dcu1_squad.log

/public/home/xuanbaby/rocm3.9-python3.6.8-tf1.15/bin/python3
/public/home/xuanbaby/rocm3.9-python3.6.8-tf1.15/lib/python3.6/site-packages/absl/flags/_validators.py:359: UserWarning: Flag --model_dir has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
  'command line!' % flag_name)
2021-04-21 10:30:52.698447: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libamdhip64.so
2021-04-21 10:30:54.255795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 0 with properties: 
pciBusID: 0000:04:00.0 name: Device 66a1     ROCm AMD GPU ISA: gfx906
coreClock: 1.7GHz coreCount: 64 deviceMemorySize: 15.98GiB deviceMemoryBandwidth: 953.67GiB/s
2021-04-21 10:30:54.256007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 1 with properties: 
pciBusID: 0000:26:00.0 name: Device 66a1     ROCm AMD GPU ISA: gfx906
coreClock: 1.7GHz coreCount: 64 deviceMemorySize: 15.98GiB deviceMemoryBandwidth: 953.67GiB/s
2021-04-21 10:30:54.256128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 2 with properties: 
pciBusID: 0000:43:00.0 name: Device 66a1     ROCm AMD GPU ISA: gfx906
coreClock: 1.7GHz coreCount: 64 deviceMemorySize: 15.98GiB deviceMemoryBandwidth: 953.67GiB/s
2021-04-21 10:30:54.256246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 3 with properties: 
pciBusID: 0000:63:00.0 name: Device 66a1     ROCm AMD GPU ISA: gfx906
coreClock: 1.7GHz coreCount: 64 deviceMemorySize: 15.98GiB deviceMemoryBandwidth: 953.67GiB/s
2021-04-21 10:30:54.809456: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library librocblas.so
2021-04-21 10:30:55.032434: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libMIOpen.so
2021-04-21 10:30:55.784559: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library librocfft.so
2021-04-21 10:30:55.838353: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library librocrand.so
2021-04-21 10:30:55.839140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1, 2, 3
2021-04-21 10:30:55.925194: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 1999885000 Hz
2021-04-21 10:30:55.928441: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5afeec0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-04-21 10:30:55.928565: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2021-04-21 10:30:55.935554: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5b6ba20 initialized for platform ROCM (this does not guarantee that XLA will be used). Devices:
2021-04-21 10:30:55.935628: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Device 66a1, AMDGPU ISA version: gfx906
2021-04-21 10:30:55.935667: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): Device 66a1, AMDGPU ISA version: gfx906
2021-04-21 10:30:55.935702: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (2): Device 66a1, AMDGPU ISA version: gfx906
2021-04-21 10:30:55.935752: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (3): Device 66a1, AMDGPU ISA version: gfx906
2021-04-21 10:31:00.379226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 0 with properties: 
pciBusID: 0000:04:00.0 name: Device 66a1     ROCm AMD GPU ISA: gfx906
coreClock: 1.7GHz coreCount: 64 deviceMemorySize: 15.98GiB deviceMemoryBandwidth: 953.67GiB/s
2021-04-21 10:31:00.379449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 1 with properties: 
pciBusID: 0000:26:00.0 name: Device 66a1     ROCm AMD GPU ISA: gfx906
coreClock: 1.7GHz coreCount: 64 deviceMemorySize: 15.98GiB deviceMemoryBandwidth: 953.67GiB/s
2021-04-21 10:31:00.379572: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 2 with properties: 
pciBusID: 0000:43:00.0 name: Device 66a1     ROCm AMD GPU ISA: gfx906
coreClock: 1.7GHz coreCount: 64 deviceMemorySize: 15.98GiB deviceMemoryBandwidth: 953.67GiB/s
2021-04-21 10:31:00.379700: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 3 with properties: 
pciBusID: 0000:63:00.0 name: Device 66a1     ROCm AMD GPU ISA: gfx906
coreClock: 1.7GHz coreCount: 64 deviceMemorySize: 15.98GiB deviceMemoryBandwidth: 953.67GiB/s
2021-04-21 10:31:00.379778: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library librocblas.so
2021-04-21 10:31:00.379829: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libMIOpen.so
2021-04-21 10:31:00.379877: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library librocfft.so
2021-04-21 10:31:00.379925: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library librocrand.so
2021-04-21 10:31:00.380484: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1, 2, 3
2021-04-21 10:31:00.380595: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-21 10:31:00.380641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 1 2 3 
2021-04-21 10:31:00.380680: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N Y Y Y 
2021-04-21 10:31:00.380716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 1:   Y N Y Y 
2021-04-21 10:31:00.380752: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 2:   Y Y N Y 
2021-04-21 10:31:00.380787: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 3:   Y Y Y N 
2021-04-21 10:31:00.381376: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15385 MB memory) -> physical GPU (device: 0, name: Device 66a1, pci bus id: 0000:04:00.0)
2021-04-21 10:31:00.386255: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 15385 MB memory) -> physical GPU (device: 1, name: Device 66a1, pci bus id: 0000:26:00.0)
2021-04-21 10:31:00.391170: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 15385 MB memory) -> physical GPU (device: 2, name: Device 66a1, pci bus id: 0000:43:00.0)
2021-04-21 10:31:00.395517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 15385 MB memory) -> physical GPU (device: 3, name: Device 66a1, pci bus id: 0000:63:00.0)
I0421 10:31:00.405918 47076539613632 mirrored_strategy.py:341] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3')
I0421 10:31:00.471962 47076539613632 run_squad_helper.py:236] Training using customized training loop with distribution strategy.
W0421 10:31:00.472802 47076539613632 device_compatibility_check.py:111] Mixed precision compatibility check (mixed_float16): WARNING
Your GPUs may run slowly with dtype policy mixed_float16 because they do not have compute capability of at least 7.0. Your GPUs:
  Device 66a1, no compute capability (probably not an Nvidia GPU) (x4)
See https://developer.nvidia.com/cuda-gpus for a list of GPUs and their compute capabilities.
If you will use compatible GPU(s) not attached to this host, e.g. by running a multi-worker model, you can ignore this warning. This message will only be logged once
W0421 10:31:00.473031 47076539613632 deprecation.py:323] From /public/home/xuanbaby/DL-TensorFlow/models_r2.3.0/official/nlp/bert/run_squad_helper.py:295: run_customized_training_loop (from official.nlp.bert.model_training_utils) is deprecated and will be removed in a future version.
Instructions for updating:
This function is deprecated. Please use Keras compile/fit instead.
I0421 10:31:00.473180 47076539613632 model_training_utils.py:228] steps_per_loop not specified. Using steps_per_loop=1
2021-04-21 10:31:01.384660: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:31:01.534850: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:31:01.542040: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:31:01.547573: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:31:01.553395: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:31:01.558656: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:31:01.564427: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:31:01.569636: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:31:01.575360: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
I0421 10:31:07.485593 47076539613632 optimization.py:89] using Adamw optimizer
I0421 10:31:07.494319 47076539613632 model_training_utils.py:273] Checkpoint file /public/home/xuanbaby/DL-TensorFlow/models_r2.3.0/official/nlp/bert/pre_tf2x/bert_model.ckpt found and restoring from initial checkpoint for core model.
I0421 10:31:13.980253 47076539613632 model_training_utils.py:276] Loading from checkpoint file completed
I0421 10:31:14.110336 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0421 10:31:14.113797 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0421 10:31:27.251605 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
W0421 10:31:27.837565 47109411751680 optimizer_v2.py:1275] Gradients do not exist for variables ['pooler_transform/kernel:0', 'pooler_transform/bias:0'] when minimizing the loss.
W0421 10:31:30.246420 47109409650432 optimizer_v2.py:1275] Gradients do not exist for variables ['pooler_transform/kernel:0', 'pooler_transform/bias:0'] when minimizing the loss.
W0421 10:31:30.812205 47109407549184 optimizer_v2.py:1275] Gradients do not exist for variables ['pooler_transform/kernel:0', 'pooler_transform/bias:0'] when minimizing the loss.
W0421 10:31:31.555347 47112398051072 optimizer_v2.py:1275] Gradients do not exist for variables ['pooler_transform/kernel:0', 'pooler_transform/bias:0'] when minimizing the loss.
I0421 10:31:31.704301 47076539613632 cross_device_ops.py:702] batch_all_reduce: 198 all-reduces with algorithm = nccl, num_packs = 1
W0421 10:31:33.933699 47076539613632 cross_device_ops.py:731] Efficient allreduce is not supported for 1 IndexedSlices
I0421 10:31:33.934081 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3').
I0421 10:31:57.373833 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
W0421 10:31:57.960069 47109409650432 optimizer_v2.py:1275] Gradients do not exist for variables ['pooler_transform/kernel:0', 'pooler_transform/bias:0'] when minimizing the loss.
W0421 10:31:58.537007 47109407549184 optimizer_v2.py:1275] Gradients do not exist for variables ['pooler_transform/kernel:0', 'pooler_transform/bias:0'] when minimizing the loss.
W0421 10:31:59.115440 47109411751680 optimizer_v2.py:1275] Gradients do not exist for variables ['pooler_transform/kernel:0', 'pooler_transform/bias:0'] when minimizing the loss.
W0421 10:31:59.691951 47112398051072 optimizer_v2.py:1275] Gradients do not exist for variables ['pooler_transform/kernel:0', 'pooler_transform/bias:0'] when minimizing the loss.
I0421 10:31:59.841835 47076539613632 cross_device_ops.py:702] batch_all_reduce: 198 all-reduces with algorithm = nccl, num_packs = 1
W0421 10:32:02.086198 47076539613632 cross_device_ops.py:731] Efficient allreduce is not supported for 1 IndexedSlices
I0421 10:32:02.086614 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3').
2021-04-21 10:32:31.860832: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:32:33.437892: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library librocblas.so
2021-04-21 10:32:33.628382: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 10:32:33.635352: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
I0421 10:32:48.270071 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0421 10:32:48.271997 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0421 10:32:48.298555 47076539613632 model_training_utils.py:505] Train Step: 1/2100  / loss = 5.9443359375
I0421 10:32:48.299917 47076539613632 keras_utils.py:133] TimeHistory: 94.18 seconds, 1.36 examples/second between steps 0 and 1
I0421 10:32:48.321353 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0421 10:32:48.324232 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0421 10:32:49.396447 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0421 10:32:49.398155 47076539613632 cross_device_ops.py:443] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0421 10:32:49.402400 47076539613632 model_training_utils.py:505] Train Step: 2/2100  / loss = 5.9453125
I0421 10:32:49.402653 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1 and 2
I0421 10:32:50.484909 47076539613632 model_training_utils.py:505] Train Step: 3/2100  / loss = 5.9326171875
I0421 10:32:50.485297 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 2 and 3
I0421 10:32:51.562718 47076539613632 model_training_utils.py:505] Train Step: 4/2100  / loss = 5.9365234375
I0421 10:32:51.563096 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.71 examples/second between steps 3 and 4
I0421 10:32:52.648627 47076539613632 model_training_utils.py:505] Train Step: 5/2100  / loss = 5.9501953125
I0421 10:32:52.649005 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 4 and 5
I0421 10:32:53.734864 47076539613632 model_training_utils.py:505] Train Step: 6/2100  / loss = 5.951171875
I0421 10:32:53.735242 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 5 and 6
I0421 10:32:54.820068 47076539613632 model_training_utils.py:505] Train Step: 7/2100  / loss = 5.91796875
I0421 10:32:54.820461 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 6 and 7
I0421 10:32:55.906687 47076539613632 model_training_utils.py:505] Train Step: 8/2100  / loss = 5.9443359375
I0421 10:32:55.907068 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 7 and 8
I0421 10:32:56.992115 47076539613632 model_training_utils.py:505] Train Step: 9/2100  / loss = 5.93359375
I0421 10:32:56.992497 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 8 and 9
I0421 10:32:58.077168 47076539613632 model_training_utils.py:505] Train Step: 10/2100  / loss = 5.9287109375
I0421 10:32:58.077557 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 9 and 10
I0421 10:32:59.163030 47076539613632 model_training_utils.py:505] Train Step: 11/2100  / loss = 5.96875
I0421 10:32:59.163427 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 10 and 11
I0421 10:33:00.243196 47076539613632 model_training_utils.py:505] Train Step: 12/2100  / loss = 5.9521484375
I0421 10:33:00.243588 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.44 examples/second between steps 11 and 12
I0421 10:33:01.327911 47076539613632 model_training_utils.py:505] Train Step: 13/2100  / loss = 5.919921875
I0421 10:33:01.328298 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 12 and 13
I0421 10:33:02.410461 47076539613632 model_training_utils.py:505] Train Step: 14/2100  / loss = 5.904296875
I0421 10:33:02.410841 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 13 and 14
I0421 10:33:03.493039 47076539613632 model_training_utils.py:505] Train Step: 15/2100  / loss = 5.9169921875
I0421 10:33:03.493431 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 14 and 15
I0421 10:33:04.580993 47076539613632 model_training_utils.py:505] Train Step: 16/2100  / loss = 5.90625
I0421 10:33:04.581379 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.53 examples/second between steps 15 and 16
I0421 10:33:05.666996 47076539613632 model_training_utils.py:505] Train Step: 17/2100  / loss = 5.9169921875
I0421 10:33:05.667385 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 16 and 17
I0421 10:33:06.754867 47076539613632 model_training_utils.py:505] Train Step: 18/2100  / loss = 5.8935546875
I0421 10:33:06.755258 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 17 and 18
I0421 10:33:07.838564 47076539613632 model_training_utils.py:505] Train Step: 19/2100  / loss = 5.91015625
I0421 10:33:07.838946 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 18 and 19
I0421 10:33:08.924276 47076539613632 model_training_utils.py:505] Train Step: 20/2100  / loss = 5.890625
I0421 10:33:08.924668 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 19 and 20
I0421 10:33:10.012882 47076539613632 model_training_utils.py:505] Train Step: 21/2100  / loss = 5.900390625
I0421 10:33:10.013268 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.49 examples/second between steps 20 and 21
I0421 10:33:11.104462 47076539613632 model_training_utils.py:505] Train Step: 22/2100  / loss = 5.888671875
I0421 10:33:11.104846 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.15 examples/second between steps 21 and 22
I0421 10:33:12.192351 47076539613632 model_training_utils.py:505] Train Step: 23/2100  / loss = 5.8671875
I0421 10:33:12.192731 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 22 and 23
I0421 10:33:13.278066 47076539613632 model_training_utils.py:505] Train Step: 24/2100  / loss = 5.880859375
I0421 10:33:13.278454 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 23 and 24
I0421 10:33:14.365594 47076539613632 model_training_utils.py:505] Train Step: 25/2100  / loss = 5.8662109375
I0421 10:33:14.365981 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 24 and 25
I0421 10:33:15.452601 47076539613632 model_training_utils.py:505] Train Step: 26/2100  / loss = 5.865234375
I0421 10:33:15.452991 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 25 and 26
I0421 10:33:16.539515 47076539613632 model_training_utils.py:505] Train Step: 27/2100  / loss = 5.876953125
I0421 10:33:16.539895 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 26 and 27
I0421 10:33:17.628303 47076539613632 model_training_utils.py:505] Train Step: 28/2100  / loss = 5.8125
I0421 10:33:17.628740 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 27 and 28
I0421 10:33:18.727007 47076539613632 model_training_utils.py:505] Train Step: 29/2100  / loss = 5.814453125
I0421 10:33:18.727447 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.40 examples/second between steps 28 and 29
I0421 10:33:19.820886 47076539613632 model_training_utils.py:505] Train Step: 30/2100  / loss = 5.822265625
I0421 10:33:19.821330 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.92 examples/second between steps 29 and 30
I0421 10:33:20.913674 47076539613632 model_training_utils.py:505] Train Step: 31/2100  / loss = 5.7841796875
I0421 10:33:20.914122 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.05 examples/second between steps 30 and 31
I0421 10:33:22.004764 47076539613632 model_training_utils.py:505] Train Step: 32/2100  / loss = 5.7919921875
I0421 10:33:22.005201 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.23 examples/second between steps 31 and 32
I0421 10:33:23.096519 47076539613632 model_training_utils.py:505] Train Step: 33/2100  / loss = 5.7685546875
I0421 10:33:23.096954 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.17 examples/second between steps 32 and 33
I0421 10:33:24.181210 47076539613632 model_training_utils.py:505] Train Step: 34/2100  / loss = 5.7822265625
I0421 10:33:24.181681 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 33 and 34
I0421 10:33:25.272241 47076539613632 model_training_utils.py:505] Train Step: 35/2100  / loss = 5.751953125
I0421 10:33:25.272684 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 34 and 35
I0421 10:33:26.363500 47076539613632 model_training_utils.py:505] Train Step: 36/2100  / loss = 5.7421875
I0421 10:33:26.363940 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.20 examples/second between steps 35 and 36
I0421 10:33:27.455332 47076539613632 model_training_utils.py:505] Train Step: 37/2100  / loss = 5.724609375
I0421 10:33:27.455775 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.17 examples/second between steps 36 and 37
I0421 10:33:28.539311 47076539613632 model_training_utils.py:505] Train Step: 38/2100  / loss = 5.748046875
I0421 10:33:28.539747 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 37 and 38
I0421 10:33:29.628311 47076539613632 model_training_utils.py:505] Train Step: 39/2100  / loss = 5.724609375
I0421 10:33:29.628753 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 38 and 39
I0421 10:33:30.714823 47076539613632 model_training_utils.py:505] Train Step: 40/2100  / loss = 5.767578125
I0421 10:33:30.715259 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 39 and 40
I0421 10:33:31.807786 47076539613632 model_training_utils.py:505] Train Step: 41/2100  / loss = 5.748046875
I0421 10:33:31.808224 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.05 examples/second between steps 40 and 41
I0421 10:33:32.893361 47076539613632 model_training_utils.py:505] Train Step: 42/2100  / loss = 5.7373046875
I0421 10:33:32.893798 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 41 and 42
I0421 10:33:33.984423 47076539613632 model_training_utils.py:505] Train Step: 43/2100  / loss = 5.69140625
I0421 10:33:33.984850 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 42 and 43
I0421 10:33:35.077356 47076539613632 model_training_utils.py:505] Train Step: 44/2100  / loss = 5.6630859375
I0421 10:33:35.077801 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.02 examples/second between steps 43 and 44
I0421 10:33:36.164971 47076539613632 model_training_utils.py:505] Train Step: 45/2100  / loss = 5.63671875
I0421 10:33:36.165435 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 44 and 45
I0421 10:33:37.251868 47076539613632 model_training_utils.py:505] Train Step: 46/2100  / loss = 5.6240234375
I0421 10:33:37.252318 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 45 and 46
I0421 10:33:38.336815 47076539613632 model_training_utils.py:505] Train Step: 47/2100  / loss = 5.5634765625
I0421 10:33:38.337260 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 46 and 47
I0421 10:33:39.423032 47076539613632 model_training_utils.py:505] Train Step: 48/2100  / loss = 5.56640625
I0421 10:33:39.423485 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 47 and 48
I0421 10:33:40.504391 47076539613632 model_training_utils.py:505] Train Step: 49/2100  / loss = 5.6220703125
I0421 10:33:40.504829 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 48 and 49
I0421 10:33:41.589461 47076539613632 model_training_utils.py:505] Train Step: 50/2100  / loss = 5.587890625
I0421 10:33:41.589900 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 49 and 50
I0421 10:33:42.675948 47076539613632 model_training_utils.py:505] Train Step: 51/2100  / loss = 5.591796875
I0421 10:33:42.676396 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 50 and 51
I0421 10:33:43.769904 47076539613632 model_training_utils.py:505] Train Step: 52/2100  / loss = 5.501953125
I0421 10:33:43.770345 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.93 examples/second between steps 51 and 52
I0421 10:33:44.866144 47076539613632 model_training_utils.py:505] Train Step: 53/2100  / loss = 5.529296875
I0421 10:33:44.866591 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.69 examples/second between steps 52 and 53
I0421 10:33:45.962362 47076539613632 model_training_utils.py:505] Train Step: 54/2100  / loss = 5.447265625
I0421 10:33:45.962801 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.67 examples/second between steps 53 and 54
I0421 10:33:47.056687 47076539613632 model_training_utils.py:505] Train Step: 55/2100  / loss = 5.359375
I0421 10:33:47.057127 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.90 examples/second between steps 54 and 55
I0421 10:33:48.151849 47076539613632 model_training_utils.py:505] Train Step: 56/2100  / loss = 5.287109375
I0421 10:33:48.152288 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.79 examples/second between steps 55 and 56
I0421 10:33:49.242059 47076539613632 model_training_utils.py:505] Train Step: 57/2100  / loss = 5.318359375
I0421 10:33:49.242503 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.38 examples/second between steps 56 and 57
I0421 10:33:50.339562 47076539613632 model_training_utils.py:505] Train Step: 58/2100  / loss = 5.26171875
I0421 10:33:50.340005 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.53 examples/second between steps 57 and 58
I0421 10:33:51.443152 47076539613632 model_training_utils.py:505] Train Step: 59/2100  / loss = 5.203125
I0421 10:33:51.443610 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.88 examples/second between steps 58 and 59
I0421 10:33:52.547581 47076539613632 model_training_utils.py:505] Train Step: 60/2100  / loss = 5.177734375
I0421 10:33:52.548020 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.78 examples/second between steps 59 and 60
I0421 10:33:53.637635 47076539613632 model_training_utils.py:505] Train Step: 61/2100  / loss = 5.1201171875
I0421 10:33:53.638072 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.34 examples/second between steps 60 and 61
I0421 10:33:54.727105 47076539613632 model_training_utils.py:505] Train Step: 62/2100  / loss = 5.150390625
I0421 10:33:54.727555 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.38 examples/second between steps 61 and 62
I0421 10:33:55.815848 47076539613632 model_training_utils.py:505] Train Step: 63/2100  / loss = 5.130859375
I0421 10:33:55.816307 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.47 examples/second between steps 62 and 63
I0421 10:33:56.908647 47076539613632 model_training_utils.py:505] Train Step: 64/2100  / loss = 5.1796875
I0421 10:33:56.909087 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.03 examples/second between steps 63 and 64
I0421 10:33:58.003484 47076539613632 model_training_utils.py:505] Train Step: 65/2100  / loss = 5.1591796875
I0421 10:33:58.003915 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.82 examples/second between steps 64 and 65
I0421 10:33:59.088458 47076539613632 model_training_utils.py:505] Train Step: 66/2100  / loss = 5.171875
I0421 10:33:59.088898 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 65 and 66
I0421 10:34:00.169981 47076539613632 model_training_utils.py:505] Train Step: 67/2100  / loss = 5.185546875
I0421 10:34:00.170428 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 66 and 67
I0421 10:34:01.251944 47076539613632 model_training_utils.py:505] Train Step: 68/2100  / loss = 5.2021484375
I0421 10:34:01.252393 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 67 and 68
I0421 10:34:02.332823 47076539613632 model_training_utils.py:505] Train Step: 69/2100  / loss = 5.1845703125
I0421 10:34:02.333277 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.34 examples/second between steps 68 and 69
I0421 10:34:03.417798 47076539613632 model_training_utils.py:505] Train Step: 70/2100  / loss = 5.0703125
I0421 10:34:03.418241 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 69 and 70
I0421 10:34:04.506007 47076539613632 model_training_utils.py:505] Train Step: 71/2100  / loss = 4.97265625
I0421 10:34:04.506458 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 70 and 71
I0421 10:34:05.593928 47076539613632 model_training_utils.py:505] Train Step: 72/2100  / loss = 4.994140625
I0421 10:34:05.594376 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.58 examples/second between steps 71 and 72
I0421 10:34:06.681665 47076539613632 model_training_utils.py:505] Train Step: 73/2100  / loss = 4.84765625
I0421 10:34:06.682108 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 72 and 73
I0421 10:34:07.769192 47076539613632 model_training_utils.py:505] Train Step: 74/2100  / loss = 4.69921875
I0421 10:34:07.769628 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 73 and 74
I0421 10:34:08.853453 47076539613632 model_training_utils.py:505] Train Step: 75/2100  / loss = 4.6982421875
I0421 10:34:08.853891 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 74 and 75
I0421 10:34:09.946589 47076539613632 model_training_utils.py:505] Train Step: 76/2100  / loss = 4.580078125
I0421 10:34:09.947052 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.00 examples/second between steps 75 and 76
I0421 10:34:11.047191 47076539613632 model_training_utils.py:505] Train Step: 77/2100  / loss = 4.3583984375
I0421 10:34:11.047638 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.18 examples/second between steps 76 and 77
I0421 10:34:12.143185 47076539613632 model_training_utils.py:505] Train Step: 78/2100  / loss = 4.2734375
I0421 10:34:12.143626 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.68 examples/second between steps 77 and 78
I0421 10:34:13.242194 47076539613632 model_training_utils.py:505] Train Step: 79/2100  / loss = 4.1494140625
I0421 10:34:13.242636 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.36 examples/second between steps 78 and 79
I0421 10:34:14.339607 47076539613632 model_training_utils.py:505] Train Step: 80/2100  / loss = 4.10986328125
I0421 10:34:14.340050 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.58 examples/second between steps 79 and 80
I0421 10:34:15.435370 47076539613632 model_training_utils.py:505] Train Step: 81/2100  / loss = 4.177734375
I0421 10:34:15.435805 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.73 examples/second between steps 80 and 81
I0421 10:34:16.534599 47076539613632 model_training_utils.py:505] Train Step: 82/2100  / loss = 4.24609375
I0421 10:34:16.535030 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.35 examples/second between steps 81 and 82
I0421 10:34:17.630459 47076539613632 model_training_utils.py:505] Train Step: 83/2100  / loss = 4.333984375
I0421 10:34:17.630895 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.72 examples/second between steps 82 and 83
I0421 10:34:18.724392 47076539613632 model_training_utils.py:505] Train Step: 84/2100  / loss = 4.33984375
I0421 10:34:18.724826 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.92 examples/second between steps 83 and 84
I0421 10:34:19.816267 47076539613632 model_training_utils.py:505] Train Step: 85/2100  / loss = 4.14453125
I0421 10:34:19.816704 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.16 examples/second between steps 84 and 85
I0421 10:34:20.912571 47076539613632 model_training_utils.py:505] Train Step: 86/2100  / loss = 4.12890625
I0421 10:34:20.913006 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.67 examples/second between steps 85 and 86
I0421 10:34:22.010862 47076539613632 model_training_utils.py:505] Train Step: 87/2100  / loss = 4.02197265625
I0421 10:34:22.011304 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.47 examples/second between steps 86 and 87
I0421 10:34:23.109011 47076539613632 model_training_utils.py:505] Train Step: 88/2100  / loss = 3.880859375
I0421 10:34:23.109453 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.49 examples/second between steps 87 and 88
I0421 10:34:24.211528 47076539613632 model_training_utils.py:505] Train Step: 89/2100  / loss = 3.6865234375
I0421 10:34:24.211975 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 116.99 examples/second between steps 88 and 89
I0421 10:34:25.310386 47076539613632 model_training_utils.py:505] Train Step: 90/2100  / loss = 3.8046875
I0421 10:34:25.310827 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.39 examples/second between steps 89 and 90
I0421 10:34:26.396572 47076539613632 model_training_utils.py:505] Train Step: 91/2100  / loss = 3.94287109375
I0421 10:34:26.397000 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 90 and 91
I0421 10:34:27.490232 47076539613632 model_training_utils.py:505] Train Step: 92/2100  / loss = 4.060546875
I0421 10:34:27.490672 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.97 examples/second between steps 91 and 92
I0421 10:34:28.581966 47076539613632 model_training_utils.py:505] Train Step: 93/2100  / loss = 3.89501953125
I0421 10:34:28.582418 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.17 examples/second between steps 92 and 93
I0421 10:34:29.672203 47076539613632 model_training_utils.py:505] Train Step: 94/2100  / loss = 3.7763671875
I0421 10:34:29.672649 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.32 examples/second between steps 93 and 94
I0421 10:34:30.760814 47076539613632 model_training_utils.py:505] Train Step: 95/2100  / loss = 3.6962890625
I0421 10:34:30.761252 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 94 and 95
I0421 10:34:31.854213 47076539613632 model_training_utils.py:505] Train Step: 96/2100  / loss = 3.724609375
I0421 10:34:31.854670 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.97 examples/second between steps 95 and 96
I0421 10:34:32.938133 47076539613632 model_training_utils.py:505] Train Step: 97/2100  / loss = 3.7607421875
I0421 10:34:32.938577 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 96 and 97
I0421 10:34:34.027951 47076539613632 model_training_utils.py:505] Train Step: 98/2100  / loss = 3.64453125
I0421 10:34:34.028393 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.38 examples/second between steps 97 and 98
I0421 10:34:35.117880 47076539613632 model_training_utils.py:505] Train Step: 99/2100  / loss = 3.72607421875
I0421 10:34:35.118323 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.36 examples/second between steps 98 and 99
I0421 10:34:36.212108 47076539613632 model_training_utils.py:505] Train Step: 100/2100  / loss = 3.4716796875
I0421 10:34:36.212553 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.91 examples/second between steps 99 and 100
I0421 10:34:37.305122 47076539613632 model_training_utils.py:505] Train Step: 101/2100  / loss = 3.4677734375
I0421 10:34:37.305563 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.07 examples/second between steps 100 and 101
I0421 10:34:38.400527 47076539613632 model_training_utils.py:505] Train Step: 102/2100  / loss = 3.6123046875
I0421 10:34:38.400963 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.77 examples/second between steps 101 and 102
I0421 10:34:39.495639 47076539613632 model_training_utils.py:505] Train Step: 103/2100  / loss = 3.34814453125
I0421 10:34:39.496074 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.82 examples/second between steps 102 and 103
I0421 10:34:40.591315 47076539613632 model_training_utils.py:505] Train Step: 104/2100  / loss = 3.3125
I0421 10:34:40.591757 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.73 examples/second between steps 103 and 104
I0421 10:34:41.690109 47076539613632 model_training_utils.py:505] Train Step: 105/2100  / loss = 3.4462890625
I0421 10:34:41.690551 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.37 examples/second between steps 104 and 105
I0421 10:34:42.787414 47076539613632 model_training_utils.py:505] Train Step: 106/2100  / loss = 3.361328125
I0421 10:34:42.787856 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.55 examples/second between steps 105 and 106
I0421 10:34:43.880080 47076539613632 model_training_utils.py:505] Train Step: 107/2100  / loss = 3.40771484375
I0421 10:34:43.880532 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.04 examples/second between steps 106 and 107
I0421 10:34:44.970167 47076539613632 model_training_utils.py:505] Train Step: 108/2100  / loss = 3.5498046875
I0421 10:34:44.970622 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.32 examples/second between steps 107 and 108
I0421 10:34:46.058837 47076539613632 model_training_utils.py:505] Train Step: 109/2100  / loss = 3.55224609375
I0421 10:34:46.059267 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 108 and 109
I0421 10:34:47.146686 47076539613632 model_training_utils.py:505] Train Step: 110/2100  / loss = 3.6591796875
I0421 10:34:47.147127 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 109 and 110
I0421 10:34:48.237298 47076539613632 model_training_utils.py:505] Train Step: 111/2100  / loss = 3.4384765625
I0421 10:34:48.237743 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.28 examples/second between steps 110 and 111
I0421 10:34:49.331591 47076539613632 model_training_utils.py:505] Train Step: 112/2100  / loss = 3.52392578125
I0421 10:34:49.332030 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.86 examples/second between steps 111 and 112
I0421 10:34:50.422046 47076539613632 model_training_utils.py:505] Train Step: 113/2100  / loss = 3.63720703125
I0421 10:34:50.422496 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.27 examples/second between steps 112 and 113
I0421 10:34:51.519348 47076539613632 model_training_utils.py:505] Train Step: 114/2100  / loss = 3.17041015625
I0421 10:34:51.519803 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.54 examples/second between steps 113 and 114
I0421 10:34:52.624500 47076539613632 model_training_utils.py:505] Train Step: 115/2100  / loss = 3.16259765625
I0421 10:34:52.624935 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.73 examples/second between steps 114 and 115
I0421 10:34:53.721354 47076539613632 model_training_utils.py:505] Train Step: 116/2100  / loss = 3.3212890625
I0421 10:34:53.721795 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.60 examples/second between steps 115 and 116
I0421 10:34:54.820021 47076539613632 model_training_utils.py:505] Train Step: 117/2100  / loss = 3.27587890625
I0421 10:34:54.820466 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.43 examples/second between steps 116 and 117
I0421 10:34:55.917998 47076539613632 model_training_utils.py:505] Train Step: 118/2100  / loss = 3.27587890625
I0421 10:34:55.918444 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.47 examples/second between steps 117 and 118
I0421 10:34:57.004701 47076539613632 model_training_utils.py:505] Train Step: 119/2100  / loss = 3.50048828125
I0421 10:34:57.005135 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 118 and 119
I0421 10:34:58.094247 47076539613632 model_training_utils.py:505] Train Step: 120/2100  / loss = 3.517578125
I0421 10:34:58.094697 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.39 examples/second between steps 119 and 120
I0421 10:34:59.192052 47076539613632 model_training_utils.py:505] Train Step: 121/2100  / loss = 3.173828125
I0421 10:34:59.192493 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.48 examples/second between steps 120 and 121
I0421 10:35:00.288325 47076539613632 model_training_utils.py:505] Train Step: 122/2100  / loss = 3.3505859375
I0421 10:35:00.288765 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.66 examples/second between steps 121 and 122
I0421 10:35:01.386429 47076539613632 model_training_utils.py:505] Train Step: 123/2100  / loss = 3.208984375
I0421 10:35:01.386867 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.46 examples/second between steps 122 and 123
I0421 10:35:02.486585 47076539613632 model_training_utils.py:505] Train Step: 124/2100  / loss = 3.197265625
I0421 10:35:02.487018 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.26 examples/second between steps 123 and 124
I0421 10:35:03.593462 47076539613632 model_training_utils.py:505] Train Step: 125/2100  / loss = 3.03369140625
I0421 10:35:03.593901 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.51 examples/second between steps 124 and 125
I0421 10:35:04.701143 47076539613632 model_training_utils.py:505] Train Step: 126/2100  / loss = 2.90771484375
I0421 10:35:04.701593 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.43 examples/second between steps 125 and 126
I0421 10:35:05.801792 47076539613632 model_training_utils.py:505] Train Step: 127/2100  / loss = 2.9501953125
I0421 10:35:05.802234 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.19 examples/second between steps 126 and 127
I0421 10:35:06.904639 47076539613632 model_training_utils.py:505] Train Step: 128/2100  / loss = 3.0849609375
I0421 10:35:06.905081 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 116.96 examples/second between steps 127 and 128
I0421 10:35:07.994532 47076539613632 model_training_utils.py:505] Train Step: 129/2100  / loss = 2.919921875
I0421 10:35:07.994969 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 128 and 129
I0421 10:35:09.090296 47076539613632 model_training_utils.py:505] Train Step: 130/2100  / loss = 2.97021484375
I0421 10:35:09.090724 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.72 examples/second between steps 129 and 130
I0421 10:35:10.189189 47076539613632 model_training_utils.py:505] Train Step: 131/2100  / loss = 2.72900390625
I0421 10:35:10.189634 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.40 examples/second between steps 130 and 131
I0421 10:35:11.283447 47076539613632 model_training_utils.py:505] Train Step: 132/2100  / loss = 2.85693359375
I0421 10:35:11.283894 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.88 examples/second between steps 131 and 132
I0421 10:35:12.377050 47076539613632 model_training_utils.py:505] Train Step: 133/2100  / loss = 2.880859375
I0421 10:35:12.377496 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.94 examples/second between steps 132 and 133
I0421 10:35:13.469250 47076539613632 model_training_utils.py:505] Train Step: 134/2100  / loss = 3.15234375
I0421 10:35:13.469692 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.12 examples/second between steps 133 and 134
I0421 10:35:14.556288 47076539613632 model_training_utils.py:505] Train Step: 135/2100  / loss = 3.20263671875
I0421 10:35:14.556720 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 134 and 135
I0421 10:35:15.646571 47076539613632 model_training_utils.py:505] Train Step: 136/2100  / loss = 3.14892578125
I0421 10:35:15.647006 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.32 examples/second between steps 135 and 136
I0421 10:35:16.744681 47076539613632 model_training_utils.py:505] Train Step: 137/2100  / loss = 2.73291015625
I0421 10:35:16.745126 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.45 examples/second between steps 136 and 137
I0421 10:35:17.839482 47076539613632 model_training_utils.py:505] Train Step: 138/2100  / loss = 2.64404296875
I0421 10:35:17.839912 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.84 examples/second between steps 137 and 138
I0421 10:35:18.936151 47076539613632 model_training_utils.py:505] Train Step: 139/2100  / loss = 2.7490234375
I0421 10:35:18.936597 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.59 examples/second between steps 138 and 139
I0421 10:35:20.035408 47076539613632 model_training_utils.py:505] Train Step: 140/2100  / loss = 2.8759765625
I0421 10:35:20.035847 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.33 examples/second between steps 139 and 140
I0421 10:35:21.133386 47076539613632 model_training_utils.py:505] Train Step: 141/2100  / loss = 2.7998046875
I0421 10:35:21.133822 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.48 examples/second between steps 140 and 141
I0421 10:35:22.235810 47076539613632 model_training_utils.py:505] Train Step: 142/2100  / loss = 2.81640625
I0421 10:35:22.236242 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 116.98 examples/second between steps 141 and 142
I0421 10:35:23.338584 47076539613632 model_training_utils.py:505] Train Step: 143/2100  / loss = 2.73681640625
I0421 10:35:23.339012 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 116.97 examples/second between steps 142 and 143
I0421 10:35:24.440233 47076539613632 model_training_utils.py:505] Train Step: 144/2100  / loss = 2.51513671875
I0421 10:35:24.440684 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.09 examples/second between steps 143 and 144
I0421 10:35:25.534200 47076539613632 model_training_utils.py:505] Train Step: 145/2100  / loss = 2.6640625
I0421 10:35:25.534649 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.94 examples/second between steps 144 and 145
I0421 10:35:26.621137 47076539613632 model_training_utils.py:505] Train Step: 146/2100  / loss = 2.505859375
I0421 10:35:26.621587 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 145 and 146
I0421 10:35:27.712854 47076539613632 model_training_utils.py:505] Train Step: 147/2100  / loss = 2.638671875
I0421 10:35:27.713296 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.15 examples/second between steps 146 and 147
I0421 10:35:28.808706 47076539613632 model_training_utils.py:505] Train Step: 148/2100  / loss = 2.4208984375
I0421 10:35:28.809145 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.74 examples/second between steps 147 and 148
I0421 10:35:29.898995 47076539613632 model_training_utils.py:505] Train Step: 149/2100  / loss = 2.4814453125
I0421 10:35:29.899448 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.32 examples/second between steps 148 and 149
I0421 10:35:30.984569 47076539613632 model_training_utils.py:505] Train Step: 150/2100  / loss = 2.48876953125
I0421 10:35:30.985006 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 149 and 150
I0421 10:35:32.078787 47076539613632 model_training_utils.py:505] Train Step: 151/2100  / loss = 2.05126953125
I0421 10:35:32.079227 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.88 examples/second between steps 150 and 151
I0421 10:35:33.174210 47076539613632 model_training_utils.py:505] Train Step: 152/2100  / loss = 2.05859375
I0421 10:35:33.174657 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.77 examples/second between steps 151 and 152
I0421 10:35:34.272821 47076539613632 model_training_utils.py:505] Train Step: 153/2100  / loss = 2.203125
I0421 10:35:34.273270 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.40 examples/second between steps 152 and 153
I0421 10:35:35.373723 47076539613632 model_training_utils.py:505] Train Step: 154/2100  / loss = 2.36669921875
I0421 10:35:35.374161 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.18 examples/second between steps 153 and 154
I0421 10:35:36.472821 47076539613632 model_training_utils.py:505] Train Step: 155/2100  / loss = 2.64501953125
I0421 10:35:36.473256 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.39 examples/second between steps 154 and 155
I0421 10:35:37.567892 47076539613632 model_training_utils.py:505] Train Step: 156/2100  / loss = 2.6416015625
I0421 10:35:37.568335 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.81 examples/second between steps 155 and 156
I0421 10:35:38.656911 47076539613632 model_training_utils.py:505] Train Step: 157/2100  / loss = 2.3388671875
I0421 10:35:38.657365 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.44 examples/second between steps 156 and 157
I0421 10:35:39.746025 47076539613632 model_training_utils.py:505] Train Step: 158/2100  / loss = 2.3974609375
I0421 10:35:39.746474 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.44 examples/second between steps 157 and 158
I0421 10:35:40.843460 47076539613632 model_training_utils.py:505] Train Step: 159/2100  / loss = 2.3994140625
I0421 10:35:40.843899 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.54 examples/second between steps 158 and 159
I0421 10:35:41.942160 47076539613632 model_training_utils.py:505] Train Step: 160/2100  / loss = 2.3125
I0421 10:35:41.942608 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.42 examples/second between steps 159 and 160
I0421 10:35:43.037592 47076539613632 model_training_utils.py:505] Train Step: 161/2100  / loss = 2.474609375
I0421 10:35:43.038023 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.76 examples/second between steps 160 and 161
I0421 10:35:44.129074 47076539613632 model_training_utils.py:505] Train Step: 162/2100  / loss = 3.03076171875
I0421 10:35:44.129523 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.17 examples/second between steps 161 and 162
I0421 10:35:45.218172 47076539613632 model_training_utils.py:505] Train Step: 163/2100  / loss = 2.873046875
I0421 10:35:45.218612 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.44 examples/second between steps 162 and 163
I0421 10:35:46.303110 47076539613632 model_training_utils.py:505] Train Step: 164/2100  / loss = 2.30419921875
I0421 10:35:46.303547 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 163 and 164
I0421 10:35:47.392338 47076539613632 model_training_utils.py:505] Train Step: 165/2100  / loss = 2.318359375
I0421 10:35:47.392777 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.42 examples/second between steps 164 and 165
I0421 10:35:48.482771 47076539613632 model_training_utils.py:505] Train Step: 166/2100  / loss = 1.911865234375
I0421 10:35:48.483205 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.27 examples/second between steps 165 and 166
I0421 10:35:49.571064 47076539613632 model_training_utils.py:505] Train Step: 167/2100  / loss = 1.981201171875
I0421 10:35:49.571510 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.50 examples/second between steps 166 and 167
I0421 10:35:50.662383 47076539613632 model_training_utils.py:505] Train Step: 168/2100  / loss = 2.23876953125
I0421 10:35:50.662821 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.18 examples/second between steps 167 and 168
I0421 10:35:51.757626 47076539613632 model_training_utils.py:505] Train Step: 169/2100  / loss = 1.921875
I0421 10:35:51.758062 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.74 examples/second between steps 168 and 169
I0421 10:35:52.860630 47076539613632 model_training_utils.py:505] Train Step: 170/2100  / loss = 1.87451171875
I0421 10:35:52.861064 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 116.93 examples/second between steps 169 and 170
I0421 10:35:53.971435 47076539613632 model_training_utils.py:505] Train Step: 171/2100  / loss = 1.97216796875
I0421 10:35:53.971843 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.10 examples/second between steps 170 and 171
I0421 10:35:55.076320 47076539613632 model_training_utils.py:505] Train Step: 172/2100  / loss = 2.0751953125
I0421 10:35:55.076759 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.71 examples/second between steps 171 and 172
I0421 10:35:56.171914 47076539613632 model_training_utils.py:505] Train Step: 173/2100  / loss = 2.2607421875
I0421 10:35:56.172364 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.72 examples/second between steps 172 and 173
I0421 10:35:57.265091 47076539613632 model_training_utils.py:505] Train Step: 174/2100  / loss = 2.108642578125
I0421 10:35:57.265536 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.02 examples/second between steps 173 and 174
I0421 10:35:58.357677 47076539613632 model_training_utils.py:505] Train Step: 175/2100  / loss = 1.859375
I0421 10:35:58.358111 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.04 examples/second between steps 174 and 175
I0421 10:35:59.447927 47076539613632 model_training_utils.py:505] Train Step: 176/2100  / loss = 1.919189453125
I0421 10:35:59.448372 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.30 examples/second between steps 175 and 176
I0421 10:36:00.539553 47076539613632 model_training_utils.py:505] Train Step: 177/2100  / loss = 1.9541015625
I0421 10:36:00.539986 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.17 examples/second between steps 176 and 177
I0421 10:36:01.638194 47076539613632 model_training_utils.py:505] Train Step: 178/2100  / loss = 1.75390625
I0421 10:36:01.638634 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.38 examples/second between steps 177 and 178
I0421 10:36:02.739884 47076539613632 model_training_utils.py:505] Train Step: 179/2100  / loss = 2.08203125
I0421 10:36:02.740333 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.06 examples/second between steps 178 and 179
I0421 10:36:03.841526 47076539613632 model_training_utils.py:505] Train Step: 180/2100  / loss = 2.353515625
I0421 10:36:03.841967 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.08 examples/second between steps 179 and 180
I0421 10:36:04.941748 47076539613632 model_training_utils.py:505] Train Step: 181/2100  / loss = 2.74755859375
I0421 10:36:04.942192 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.22 examples/second between steps 180 and 181
I0421 10:36:06.044015 47076539613632 model_training_utils.py:505] Train Step: 182/2100  / loss = 2.58251953125
I0421 10:36:06.044461 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.01 examples/second between steps 181 and 182
I0421 10:36:07.136699 47076539613632 model_training_utils.py:505] Train Step: 183/2100  / loss = 2.5166015625
I0421 10:36:07.137087 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.08 examples/second between steps 182 and 183
I0421 10:36:08.227677 47076539613632 model_training_utils.py:505] Train Step: 184/2100  / loss = 2.206298828125
I0421 10:36:08.228062 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.21 examples/second between steps 183 and 184
I0421 10:36:09.317612 47076539613632 model_training_utils.py:505] Train Step: 185/2100  / loss = 2.0595703125
I0421 10:36:09.318001 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.38 examples/second between steps 184 and 185
I0421 10:36:10.399769 47076539613632 model_training_utils.py:505] Train Step: 186/2100  / loss = 2.02099609375
I0421 10:36:10.400155 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 185 and 186
I0421 10:36:11.481112 47076539613632 model_training_utils.py:505] Train Step: 187/2100  / loss = 1.963623046875
I0421 10:36:11.481510 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.47 examples/second between steps 186 and 187
I0421 10:36:12.561995 47076539613632 model_training_utils.py:505] Train Step: 188/2100  / loss = 1.969482421875
I0421 10:36:12.562394 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 187 and 188
I0421 10:36:13.648635 47076539613632 model_training_utils.py:505] Train Step: 189/2100  / loss = 2.1064453125
I0421 10:36:13.649017 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 188 and 189
I0421 10:36:14.748042 47076539613632 model_training_utils.py:505] Train Step: 190/2100  / loss = 1.982421875
I0421 10:36:14.748435 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.32 examples/second between steps 189 and 190
I0421 10:36:15.846697 47076539613632 model_training_utils.py:505] Train Step: 191/2100  / loss = 2.490234375
I0421 10:36:15.847087 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.42 examples/second between steps 190 and 191
I0421 10:36:16.945017 47076539613632 model_training_utils.py:505] Train Step: 192/2100  / loss = 2.7373046875
I0421 10:36:16.945409 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.43 examples/second between steps 191 and 192
I0421 10:36:18.035637 47076539613632 model_training_utils.py:505] Train Step: 193/2100  / loss = 3.0576171875
I0421 10:36:18.036022 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 192 and 193
I0421 10:36:19.131561 47076539613632 model_training_utils.py:505] Train Step: 194/2100  / loss = 2.26318359375
I0421 10:36:19.131948 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.69 examples/second between steps 193 and 194
I0421 10:36:20.230017 47076539613632 model_training_utils.py:505] Train Step: 195/2100  / loss = 1.943115234375
I0421 10:36:20.230407 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.40 examples/second between steps 194 and 195
I0421 10:36:21.328803 47076539613632 model_training_utils.py:505] Train Step: 196/2100  / loss = 1.876220703125
I0421 10:36:21.329184 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.38 examples/second between steps 195 and 196
I0421 10:36:22.421467 47076539613632 model_training_utils.py:505] Train Step: 197/2100  / loss = 2.02978515625
I0421 10:36:22.421845 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.05 examples/second between steps 196 and 197
I0421 10:36:23.515832 47076539613632 model_training_utils.py:505] Train Step: 198/2100  / loss = 2.3974609375
I0421 10:36:23.516227 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.86 examples/second between steps 197 and 198
I0421 10:36:24.613873 47076539613632 model_training_utils.py:505] Train Step: 199/2100  / loss = 2.5244140625
I0421 10:36:24.614255 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.47 examples/second between steps 198 and 199
I0421 10:36:25.708716 47076539613632 model_training_utils.py:505] Train Step: 200/2100  / loss = 1.859130859375
I0421 10:36:25.709094 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.83 examples/second between steps 199 and 200
I0421 10:36:26.797033 47076539613632 model_training_utils.py:505] Train Step: 201/2100  / loss = 1.78759765625
I0421 10:36:26.797431 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 200 and 201
I0421 10:36:27.890554 47076539613632 model_training_utils.py:505] Train Step: 202/2100  / loss = 2.07958984375
I0421 10:36:27.890937 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.93 examples/second between steps 201 and 202
I0421 10:36:28.984051 47076539613632 model_training_utils.py:505] Train Step: 203/2100  / loss = 2.04736328125
I0421 10:36:28.984441 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.94 examples/second between steps 202 and 203
I0421 10:36:30.081305 47076539613632 model_training_utils.py:505] Train Step: 204/2100  / loss = 2.20654296875
I0421 10:36:30.081685 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.54 examples/second between steps 203 and 204
I0421 10:36:31.172506 47076539613632 model_training_utils.py:505] Train Step: 205/2100  / loss = 2.078369140625
I0421 10:36:31.172886 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.21 examples/second between steps 204 and 205
I0421 10:36:32.263342 47076539613632 model_training_utils.py:505] Train Step: 206/2100  / loss = 1.963623046875
I0421 10:36:32.263725 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.24 examples/second between steps 205 and 206
I0421 10:36:33.348359 47076539613632 model_training_utils.py:505] Train Step: 207/2100  / loss = 1.90673828125
I0421 10:36:33.348739 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 206 and 207
I0421 10:36:34.432911 47076539613632 model_training_utils.py:505] Train Step: 208/2100  / loss = 1.884765625
I0421 10:36:34.433306 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 207 and 208
I0421 10:36:35.520555 47076539613632 model_training_utils.py:505] Train Step: 209/2100  / loss = 1.528076171875
I0421 10:36:35.520941 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 208 and 209
I0421 10:36:36.608789 47076539613632 model_training_utils.py:505] Train Step: 210/2100  / loss = 1.675537109375
I0421 10:36:36.609187 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 209 and 210
I0421 10:36:37.698480 47076539613632 model_training_utils.py:505] Train Step: 211/2100  / loss = 1.9228515625
I0421 10:36:37.698879 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 210 and 211
I0421 10:36:38.783445 47076539613632 model_training_utils.py:505] Train Step: 212/2100  / loss = 2.302734375
I0421 10:36:38.783826 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 211 and 212
I0421 10:36:39.875589 47076539613632 model_training_utils.py:505] Train Step: 213/2100  / loss = 2.113037109375
I0421 10:36:39.875974 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.10 examples/second between steps 212 and 213
I0421 10:36:40.966915 47076539613632 model_training_utils.py:505] Train Step: 214/2100  / loss = 2.05712890625
I0421 10:36:40.967299 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.19 examples/second between steps 213 and 214
I0421 10:36:42.054537 47076539613632 model_training_utils.py:505] Train Step: 215/2100  / loss = 2.138671875
I0421 10:36:42.054910 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 214 and 215
I0421 10:36:43.147522 47076539613632 model_training_utils.py:505] Train Step: 216/2100  / loss = 2.02392578125
I0421 10:36:43.147897 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.03 examples/second between steps 215 and 216
I0421 10:36:44.236444 47076539613632 model_training_utils.py:505] Train Step: 217/2100  / loss = 1.916259765625
I0421 10:36:44.236830 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 216 and 217
I0421 10:36:45.320531 47076539613632 model_training_utils.py:505] Train Step: 218/2100  / loss = 1.693359375
I0421 10:36:45.320907 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 217 and 218
I0421 10:36:46.407844 47076539613632 model_training_utils.py:505] Train Step: 219/2100  / loss = 1.822265625
I0421 10:36:46.408226 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 218 and 219
I0421 10:36:47.494579 47076539613632 model_training_utils.py:505] Train Step: 220/2100  / loss = 2.04150390625
I0421 10:36:47.494957 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 219 and 220
I0421 10:36:48.580131 47076539613632 model_training_utils.py:505] Train Step: 221/2100  / loss = 1.9169921875
I0421 10:36:48.580522 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 220 and 221
I0421 10:36:49.667216 47076539613632 model_training_utils.py:505] Train Step: 222/2100  / loss = 1.70849609375
I0421 10:36:49.667606 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 221 and 222
I0421 10:36:50.753705 47076539613632 model_training_utils.py:505] Train Step: 223/2100  / loss = 2.052001953125
I0421 10:36:50.754083 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 222 and 223
I0421 10:36:51.843742 47076539613632 model_training_utils.py:505] Train Step: 224/2100  / loss = 1.72412109375
I0421 10:36:51.844120 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.31 examples/second between steps 223 and 224
I0421 10:36:52.930403 47076539613632 model_training_utils.py:505] Train Step: 225/2100  / loss = 1.67333984375
I0421 10:36:52.930780 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 224 and 225
I0421 10:36:54.013795 47076539613632 model_training_utils.py:505] Train Step: 226/2100  / loss = 1.578857421875
I0421 10:36:54.014178 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 225 and 226
I0421 10:36:55.094019 47076539613632 model_training_utils.py:505] Train Step: 227/2100  / loss = 1.9794921875
I0421 10:36:55.094408 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.40 examples/second between steps 226 and 227
I0421 10:36:56.181170 47076539613632 model_training_utils.py:505] Train Step: 228/2100  / loss = 1.74462890625
I0421 10:36:56.181609 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 227 and 228
I0421 10:36:57.272562 47076539613632 model_training_utils.py:505] Train Step: 229/2100  / loss = 1.9248046875
I0421 10:36:57.273015 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.19 examples/second between steps 228 and 229
I0421 10:36:58.364504 47076539613632 model_training_utils.py:505] Train Step: 230/2100  / loss = 2.0712890625
I0421 10:36:58.364936 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.18 examples/second between steps 229 and 230
I0421 10:36:59.452755 47076539613632 model_training_utils.py:505] Train Step: 231/2100  / loss = 1.64599609375
I0421 10:36:59.453196 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.52 examples/second between steps 230 and 231
I0421 10:37:00.538913 47076539613632 model_training_utils.py:505] Train Step: 232/2100  / loss = 1.5927734375
I0421 10:37:00.539364 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 231 and 232
I0421 10:37:01.629975 47076539613632 model_training_utils.py:505] Train Step: 233/2100  / loss = 2.10498046875
I0421 10:37:01.630430 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.22 examples/second between steps 232 and 233
I0421 10:37:02.720572 47076539613632 model_training_utils.py:505] Train Step: 234/2100  / loss = 2.256591796875
I0421 10:37:02.721011 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 233 and 234
I0421 10:37:03.807444 47076539613632 model_training_utils.py:505] Train Step: 235/2100  / loss = 2.27099609375
I0421 10:37:03.807877 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.69 examples/second between steps 234 and 235
I0421 10:37:04.892779 47076539613632 model_training_utils.py:505] Train Step: 236/2100  / loss = 2.05224609375
I0421 10:37:04.893216 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 235 and 236
I0421 10:37:05.972440 47076539613632 model_training_utils.py:505] Train Step: 237/2100  / loss = 2.333251953125
I0421 10:37:05.972879 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.47 examples/second between steps 236 and 237
I0421 10:37:07.055802 47076539613632 model_training_utils.py:505] Train Step: 238/2100  / loss = 1.96630859375
I0421 10:37:07.056239 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 237 and 238
I0421 10:37:08.144304 47076539613632 model_training_utils.py:505] Train Step: 239/2100  / loss = 1.80517578125
I0421 10:37:08.144689 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 238 and 239
I0421 10:37:09.233993 47076539613632 model_training_utils.py:505] Train Step: 240/2100  / loss = 1.977294921875
I0421 10:37:09.234385 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 239 and 240
I0421 10:37:10.324296 47076539613632 model_training_utils.py:505] Train Step: 241/2100  / loss = 2.26953125
I0421 10:37:10.324686 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.29 examples/second between steps 240 and 241
I0421 10:37:11.411832 47076539613632 model_training_utils.py:505] Train Step: 242/2100  / loss = 2.10498046875
I0421 10:37:11.412266 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 241 and 242
I0421 10:37:12.497775 47076539613632 model_training_utils.py:505] Train Step: 243/2100  / loss = 2.392578125
I0421 10:37:12.498204 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 242 and 243
I0421 10:37:13.586014 47076539613632 model_training_utils.py:505] Train Step: 244/2100  / loss = 2.3408203125
I0421 10:37:13.586451 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 243 and 244
I0421 10:37:14.671746 47076539613632 model_training_utils.py:505] Train Step: 245/2100  / loss = 2.14453125
I0421 10:37:14.672190 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 244 and 245
I0421 10:37:15.757756 47076539613632 model_training_utils.py:505] Train Step: 246/2100  / loss = 2.69482421875
I0421 10:37:15.758199 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 245 and 246
I0421 10:37:16.846297 47076539613632 model_training_utils.py:505] Train Step: 247/2100  / loss = 2.28076171875
I0421 10:37:16.846732 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.49 examples/second between steps 246 and 247
I0421 10:37:17.933180 47076539613632 model_training_utils.py:505] Train Step: 248/2100  / loss = 1.96826171875
I0421 10:37:17.933619 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 247 and 248
I0421 10:37:19.019651 47076539613632 model_training_utils.py:505] Train Step: 249/2100  / loss = 2.0517578125
I0421 10:37:19.020088 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 248 and 249
I0421 10:37:20.105462 47076539613632 model_training_utils.py:505] Train Step: 250/2100  / loss = 1.95849609375
I0421 10:37:20.105892 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 249 and 250
I0421 10:37:21.195276 47076539613632 model_training_utils.py:505] Train Step: 251/2100  / loss = 1.484619140625
I0421 10:37:21.195712 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.35 examples/second between steps 250 and 251
I0421 10:37:22.281736 47076539613632 model_training_utils.py:505] Train Step: 252/2100  / loss = 1.4638671875
I0421 10:37:22.282172 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 251 and 252
I0421 10:37:23.361393 47076539613632 model_training_utils.py:505] Train Step: 253/2100  / loss = 1.204833984375
I0421 10:37:23.361828 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.46 examples/second between steps 252 and 253
I0421 10:37:24.446151 47076539613632 model_training_utils.py:505] Train Step: 254/2100  / loss = 1.101318359375
I0421 10:37:24.446596 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 253 and 254
I0421 10:37:25.531286 47076539613632 model_training_utils.py:505] Train Step: 255/2100  / loss = 1.52685546875
I0421 10:37:25.531720 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 254 and 255
I0421 10:37:26.611370 47076539613632 model_training_utils.py:505] Train Step: 256/2100  / loss = 1.5205078125
I0421 10:37:26.611806 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.41 examples/second between steps 255 and 256
I0421 10:37:27.699063 47076539613632 model_training_utils.py:505] Train Step: 257/2100  / loss = 1.759521484375
I0421 10:37:27.699530 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.58 examples/second between steps 256 and 257
I0421 10:37:28.783625 47076539613632 model_training_utils.py:505] Train Step: 258/2100  / loss = 1.6201171875
I0421 10:37:28.784062 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 257 and 258
I0421 10:37:29.872351 47076539613632 model_training_utils.py:505] Train Step: 259/2100  / loss = 1.42236328125
I0421 10:37:29.872783 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.47 examples/second between steps 258 and 259
I0421 10:37:30.960450 47076539613632 model_training_utils.py:505] Train Step: 260/2100  / loss = 1.73095703125
I0421 10:37:30.960888 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.53 examples/second between steps 259 and 260
I0421 10:37:32.042728 47076539613632 model_training_utils.py:505] Train Step: 261/2100  / loss = 1.8154296875
I0421 10:37:32.043156 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 260 and 261
I0421 10:37:33.124570 47076539613632 model_training_utils.py:505] Train Step: 262/2100  / loss = 1.322021484375
I0421 10:37:33.125001 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 261 and 262
I0421 10:37:34.209725 47076539613632 model_training_utils.py:505] Train Step: 263/2100  / loss = 1.29345703125
I0421 10:37:34.210155 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 262 and 263
I0421 10:37:35.294511 47076539613632 model_training_utils.py:505] Train Step: 264/2100  / loss = 1.447998046875
I0421 10:37:35.294948 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 263 and 264
I0421 10:37:36.379417 47076539613632 model_training_utils.py:505] Train Step: 265/2100  / loss = 1.773681640625
I0421 10:37:36.379853 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 264 and 265
I0421 10:37:37.465814 47076539613632 model_training_utils.py:505] Train Step: 266/2100  / loss = 1.57421875
I0421 10:37:37.466248 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 265 and 266
I0421 10:37:38.555461 47076539613632 model_training_utils.py:505] Train Step: 267/2100  / loss = 1.718505859375
I0421 10:37:38.555895 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.39 examples/second between steps 266 and 267
I0421 10:37:39.643021 47076539613632 model_training_utils.py:505] Train Step: 268/2100  / loss = 1.618896484375
I0421 10:37:39.643476 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 267 and 268
I0421 10:37:40.727110 47076539613632 model_training_utils.py:505] Train Step: 269/2100  / loss = 1.542724609375
I0421 10:37:40.727553 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 268 and 269
I0421 10:37:41.814102 47076539613632 model_training_utils.py:505] Train Step: 270/2100  / loss = 1.50537109375
I0421 10:37:41.814551 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 269 and 270
I0421 10:37:42.897418 47076539613632 model_training_utils.py:505] Train Step: 271/2100  / loss = 1.71337890625
I0421 10:37:42.897860 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 270 and 271
I0421 10:37:43.981513 47076539613632 model_training_utils.py:505] Train Step: 272/2100  / loss = 1.6259765625
I0421 10:37:43.981955 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 271 and 272
I0421 10:37:45.067598 47076539613632 model_training_utils.py:505] Train Step: 273/2100  / loss = 1.76611328125
I0421 10:37:45.068038 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 272 and 273
I0421 10:37:46.153645 47076539613632 model_training_utils.py:505] Train Step: 274/2100  / loss = 1.712890625
I0421 10:37:46.154095 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 273 and 274
I0421 10:37:47.238104 47076539613632 model_training_utils.py:505] Train Step: 275/2100  / loss = 2.099853515625
I0421 10:37:47.238545 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 274 and 275
I0421 10:37:48.324071 47076539613632 model_training_utils.py:505] Train Step: 276/2100  / loss = 3.19482421875
I0421 10:37:48.324517 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 275 and 276
I0421 10:37:49.415494 47076539613632 model_training_utils.py:505] Train Step: 277/2100  / loss = 3.095703125
I0421 10:37:49.415925 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.22 examples/second between steps 276 and 277
I0421 10:37:50.500530 47076539613632 model_training_utils.py:505] Train Step: 278/2100  / loss = 2.310546875
I0421 10:37:50.500961 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 277 and 278
I0421 10:37:51.586648 47076539613632 model_training_utils.py:505] Train Step: 279/2100  / loss = 1.547607421875
I0421 10:37:51.587082 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 278 and 279
I0421 10:37:52.674976 47076539613632 model_training_utils.py:505] Train Step: 280/2100  / loss = 1.621826171875
I0421 10:37:52.675422 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 279 and 280
I0421 10:37:53.760400 47076539613632 model_training_utils.py:505] Train Step: 281/2100  / loss = 1.564208984375
I0421 10:37:53.760834 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 280 and 281
I0421 10:37:54.845687 47076539613632 model_training_utils.py:505] Train Step: 282/2100  / loss = 1.84716796875
I0421 10:37:54.846131 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 281 and 282
I0421 10:37:55.928692 47076539613632 model_training_utils.py:505] Train Step: 283/2100  / loss = 1.88720703125
I0421 10:37:55.929127 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 282 and 283
I0421 10:37:57.012435 47076539613632 model_training_utils.py:505] Train Step: 284/2100  / loss = 1.80908203125
I0421 10:37:57.012863 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 283 and 284
I0421 10:37:58.097065 47076539613632 model_training_utils.py:505] Train Step: 285/2100  / loss = 2.8671875
I0421 10:37:58.097504 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 284 and 285
I0421 10:37:59.180255 47076539613632 model_training_utils.py:505] Train Step: 286/2100  / loss = 2.86083984375
I0421 10:37:59.180702 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 285 and 286
I0421 10:38:00.267987 47076539613632 model_training_utils.py:505] Train Step: 287/2100  / loss = 2.06640625
I0421 10:38:00.268426 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 286 and 287
I0421 10:38:01.360349 47076539613632 model_training_utils.py:505] Train Step: 288/2100  / loss = 2.633544921875
I0421 10:38:01.360787 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.05 examples/second between steps 287 and 288
I0421 10:38:02.448136 47076539613632 model_training_utils.py:505] Train Step: 289/2100  / loss = 3.0361328125
I0421 10:38:02.448577 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 288 and 289
I0421 10:38:03.537286 47076539613632 model_training_utils.py:505] Train Step: 290/2100  / loss = 2.70947265625
I0421 10:38:03.537724 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.43 examples/second between steps 289 and 290
I0421 10:38:04.623644 47076539613632 model_training_utils.py:505] Train Step: 291/2100  / loss = 2.33642578125
I0421 10:38:04.624069 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 290 and 291
I0421 10:38:05.707680 47076539613632 model_training_utils.py:505] Train Step: 292/2100  / loss = 1.6201171875
I0421 10:38:05.708124 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 291 and 292
I0421 10:38:06.796768 47076539613632 model_training_utils.py:505] Train Step: 293/2100  / loss = 1.678955078125
I0421 10:38:06.797196 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.45 examples/second between steps 292 and 293
I0421 10:38:07.883083 47076539613632 model_training_utils.py:505] Train Step: 294/2100  / loss = 1.6884765625
I0421 10:38:07.883518 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 293 and 294
I0421 10:38:08.970413 47076539613632 model_training_utils.py:505] Train Step: 295/2100  / loss = 1.765625
I0421 10:38:08.971602 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 294 and 295
I0421 10:38:10.055443 47076539613632 model_training_utils.py:505] Train Step: 296/2100  / loss = 1.902587890625
I0421 10:38:10.055874 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 295 and 296
I0421 10:38:11.141615 47076539613632 model_training_utils.py:505] Train Step: 297/2100  / loss = 1.730712890625
I0421 10:38:11.142053 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 296 and 297
I0421 10:38:12.225957 47076539613632 model_training_utils.py:505] Train Step: 298/2100  / loss = 1.768310546875
I0421 10:38:12.226391 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 297 and 298
I0421 10:38:13.312524 47076539613632 model_training_utils.py:505] Train Step: 299/2100  / loss = 1.8427734375
I0421 10:38:13.312957 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 298 and 299
I0421 10:38:14.398540 47076539613632 model_training_utils.py:505] Train Step: 300/2100  / loss = 2.079345703125
I0421 10:38:14.398971 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 299 and 300
I0421 10:38:15.484151 47076539613632 model_training_utils.py:505] Train Step: 301/2100  / loss = 1.74462890625
I0421 10:38:15.484595 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 300 and 301
I0421 10:38:16.573475 47076539613632 model_training_utils.py:505] Train Step: 302/2100  / loss = 1.829833984375
I0421 10:38:16.573924 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.40 examples/second between steps 301 and 302
I0421 10:38:17.663332 47076539613632 model_training_utils.py:505] Train Step: 303/2100  / loss = 1.9140625
I0421 10:38:17.663762 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.40 examples/second between steps 302 and 303
I0421 10:38:18.749788 47076539613632 model_training_utils.py:505] Train Step: 304/2100  / loss = 1.567138671875
I0421 10:38:18.750222 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 303 and 304
I0421 10:38:19.835153 47076539613632 model_training_utils.py:505] Train Step: 305/2100  / loss = 1.332275390625
I0421 10:38:19.835597 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 304 and 305
I0421 10:38:20.919651 47076539613632 model_training_utils.py:505] Train Step: 306/2100  / loss = 1.061767578125
I0421 10:38:20.920087 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 305 and 306
I0421 10:38:22.003611 47076539613632 model_training_utils.py:505] Train Step: 307/2100  / loss = 1.541748046875
I0421 10:38:22.004045 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 306 and 307
I0421 10:38:23.087404 47076539613632 model_training_utils.py:505] Train Step: 308/2100  / loss = 1.0927734375
I0421 10:38:23.087837 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 307 and 308
I0421 10:38:24.169559 47076539613632 model_training_utils.py:505] Train Step: 309/2100  / loss = 1.314697265625
I0421 10:38:24.170013 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 308 and 309
I0421 10:38:25.254856 47076539613632 model_training_utils.py:505] Train Step: 310/2100  / loss = 1.45166015625
I0421 10:38:25.255253 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 309 and 310
I0421 10:38:26.341274 47076539613632 model_training_utils.py:505] Train Step: 311/2100  / loss = 1.30419921875
I0421 10:38:26.341703 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 310 and 311
I0421 10:38:27.425168 47076539613632 model_training_utils.py:505] Train Step: 312/2100  / loss = 1.178955078125
I0421 10:38:27.425615 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 311 and 312
I0421 10:38:28.509448 47076539613632 model_training_utils.py:505] Train Step: 313/2100  / loss = 1.134765625
I0421 10:38:28.509877 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 312 and 313
I0421 10:38:29.597739 47076539613632 model_training_utils.py:505] Train Step: 314/2100  / loss = 1.88330078125
I0421 10:38:29.598181 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 313 and 314
I0421 10:38:30.685404 47076539613632 model_training_utils.py:505] Train Step: 315/2100  / loss = 2.375
I0421 10:38:30.685853 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 314 and 315
I0421 10:38:31.769310 47076539613632 model_training_utils.py:505] Train Step: 316/2100  / loss = 1.4326171875
I0421 10:38:31.769736 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 315 and 316
I0421 10:38:32.854553 47076539613632 model_training_utils.py:505] Train Step: 317/2100  / loss = 1.6572265625
I0421 10:38:32.854992 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 316 and 317
I0421 10:38:33.939206 47076539613632 model_training_utils.py:505] Train Step: 318/2100  / loss = 1.01025390625
I0421 10:38:33.939652 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 317 and 318
I0421 10:38:35.024049 47076539613632 model_training_utils.py:505] Train Step: 319/2100  / loss = 1.052490234375
I0421 10:38:35.024492 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 318 and 319
I0421 10:38:36.109975 47076539613632 model_training_utils.py:505] Train Step: 320/2100  / loss = 2.75048828125
I0421 10:38:36.110430 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 319 and 320
I0421 10:38:37.200154 47076539613632 model_training_utils.py:505] Train Step: 321/2100  / loss = 2.43212890625
I0421 10:38:37.200596 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.34 examples/second between steps 320 and 321
I0421 10:38:38.286348 47076539613632 model_training_utils.py:505] Train Step: 322/2100  / loss = 1.92431640625
I0421 10:38:38.286773 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 321 and 322
I0421 10:38:39.373057 47076539613632 model_training_utils.py:505] Train Step: 323/2100  / loss = 2.130859375
I0421 10:38:39.373486 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 322 and 323
I0421 10:38:40.463286 47076539613632 model_training_utils.py:505] Train Step: 324/2100  / loss = 1.650390625
I0421 10:38:40.463715 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.29 examples/second between steps 323 and 324
I0421 10:38:41.549247 47076539613632 model_training_utils.py:505] Train Step: 325/2100  / loss = 1.492919921875
I0421 10:38:41.549694 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 324 and 325
I0421 10:38:42.636014 47076539613632 model_training_utils.py:505] Train Step: 326/2100  / loss = 1.71484375
I0421 10:38:42.636457 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 325 and 326
I0421 10:38:43.724728 47076539613632 model_training_utils.py:505] Train Step: 327/2100  / loss = 2.143310546875
I0421 10:38:43.725167 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.47 examples/second between steps 326 and 327
I0421 10:38:44.818813 47076539613632 model_training_utils.py:505] Train Step: 328/2100  / loss = 1.66552734375
I0421 10:38:44.819251 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.91 examples/second between steps 327 and 328
I0421 10:38:45.905325 47076539613632 model_training_utils.py:505] Train Step: 329/2100  / loss = 1.24951171875
I0421 10:38:45.905770 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 328 and 329
I0421 10:38:46.994952 47076539613632 model_training_utils.py:505] Train Step: 330/2100  / loss = 1.25537109375
I0421 10:38:46.995393 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.38 examples/second between steps 329 and 330
I0421 10:38:48.085377 47076539613632 model_training_utils.py:505] Train Step: 331/2100  / loss = 1.53857421875
I0421 10:38:48.085815 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.28 examples/second between steps 330 and 331
I0421 10:38:49.177045 47076539613632 model_training_utils.py:505] Train Step: 332/2100  / loss = 1.911865234375
I0421 10:38:49.177488 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.15 examples/second between steps 331 and 332
I0421 10:38:50.262437 47076539613632 model_training_utils.py:505] Train Step: 333/2100  / loss = 2.67626953125
I0421 10:38:50.262840 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 332 and 333
I0421 10:38:51.349210 47076539613632 model_training_utils.py:505] Train Step: 334/2100  / loss = 2.0205078125
I0421 10:38:51.349642 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 333 and 334
I0421 10:38:52.443220 47076539613632 model_training_utils.py:505] Train Step: 335/2100  / loss = 2.02734375
I0421 10:38:52.443661 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.89 examples/second between steps 334 and 335
I0421 10:38:53.528214 47076539613632 model_training_utils.py:505] Train Step: 336/2100  / loss = 1.795166015625
I0421 10:38:53.528657 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 335 and 336
I0421 10:38:54.613633 47076539613632 model_training_utils.py:505] Train Step: 337/2100  / loss = 1.3046875
I0421 10:38:54.614074 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 336 and 337
I0421 10:38:55.699162 47076539613632 model_training_utils.py:505] Train Step: 338/2100  / loss = 1.416015625
I0421 10:38:55.699598 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 337 and 338
I0421 10:38:56.785155 47076539613632 model_training_utils.py:505] Train Step: 339/2100  / loss = 1.08447265625
I0421 10:38:56.785596 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 338 and 339
I0421 10:38:57.872408 47076539613632 model_training_utils.py:505] Train Step: 340/2100  / loss = 1.51123046875
I0421 10:38:57.872846 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 339 and 340
I0421 10:38:58.963851 47076539613632 model_training_utils.py:505] Train Step: 341/2100  / loss = 1.9267578125
I0421 10:38:58.964295 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.18 examples/second between steps 340 and 341
I0421 10:39:00.056198 47076539613632 model_training_utils.py:505] Train Step: 342/2100  / loss = 1.530029296875
I0421 10:39:00.056620 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.15 examples/second between steps 341 and 342
I0421 10:39:01.146629 47076539613632 model_training_utils.py:505] Train Step: 343/2100  / loss = 2.79833984375
I0421 10:39:01.147047 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.31 examples/second between steps 342 and 343
I0421 10:39:02.240182 47076539613632 model_training_utils.py:505] Train Step: 344/2100  / loss = 2.035888671875
I0421 10:39:02.240573 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.95 examples/second between steps 343 and 344
I0421 10:39:03.334143 47076539613632 model_training_utils.py:505] Train Step: 345/2100  / loss = 1.9658203125
I0421 10:39:03.334534 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.92 examples/second between steps 344 and 345
I0421 10:39:04.428382 47076539613632 model_training_utils.py:505] Train Step: 346/2100  / loss = 1.9287109375
I0421 10:39:04.428781 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.86 examples/second between steps 345 and 346
I0421 10:39:05.516049 47076539613632 model_training_utils.py:505] Train Step: 347/2100  / loss = 1.83935546875
I0421 10:39:05.516443 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 346 and 347
I0421 10:39:06.603738 47076539613632 model_training_utils.py:505] Train Step: 348/2100  / loss = 1.912353515625
I0421 10:39:06.604121 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 347 and 348
I0421 10:39:07.682903 47076539613632 model_training_utils.py:505] Train Step: 349/2100  / loss = 1.7158203125
I0421 10:39:07.683290 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.56 examples/second between steps 348 and 349
I0421 10:39:08.772351 47076539613632 model_training_utils.py:505] Train Step: 350/2100  / loss = 1.451416015625
I0421 10:39:08.772736 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.41 examples/second between steps 349 and 350
I0421 10:39:09.859643 47076539613632 model_training_utils.py:505] Train Step: 351/2100  / loss = 1.799072265625
I0421 10:39:09.860026 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 350 and 351
I0421 10:39:10.945577 47076539613632 model_training_utils.py:505] Train Step: 352/2100  / loss = 1.81591796875
I0421 10:39:10.945961 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 351 and 352
I0421 10:39:12.032278 47076539613632 model_training_utils.py:505] Train Step: 353/2100  / loss = 1.724609375
I0421 10:39:12.032680 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 352 and 353
I0421 10:39:13.115339 47076539613632 model_training_utils.py:505] Train Step: 354/2100  / loss = 1.543212890625
I0421 10:39:13.115725 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 353 and 354
I0421 10:39:14.202596 47076539613632 model_training_utils.py:505] Train Step: 355/2100  / loss = 1.6552734375
I0421 10:39:14.202981 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 354 and 355
I0421 10:39:15.287914 47076539613632 model_training_utils.py:505] Train Step: 356/2100  / loss = 1.632080078125
I0421 10:39:15.288303 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 355 and 356
I0421 10:39:16.373405 47076539613632 model_training_utils.py:505] Train Step: 357/2100  / loss = 1.599853515625
I0421 10:39:16.373788 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 356 and 357
I0421 10:39:17.459241 47076539613632 model_training_utils.py:505] Train Step: 358/2100  / loss = 1.43115234375
I0421 10:39:17.459629 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 357 and 358
I0421 10:39:18.545299 47076539613632 model_training_utils.py:505] Train Step: 359/2100  / loss = 2.122314453125
I0421 10:39:18.545689 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 358 and 359
I0421 10:39:19.630056 47076539613632 model_training_utils.py:505] Train Step: 360/2100  / loss = 2.619140625
I0421 10:39:19.630446 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 359 and 360
I0421 10:39:20.714480 47076539613632 model_training_utils.py:505] Train Step: 361/2100  / loss = 1.74169921875
I0421 10:39:20.714870 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 360 and 361
I0421 10:39:21.796844 47076539613632 model_training_utils.py:505] Train Step: 362/2100  / loss = 1.75341796875
I0421 10:39:21.797224 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 361 and 362
I0421 10:39:22.880102 47076539613632 model_training_utils.py:505] Train Step: 363/2100  / loss = 1.482421875
I0421 10:39:22.880493 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 362 and 363
I0421 10:39:23.963794 47076539613632 model_training_utils.py:505] Train Step: 364/2100  / loss = 2.209228515625
I0421 10:39:23.964174 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 363 and 364
I0421 10:39:25.050236 47076539613632 model_training_utils.py:505] Train Step: 365/2100  / loss = 2.3818359375
I0421 10:39:25.050626 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 364 and 365
I0421 10:39:26.133832 47076539613632 model_training_utils.py:505] Train Step: 366/2100  / loss = 2.22509765625
I0421 10:39:26.134209 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 365 and 366
I0421 10:39:27.217152 47076539613632 model_training_utils.py:505] Train Step: 367/2100  / loss = 1.7421875
I0421 10:39:27.217537 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 366 and 367
I0421 10:39:28.301435 47076539613632 model_training_utils.py:505] Train Step: 368/2100  / loss = 1.29541015625
I0421 10:39:28.301821 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 367 and 368
I0421 10:39:29.385930 47076539613632 model_training_utils.py:505] Train Step: 369/2100  / loss = 1.7861328125
I0421 10:39:29.386322 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 368 and 369
I0421 10:39:30.470473 47076539613632 model_training_utils.py:505] Train Step: 370/2100  / loss = 2.54150390625
I0421 10:39:30.470860 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 369 and 370
I0421 10:39:31.557722 47076539613632 model_training_utils.py:505] Train Step: 371/2100  / loss = 2.1982421875
I0421 10:39:31.558102 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 370 and 371
I0421 10:39:32.643596 47076539613632 model_training_utils.py:505] Train Step: 372/2100  / loss = 1.394287109375
I0421 10:39:32.643977 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 371 and 372
I0421 10:39:33.723816 47076539613632 model_training_utils.py:505] Train Step: 373/2100  / loss = 1.514404296875
I0421 10:39:33.724196 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.39 examples/second between steps 372 and 373
I0421 10:39:34.809803 47076539613632 model_training_utils.py:505] Train Step: 374/2100  / loss = 1.431396484375
I0421 10:39:34.810189 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 373 and 374
I0421 10:39:35.897136 47076539613632 model_training_utils.py:505] Train Step: 375/2100  / loss = 1.524658203125
I0421 10:39:35.897531 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 374 and 375
I0421 10:39:36.991240 47076539613632 model_training_utils.py:505] Train Step: 376/2100  / loss = 1.32470703125
I0421 10:39:36.991638 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.88 examples/second between steps 375 and 376
I0421 10:39:38.089576 47076539613632 model_training_utils.py:505] Train Step: 377/2100  / loss = 1.681884765625
I0421 10:39:38.089957 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.43 examples/second between steps 376 and 377
I0421 10:39:39.180543 47076539613632 model_training_utils.py:505] Train Step: 378/2100  / loss = 2.443359375
I0421 10:39:39.180927 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.23 examples/second between steps 377 and 378
I0421 10:39:40.274457 47076539613632 model_training_utils.py:505] Train Step: 379/2100  / loss = 2.86328125
I0421 10:39:40.274840 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.91 examples/second between steps 378 and 379
I0421 10:39:41.368330 47076539613632 model_training_utils.py:505] Train Step: 380/2100  / loss = 3.4267578125
I0421 10:39:41.368717 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.01 examples/second between steps 379 and 380
I0421 10:39:42.454418 47076539613632 model_training_utils.py:505] Train Step: 381/2100  / loss = 2.82666015625
I0421 10:39:42.454809 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 380 and 381
I0421 10:39:43.540168 47076539613632 model_training_utils.py:505] Train Step: 382/2100  / loss = 1.896728515625
I0421 10:39:43.540552 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 381 and 382
I0421 10:39:44.628319 47076539613632 model_training_utils.py:505] Train Step: 383/2100  / loss = 1.648193359375
I0421 10:39:44.628702 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 382 and 383
I0421 10:39:45.721182 47076539613632 model_training_utils.py:505] Train Step: 384/2100  / loss = 1.7333984375
I0421 10:39:45.721571 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.03 examples/second between steps 383 and 384
I0421 10:39:46.810573 47076539613632 model_training_utils.py:505] Train Step: 385/2100  / loss = 1.60693359375
I0421 10:39:46.810966 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.41 examples/second between steps 384 and 385
I0421 10:39:47.896821 47076539613632 model_training_utils.py:505] Train Step: 386/2100  / loss = 1.568115234375
I0421 10:39:47.897206 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 385 and 386
I0421 10:39:48.984844 47076539613632 model_training_utils.py:505] Train Step: 387/2100  / loss = 1.3328857421875
I0421 10:39:48.985229 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 386 and 387
I0421 10:39:50.073522 47076539613632 model_training_utils.py:505] Train Step: 388/2100  / loss = 1.654052734375
I0421 10:39:50.073906 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.45 examples/second between steps 387 and 388
I0421 10:39:51.155300 47076539613632 model_training_utils.py:505] Train Step: 389/2100  / loss = 2.49462890625
I0421 10:39:51.155677 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 388 and 389
I0421 10:39:52.238724 47076539613632 model_training_utils.py:505] Train Step: 390/2100  / loss = 2.3427734375
I0421 10:39:52.239104 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 389 and 390
I0421 10:39:53.322158 47076539613632 model_training_utils.py:505] Train Step: 391/2100  / loss = 2.06396484375
I0421 10:39:53.322549 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 390 and 391
I0421 10:39:54.407061 47076539613632 model_training_utils.py:505] Train Step: 392/2100  / loss = 1.482666015625
I0421 10:39:54.407454 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 391 and 392
I0421 10:39:55.491780 47076539613632 model_training_utils.py:505] Train Step: 393/2100  / loss = 1.1932373046875
I0421 10:39:55.492161 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 392 and 393
I0421 10:39:56.578824 47076539613632 model_training_utils.py:505] Train Step: 394/2100  / loss = 1.1162109375
I0421 10:39:56.579201 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 393 and 394
I0421 10:39:57.666678 47076539613632 model_training_utils.py:505] Train Step: 395/2100  / loss = 1.519775390625
I0421 10:39:57.667057 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 394 and 395
I0421 10:39:58.751467 47076539613632 model_training_utils.py:505] Train Step: 396/2100  / loss = 1.77734375
I0421 10:39:58.751851 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 395 and 396
I0421 10:39:59.835806 47076539613632 model_training_utils.py:505] Train Step: 397/2100  / loss = 1.5361328125
I0421 10:39:59.836192 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 396 and 397
I0421 10:40:00.922264 47076539613632 model_training_utils.py:505] Train Step: 398/2100  / loss = 1.4033203125
I0421 10:40:00.922650 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 397 and 398
I0421 10:40:02.009158 47076539613632 model_training_utils.py:505] Train Step: 399/2100  / loss = 1.443115234375
I0421 10:40:02.009553 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 398 and 399
I0421 10:40:03.096000 47076539613632 model_training_utils.py:505] Train Step: 400/2100  / loss = 1.2431640625
I0421 10:40:03.096395 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 399 and 400
I0421 10:40:04.183169 47076539613632 model_training_utils.py:505] Train Step: 401/2100  / loss = 1.28125
I0421 10:40:04.183565 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 400 and 401
I0421 10:40:05.269026 47076539613632 model_training_utils.py:505] Train Step: 402/2100  / loss = 1.3994140625
I0421 10:40:05.269415 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 401 and 402
I0421 10:40:06.350570 47076539613632 model_training_utils.py:505] Train Step: 403/2100  / loss = 1.551025390625
I0421 10:40:06.350955 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 402 and 403
I0421 10:40:07.434485 47076539613632 model_training_utils.py:505] Train Step: 404/2100  / loss = 1.633056640625
I0421 10:40:07.434868 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 403 and 404
I0421 10:40:08.518422 47076539613632 model_training_utils.py:505] Train Step: 405/2100  / loss = 1.4739990234375
I0421 10:40:08.518802 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 404 and 405
I0421 10:40:09.604782 47076539613632 model_training_utils.py:505] Train Step: 406/2100  / loss = 2.21044921875
I0421 10:40:09.605165 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 405 and 406
I0421 10:40:10.690788 47076539613632 model_training_utils.py:505] Train Step: 407/2100  / loss = 1.629638671875
I0421 10:40:10.691168 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 406 and 407
I0421 10:40:11.781507 47076539613632 model_training_utils.py:505] Train Step: 408/2100  / loss = 1.3699951171875
I0421 10:40:11.781885 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 407 and 408
I0421 10:40:12.865730 47076539613632 model_training_utils.py:505] Train Step: 409/2100  / loss = 1.2578125
I0421 10:40:12.866117 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 408 and 409
I0421 10:40:13.949761 47076539613632 model_training_utils.py:505] Train Step: 410/2100  / loss = 1.537109375
I0421 10:40:13.950145 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 409 and 410
I0421 10:40:15.041076 47076539613632 model_training_utils.py:505] Train Step: 411/2100  / loss = 2.052734375
I0421 10:40:15.041463 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.16 examples/second between steps 410 and 411
I0421 10:40:16.130613 47076539613632 model_training_utils.py:505] Train Step: 412/2100  / loss = 2.089599609375
I0421 10:40:16.130990 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 411 and 412
I0421 10:40:17.214931 47076539613632 model_training_utils.py:505] Train Step: 413/2100  / loss = 1.66845703125
I0421 10:40:17.215322 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 412 and 413
I0421 10:40:18.301891 47076539613632 model_training_utils.py:505] Train Step: 414/2100  / loss = 1.67333984375
I0421 10:40:18.302275 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 413 and 414
I0421 10:40:19.388364 47076539613632 model_training_utils.py:505] Train Step: 415/2100  / loss = 1.22216796875
I0421 10:40:19.388756 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 414 and 415
I0421 10:40:20.474512 47076539613632 model_training_utils.py:505] Train Step: 416/2100  / loss = 1.2392578125
I0421 10:40:20.474895 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 415 and 416
I0421 10:40:21.562211 47076539613632 model_training_utils.py:505] Train Step: 417/2100  / loss = 1.530517578125
I0421 10:40:21.562602 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 416 and 417
I0421 10:40:22.652662 47076539613632 model_training_utils.py:505] Train Step: 418/2100  / loss = 1.85205078125
I0421 10:40:22.653043 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.30 examples/second between steps 417 and 418
I0421 10:40:23.742236 47076539613632 model_training_utils.py:505] Train Step: 419/2100  / loss = 1.720458984375
I0421 10:40:23.742627 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 418 and 419
I0421 10:40:24.828571 47076539613632 model_training_utils.py:505] Train Step: 420/2100  / loss = 1.53173828125
I0421 10:40:24.828954 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 419 and 420
I0421 10:40:25.915418 47076539613632 model_training_utils.py:505] Train Step: 421/2100  / loss = 1.6787109375
I0421 10:40:25.915802 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 420 and 421
I0421 10:40:27.001512 47076539613632 model_training_utils.py:505] Train Step: 422/2100  / loss = 2.03076171875
I0421 10:40:27.001892 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 421 and 422
I0421 10:40:28.089389 47076539613632 model_training_utils.py:505] Train Step: 423/2100  / loss = 1.39306640625
I0421 10:40:28.089783 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 422 and 423
I0421 10:40:29.177295 47076539613632 model_training_utils.py:505] Train Step: 424/2100  / loss = 1.773681640625
I0421 10:40:29.177679 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 423 and 424
I0421 10:40:30.258994 47076539613632 model_training_utils.py:505] Train Step: 425/2100  / loss = 2.32958984375
I0421 10:40:30.259385 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 424 and 425
I0421 10:40:31.343122 47076539613632 model_training_utils.py:505] Train Step: 426/2100  / loss = 2.0205078125
I0421 10:40:31.343528 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 425 and 426
I0421 10:40:32.426244 47076539613632 model_training_utils.py:505] Train Step: 427/2100  / loss = 2.4775390625
I0421 10:40:32.426633 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 426 and 427
I0421 10:40:33.510426 47076539613632 model_training_utils.py:505] Train Step: 428/2100  / loss = 2.147705078125
I0421 10:40:33.510809 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 427 and 428
I0421 10:40:34.596785 47076539613632 model_training_utils.py:505] Train Step: 429/2100  / loss = 2.05615234375
I0421 10:40:34.597170 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 428 and 429
I0421 10:40:35.683142 47076539613632 model_training_utils.py:505] Train Step: 430/2100  / loss = 1.56982421875
I0421 10:40:35.683535 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 429 and 430
I0421 10:40:36.767720 47076539613632 model_training_utils.py:505] Train Step: 431/2100  / loss = 1.450927734375
I0421 10:40:36.768118 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 430 and 431
I0421 10:40:37.852011 47076539613632 model_training_utils.py:505] Train Step: 432/2100  / loss = 1.63720703125
I0421 10:40:37.852403 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 431 and 432
I0421 10:40:38.936460 47076539613632 model_training_utils.py:505] Train Step: 433/2100  / loss = 1.48095703125
I0421 10:40:38.936843 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 432 and 433
I0421 10:40:40.021685 47076539613632 model_training_utils.py:505] Train Step: 434/2100  / loss = 1.731689453125
I0421 10:40:40.022075 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 433 and 434
I0421 10:40:41.109047 47076539613632 model_training_utils.py:505] Train Step: 435/2100  / loss = 1.755126953125
I0421 10:40:41.109439 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 434 and 435
I0421 10:40:42.195044 47076539613632 model_training_utils.py:505] Train Step: 436/2100  / loss = 1.84375
I0421 10:40:42.195464 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 435 and 436
I0421 10:40:43.283028 47076539613632 model_training_utils.py:505] Train Step: 437/2100  / loss = 1.78271484375
I0421 10:40:43.283417 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 436 and 437
I0421 10:40:44.370090 47076539613632 model_training_utils.py:505] Train Step: 438/2100  / loss = 1.63916015625
I0421 10:40:44.370490 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 437 and 438
I0421 10:40:45.454013 47076539613632 model_training_utils.py:505] Train Step: 439/2100  / loss = 1.396240234375
I0421 10:40:45.454409 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 438 and 439
I0421 10:40:46.541704 47076539613632 model_training_utils.py:505] Train Step: 440/2100  / loss = 1.58251953125
I0421 10:40:46.542088 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 439 and 440
I0421 10:40:47.627062 47076539613632 model_training_utils.py:505] Train Step: 441/2100  / loss = 2.65966796875
I0421 10:40:47.627456 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 440 and 441
I0421 10:40:48.711026 47076539613632 model_training_utils.py:505] Train Step: 442/2100  / loss = 2.497314453125
I0421 10:40:48.711419 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 441 and 442
I0421 10:40:49.795244 47076539613632 model_training_utils.py:505] Train Step: 443/2100  / loss = 1.43408203125
I0421 10:40:49.795637 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 442 and 443
I0421 10:40:50.883152 47076539613632 model_training_utils.py:505] Train Step: 444/2100  / loss = 1.95556640625
I0421 10:40:50.883540 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 443 and 444
I0421 10:40:51.968538 47076539613632 model_training_utils.py:505] Train Step: 445/2100  / loss = 1.79736328125
I0421 10:40:51.968920 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 444 and 445
I0421 10:40:53.053330 47076539613632 model_training_utils.py:505] Train Step: 446/2100  / loss = 1.515380859375
I0421 10:40:53.053714 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 445 and 446
I0421 10:40:54.138927 47076539613632 model_training_utils.py:505] Train Step: 447/2100  / loss = 1.1475830078125
I0421 10:40:54.139338 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 446 and 447
I0421 10:40:55.225168 47076539613632 model_training_utils.py:505] Train Step: 448/2100  / loss = 1.07421875
I0421 10:40:55.225553 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 447 and 448
I0421 10:40:56.307857 47076539613632 model_training_utils.py:505] Train Step: 449/2100  / loss = 1.56982421875
I0421 10:40:56.308241 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 448 and 449
I0421 10:40:57.394321 47076539613632 model_training_utils.py:505] Train Step: 450/2100  / loss = 2.082763671875
I0421 10:40:57.394710 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 449 and 450
I0421 10:40:58.480298 47076539613632 model_training_utils.py:505] Train Step: 451/2100  / loss = 2.02392578125
I0421 10:40:58.480684 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 450 and 451
I0421 10:40:59.567233 47076539613632 model_training_utils.py:505] Train Step: 452/2100  / loss = 1.502197265625
I0421 10:40:59.567626 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 451 and 452
I0421 10:41:00.653912 47076539613632 model_training_utils.py:505] Train Step: 453/2100  / loss = 1.94921875
I0421 10:41:00.654314 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 452 and 453
I0421 10:41:01.740939 47076539613632 model_training_utils.py:505] Train Step: 454/2100  / loss = 2.2197265625
I0421 10:41:01.741325 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 453 and 454
I0421 10:41:02.825470 47076539613632 model_training_utils.py:505] Train Step: 455/2100  / loss = 1.76318359375
I0421 10:41:02.825851 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 454 and 455
I0421 10:41:03.915193 47076539613632 model_training_utils.py:505] Train Step: 456/2100  / loss = 1.8232421875
I0421 10:41:03.915581 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 455 and 456
I0421 10:41:05.005789 47076539613632 model_training_utils.py:505] Train Step: 457/2100  / loss = 2.4482421875
I0421 10:41:05.006172 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.27 examples/second between steps 456 and 457
I0421 10:41:06.088326 47076539613632 model_training_utils.py:505] Train Step: 458/2100  / loss = 2.48046875
I0421 10:41:06.088709 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 457 and 458
I0421 10:41:07.174426 47076539613632 model_training_utils.py:505] Train Step: 459/2100  / loss = 2.156005859375
I0421 10:41:07.174810 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 458 and 459
I0421 10:41:08.261306 47076539613632 model_training_utils.py:505] Train Step: 460/2100  / loss = 2.24365234375
I0421 10:41:08.261686 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 459 and 460
I0421 10:41:09.348405 47076539613632 model_training_utils.py:505] Train Step: 461/2100  / loss = 2.2890625
I0421 10:41:09.348790 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 460 and 461
I0421 10:41:10.434391 47076539613632 model_training_utils.py:505] Train Step: 462/2100  / loss = 1.622802734375
I0421 10:41:10.434780 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 461 and 462
I0421 10:41:11.519661 47076539613632 model_training_utils.py:505] Train Step: 463/2100  / loss = 1.44677734375
I0421 10:41:11.520041 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 462 and 463
I0421 10:41:12.603363 47076539613632 model_training_utils.py:505] Train Step: 464/2100  / loss = 1.601318359375
I0421 10:41:12.603746 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 463 and 464
I0421 10:41:13.687149 47076539613632 model_training_utils.py:505] Train Step: 465/2100  / loss = 1.8955078125
I0421 10:41:13.687552 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 464 and 465
I0421 10:41:14.774652 47076539613632 model_training_utils.py:505] Train Step: 466/2100  / loss = 1.71142578125
I0421 10:41:14.775038 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 465 and 466
I0421 10:41:15.859898 47076539613632 model_training_utils.py:505] Train Step: 467/2100  / loss = 1.6357421875
I0421 10:41:15.860289 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 466 and 467
I0421 10:41:16.945927 47076539613632 model_training_utils.py:505] Train Step: 468/2100  / loss = 1.62646484375
I0421 10:41:16.946314 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 467 and 468
I0421 10:41:18.035357 47076539613632 model_training_utils.py:505] Train Step: 469/2100  / loss = 1.7939453125
I0421 10:41:18.035739 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 468 and 469
I0421 10:41:19.118366 47076539613632 model_training_utils.py:505] Train Step: 470/2100  / loss = 2.029052734375
I0421 10:41:19.118748 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 469 and 470
I0421 10:41:20.203003 47076539613632 model_training_utils.py:505] Train Step: 471/2100  / loss = 1.457275390625
I0421 10:41:20.203394 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 470 and 471
I0421 10:41:21.284784 47076539613632 model_training_utils.py:505] Train Step: 472/2100  / loss = 1.226318359375
I0421 10:41:21.285162 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 471 and 472
I0421 10:41:22.374432 47076539613632 model_training_utils.py:505] Train Step: 473/2100  / loss = 1.537109375
I0421 10:41:22.374820 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 472 and 473
I0421 10:41:23.457965 47076539613632 model_training_utils.py:505] Train Step: 474/2100  / loss = 1.295166015625
I0421 10:41:23.458356 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 473 and 474
I0421 10:41:24.541465 47076539613632 model_training_utils.py:505] Train Step: 475/2100  / loss = 1.4853515625
I0421 10:41:24.541851 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 474 and 475
I0421 10:41:25.626637 47076539613632 model_training_utils.py:505] Train Step: 476/2100  / loss = 1.522216796875
I0421 10:41:25.627016 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 475 and 476
I0421 10:41:26.714933 47076539613632 model_training_utils.py:505] Train Step: 477/2100  / loss = 1.37548828125
I0421 10:41:26.715322 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.52 examples/second between steps 476 and 477
I0421 10:41:27.804724 47076539613632 model_training_utils.py:505] Train Step: 478/2100  / loss = 1.45361328125
I0421 10:41:27.805107 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 477 and 478
I0421 10:41:28.890561 47076539613632 model_training_utils.py:505] Train Step: 479/2100  / loss = 1.341796875
I0421 10:41:28.890941 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 478 and 479
I0421 10:41:29.976774 47076539613632 model_training_utils.py:505] Train Step: 480/2100  / loss = 1.663818359375
I0421 10:41:29.977171 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 479 and 480
I0421 10:41:31.065073 47076539613632 model_training_utils.py:505] Train Step: 481/2100  / loss = 1.284423828125
I0421 10:41:31.065465 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 480 and 481
I0421 10:41:32.152190 47076539613632 model_training_utils.py:505] Train Step: 482/2100  / loss = 0.9088134765625
I0421 10:41:32.152574 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 481 and 482
I0421 10:41:33.235312 47076539613632 model_training_utils.py:505] Train Step: 483/2100  / loss = 1.0806884765625
I0421 10:41:33.235697 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 482 and 483
I0421 10:41:34.321370 47076539613632 model_training_utils.py:505] Train Step: 484/2100  / loss = 0.9891357421875
I0421 10:41:34.321749 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 483 and 484
I0421 10:41:35.410709 47076539613632 model_training_utils.py:505] Train Step: 485/2100  / loss = 1.046630859375
I0421 10:41:35.411093 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.38 examples/second between steps 484 and 485
I0421 10:41:36.499203 47076539613632 model_training_utils.py:505] Train Step: 486/2100  / loss = 1.2412109375
I0421 10:41:36.499592 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.58 examples/second between steps 485 and 486
I0421 10:41:37.582301 47076539613632 model_training_utils.py:505] Train Step: 487/2100  / loss = 1.21826171875
I0421 10:41:37.582686 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 486 and 487
I0421 10:41:38.668452 47076539613632 model_training_utils.py:505] Train Step: 488/2100  / loss = 1.4439697265625
I0421 10:41:38.668830 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 487 and 488
I0421 10:41:39.752951 47076539613632 model_training_utils.py:505] Train Step: 489/2100  / loss = 1.16015625
I0421 10:41:39.753344 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 488 and 489
I0421 10:41:40.838794 47076539613632 model_training_utils.py:505] Train Step: 490/2100  / loss = 1.163818359375
I0421 10:41:40.839175 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 489 and 490
I0421 10:41:41.923044 47076539613632 model_training_utils.py:505] Train Step: 491/2100  / loss = 1.764892578125
I0421 10:41:41.923436 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 490 and 491
I0421 10:41:43.011649 47076539613632 model_training_utils.py:505] Train Step: 492/2100  / loss = 1.787353515625
I0421 10:41:43.012033 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.49 examples/second between steps 491 and 492
I0421 10:41:44.093919 47076539613632 model_training_utils.py:505] Train Step: 493/2100  / loss = 1.35009765625
I0421 10:41:44.094312 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 492 and 493
I0421 10:41:45.176827 47076539613632 model_training_utils.py:505] Train Step: 494/2100  / loss = 1.40380859375
I0421 10:41:45.177234 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 493 and 494
I0421 10:41:46.261704 47076539613632 model_training_utils.py:505] Train Step: 495/2100  / loss = 1.681884765625
I0421 10:41:46.262089 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 494 and 495
I0421 10:41:47.345225 47076539613632 model_training_utils.py:505] Train Step: 496/2100  / loss = 1.380615234375
I0421 10:41:47.345613 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 495 and 496
I0421 10:41:48.427760 47076539613632 model_training_utils.py:505] Train Step: 497/2100  / loss = 1.53564453125
I0421 10:41:48.428156 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 496 and 497
I0421 10:41:49.514214 47076539613632 model_training_utils.py:505] Train Step: 498/2100  / loss = 1.528564453125
I0421 10:41:49.514605 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 497 and 498
I0421 10:41:50.599313 47076539613632 model_training_utils.py:505] Train Step: 499/2100  / loss = 1.62353515625
I0421 10:41:50.599693 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 498 and 499
I0421 10:41:51.684388 47076539613632 model_training_utils.py:505] Train Step: 500/2100  / loss = 1.51904296875
I0421 10:41:51.684772 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 499 and 500
I0421 10:41:52.772576 47076539613632 model_training_utils.py:505] Train Step: 501/2100  / loss = 1.682861328125
I0421 10:41:52.772957 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 500 and 501
I0421 10:41:53.866122 47076539613632 model_training_utils.py:505] Train Step: 502/2100  / loss = 1.630126953125
I0421 10:41:53.866521 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.95 examples/second between steps 501 and 502
I0421 10:41:54.951194 47076539613632 model_training_utils.py:505] Train Step: 503/2100  / loss = 2.096435546875
I0421 10:41:54.951582 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 502 and 503
I0421 10:41:56.035248 47076539613632 model_training_utils.py:505] Train Step: 504/2100  / loss = 1.791259765625
I0421 10:41:56.035640 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 503 and 504
I0421 10:41:57.117562 47076539613632 model_training_utils.py:505] Train Step: 505/2100  / loss = 1.6337890625
I0421 10:41:57.117945 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 504 and 505
I0421 10:41:58.201441 47076539613632 model_training_utils.py:505] Train Step: 506/2100  / loss = 1.74609375
I0421 10:41:58.201822 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 505 and 506
I0421 10:41:59.286799 47076539613632 model_training_utils.py:505] Train Step: 507/2100  / loss = 1.642333984375
I0421 10:41:59.287182 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 506 and 507
I0421 10:42:00.373651 47076539613632 model_training_utils.py:505] Train Step: 508/2100  / loss = 1.537353515625
I0421 10:42:00.374091 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 507 and 508
I0421 10:42:01.459945 47076539613632 model_training_utils.py:505] Train Step: 509/2100  / loss = 1.1630859375
I0421 10:42:01.460398 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 508 and 509
I0421 10:42:02.544161 47076539613632 model_training_utils.py:505] Train Step: 510/2100  / loss = 1.238037109375
I0421 10:42:02.544624 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 509 and 510
I0421 10:42:03.631264 47076539613632 model_training_utils.py:505] Train Step: 511/2100  / loss = 1.2734375
I0421 10:42:03.631710 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 510 and 511
I0421 10:42:04.718500 47076539613632 model_training_utils.py:505] Train Step: 512/2100  / loss = 1.3798828125
I0421 10:42:04.718942 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 511 and 512
I0421 10:42:05.808909 47076539613632 model_training_utils.py:505] Train Step: 513/2100  / loss = 1.3388671875
I0421 10:42:05.809363 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.30 examples/second between steps 512 and 513
I0421 10:42:06.898506 47076539613632 model_training_utils.py:505] Train Step: 514/2100  / loss = 1.74755859375
I0421 10:42:06.898949 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.41 examples/second between steps 513 and 514
I0421 10:42:07.988959 47076539613632 model_training_utils.py:505] Train Step: 515/2100  / loss = 1.69580078125
I0421 10:42:07.989410 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.31 examples/second between steps 514 and 515
I0421 10:42:09.075988 47076539613632 model_training_utils.py:505] Train Step: 516/2100  / loss = 1.3037109375
I0421 10:42:09.076441 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.69 examples/second between steps 515 and 516
I0421 10:42:10.161685 47076539613632 model_training_utils.py:505] Train Step: 517/2100  / loss = 1.4912109375
I0421 10:42:10.162125 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 516 and 517
I0421 10:42:11.248133 47076539613632 model_training_utils.py:505] Train Step: 518/2100  / loss = 1.3154296875
I0421 10:42:11.248584 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 517 and 518
I0421 10:42:12.333856 47076539613632 model_training_utils.py:505] Train Step: 519/2100  / loss = 1.41162109375
I0421 10:42:12.334307 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 518 and 519
I0421 10:42:13.418418 47076539613632 model_training_utils.py:505] Train Step: 520/2100  / loss = 1.294921875
I0421 10:42:13.418848 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 519 and 520
I0421 10:42:14.501243 47076539613632 model_training_utils.py:505] Train Step: 521/2100  / loss = 1.2039794921875
I0421 10:42:14.501670 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 520 and 521
I0421 10:42:15.587245 47076539613632 model_training_utils.py:505] Train Step: 522/2100  / loss = 0.9847412109375
I0421 10:42:15.587685 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 521 and 522
I0421 10:42:16.668939 47076539613632 model_training_utils.py:505] Train Step: 523/2100  / loss = 0.931640625
I0421 10:42:16.669378 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 522 and 523
I0421 10:42:17.756553 47076539613632 model_training_utils.py:505] Train Step: 524/2100  / loss = 1.0362548828125
I0421 10:42:17.756986 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 523 and 524
I0421 10:42:18.844296 47076539613632 model_training_utils.py:505] Train Step: 525/2100  / loss = 0.9427490234375
I0421 10:42:18.844731 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 524 and 525
I0421 10:42:19.924770 47076539613632 model_training_utils.py:505] Train Step: 526/2100  / loss = 1.274658203125
I0421 10:42:19.925209 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.37 examples/second between steps 525 and 526
I0421 10:42:21.013316 47076539613632 model_training_utils.py:505] Train Step: 527/2100  / loss = 1.764404296875
I0421 10:42:21.013760 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.49 examples/second between steps 526 and 527
I0421 10:42:22.097192 47076539613632 model_training_utils.py:505] Train Step: 528/2100  / loss = 1.70361328125
I0421 10:42:22.097624 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 527 and 528
I0421 10:42:23.182723 47076539613632 model_training_utils.py:505] Train Step: 529/2100  / loss = 1.2474365234375
I0421 10:42:23.183154 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 528 and 529
I0421 10:42:24.268548 47076539613632 model_training_utils.py:505] Train Step: 530/2100  / loss = 1.4296875
I0421 10:42:24.268984 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 529 and 530
I0421 10:42:25.355668 47076539613632 model_training_utils.py:505] Train Step: 531/2100  / loss = 1.753173828125
I0421 10:42:25.356081 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 530 and 531
I0421 10:42:26.438841 47076539613632 model_training_utils.py:505] Train Step: 532/2100  / loss = 2.24072265625
I0421 10:42:26.439264 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 531 and 532
I0421 10:42:27.523907 47076539613632 model_training_utils.py:505] Train Step: 533/2100  / loss = 1.221923828125
I0421 10:42:27.524336 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 532 and 533
I0421 10:42:28.609352 47076539613632 model_training_utils.py:505] Train Step: 534/2100  / loss = 1.296875
I0421 10:42:28.609775 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 533 and 534
I0421 10:42:29.694147 47076539613632 model_training_utils.py:505] Train Step: 535/2100  / loss = 1.3255615234375
I0421 10:42:29.694587 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 534 and 535
I0421 10:42:30.785215 47076539613632 model_training_utils.py:505] Train Step: 536/2100  / loss = 1.50048828125
I0421 10:42:30.785649 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.20 examples/second between steps 535 and 536
I0421 10:42:31.871887 47076539613632 model_training_utils.py:505] Train Step: 537/2100  / loss = 1.531005859375
I0421 10:42:31.872328 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 536 and 537
I0421 10:42:32.957026 47076539613632 model_training_utils.py:505] Train Step: 538/2100  / loss = 1.471923828125
I0421 10:42:32.957467 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 537 and 538
I0421 10:42:34.039138 47076539613632 model_training_utils.py:505] Train Step: 539/2100  / loss = 1.167236328125
I0421 10:42:34.039576 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 538 and 539
I0421 10:42:35.125748 47076539613632 model_training_utils.py:505] Train Step: 540/2100  / loss = 1.1864013671875
I0421 10:42:35.126177 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 539 and 540
I0421 10:42:36.209130 47076539613632 model_training_utils.py:505] Train Step: 541/2100  / loss = 1.158935546875
I0421 10:42:36.209562 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 540 and 541
I0421 10:42:37.294137 47076539613632 model_training_utils.py:505] Train Step: 542/2100  / loss = 1.239501953125
I0421 10:42:37.294570 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 541 and 542
I0421 10:42:38.377949 47076539613632 model_training_utils.py:505] Train Step: 543/2100  / loss = 1.319091796875
I0421 10:42:38.378377 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 542 and 543
I0421 10:42:39.466773 47076539613632 model_training_utils.py:505] Train Step: 544/2100  / loss = 1.2958984375
I0421 10:42:39.467192 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.47 examples/second between steps 543 and 544
I0421 10:42:40.549877 47076539613632 model_training_utils.py:505] Train Step: 545/2100  / loss = 1.6591796875
I0421 10:42:40.550305 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 544 and 545
I0421 10:42:41.640952 47076539613632 model_training_utils.py:505] Train Step: 546/2100  / loss = 1.74462890625
I0421 10:42:41.641383 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.20 examples/second between steps 545 and 546
I0421 10:42:42.721530 47076539613632 model_training_utils.py:505] Train Step: 547/2100  / loss = 1.71875
I0421 10:42:42.721963 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.38 examples/second between steps 546 and 547
I0421 10:42:43.804792 47076539613632 model_training_utils.py:505] Train Step: 548/2100  / loss = 1.993896484375
I0421 10:42:43.805211 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 547 and 548
I0421 10:42:44.890549 47076539613632 model_training_utils.py:505] Train Step: 549/2100  / loss = 1.3466796875
I0421 10:42:44.890987 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 548 and 549
I0421 10:42:45.973413 47076539613632 model_training_utils.py:505] Train Step: 550/2100  / loss = 1.53955078125
I0421 10:42:45.973851 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 549 and 550
I0421 10:42:47.055010 47076539613632 model_training_utils.py:505] Train Step: 551/2100  / loss = 1.580322265625
I0421 10:42:47.055442 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 550 and 551
I0421 10:42:48.135042 47076539613632 model_training_utils.py:505] Train Step: 552/2100  / loss = 1.4345703125
I0421 10:42:48.135479 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.45 examples/second between steps 551 and 552
I0421 10:42:49.217761 47076539613632 model_training_utils.py:505] Train Step: 553/2100  / loss = 1.1700439453125
I0421 10:42:49.218202 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 552 and 553
I0421 10:42:50.299826 47076539613632 model_training_utils.py:505] Train Step: 554/2100  / loss = 1.68408203125
I0421 10:42:50.300253 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 553 and 554
I0421 10:42:51.381032 47076539613632 model_training_utils.py:505] Train Step: 555/2100  / loss = 1.6318359375
I0421 10:42:51.381459 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 554 and 555
I0421 10:42:52.465617 47076539613632 model_training_utils.py:505] Train Step: 556/2100  / loss = 1.734375
I0421 10:42:52.466033 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 555 and 556
I0421 10:42:53.549742 47076539613632 model_training_utils.py:505] Train Step: 557/2100  / loss = 1.263671875
I0421 10:42:53.550126 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 556 and 557
I0421 10:42:54.634962 47076539613632 model_training_utils.py:505] Train Step: 558/2100  / loss = 1.4140625
I0421 10:42:54.635354 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 557 and 558
I0421 10:42:55.721018 47076539613632 model_training_utils.py:505] Train Step: 559/2100  / loss = 1.510986328125
I0421 10:42:55.721416 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 558 and 559
I0421 10:42:56.804304 47076539613632 model_training_utils.py:505] Train Step: 560/2100  / loss = 1.947509765625
I0421 10:42:56.804694 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 559 and 560
I0421 10:42:57.890010 47076539613632 model_training_utils.py:505] Train Step: 561/2100  / loss = 2.6337890625
I0421 10:42:57.890402 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 560 and 561
I0421 10:42:58.972122 47076539613632 model_training_utils.py:505] Train Step: 562/2100  / loss = 3.4287109375
I0421 10:42:58.972515 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 561 and 562
I0421 10:43:00.055675 47076539613632 model_training_utils.py:505] Train Step: 563/2100  / loss = 2.85302734375
I0421 10:43:00.056059 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 562 and 563
I0421 10:43:01.142221 47076539613632 model_training_utils.py:505] Train Step: 564/2100  / loss = 2.18310546875
I0421 10:43:01.142615 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 563 and 564
I0421 10:43:02.226521 47076539613632 model_training_utils.py:505] Train Step: 565/2100  / loss = 2.36083984375
I0421 10:43:02.226897 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 564 and 565
I0421 10:43:03.311867 47076539613632 model_training_utils.py:505] Train Step: 566/2100  / loss = 2.0771484375
I0421 10:43:03.312247 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 565 and 566
I0421 10:43:04.395390 47076539613632 model_training_utils.py:505] Train Step: 567/2100  / loss = 1.5810546875
I0421 10:43:04.395780 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 566 and 567
I0421 10:43:05.477968 47076539613632 model_training_utils.py:505] Train Step: 568/2100  / loss = 1.396484375
I0421 10:43:05.478355 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 567 and 568
I0421 10:43:06.563596 47076539613632 model_training_utils.py:505] Train Step: 569/2100  / loss = 1.07080078125
I0421 10:43:06.563980 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 568 and 569
I0421 10:43:07.648089 47076539613632 model_training_utils.py:505] Train Step: 570/2100  / loss = 1.364501953125
I0421 10:43:07.648482 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 569 and 570
I0421 10:43:08.730465 47076539613632 model_training_utils.py:505] Train Step: 571/2100  / loss = 1.3291015625
I0421 10:43:08.730849 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 570 and 571
I0421 10:43:09.814360 47076539613632 model_training_utils.py:505] Train Step: 572/2100  / loss = 1.571533203125
I0421 10:43:09.814746 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 571 and 572
I0421 10:43:10.902310 47076539613632 model_training_utils.py:505] Train Step: 573/2100  / loss = 1.53369140625
I0421 10:43:10.902704 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 572 and 573
I0421 10:43:11.988309 47076539613632 model_training_utils.py:505] Train Step: 574/2100  / loss = 1.695068359375
I0421 10:43:11.988693 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 573 and 574
I0421 10:43:13.078194 47076539613632 model_training_utils.py:505] Train Step: 575/2100  / loss = 1.4853515625
I0421 10:43:13.078588 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.35 examples/second between steps 574 and 575
I0421 10:43:14.169315 47076539613632 model_training_utils.py:505] Train Step: 576/2100  / loss = 1.5595703125
I0421 10:43:14.169705 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.20 examples/second between steps 575 and 576
I0421 10:43:15.255740 47076539613632 model_training_utils.py:505] Train Step: 577/2100  / loss = 2.1953125
I0421 10:43:15.256119 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 576 and 577
I0421 10:43:16.344471 47076539613632 model_training_utils.py:505] Train Step: 578/2100  / loss = 2.89453125
I0421 10:43:16.344852 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 577 and 578
I0421 10:43:17.432602 47076539613632 model_training_utils.py:505] Train Step: 579/2100  / loss = 1.6064453125
I0421 10:43:17.432984 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 578 and 579
I0421 10:43:18.523050 47076539613632 model_training_utils.py:505] Train Step: 580/2100  / loss = 1.244384765625
I0421 10:43:18.523437 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.29 examples/second between steps 579 and 580
I0421 10:43:19.611999 47076539613632 model_training_utils.py:505] Train Step: 581/2100  / loss = 1.341796875
I0421 10:43:19.612398 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.44 examples/second between steps 580 and 581
I0421 10:43:20.699590 47076539613632 model_training_utils.py:505] Train Step: 582/2100  / loss = 1.134033203125
I0421 10:43:20.699985 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 581 and 582
I0421 10:43:21.787599 47076539613632 model_training_utils.py:505] Train Step: 583/2100  / loss = 1.216064453125
I0421 10:43:21.787975 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.52 examples/second between steps 582 and 583
I0421 10:43:22.875586 47076539613632 model_training_utils.py:505] Train Step: 584/2100  / loss = 1.3050537109375
I0421 10:43:22.875967 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 583 and 584
I0421 10:43:23.962793 47076539613632 model_training_utils.py:505] Train Step: 585/2100  / loss = 1.6875
I0421 10:43:23.963177 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 584 and 585
I0421 10:43:25.053447 47076539613632 model_training_utils.py:505] Train Step: 586/2100  / loss = 1.836181640625
I0421 10:43:25.053831 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.25 examples/second between steps 585 and 586
I0421 10:43:26.139171 47076539613632 model_training_utils.py:505] Train Step: 587/2100  / loss = 1.708251953125
I0421 10:43:26.139564 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 586 and 587
I0421 10:43:27.221878 47076539613632 model_training_utils.py:505] Train Step: 588/2100  / loss = 1.346435546875
I0421 10:43:27.222260 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 587 and 588
I0421 10:43:28.310820 47076539613632 model_training_utils.py:505] Train Step: 589/2100  / loss = 1.316162109375
I0421 10:43:28.311199 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.43 examples/second between steps 588 and 589
I0421 10:43:29.395480 47076539613632 model_training_utils.py:505] Train Step: 590/2100  / loss = 2.57275390625
I0421 10:43:29.395864 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 589 and 590
I0421 10:43:30.478429 47076539613632 model_training_utils.py:505] Train Step: 591/2100  / loss = 2.89013671875
I0421 10:43:30.478818 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 590 and 591
I0421 10:43:31.561298 47076539613632 model_training_utils.py:505] Train Step: 592/2100  / loss = 2.180419921875
I0421 10:43:31.561680 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 591 and 592
I0421 10:43:32.644455 47076539613632 model_training_utils.py:505] Train Step: 593/2100  / loss = 1.87060546875
I0421 10:43:32.644843 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 592 and 593
I0421 10:43:33.730708 47076539613632 model_training_utils.py:505] Train Step: 594/2100  / loss = 1.50048828125
I0421 10:43:33.731093 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 593 and 594
I0421 10:43:34.821319 47076539613632 model_training_utils.py:505] Train Step: 595/2100  / loss = 1.3671875
I0421 10:43:34.821706 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.27 examples/second between steps 594 and 595
I0421 10:43:35.908874 47076539613632 model_training_utils.py:505] Train Step: 596/2100  / loss = 1.509521484375
I0421 10:43:35.909252 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 595 and 596
I0421 10:43:36.999645 47076539613632 model_training_utils.py:505] Train Step: 597/2100  / loss = 1.576171875
I0421 10:43:37.000028 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 596 and 597
I0421 10:43:38.087806 47076539613632 model_training_utils.py:505] Train Step: 598/2100  / loss = 1.548583984375
I0421 10:43:38.088185 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 597 and 598
I0421 10:43:39.171206 47076539613632 model_training_utils.py:505] Train Step: 599/2100  / loss = 1.49169921875
I0421 10:43:39.171595 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 598 and 599
I0421 10:43:40.257179 47076539613632 model_training_utils.py:505] Train Step: 600/2100  / loss = 1.656494140625
I0421 10:43:40.257563 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 599 and 600
I0421 10:43:41.342077 47076539613632 model_training_utils.py:505] Train Step: 601/2100  / loss = 1.6357421875
I0421 10:43:41.342473 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 600 and 601
I0421 10:43:42.429971 47076539613632 model_training_utils.py:505] Train Step: 602/2100  / loss = 2.330078125
I0421 10:43:42.430367 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 601 and 602
I0421 10:43:43.520302 47076539613632 model_training_utils.py:505] Train Step: 603/2100  / loss = 1.98681640625
I0421 10:43:43.520684 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.28 examples/second between steps 602 and 603
I0421 10:43:44.603439 47076539613632 model_training_utils.py:505] Train Step: 604/2100  / loss = 1.5146484375
I0421 10:43:44.603826 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 603 and 604
I0421 10:43:45.681530 47076539613632 model_training_utils.py:505] Train Step: 605/2100  / loss = 1.34765625
I0421 10:43:45.681915 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.65 examples/second between steps 604 and 605
I0421 10:43:46.764645 47076539613632 model_training_utils.py:505] Train Step: 606/2100  / loss = 1.315673828125
I0421 10:43:46.765041 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 605 and 606
I0421 10:43:47.848131 47076539613632 model_training_utils.py:505] Train Step: 607/2100  / loss = 1.25390625
I0421 10:43:47.848522 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 606 and 607
I0421 10:43:48.932395 47076539613632 model_training_utils.py:505] Train Step: 608/2100  / loss = 1.201416015625
I0421 10:43:48.932774 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 607 and 608
I0421 10:43:50.018388 47076539613632 model_training_utils.py:505] Train Step: 609/2100  / loss = 1.0877685546875
I0421 10:43:50.018774 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 608 and 609
I0421 10:43:51.105645 47076539613632 model_training_utils.py:505] Train Step: 610/2100  / loss = 1.391845703125
I0421 10:43:51.106032 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 609 and 610
I0421 10:43:52.193206 47076539613632 model_training_utils.py:505] Train Step: 611/2100  / loss = 1.63671875
I0421 10:43:52.193593 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 610 and 611
I0421 10:43:53.279977 47076539613632 model_training_utils.py:505] Train Step: 612/2100  / loss = 1.53271484375
I0421 10:43:53.280368 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 611 and 612
I0421 10:43:54.366604 47076539613632 model_training_utils.py:505] Train Step: 613/2100  / loss = 1.13818359375
I0421 10:43:54.367000 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 612 and 613
I0421 10:43:55.448639 47076539613632 model_training_utils.py:505] Train Step: 614/2100  / loss = 1.273681640625
I0421 10:43:55.449026 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 613 and 614
I0421 10:43:56.533185 47076539613632 model_training_utils.py:505] Train Step: 615/2100  / loss = 1.1884765625
I0421 10:43:56.533572 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 614 and 615
I0421 10:43:57.621979 47076539613632 model_training_utils.py:505] Train Step: 616/2100  / loss = 1.409912109375
I0421 10:43:57.622370 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 615 and 616
I0421 10:43:58.706182 47076539613632 model_training_utils.py:505] Train Step: 617/2100  / loss = 1.811767578125
I0421 10:43:58.706586 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 616 and 617
I0421 10:43:59.793736 47076539613632 model_training_utils.py:505] Train Step: 618/2100  / loss = 1.96826171875
I0421 10:43:59.794120 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.58 examples/second between steps 617 and 618
I0421 10:44:00.874525 47076539613632 model_training_utils.py:505] Train Step: 619/2100  / loss = 1.72900390625
I0421 10:44:00.874905 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 618 and 619
I0421 10:44:01.966198 47076539613632 model_training_utils.py:505] Train Step: 620/2100  / loss = 2.293212890625
I0421 10:44:01.966587 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.11 examples/second between steps 619 and 620
I0421 10:44:03.048389 47076539613632 model_training_utils.py:505] Train Step: 621/2100  / loss = 2.88134765625
I0421 10:44:03.048772 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 620 and 621
I0421 10:44:04.134586 47076539613632 model_training_utils.py:505] Train Step: 622/2100  / loss = 2.81396484375
I0421 10:44:04.134976 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 621 and 622
I0421 10:44:05.217406 47076539613632 model_training_utils.py:505] Train Step: 623/2100  / loss = 1.847900390625
I0421 10:44:05.217785 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 622 and 623
I0421 10:44:06.300464 47076539613632 model_training_utils.py:505] Train Step: 624/2100  / loss = 1.392578125
I0421 10:44:06.300846 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 623 and 624
I0421 10:44:07.384650 47076539613632 model_training_utils.py:505] Train Step: 625/2100  / loss = 1.62060546875
I0421 10:44:07.385029 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 624 and 625
I0421 10:44:08.466833 47076539613632 model_training_utils.py:505] Train Step: 626/2100  / loss = 1.36376953125
I0421 10:44:08.467219 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 625 and 626
I0421 10:44:09.550914 47076539613632 model_training_utils.py:505] Train Step: 627/2100  / loss = 1.07861328125
I0421 10:44:09.551310 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 626 and 627
I0421 10:44:10.633571 47076539613632 model_training_utils.py:505] Train Step: 628/2100  / loss = 1.080322265625
I0421 10:44:10.633949 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 627 and 628
I0421 10:44:11.717386 47076539613632 model_training_utils.py:505] Train Step: 629/2100  / loss = 1.184326171875
I0421 10:44:11.717762 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 628 and 629
I0421 10:44:12.804082 47076539613632 model_training_utils.py:505] Train Step: 630/2100  / loss = 1.21533203125
I0421 10:44:12.804469 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 629 and 630
I0421 10:44:13.889343 47076539613632 model_training_utils.py:505] Train Step: 631/2100  / loss = 1.33203125
I0421 10:44:13.889723 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 630 and 631
I0421 10:44:14.971918 47076539613632 model_training_utils.py:505] Train Step: 632/2100  / loss = 1.25927734375
I0421 10:44:14.972309 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 631 and 632
I0421 10:44:16.054768 47076539613632 model_training_utils.py:505] Train Step: 633/2100  / loss = 1.188720703125
I0421 10:44:16.055152 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 632 and 633
I0421 10:44:17.138791 47076539613632 model_training_utils.py:505] Train Step: 634/2100  / loss = 1.200927734375
I0421 10:44:17.139173 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 633 and 634
I0421 10:44:18.226878 47076539613632 model_training_utils.py:505] Train Step: 635/2100  / loss = 1.4296875
I0421 10:44:18.227264 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.48 examples/second between steps 634 and 635
I0421 10:44:19.312072 47076539613632 model_training_utils.py:505] Train Step: 636/2100  / loss = 1.25927734375
I0421 10:44:19.312462 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 635 and 636
I0421 10:44:20.391440 47076539613632 model_training_utils.py:505] Train Step: 637/2100  / loss = 1.5615234375
I0421 10:44:20.391822 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.46 examples/second between steps 636 and 637
I0421 10:44:21.477019 47076539613632 model_training_utils.py:505] Train Step: 638/2100  / loss = 1.2210693359375
I0421 10:44:21.477405 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 637 and 638
I0421 10:44:22.559295 47076539613632 model_training_utils.py:505] Train Step: 639/2100  / loss = 1.01220703125
I0421 10:44:22.559678 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 638 and 639
I0421 10:44:23.652218 47076539613632 model_training_utils.py:505] Train Step: 640/2100  / loss = 1.3466796875
I0421 10:44:23.652603 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.01 examples/second between steps 639 and 640
I0421 10:44:24.745176 47076539613632 model_training_utils.py:505] Train Step: 641/2100  / loss = 1.517578125
I0421 10:44:24.745562 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.00 examples/second between steps 640 and 641
I0421 10:44:25.833340 47076539613632 model_training_utils.py:505] Train Step: 642/2100  / loss = 1.434814453125
I0421 10:44:25.833722 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.50 examples/second between steps 641 and 642
I0421 10:44:26.923656 47076539613632 model_training_utils.py:505] Train Step: 643/2100  / loss = 1.504150390625
I0421 10:44:26.924043 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.28 examples/second between steps 642 and 643
I0421 10:44:28.011352 47076539613632 model_training_utils.py:505] Train Step: 644/2100  / loss = 0.82373046875
I0421 10:44:28.011733 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 643 and 644
I0421 10:44:29.097480 47076539613632 model_training_utils.py:505] Train Step: 645/2100  / loss = 1.203125
I0421 10:44:29.097867 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 644 and 645
I0421 10:44:30.184471 47076539613632 model_training_utils.py:505] Train Step: 646/2100  / loss = 1.51025390625
I0421 10:44:30.184864 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 645 and 646
I0421 10:44:31.269601 47076539613632 model_training_utils.py:505] Train Step: 647/2100  / loss = 1.08544921875
I0421 10:44:31.269987 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 646 and 647
I0421 10:44:32.350884 47076539613632 model_training_utils.py:505] Train Step: 648/2100  / loss = 1.123779296875
I0421 10:44:32.351275 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 647 and 648
I0421 10:44:33.438771 47076539613632 model_training_utils.py:505] Train Step: 649/2100  / loss = 1.60498046875
I0421 10:44:33.439157 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 648 and 649
I0421 10:44:34.526220 47076539613632 model_training_utils.py:505] Train Step: 650/2100  / loss = 1.82421875
I0421 10:44:34.526609 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 649 and 650
I0421 10:44:35.612705 47076539613632 model_training_utils.py:505] Train Step: 651/2100  / loss = 2.574951171875
I0421 10:44:35.613089 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 650 and 651
I0421 10:44:36.703492 47076539613632 model_training_utils.py:505] Train Step: 652/2100  / loss = 1.600341796875
I0421 10:44:36.703873 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.23 examples/second between steps 651 and 652
I0421 10:44:37.789536 47076539613632 model_training_utils.py:505] Train Step: 653/2100  / loss = 1.4482421875
I0421 10:44:37.789913 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 652 and 653
I0421 10:44:38.878498 47076539613632 model_training_utils.py:505] Train Step: 654/2100  / loss = 1.532470703125
I0421 10:44:38.878877 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.41 examples/second between steps 653 and 654
I0421 10:44:39.967799 47076539613632 model_training_utils.py:505] Train Step: 655/2100  / loss = 1.4287109375
I0421 10:44:39.968179 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.41 examples/second between steps 654 and 655
I0421 10:44:41.054824 47076539613632 model_training_utils.py:505] Train Step: 656/2100  / loss = 1.3525390625
I0421 10:44:41.055204 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 655 and 656
I0421 10:44:42.139502 47076539613632 model_training_utils.py:505] Train Step: 657/2100  / loss = 1.405029296875
I0421 10:44:42.139877 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 656 and 657
I0421 10:44:43.224071 47076539613632 model_training_utils.py:505] Train Step: 658/2100  / loss = 1.1180419921875
I0421 10:44:43.224455 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 657 and 658
I0421 10:44:44.306572 47076539613632 model_training_utils.py:505] Train Step: 659/2100  / loss = 1.318603515625
I0421 10:44:44.306953 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 658 and 659
I0421 10:44:45.387191 47076539613632 model_training_utils.py:505] Train Step: 660/2100  / loss = 1.44384765625
I0421 10:44:45.387579 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.34 examples/second between steps 659 and 660
I0421 10:44:46.464014 47076539613632 model_training_utils.py:505] Train Step: 661/2100  / loss = 1.50732421875
I0421 10:44:46.464403 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.78 examples/second between steps 660 and 661
I0421 10:44:47.546672 47076539613632 model_training_utils.py:505] Train Step: 662/2100  / loss = 1.35498046875
I0421 10:44:47.547060 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 661 and 662
I0421 10:44:48.631193 47076539613632 model_training_utils.py:505] Train Step: 663/2100  / loss = 1.14892578125
I0421 10:44:48.631589 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 662 and 663
I0421 10:44:49.717002 47076539613632 model_training_utils.py:505] Train Step: 664/2100  / loss = 0.997314453125
I0421 10:44:49.717395 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 663 and 664
I0421 10:44:50.804101 47076539613632 model_training_utils.py:505] Train Step: 665/2100  / loss = 1.22509765625
I0421 10:44:50.804491 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 664 and 665
I0421 10:44:51.894896 47076539613632 model_training_utils.py:505] Train Step: 666/2100  / loss = 1.222412109375
I0421 10:44:51.895291 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.23 examples/second between steps 665 and 666
I0421 10:44:52.979215 47076539613632 model_training_utils.py:505] Train Step: 667/2100  / loss = 1.14501953125
I0421 10:44:52.979614 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 666 and 667
I0421 10:44:54.065119 47076539613632 model_training_utils.py:505] Train Step: 668/2100  / loss = 1.451416015625
I0421 10:44:54.065507 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 667 and 668
I0421 10:44:55.151186 47076539613632 model_training_utils.py:505] Train Step: 669/2100  / loss = 1.565185546875
I0421 10:44:55.151571 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 668 and 669
I0421 10:44:56.235175 47076539613632 model_training_utils.py:505] Train Step: 670/2100  / loss = 1.585693359375
I0421 10:44:56.235565 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 669 and 670
I0421 10:44:57.322276 47076539613632 model_training_utils.py:505] Train Step: 671/2100  / loss = 1.7314453125
I0421 10:44:57.322689 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 670 and 671
I0421 10:44:58.405473 47076539613632 model_training_utils.py:505] Train Step: 672/2100  / loss = 1.696044921875
I0421 10:44:58.405854 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 671 and 672
I0421 10:44:59.492201 47076539613632 model_training_utils.py:505] Train Step: 673/2100  / loss = 1.277099609375
I0421 10:44:59.492589 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 672 and 673
I0421 10:45:00.579190 47076539613632 model_training_utils.py:505] Train Step: 674/2100  / loss = 1.24853515625
I0421 10:45:00.579579 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 673 and 674
I0421 10:45:01.670888 47076539613632 model_training_utils.py:505] Train Step: 675/2100  / loss = 1.3271484375
I0421 10:45:01.671272 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.14 examples/second between steps 674 and 675
I0421 10:45:02.757252 47076539613632 model_training_utils.py:505] Train Step: 676/2100  / loss = 0.95849609375
I0421 10:45:02.757650 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 675 and 676
I0421 10:45:03.845877 47076539613632 model_training_utils.py:505] Train Step: 677/2100  / loss = 1.044677734375
I0421 10:45:03.846261 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.48 examples/second between steps 676 and 677
I0421 10:45:04.922216 47076539613632 model_training_utils.py:505] Train Step: 678/2100  / loss = 1.209228515625
I0421 10:45:04.922612 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.81 examples/second between steps 677 and 678
I0421 10:45:06.008221 47076539613632 model_training_utils.py:505] Train Step: 679/2100  / loss = 1.0667724609375
I0421 10:45:06.008608 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 678 and 679
I0421 10:45:07.094058 47076539613632 model_training_utils.py:505] Train Step: 680/2100  / loss = 1.12890625
I0421 10:45:07.094449 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 679 and 680
I0421 10:45:08.180089 47076539613632 model_training_utils.py:505] Train Step: 681/2100  / loss = 1.7080078125
I0421 10:45:08.180488 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 680 and 681
I0421 10:45:09.262519 47076539613632 model_training_utils.py:505] Train Step: 682/2100  / loss = 1.328857421875
I0421 10:45:09.262900 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 681 and 682
I0421 10:45:10.343711 47076539613632 model_training_utils.py:505] Train Step: 683/2100  / loss = 1.04931640625
I0421 10:45:10.344092 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 682 and 683
I0421 10:45:11.428914 47076539613632 model_training_utils.py:505] Train Step: 684/2100  / loss = 1.166748046875
I0421 10:45:11.429310 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 683 and 684
I0421 10:45:12.516049 47076539613632 model_training_utils.py:505] Train Step: 685/2100  / loss = 1.127685546875
I0421 10:45:12.516457 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 684 and 685
I0421 10:45:13.600478 47076539613632 model_training_utils.py:505] Train Step: 686/2100  / loss = 1.184326171875
I0421 10:45:13.600865 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 685 and 686
I0421 10:45:14.685941 47076539613632 model_training_utils.py:505] Train Step: 687/2100  / loss = 1.1776123046875
I0421 10:45:14.686342 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 686 and 687
I0421 10:45:15.774722 47076539613632 model_training_utils.py:505] Train Step: 688/2100  / loss = 1.32373046875
I0421 10:45:15.775116 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.43 examples/second between steps 687 and 688
I0421 10:45:16.859976 47076539613632 model_training_utils.py:505] Train Step: 689/2100  / loss = 1.2239990234375
I0421 10:45:16.860368 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 688 and 689
I0421 10:45:17.941788 47076539613632 model_training_utils.py:505] Train Step: 690/2100  / loss = 1.71875
I0421 10:45:17.942172 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 689 and 690
I0421 10:45:19.024093 47076539613632 model_training_utils.py:505] Train Step: 691/2100  / loss = 1.412109375
I0421 10:45:19.024484 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 690 and 691
I0421 10:45:20.109707 47076539613632 model_training_utils.py:505] Train Step: 692/2100  / loss = 1.465087890625
I0421 10:45:20.110089 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 691 and 692
I0421 10:45:21.198521 47076539613632 model_training_utils.py:505] Train Step: 693/2100  / loss = 1.39208984375
I0421 10:45:21.198901 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.43 examples/second between steps 692 and 693
I0421 10:45:22.284507 47076539613632 model_training_utils.py:505] Train Step: 694/2100  / loss = 1.365478515625
I0421 10:45:22.284894 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 693 and 694
I0421 10:45:23.369710 47076539613632 model_training_utils.py:505] Train Step: 695/2100  / loss = 1.259765625
I0421 10:45:23.370094 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 694 and 695
I0421 10:45:24.455134 47076539613632 model_training_utils.py:505] Train Step: 696/2100  / loss = 1.013916015625
I0421 10:45:24.455540 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 695 and 696
I0421 10:45:25.540038 47076539613632 model_training_utils.py:505] Train Step: 697/2100  / loss = 0.97607421875
I0421 10:45:25.540440 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 696 and 697
I0421 10:45:26.621473 47076539613632 model_training_utils.py:505] Train Step: 698/2100  / loss = 1.000244140625
I0421 10:45:26.621858 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 697 and 698
I0421 10:45:27.703630 47076539613632 model_training_utils.py:505] Train Step: 699/2100  / loss = 1.166748046875
I0421 10:45:27.704011 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 698 and 699
I0421 10:45:28.789150 47076539613632 model_training_utils.py:505] Train Step: 700/2100  / loss = 1.030029296875
I0421 10:45:37.176540 47076539613632 model_training_utils.py:49] Saving model as TF checkpoint: /public/home/xuanbaby/DL-TensorFlow/models_r2.3.0/official/nlp/bert/model_squad_v2/ctl_step_700.ckpt-1
I0421 10:45:37.408300 47076539613632 keras_utils.py:133] TimeHistory: 9.58 seconds, 13.36 examples/second between steps 699 and 700
I0421 10:45:38.737152 47076539613632 model_training_utils.py:505] Train Step: 701/2100  / loss = 1.2469482421875
I0421 10:45:38.738032 47076539613632 keras_utils.py:133] TimeHistory: 1.21 seconds, 74354.83 examples/second between steps 700 and 1401
I0421 10:45:39.820216 47076539613632 model_training_utils.py:505] Train Step: 702/2100  / loss = 0.9532470703125
I0421 10:45:39.820623 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 1401 and 1402
I0421 10:45:40.902045 47076539613632 model_training_utils.py:505] Train Step: 703/2100  / loss = 1.380126953125
I0421 10:45:40.902448 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 1402 and 1403
I0421 10:45:41.989025 47076539613632 model_training_utils.py:505] Train Step: 704/2100  / loss = 1.504638671875
I0421 10:45:41.989432 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 1403 and 1404
I0421 10:45:43.075928 47076539613632 model_training_utils.py:505] Train Step: 705/2100  / loss = 1.27392578125
I0421 10:45:43.076340 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 1404 and 1405
I0421 10:45:44.162086 47076539613632 model_training_utils.py:505] Train Step: 706/2100  / loss = 1.550048828125
I0421 10:45:44.162497 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 1405 and 1406
I0421 10:45:45.247252 47076539613632 model_training_utils.py:505] Train Step: 707/2100  / loss = 1.4892578125
I0421 10:45:45.247696 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 1406 and 1407
I0421 10:45:46.335277 47076539613632 model_training_utils.py:505] Train Step: 708/2100  / loss = 1.344482421875
I0421 10:45:46.335704 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 1407 and 1408
I0421 10:45:47.421876 47076539613632 model_training_utils.py:505] Train Step: 709/2100  / loss = 1.0712890625
I0421 10:45:47.422302 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 1408 and 1409
I0421 10:45:48.509137 47076539613632 model_training_utils.py:505] Train Step: 710/2100  / loss = 1.25048828125
I0421 10:45:48.509560 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 1409 and 1410
I0421 10:45:49.596687 47076539613632 model_training_utils.py:505] Train Step: 711/2100  / loss = 1.4609375
I0421 10:45:49.597111 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 1410 and 1411
I0421 10:45:50.679113 47076539613632 model_training_utils.py:505] Train Step: 712/2100  / loss = 1.628662109375
I0421 10:45:50.679545 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 1411 and 1412
I0421 10:45:51.764613 47076539613632 model_training_utils.py:505] Train Step: 713/2100  / loss = 1.073486328125
I0421 10:45:51.765036 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 1412 and 1413
I0421 10:45:52.849865 47076539613632 model_training_utils.py:505] Train Step: 714/2100  / loss = 1.19140625
I0421 10:45:52.850298 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 1413 and 1414
I0421 10:45:53.935842 47076539613632 model_training_utils.py:505] Train Step: 715/2100  / loss = 1.3719482421875
I0421 10:45:53.936269 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 1414 and 1415
I0421 10:45:55.023673 47076539613632 model_training_utils.py:505] Train Step: 716/2100  / loss = 1.1033935546875
I0421 10:45:55.024100 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 1415 and 1416
I0421 10:45:56.112215 47076539613632 model_training_utils.py:505] Train Step: 717/2100  / loss = 1.0306396484375
I0421 10:45:56.112655 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.48 examples/second between steps 1416 and 1417
I0421 10:45:57.197658 47076539613632 model_training_utils.py:505] Train Step: 718/2100  / loss = 1.209716796875
I0421 10:45:57.198084 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 1417 and 1418
I0421 10:45:58.280993 47076539613632 model_training_utils.py:505] Train Step: 719/2100  / loss = 1.626220703125
I0421 10:45:58.281429 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1418 and 1419
I0421 10:45:59.364000 47076539613632 model_training_utils.py:505] Train Step: 720/2100  / loss = 1.612548828125
I0421 10:45:59.364407 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1419 and 1420
I0421 10:46:00.462738 47076539613632 model_training_utils.py:505] Train Step: 721/2100  / loss = 1.56884765625
I0421 10:46:00.463166 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.32 examples/second between steps 1420 and 1421
I0421 10:46:01.556326 47076539613632 model_training_utils.py:505] Train Step: 722/2100  / loss = 1.491943359375
I0421 10:46:01.556756 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 117.98 examples/second between steps 1421 and 1422
I0421 10:46:02.648989 47076539613632 model_training_utils.py:505] Train Step: 723/2100  / loss = 1.358154296875
I0421 10:46:02.649419 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.06 examples/second between steps 1422 and 1423
I0421 10:46:03.731360 47076539613632 model_training_utils.py:505] Train Step: 724/2100  / loss = 1.244140625
I0421 10:46:03.731771 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1423 and 1424
I0421 10:46:04.820967 47076539613632 model_training_utils.py:505] Train Step: 725/2100  / loss = 1.341552734375
I0421 10:46:04.821419 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 1424 and 1425
I0421 10:46:05.908252 47076539613632 model_training_utils.py:505] Train Step: 726/2100  / loss = 1.3779296875
I0421 10:46:05.908677 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 1425 and 1426
I0421 10:46:06.991475 47076539613632 model_training_utils.py:505] Train Step: 727/2100  / loss = 1.47314453125
I0421 10:46:06.991911 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1426 and 1427
I0421 10:46:08.084931 47076539613632 model_training_utils.py:505] Train Step: 728/2100  / loss = 1.179443359375
I0421 10:46:08.085366 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.92 examples/second between steps 1427 and 1428
I0421 10:46:09.180355 47076539613632 model_training_utils.py:505] Train Step: 729/2100  / loss = 1.417724609375
I0421 10:46:09.180784 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.74 examples/second between steps 1428 and 1429
I0421 10:46:10.269257 47076539613632 model_training_utils.py:505] Train Step: 730/2100  / loss = 1.31298828125
I0421 10:46:10.269693 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.44 examples/second between steps 1429 and 1430
I0421 10:46:11.355356 47076539613632 model_training_utils.py:505] Train Step: 731/2100  / loss = 1.1357421875
I0421 10:46:11.355787 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 1430 and 1431
I0421 10:46:12.447300 47076539613632 model_training_utils.py:505] Train Step: 732/2100  / loss = 0.833251953125
I0421 10:46:12.447731 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.09 examples/second between steps 1431 and 1432
I0421 10:46:13.539188 47076539613632 model_training_utils.py:505] Train Step: 733/2100  / loss = 1.05419921875
I0421 10:46:13.539617 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.11 examples/second between steps 1432 and 1433
I0421 10:46:14.627042 47076539613632 model_training_utils.py:505] Train Step: 734/2100  / loss = 1.1300048828125
I0421 10:46:14.627472 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 1433 and 1434
I0421 10:46:15.715484 47076539613632 model_training_utils.py:505] Train Step: 735/2100  / loss = 0.992919921875
I0421 10:46:15.715905 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 1434 and 1435
I0421 10:46:16.807507 47076539613632 model_training_utils.py:505] Train Step: 736/2100  / loss = 1.007568359375
I0421 10:46:16.807928 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.13 examples/second between steps 1435 and 1436
I0421 10:46:17.898707 47076539613632 model_training_utils.py:505] Train Step: 737/2100  / loss = 0.8719482421875
I0421 10:46:17.899128 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.21 examples/second between steps 1436 and 1437
I0421 10:46:18.986259 47076539613632 model_training_utils.py:505] Train Step: 738/2100  / loss = 1.1715087890625
I0421 10:46:18.986695 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 1437 and 1438
I0421 10:46:20.072453 47076539613632 model_training_utils.py:505] Train Step: 739/2100  / loss = 1.297607421875
I0421 10:46:20.072876 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 1438 and 1439
I0421 10:46:21.160135 47076539613632 model_training_utils.py:505] Train Step: 740/2100  / loss = 1.576904296875
I0421 10:46:21.160567 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 1439 and 1440
I0421 10:46:22.249179 47076539613632 model_training_utils.py:505] Train Step: 741/2100  / loss = 1.447998046875
I0421 10:46:22.249614 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.43 examples/second between steps 1440 and 1441
I0421 10:46:23.331606 47076539613632 model_training_utils.py:505] Train Step: 742/2100  / loss = 1.65771484375
I0421 10:46:23.332027 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1441 and 1442
I0421 10:46:24.417267 47076539613632 model_training_utils.py:505] Train Step: 743/2100  / loss = 1.41455078125
I0421 10:46:24.417701 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 1442 and 1443
I0421 10:46:25.506669 47076539613632 model_training_utils.py:505] Train Step: 744/2100  / loss = 1.18115234375
I0421 10:46:25.507083 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 1443 and 1444
I0421 10:46:26.592749 47076539613632 model_training_utils.py:505] Train Step: 745/2100  / loss = 1.316650390625
I0421 10:46:26.593169 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 1444 and 1445
I0421 10:46:27.679553 47076539613632 model_training_utils.py:505] Train Step: 746/2100  / loss = 1.0540771484375
I0421 10:46:27.679975 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 1445 and 1446
I0421 10:46:28.768438 47076539613632 model_training_utils.py:505] Train Step: 747/2100  / loss = 1.141357421875
I0421 10:46:28.768857 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.45 examples/second between steps 1446 and 1447
I0421 10:46:29.855354 47076539613632 model_training_utils.py:505] Train Step: 748/2100  / loss = 1.465576171875
I0421 10:46:29.855776 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 1447 and 1448
I0421 10:46:30.939173 47076539613632 model_training_utils.py:505] Train Step: 749/2100  / loss = 1.004638671875
I0421 10:46:30.939602 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 1448 and 1449
I0421 10:46:32.023627 47076539613632 model_training_utils.py:505] Train Step: 750/2100  / loss = 1.531494140625
I0421 10:46:32.024043 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 1449 and 1450
I0421 10:46:33.108814 47076539613632 model_training_utils.py:505] Train Step: 751/2100  / loss = 1.543212890625
I0421 10:46:33.109235 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 1450 and 1451
I0421 10:46:34.200939 47076539613632 model_training_utils.py:505] Train Step: 752/2100  / loss = 1.673828125
I0421 10:46:34.201373 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.14 examples/second between steps 1451 and 1452
I0421 10:46:35.291742 47076539613632 model_training_utils.py:505] Train Step: 753/2100  / loss = 1.551513671875
I0421 10:46:35.292165 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.23 examples/second between steps 1452 and 1453
I0421 10:46:36.382927 47076539613632 model_training_utils.py:505] Train Step: 754/2100  / loss = 1.57275390625
I0421 10:46:36.383347 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.22 examples/second between steps 1453 and 1454
I0421 10:46:37.472649 47076539613632 model_training_utils.py:505] Train Step: 755/2100  / loss = 1.445556640625
I0421 10:46:37.473055 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.34 examples/second between steps 1454 and 1455
I0421 10:46:38.567592 47076539613632 model_training_utils.py:505] Train Step: 756/2100  / loss = 1.4521484375
I0421 10:46:38.568008 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.78 examples/second between steps 1455 and 1456
I0421 10:46:39.649745 47076539613632 model_training_utils.py:505] Train Step: 757/2100  / loss = 1.593994140625
I0421 10:46:39.650160 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1456 and 1457
I0421 10:46:40.748856 47076539613632 model_training_utils.py:505] Train Step: 758/2100  / loss = 1.371337890625
I0421 10:46:40.749279 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.31 examples/second between steps 1457 and 1458
I0421 10:46:41.846483 47076539613632 model_training_utils.py:505] Train Step: 759/2100  / loss = 1.12548828125
I0421 10:46:41.846907 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.47 examples/second between steps 1458 and 1459
I0421 10:46:42.949684 47076539613632 model_training_utils.py:505] Train Step: 760/2100  / loss = 0.8424072265625
I0421 10:46:42.950146 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.86 examples/second between steps 1459 and 1460
I0421 10:46:44.046392 47076539613632 model_training_utils.py:505] Train Step: 761/2100  / loss = 1.159423828125
I0421 10:46:44.046830 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.62 examples/second between steps 1460 and 1461
I0421 10:46:45.134300 47076539613632 model_training_utils.py:505] Train Step: 762/2100  / loss = 1.418212890625
I0421 10:46:45.134723 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 1461 and 1462
I0421 10:46:46.233692 47076539613632 model_training_utils.py:505] Train Step: 763/2100  / loss = 1.49365234375
I0421 10:46:46.234076 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.42 examples/second between steps 1462 and 1463
I0421 10:46:47.326311 47076539613632 model_training_utils.py:505] Train Step: 764/2100  / loss = 1.61865234375
I0421 10:46:47.326707 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.04 examples/second between steps 1463 and 1464
I0421 10:46:48.424350 47076539613632 model_training_utils.py:505] Train Step: 765/2100  / loss = 1.299072265625
I0421 10:46:48.424742 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.49 examples/second between steps 1464 and 1465
I0421 10:46:49.514236 47076539613632 model_training_utils.py:505] Train Step: 766/2100  / loss = 1.4912109375
I0421 10:46:49.514643 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 1465 and 1466
I0421 10:46:50.598412 47076539613632 model_training_utils.py:505] Train Step: 767/2100  / loss = 1.257080078125
I0421 10:46:50.598797 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 1466 and 1467
I0421 10:46:51.680390 47076539613632 model_training_utils.py:505] Train Step: 768/2100  / loss = 1.225830078125
I0421 10:46:51.680774 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 1467 and 1468
I0421 10:46:52.763446 47076539613632 model_training_utils.py:505] Train Step: 769/2100  / loss = 1.21337890625
I0421 10:46:52.763819 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1468 and 1469
I0421 10:46:53.838383 47076539613632 model_training_utils.py:505] Train Step: 770/2100  / loss = 1.94140625
I0421 10:46:53.838765 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 120.01 examples/second between steps 1469 and 1470
I0421 10:46:54.924060 47076539613632 model_training_utils.py:505] Train Step: 771/2100  / loss = 1.764404296875
I0421 10:46:54.924459 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 1470 and 1471
I0421 10:46:56.009514 47076539613632 model_training_utils.py:505] Train Step: 772/2100  / loss = 2.001220703125
I0421 10:46:56.009897 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 1471 and 1472
I0421 10:46:57.097688 47076539613632 model_training_utils.py:505] Train Step: 773/2100  / loss = 1.507080078125
I0421 10:46:57.098078 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.53 examples/second between steps 1472 and 1473
I0421 10:46:58.182351 47076539613632 model_training_utils.py:505] Train Step: 774/2100  / loss = 0.987060546875
I0421 10:46:58.182735 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 1473 and 1474
I0421 10:46:59.269327 47076539613632 model_training_utils.py:505] Train Step: 775/2100  / loss = 0.9698486328125
I0421 10:46:59.269714 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 1474 and 1475
I0421 10:47:00.363641 47076539613632 model_training_utils.py:505] Train Step: 776/2100  / loss = 1.056396484375
I0421 10:47:00.364024 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.84 examples/second between steps 1475 and 1476
I0421 10:47:01.459914 47076539613632 model_training_utils.py:505] Train Step: 777/2100  / loss = 1.16357421875
I0421 10:47:01.460308 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.63 examples/second between steps 1476 and 1477
I0421 10:47:02.564208 47076539613632 model_training_utils.py:505] Train Step: 778/2100  / loss = 1.0927734375
I0421 10:47:02.564594 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.80 examples/second between steps 1477 and 1478
I0421 10:47:03.656044 47076539613632 model_training_utils.py:505] Train Step: 779/2100  / loss = 1.0516357421875
I0421 10:47:03.656439 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.15 examples/second between steps 1478 and 1479
I0421 10:47:04.755882 47076539613632 model_training_utils.py:505] Train Step: 780/2100  / loss = 0.943359375
I0421 10:47:04.756278 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.25 examples/second between steps 1479 and 1480
I0421 10:47:05.854066 47076539613632 model_training_utils.py:505] Train Step: 781/2100  / loss = 1.196044921875
I0421 10:47:05.854457 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.48 examples/second between steps 1480 and 1481
I0421 10:47:06.953373 47076539613632 model_training_utils.py:505] Train Step: 782/2100  / loss = 1.45947265625
I0421 10:47:06.953804 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.40 examples/second between steps 1481 and 1482
I0421 10:47:08.049830 47076539613632 model_training_utils.py:505] Train Step: 783/2100  / loss = 1.455322265625
I0421 10:47:08.050253 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.64 examples/second between steps 1482 and 1483
I0421 10:47:09.140326 47076539613632 model_training_utils.py:505] Train Step: 784/2100  / loss = 1.352783203125
I0421 10:47:09.140758 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 1483 and 1484
I0421 10:47:10.230097 47076539613632 model_training_utils.py:505] Train Step: 785/2100  / loss = 1.33544921875
I0421 10:47:10.230528 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.36 examples/second between steps 1484 and 1485
I0421 10:47:11.318606 47076539613632 model_training_utils.py:505] Train Step: 786/2100  / loss = 1.515625
I0421 10:47:11.319035 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.50 examples/second between steps 1485 and 1486
I0421 10:47:12.408524 47076539613632 model_training_utils.py:505] Train Step: 787/2100  / loss = 1.65283203125
I0421 10:47:12.408941 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.35 examples/second between steps 1486 and 1487
I0421 10:47:13.501561 47076539613632 model_training_utils.py:505] Train Step: 788/2100  / loss = 1.241943359375
I0421 10:47:13.501983 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.00 examples/second between steps 1487 and 1488
I0421 10:47:14.607057 47076539613632 model_training_utils.py:505] Train Step: 789/2100  / loss = 1.298828125
I0421 10:47:14.607496 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.72 examples/second between steps 1488 and 1489
I0421 10:47:15.698932 47076539613632 model_training_utils.py:505] Train Step: 790/2100  / loss = 1.250732421875
I0421 10:47:15.699373 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.10 examples/second between steps 1489 and 1490
I0421 10:47:16.786372 47076539613632 model_training_utils.py:505] Train Step: 791/2100  / loss = 1.22998046875
I0421 10:47:16.786799 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 1490 and 1491
I0421 10:47:17.871682 47076539613632 model_training_utils.py:505] Train Step: 792/2100  / loss = 0.8486328125
I0421 10:47:17.872104 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 1491 and 1492
I0421 10:47:18.959922 47076539613632 model_training_utils.py:505] Train Step: 793/2100  / loss = 0.9205322265625
I0421 10:47:18.960347 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 1492 and 1493
I0421 10:47:20.054541 47076539613632 model_training_utils.py:505] Train Step: 794/2100  / loss = 1.009521484375
I0421 10:47:20.054936 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.96 examples/second between steps 1493 and 1494
I0421 10:47:21.151968 47076539613632 model_training_utils.py:505] Train Step: 795/2100  / loss = 1.1455078125
I0421 10:47:21.152375 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.53 examples/second between steps 1494 and 1495
I0421 10:47:22.242842 47076539613632 model_training_utils.py:505] Train Step: 796/2100  / loss = 1.2252197265625
I0421 10:47:22.243219 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 1495 and 1496
I0421 10:47:23.329753 47076539613632 model_training_utils.py:505] Train Step: 797/2100  / loss = 1.03857421875
I0421 10:47:23.330134 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 1496 and 1497
I0421 10:47:24.419654 47076539613632 model_training_utils.py:505] Train Step: 798/2100  / loss = 1.027099609375
I0421 10:47:24.420046 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.30 examples/second between steps 1497 and 1498
I0421 10:47:25.508262 47076539613632 model_training_utils.py:505] Train Step: 799/2100  / loss = 1.1103515625
I0421 10:47:25.508653 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 1498 and 1499
I0421 10:47:26.603827 47076539613632 model_training_utils.py:505] Train Step: 800/2100  / loss = 1.146240234375
I0421 10:47:26.604221 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.72 examples/second between steps 1499 and 1500
I0421 10:47:27.702528 47076539613632 model_training_utils.py:505] Train Step: 801/2100  / loss = 1.175048828125
I0421 10:47:27.702913 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.38 examples/second between steps 1500 and 1501
I0421 10:47:28.795742 47076539613632 model_training_utils.py:505] Train Step: 802/2100  / loss = 0.9207763671875
I0421 10:47:28.796141 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.93 examples/second between steps 1501 and 1502
I0421 10:47:29.894442 47076539613632 model_training_utils.py:505] Train Step: 803/2100  / loss = 0.945068359375
I0421 10:47:29.894822 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.39 examples/second between steps 1502 and 1503
I0421 10:47:30.993613 47076539613632 model_training_utils.py:505] Train Step: 804/2100  / loss = 0.962890625
I0421 10:47:30.993997 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.31 examples/second between steps 1503 and 1504
I0421 10:47:32.094469 47076539613632 model_training_utils.py:505] Train Step: 805/2100  / loss = 1.44775390625
I0421 10:47:32.094856 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.15 examples/second between steps 1504 and 1505
I0421 10:47:33.191532 47076539613632 model_training_utils.py:505] Train Step: 806/2100  / loss = 1.20703125
I0421 10:47:33.191913 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.59 examples/second between steps 1505 and 1506
I0421 10:47:34.287504 47076539613632 model_training_utils.py:505] Train Step: 807/2100  / loss = 0.794921875
I0421 10:47:34.287894 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.67 examples/second between steps 1506 and 1507
I0421 10:47:35.376121 47076539613632 model_training_utils.py:505] Train Step: 808/2100  / loss = 0.9906005859375
I0421 10:47:35.376517 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 1507 and 1508
I0421 10:47:36.463880 47076539613632 model_training_utils.py:505] Train Step: 809/2100  / loss = 1.118408203125
I0421 10:47:36.464266 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 1508 and 1509
I0421 10:47:37.555967 47076539613632 model_training_utils.py:505] Train Step: 810/2100  / loss = 1.2586669921875
I0421 10:47:37.556366 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.08 examples/second between steps 1509 and 1510
I0421 10:47:38.639316 47076539613632 model_training_utils.py:505] Train Step: 811/2100  / loss = 1.19140625
I0421 10:47:38.639701 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1510 and 1511
I0421 10:47:39.731784 47076539613632 model_training_utils.py:505] Train Step: 812/2100  / loss = 1.32568359375
I0421 10:47:39.732174 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.05 examples/second between steps 1511 and 1512
I0421 10:47:40.822713 47076539613632 model_training_utils.py:505] Train Step: 813/2100  / loss = 1.3233642578125
I0421 10:47:40.823099 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.22 examples/second between steps 1512 and 1513
I0421 10:47:41.915500 47076539613632 model_training_utils.py:505] Train Step: 814/2100  / loss = 0.9251708984375
I0421 10:47:41.915892 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.02 examples/second between steps 1513 and 1514
I0421 10:47:43.011878 47076539613632 model_training_utils.py:505] Train Step: 815/2100  / loss = 0.8245849609375
I0421 10:47:43.012268 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.64 examples/second between steps 1514 and 1515
I0421 10:47:44.106042 47076539613632 model_training_utils.py:505] Train Step: 816/2100  / loss = 1.1522216796875
I0421 10:47:44.106439 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.86 examples/second between steps 1515 and 1516
I0421 10:47:45.200642 47076539613632 model_training_utils.py:505] Train Step: 817/2100  / loss = 1.512451171875
I0421 10:47:45.201024 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.83 examples/second between steps 1516 and 1517
I0421 10:47:46.298630 47076539613632 model_training_utils.py:505] Train Step: 818/2100  / loss = 1.1314697265625
I0421 10:47:46.299017 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.44 examples/second between steps 1517 and 1518
I0421 10:47:47.383451 47076539613632 model_training_utils.py:505] Train Step: 819/2100  / loss = 0.888671875
I0421 10:47:47.383831 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 1518 and 1519
I0421 10:47:48.471593 47076539613632 model_training_utils.py:505] Train Step: 820/2100  / loss = 0.947509765625
I0421 10:47:48.471983 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.49 examples/second between steps 1519 and 1520
I0421 10:47:49.557766 47076539613632 model_training_utils.py:505] Train Step: 821/2100  / loss = 1.314453125
I0421 10:47:49.558157 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 1520 and 1521
I0421 10:47:50.660841 47076539613632 model_training_utils.py:505] Train Step: 822/2100  / loss = 1.2099609375
I0421 10:47:50.661225 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 116.88 examples/second between steps 1521 and 1522
I0421 10:47:51.756274 47076539613632 model_training_utils.py:505] Train Step: 823/2100  / loss = 1.46630859375
I0421 10:47:51.756664 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.74 examples/second between steps 1522 and 1523
I0421 10:47:52.850459 47076539613632 model_training_utils.py:505] Train Step: 824/2100  / loss = 1.344482421875
I0421 10:47:52.850841 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.89 examples/second between steps 1523 and 1524
I0421 10:47:53.952278 47076539613632 model_training_utils.py:505] Train Step: 825/2100  / loss = 1.1978759765625
I0421 10:47:53.952715 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.04 examples/second between steps 1524 and 1525
I0421 10:47:55.053365 47076539613632 model_training_utils.py:505] Train Step: 826/2100  / loss = 1.2626953125
I0421 10:47:55.053791 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.14 examples/second between steps 1525 and 1526
I0421 10:47:56.153141 47076539613632 model_training_utils.py:505] Train Step: 827/2100  / loss = 1.35302734375
I0421 10:47:56.153578 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.29 examples/second between steps 1526 and 1527
I0421 10:47:57.248036 47076539613632 model_training_utils.py:505] Train Step: 828/2100  / loss = 1.533203125
I0421 10:47:57.248470 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.79 examples/second between steps 1527 and 1528
I0421 10:47:58.340044 47076539613632 model_training_utils.py:505] Train Step: 829/2100  / loss = 1.324951171875
I0421 10:47:58.340475 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.11 examples/second between steps 1528 and 1529
I0421 10:47:59.431691 47076539613632 model_training_utils.py:505] Train Step: 830/2100  / loss = 1.02099609375
I0421 10:47:59.432129 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.13 examples/second between steps 1529 and 1530
I0421 10:48:00.520510 47076539613632 model_training_utils.py:505] Train Step: 831/2100  / loss = 1.1087646484375
I0421 10:48:00.520941 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.49 examples/second between steps 1530 and 1531
I0421 10:48:01.611633 47076539613632 model_training_utils.py:505] Train Step: 832/2100  / loss = 1.18603515625
I0421 10:48:01.612056 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.20 examples/second between steps 1531 and 1532
I0421 10:48:02.705230 47076539613632 model_training_utils.py:505] Train Step: 833/2100  / loss = 0.8897705078125
I0421 10:48:02.705661 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.91 examples/second between steps 1532 and 1533
I0421 10:48:03.789345 47076539613632 model_training_utils.py:505] Train Step: 834/2100  / loss = 1.03515625
I0421 10:48:03.789772 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1533 and 1534
I0421 10:48:04.874679 47076539613632 model_training_utils.py:505] Train Step: 835/2100  / loss = 1.40771484375
I0421 10:48:04.875126 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 1534 and 1535
I0421 10:48:05.966503 47076539613632 model_training_utils.py:505] Train Step: 836/2100  / loss = 1.1923828125
I0421 10:48:05.966930 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.10 examples/second between steps 1535 and 1536
I0421 10:48:07.062394 47076539613632 model_training_utils.py:505] Train Step: 837/2100  / loss = 1.24267578125
I0421 10:48:07.062819 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.68 examples/second between steps 1536 and 1537
I0421 10:48:08.155940 47076539613632 model_training_utils.py:505] Train Step: 838/2100  / loss = 1.178466796875
I0421 10:48:08.156367 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.93 examples/second between steps 1537 and 1538
I0421 10:48:09.248010 47076539613632 model_training_utils.py:505] Train Step: 839/2100  / loss = 1.244384765625
I0421 10:48:09.248450 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.10 examples/second between steps 1538 and 1539
I0421 10:48:10.340926 47076539613632 model_training_utils.py:505] Train Step: 840/2100  / loss = 1.081787109375
I0421 10:48:10.341366 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.00 examples/second between steps 1539 and 1540
I0421 10:48:11.431592 47076539613632 model_training_utils.py:505] Train Step: 841/2100  / loss = 1.2496337890625
I0421 10:48:11.432014 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.25 examples/second between steps 1540 and 1541
I0421 10:48:12.527532 47076539613632 model_training_utils.py:505] Train Step: 842/2100  / loss = 1.407470703125
I0421 10:48:12.527962 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.65 examples/second between steps 1541 and 1542
I0421 10:48:13.623888 47076539613632 model_training_utils.py:505] Train Step: 843/2100  / loss = 1.057373046875
I0421 10:48:13.624328 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.59 examples/second between steps 1542 and 1543
I0421 10:48:14.719745 47076539613632 model_training_utils.py:505] Train Step: 844/2100  / loss = 1.071044921875
I0421 10:48:14.720177 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.67 examples/second between steps 1543 and 1544
I0421 10:48:15.811988 47076539613632 model_training_utils.py:505] Train Step: 845/2100  / loss = 1.109619140625
I0421 10:48:15.812424 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.07 examples/second between steps 1544 and 1545
I0421 10:48:16.899119 47076539613632 model_training_utils.py:505] Train Step: 846/2100  / loss = 1.29052734375
I0421 10:48:16.899554 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 1545 and 1546
I0421 10:48:17.983568 47076539613632 model_training_utils.py:505] Train Step: 847/2100  / loss = 0.9256591796875
I0421 10:48:17.984001 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 1546 and 1547
I0421 10:48:19.070128 47076539613632 model_training_utils.py:505] Train Step: 848/2100  / loss = 1.114990234375
I0421 10:48:19.070564 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 1547 and 1548
I0421 10:48:20.158898 47076539613632 model_training_utils.py:505] Train Step: 849/2100  / loss = 0.916015625
I0421 10:48:20.159340 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.47 examples/second between steps 1548 and 1549
I0421 10:48:21.245922 47076539613632 model_training_utils.py:505] Train Step: 850/2100  / loss = 0.78515625
I0421 10:48:21.246354 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 1549 and 1550
I0421 10:48:22.339018 47076539613632 model_training_utils.py:505] Train Step: 851/2100  / loss = 0.63055419921875
I0421 10:48:22.339456 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.01 examples/second between steps 1550 and 1551
I0421 10:48:23.431870 47076539613632 model_training_utils.py:505] Train Step: 852/2100  / loss = 0.6890869140625
I0421 10:48:23.432317 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.00 examples/second between steps 1551 and 1552
I0421 10:48:24.522451 47076539613632 model_training_utils.py:505] Train Step: 853/2100  / loss = 0.7177734375
I0421 10:48:24.522895 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.27 examples/second between steps 1552 and 1553
I0421 10:48:25.624447 47076539613632 model_training_utils.py:505] Train Step: 854/2100  / loss = 0.8251953125
I0421 10:48:25.624876 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.04 examples/second between steps 1553 and 1554
I0421 10:48:26.725620 47076539613632 model_training_utils.py:505] Train Step: 855/2100  / loss = 1.123291015625
I0421 10:48:26.726048 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.12 examples/second between steps 1554 and 1555
I0421 10:48:27.820781 47076539613632 model_training_utils.py:505] Train Step: 856/2100  / loss = 1.2197265625
I0421 10:48:27.821206 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.76 examples/second between steps 1555 and 1556
I0421 10:48:28.903899 47076539613632 model_training_utils.py:505] Train Step: 857/2100  / loss = 1.133544921875
I0421 10:48:28.904336 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1556 and 1557
I0421 10:48:29.988299 47076539613632 model_training_utils.py:505] Train Step: 858/2100  / loss = 1.37744140625
I0421 10:48:29.988727 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 1557 and 1558
I0421 10:48:31.081651 47076539613632 model_training_utils.py:505] Train Step: 859/2100  / loss = 0.7958984375
I0421 10:48:31.082075 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.94 examples/second between steps 1558 and 1559
I0421 10:48:32.174510 47076539613632 model_training_utils.py:505] Train Step: 860/2100  / loss = 0.8917236328125
I0421 10:48:32.174938 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.00 examples/second between steps 1559 and 1560
I0421 10:48:33.270327 47076539613632 model_training_utils.py:505] Train Step: 861/2100  / loss = 0.9869384765625
I0421 10:48:33.270752 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.68 examples/second between steps 1560 and 1561
I0421 10:48:34.358778 47076539613632 model_training_utils.py:505] Train Step: 862/2100  / loss = 1.639404296875
I0421 10:48:34.359209 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.47 examples/second between steps 1561 and 1562
I0421 10:48:35.449362 47076539613632 model_training_utils.py:505] Train Step: 863/2100  / loss = 1.591796875
I0421 10:48:35.449791 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.23 examples/second between steps 1562 and 1563
I0421 10:48:36.529512 47076539613632 model_training_utils.py:505] Train Step: 864/2100  / loss = 1.1722412109375
I0421 10:48:36.529947 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.39 examples/second between steps 1563 and 1564
I0421 10:48:37.613673 47076539613632 model_training_utils.py:505] Train Step: 865/2100  / loss = 1.0703125
I0421 10:48:37.614099 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 1564 and 1565
I0421 10:48:38.699271 47076539613632 model_training_utils.py:505] Train Step: 866/2100  / loss = 0.7266845703125
I0421 10:48:38.699699 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1565 and 1566
I0421 10:48:39.782126 47076539613632 model_training_utils.py:505] Train Step: 867/2100  / loss = 0.77685546875
I0421 10:48:39.782563 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1566 and 1567
I0421 10:48:40.866231 47076539613632 model_training_utils.py:505] Train Step: 868/2100  / loss = 1.2529296875
I0421 10:48:40.866668 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1567 and 1568
I0421 10:48:41.951580 47076539613632 model_training_utils.py:505] Train Step: 869/2100  / loss = 0.9278564453125
I0421 10:48:41.952001 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 1568 and 1569
I0421 10:48:43.044303 47076539613632 model_training_utils.py:505] Train Step: 870/2100  / loss = 0.7664794921875
I0421 10:48:43.044726 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.02 examples/second between steps 1569 and 1570
I0421 10:48:44.142018 47076539613632 model_training_utils.py:505] Train Step: 871/2100  / loss = 0.9378662109375
I0421 10:48:44.142464 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.46 examples/second between steps 1570 and 1571
I0421 10:48:45.229272 47076539613632 model_training_utils.py:505] Train Step: 872/2100  / loss = 0.849853515625
I0421 10:48:45.229706 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 1571 and 1572
I0421 10:48:46.314747 47076539613632 model_training_utils.py:505] Train Step: 873/2100  / loss = 1.0556640625
I0421 10:48:46.315169 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 1572 and 1573
I0421 10:48:47.404043 47076539613632 model_training_utils.py:505] Train Step: 874/2100  / loss = 1.154541015625
I0421 10:48:47.404481 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.42 examples/second between steps 1573 and 1574
I0421 10:48:48.491350 47076539613632 model_training_utils.py:505] Train Step: 875/2100  / loss = 0.89599609375
I0421 10:48:48.491779 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 1574 and 1575
I0421 10:48:49.582478 47076539613632 model_training_utils.py:505] Train Step: 876/2100  / loss = 0.9085693359375
I0421 10:48:49.582912 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.22 examples/second between steps 1575 and 1576
I0421 10:48:50.666463 47076539613632 model_training_utils.py:505] Train Step: 877/2100  / loss = 1.00927734375
I0421 10:48:50.666887 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 1576 and 1577
I0421 10:48:51.760179 47076539613632 model_training_utils.py:505] Train Step: 878/2100  / loss = 0.743896484375
I0421 10:48:51.760604 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.92 examples/second between steps 1577 and 1578
I0421 10:48:52.855700 47076539613632 model_training_utils.py:505] Train Step: 879/2100  / loss = 0.7587890625
I0421 10:48:52.856122 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.73 examples/second between steps 1578 and 1579
I0421 10:48:53.953251 47076539613632 model_training_utils.py:505] Train Step: 880/2100  / loss = 1.10009765625
I0421 10:48:53.953683 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.47 examples/second between steps 1579 and 1580
I0421 10:48:55.047940 47076539613632 model_training_utils.py:505] Train Step: 881/2100  / loss = 1.44580078125
I0421 10:48:55.048375 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.79 examples/second between steps 1580 and 1581
I0421 10:48:56.148607 47076539613632 model_training_utils.py:505] Train Step: 882/2100  / loss = 1.35546875
I0421 10:48:56.149033 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.15 examples/second between steps 1581 and 1582
I0421 10:48:57.245627 47076539613632 model_training_utils.py:505] Train Step: 883/2100  / loss = 1.0546875
I0421 10:48:57.246049 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.54 examples/second between steps 1582 and 1583
I0421 10:48:58.334143 47076539613632 model_training_utils.py:505] Train Step: 884/2100  / loss = 1.01513671875
I0421 10:48:58.334571 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.47 examples/second between steps 1583 and 1584
I0421 10:48:59.425756 47076539613632 model_training_utils.py:505] Train Step: 885/2100  / loss = 0.747802734375
I0421 10:48:59.426184 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.11 examples/second between steps 1584 and 1585
I0421 10:49:00.509982 47076539613632 model_training_utils.py:505] Train Step: 886/2100  / loss = 0.9036865234375
I0421 10:49:00.510429 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1585 and 1586
I0421 10:49:01.591265 47076539613632 model_training_utils.py:505] Train Step: 887/2100  / loss = 0.7894287109375
I0421 10:49:01.591700 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 1586 and 1587
I0421 10:49:02.672419 47076539613632 model_training_utils.py:505] Train Step: 888/2100  / loss = 0.747314453125
I0421 10:49:02.672846 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 1587 and 1588
I0421 10:49:03.753845 47076539613632 model_training_utils.py:505] Train Step: 889/2100  / loss = 0.908447265625
I0421 10:49:03.754272 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 1588 and 1589
I0421 10:49:04.843925 47076539613632 model_training_utils.py:505] Train Step: 890/2100  / loss = 0.8060302734375
I0421 10:49:04.844352 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.29 examples/second between steps 1589 and 1590
I0421 10:49:05.938513 47076539613632 model_training_utils.py:505] Train Step: 891/2100  / loss = 1.542236328125
I0421 10:49:05.938939 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.79 examples/second between steps 1590 and 1591
I0421 10:49:07.028554 47076539613632 model_training_utils.py:505] Train Step: 892/2100  / loss = 1.3135986328125
I0421 10:49:07.028977 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.29 examples/second between steps 1591 and 1592
I0421 10:49:08.116142 47076539613632 model_training_utils.py:505] Train Step: 893/2100  / loss = 1.79638671875
I0421 10:49:08.116572 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 1592 and 1593
I0421 10:49:09.204869 47076539613632 model_training_utils.py:505] Train Step: 894/2100  / loss = 1.074462890625
I0421 10:49:09.205304 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.44 examples/second between steps 1593 and 1594
I0421 10:49:10.292535 47076539613632 model_training_utils.py:505] Train Step: 895/2100  / loss = 0.8701171875
I0421 10:49:10.292961 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 1594 and 1595
I0421 10:49:11.380311 47076539613632 model_training_utils.py:505] Train Step: 896/2100  / loss = 0.9775390625
I0421 10:49:11.380740 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.58 examples/second between steps 1595 and 1596
I0421 10:49:12.470787 47076539613632 model_training_utils.py:505] Train Step: 897/2100  / loss = 0.7843017578125
I0421 10:49:12.471214 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.25 examples/second between steps 1596 and 1597
I0421 10:49:13.558257 47076539613632 model_training_utils.py:505] Train Step: 898/2100  / loss = 0.9503173828125
I0421 10:49:13.558689 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 1597 and 1598
I0421 10:49:14.651753 47076539613632 model_training_utils.py:505] Train Step: 899/2100  / loss = 1.244140625
I0421 10:49:14.652183 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.92 examples/second between steps 1598 and 1599
I0421 10:49:15.745344 47076539613632 model_training_utils.py:505] Train Step: 900/2100  / loss = 1.05419921875
I0421 10:49:15.745760 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.91 examples/second between steps 1599 and 1600
I0421 10:49:16.832740 47076539613632 model_training_utils.py:505] Train Step: 901/2100  / loss = 0.8095703125
I0421 10:49:16.833168 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 1600 and 1601
I0421 10:49:17.924072 47076539613632 model_training_utils.py:505] Train Step: 902/2100  / loss = 0.985595703125
I0421 10:49:17.924508 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.17 examples/second between steps 1601 and 1602
I0421 10:49:19.018221 47076539613632 model_training_utils.py:505] Train Step: 903/2100  / loss = 1.0374755859375
I0421 10:49:19.018672 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.88 examples/second between steps 1602 and 1603
I0421 10:49:20.110978 47076539613632 model_training_utils.py:505] Train Step: 904/2100  / loss = 1.056396484375
I0421 10:49:20.111413 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.15 examples/second between steps 1603 and 1604
I0421 10:49:21.203457 47076539613632 model_training_utils.py:505] Train Step: 905/2100  / loss = 1.09228515625
I0421 10:49:21.203882 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.04 examples/second between steps 1604 and 1605
I0421 10:49:22.294260 47076539613632 model_training_utils.py:505] Train Step: 906/2100  / loss = 0.9730224609375
I0421 10:49:22.294688 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.21 examples/second between steps 1605 and 1606
I0421 10:49:23.375030 47076539613632 model_training_utils.py:505] Train Step: 907/2100  / loss = 1.12451171875
I0421 10:49:23.375464 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 1606 and 1607
I0421 10:49:24.456682 47076539613632 model_training_utils.py:505] Train Step: 908/2100  / loss = 0.8765869140625
I0421 10:49:24.457112 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 1607 and 1608
I0421 10:49:25.538201 47076539613632 model_training_utils.py:505] Train Step: 909/2100  / loss = 0.67626953125
I0421 10:49:25.538611 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 1608 and 1609
I0421 10:49:26.621787 47076539613632 model_training_utils.py:505] Train Step: 910/2100  / loss = 0.7398681640625
I0421 10:49:26.622211 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 1609 and 1610
I0421 10:49:27.707064 47076539613632 model_training_utils.py:505] Train Step: 911/2100  / loss = 0.7781982421875
I0421 10:49:27.707495 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 1610 and 1611
I0421 10:49:28.793417 47076539613632 model_training_utils.py:505] Train Step: 912/2100  / loss = 0.99365234375
I0421 10:49:28.793837 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.69 examples/second between steps 1611 and 1612
I0421 10:49:29.881301 47076539613632 model_training_utils.py:505] Train Step: 913/2100  / loss = 1.049072265625
I0421 10:49:29.881727 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.53 examples/second between steps 1612 and 1613
I0421 10:49:30.964700 47076539613632 model_training_utils.py:505] Train Step: 914/2100  / loss = 1.099609375
I0421 10:49:30.965138 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1613 and 1614
I0421 10:49:32.046010 47076539613632 model_training_utils.py:505] Train Step: 915/2100  / loss = 1.20849609375
I0421 10:49:32.046448 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 1614 and 1615
I0421 10:49:33.127905 47076539613632 model_training_utils.py:505] Train Step: 916/2100  / loss = 1.0286865234375
I0421 10:49:33.128337 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1615 and 1616
I0421 10:49:34.208977 47076539613632 model_training_utils.py:505] Train Step: 917/2100  / loss = 0.8787841796875
I0421 10:49:34.209401 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 1616 and 1617
I0421 10:49:35.290633 47076539613632 model_training_utils.py:505] Train Step: 918/2100  / loss = 0.989501953125
I0421 10:49:35.291061 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 1617 and 1618
I0421 10:49:36.371509 47076539613632 model_training_utils.py:505] Train Step: 919/2100  / loss = 0.96435546875
I0421 10:49:36.371934 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 1618 and 1619
I0421 10:49:37.453422 47076539613632 model_training_utils.py:505] Train Step: 920/2100  / loss = 1.028076171875
I0421 10:49:37.453850 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 1619 and 1620
I0421 10:49:38.536029 47076539613632 model_training_utils.py:505] Train Step: 921/2100  / loss = 0.926025390625
I0421 10:49:38.536465 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1620 and 1621
I0421 10:49:39.616574 47076539613632 model_training_utils.py:505] Train Step: 922/2100  / loss = 0.848876953125
I0421 10:49:39.617001 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.34 examples/second between steps 1621 and 1622
I0421 10:49:40.701636 47076539613632 model_training_utils.py:505] Train Step: 923/2100  / loss = 1.1561279296875
I0421 10:49:40.702064 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 1622 and 1623
I0421 10:49:41.782395 47076539613632 model_training_utils.py:505] Train Step: 924/2100  / loss = 1.016845703125
I0421 10:49:41.782826 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 1623 and 1624
I0421 10:49:42.864112 47076539613632 model_training_utils.py:505] Train Step: 925/2100  / loss = 0.8126220703125
I0421 10:49:42.864537 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 1624 and 1625
I0421 10:49:43.945129 47076539613632 model_training_utils.py:505] Train Step: 926/2100  / loss = 0.6988525390625
I0421 10:49:43.945564 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 1625 and 1626
I0421 10:49:45.026015 47076539613632 model_training_utils.py:505] Train Step: 927/2100  / loss = 0.796142578125
I0421 10:49:45.026453 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 1626 and 1627
I0421 10:49:46.110161 47076539613632 model_training_utils.py:505] Train Step: 928/2100  / loss = 1.037841796875
I0421 10:49:46.110596 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 1627 and 1628
I0421 10:49:47.195786 47076539613632 model_training_utils.py:505] Train Step: 929/2100  / loss = 0.8160400390625
I0421 10:49:47.196216 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 1628 and 1629
I0421 10:49:48.272788 47076539613632 model_training_utils.py:505] Train Step: 930/2100  / loss = 1.12060546875
I0421 10:49:48.273216 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.72 examples/second between steps 1629 and 1630
I0421 10:49:49.356182 47076539613632 model_training_utils.py:505] Train Step: 931/2100  / loss = 0.96630859375
I0421 10:49:49.356624 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 1630 and 1631
I0421 10:49:50.438325 47076539613632 model_training_utils.py:505] Train Step: 932/2100  / loss = 0.84326171875
I0421 10:49:50.438754 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 1631 and 1632
I0421 10:49:51.522360 47076539613632 model_training_utils.py:505] Train Step: 933/2100  / loss = 1.087890625
I0421 10:49:51.522788 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 1632 and 1633
I0421 10:49:52.605834 47076539613632 model_training_utils.py:505] Train Step: 934/2100  / loss = 1.19482421875
I0421 10:49:52.606256 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1633 and 1634
I0421 10:49:53.690090 47076539613632 model_training_utils.py:505] Train Step: 935/2100  / loss = 1.304443359375
I0421 10:49:53.690522 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 1634 and 1635
I0421 10:49:54.772022 47076539613632 model_training_utils.py:505] Train Step: 936/2100  / loss = 1.41259765625
I0421 10:49:54.772454 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 1635 and 1636
I0421 10:49:55.853628 47076539613632 model_training_utils.py:505] Train Step: 937/2100  / loss = 1.377685546875
I0421 10:49:55.854056 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 1636 and 1637
I0421 10:49:56.933461 47076539613632 model_training_utils.py:505] Train Step: 938/2100  / loss = 1.0550537109375
I0421 10:49:56.933882 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.41 examples/second between steps 1637 and 1638
I0421 10:49:58.014112 47076539613632 model_training_utils.py:505] Train Step: 939/2100  / loss = 0.806640625
I0421 10:49:58.014552 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 1638 and 1639
I0421 10:49:59.096117 47076539613632 model_training_utils.py:505] Train Step: 940/2100  / loss = 1.09228515625
I0421 10:49:59.096555 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1639 and 1640
I0421 10:50:00.178165 47076539613632 model_training_utils.py:505] Train Step: 941/2100  / loss = 1.142578125
I0421 10:50:00.178604 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 1640 and 1641
I0421 10:50:01.258883 47076539613632 model_training_utils.py:505] Train Step: 942/2100  / loss = 1.391845703125
I0421 10:50:01.259319 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 1641 and 1642
I0421 10:50:02.337626 47076539613632 model_training_utils.py:505] Train Step: 943/2100  / loss = 1.436279296875
I0421 10:50:02.338054 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.52 examples/second between steps 1642 and 1643
I0421 10:50:03.421120 47076539613632 model_training_utils.py:505] Train Step: 944/2100  / loss = 1.5224609375
I0421 10:50:03.421554 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1643 and 1644
I0421 10:50:04.504197 47076539613632 model_training_utils.py:505] Train Step: 945/2100  / loss = 1.319091796875
I0421 10:50:04.504636 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 1644 and 1645
I0421 10:50:05.586376 47076539613632 model_training_utils.py:505] Train Step: 946/2100  / loss = 1.68310546875
I0421 10:50:05.586799 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1645 and 1646
I0421 10:50:06.669510 47076539613632 model_training_utils.py:505] Train Step: 947/2100  / loss = 1.43310546875
I0421 10:50:06.669940 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 1646 and 1647
I0421 10:50:07.749755 47076539613632 model_training_utils.py:505] Train Step: 948/2100  / loss = 1.1593017578125
I0421 10:50:07.750185 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.38 examples/second between steps 1647 and 1648
I0421 10:50:08.833479 47076539613632 model_training_utils.py:505] Train Step: 949/2100  / loss = 0.96142578125
I0421 10:50:08.833899 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 1648 and 1649
I0421 10:50:09.916466 47076539613632 model_training_utils.py:505] Train Step: 950/2100  / loss = 0.9580078125
I0421 10:50:09.916893 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1649 and 1650
I0421 10:50:11.000303 47076539613632 model_training_utils.py:505] Train Step: 951/2100  / loss = 0.875
I0421 10:50:11.000724 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 1650 and 1651
I0421 10:50:12.080341 47076539613632 model_training_utils.py:505] Train Step: 952/2100  / loss = 0.7005615234375
I0421 10:50:12.080768 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.37 examples/second between steps 1651 and 1652
I0421 10:50:13.160694 47076539613632 model_training_utils.py:505] Train Step: 953/2100  / loss = 0.6920166015625
I0421 10:50:13.161120 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.34 examples/second between steps 1652 and 1653
I0421 10:50:14.240722 47076539613632 model_training_utils.py:505] Train Step: 954/2100  / loss = 0.51190185546875
I0421 10:50:14.241161 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.45 examples/second between steps 1653 and 1654
I0421 10:50:15.321116 47076539613632 model_training_utils.py:505] Train Step: 955/2100  / loss = 0.7860107421875
I0421 10:50:15.321547 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 1654 and 1655
I0421 10:50:16.400304 47076539613632 model_training_utils.py:505] Train Step: 956/2100  / loss = 0.839599609375
I0421 10:50:16.400727 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.52 examples/second between steps 1655 and 1656
I0421 10:50:17.482536 47076539613632 model_training_utils.py:505] Train Step: 957/2100  / loss = 0.98388671875
I0421 10:50:17.482961 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 1656 and 1657
I0421 10:50:18.565195 47076539613632 model_training_utils.py:505] Train Step: 958/2100  / loss = 0.762939453125
I0421 10:50:18.565627 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1657 and 1658
I0421 10:50:19.652214 47076539613632 model_training_utils.py:505] Train Step: 959/2100  / loss = 0.829345703125
I0421 10:50:19.652650 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 1658 and 1659
I0421 10:50:20.735441 47076539613632 model_training_utils.py:505] Train Step: 960/2100  / loss = 1.0306396484375
I0421 10:50:20.735884 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1659 and 1660
I0421 10:50:21.816008 47076539613632 model_training_utils.py:505] Train Step: 961/2100  / loss = 0.92193603515625
I0421 10:50:21.816439 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 1660 and 1661
I0421 10:50:22.897410 47076539613632 model_training_utils.py:505] Train Step: 962/2100  / loss = 0.67236328125
I0421 10:50:22.897835 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 1661 and 1662
I0421 10:50:23.979141 47076539613632 model_training_utils.py:505] Train Step: 963/2100  / loss = 0.52911376953125
I0421 10:50:23.979568 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 1662 and 1663
I0421 10:50:25.064355 47076539613632 model_training_utils.py:505] Train Step: 964/2100  / loss = 0.755859375
I0421 10:50:25.064781 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 1663 and 1664
I0421 10:50:26.148587 47076539613632 model_training_utils.py:505] Train Step: 965/2100  / loss = 0.934326171875
I0421 10:50:26.149006 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 1664 and 1665
I0421 10:50:27.233850 47076539613632 model_training_utils.py:505] Train Step: 966/2100  / loss = 0.9853515625
I0421 10:50:27.234275 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 1665 and 1666
I0421 10:50:28.316853 47076539613632 model_training_utils.py:505] Train Step: 967/2100  / loss = 0.8609619140625
I0421 10:50:28.317276 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1666 and 1667
I0421 10:50:29.393966 47076539613632 model_training_utils.py:505] Train Step: 968/2100  / loss = 0.926025390625
I0421 10:50:29.394406 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.70 examples/second between steps 1667 and 1668
I0421 10:50:30.478711 47076539613632 model_training_utils.py:505] Train Step: 969/2100  / loss = 0.78582763671875
I0421 10:50:30.479140 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 1668 and 1669
I0421 10:50:31.563418 47076539613632 model_training_utils.py:505] Train Step: 970/2100  / loss = 0.890625
I0421 10:50:31.563840 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 1669 and 1670
I0421 10:50:32.649370 47076539613632 model_training_utils.py:505] Train Step: 971/2100  / loss = 0.9056396484375
I0421 10:50:32.649793 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 1670 and 1671
I0421 10:50:33.731170 47076539613632 model_training_utils.py:505] Train Step: 972/2100  / loss = 1.0938720703125
I0421 10:50:33.731606 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 1671 and 1672
I0421 10:50:34.813492 47076539613632 model_training_utils.py:505] Train Step: 973/2100  / loss = 1.360595703125
I0421 10:50:34.813917 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1672 and 1673
I0421 10:50:35.892236 47076539613632 model_training_utils.py:505] Train Step: 974/2100  / loss = 0.895263671875
I0421 10:50:35.892669 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.52 examples/second between steps 1673 and 1674
I0421 10:50:36.976005 47076539613632 model_training_utils.py:505] Train Step: 975/2100  / loss = 1.241455078125
I0421 10:50:36.976434 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 1674 and 1675
I0421 10:50:38.060729 47076539613632 model_training_utils.py:505] Train Step: 976/2100  / loss = 2.0986328125
I0421 10:50:38.061155 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 1675 and 1676
I0421 10:50:39.143538 47076539613632 model_training_utils.py:505] Train Step: 977/2100  / loss = 2.18017578125
I0421 10:50:39.143966 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 1676 and 1677
I0421 10:50:40.227341 47076539613632 model_training_utils.py:505] Train Step: 978/2100  / loss = 1.768798828125
I0421 10:50:40.227783 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 1677 and 1678
I0421 10:50:41.310268 47076539613632 model_training_utils.py:505] Train Step: 979/2100  / loss = 1.01171875
I0421 10:50:41.310702 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1678 and 1679
I0421 10:50:42.392879 47076539613632 model_training_utils.py:505] Train Step: 980/2100  / loss = 0.8768310546875
I0421 10:50:42.393316 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 1679 and 1680
I0421 10:50:43.475100 47076539613632 model_training_utils.py:505] Train Step: 981/2100  / loss = 0.79345703125
I0421 10:50:43.475538 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1680 and 1681
I0421 10:50:44.557044 47076539613632 model_training_utils.py:505] Train Step: 982/2100  / loss = 0.95166015625
I0421 10:50:44.557487 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 1681 and 1682
I0421 10:50:45.635345 47076539613632 model_training_utils.py:505] Train Step: 983/2100  / loss = 0.942138671875
I0421 10:50:45.635782 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.64 examples/second between steps 1682 and 1683
I0421 10:50:46.718333 47076539613632 model_training_utils.py:505] Train Step: 984/2100  / loss = 1.2236328125
I0421 10:50:46.718757 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 1683 and 1684
I0421 10:50:47.799111 47076539613632 model_training_utils.py:505] Train Step: 985/2100  / loss = 1.515380859375
I0421 10:50:47.799545 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 1684 and 1685
I0421 10:50:48.881441 47076539613632 model_training_utils.py:505] Train Step: 986/2100  / loss = 2.03564453125
I0421 10:50:48.881865 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1685 and 1686
I0421 10:50:49.967264 47076539613632 model_training_utils.py:505] Train Step: 987/2100  / loss = 1.43701171875
I0421 10:50:49.967707 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 1686 and 1687
I0421 10:50:51.050479 47076539613632 model_training_utils.py:505] Train Step: 988/2100  / loss = 1.552734375
I0421 10:50:51.050901 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1687 and 1688
I0421 10:50:52.136704 47076539613632 model_training_utils.py:505] Train Step: 989/2100  / loss = 2.6025390625
I0421 10:50:52.137124 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 1688 and 1689
I0421 10:50:53.221307 47076539613632 model_training_utils.py:505] Train Step: 990/2100  / loss = 2.12255859375
I0421 10:50:53.221732 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 1689 and 1690
I0421 10:50:54.305856 47076539613632 model_training_utils.py:505] Train Step: 991/2100  / loss = 1.636962890625
I0421 10:50:54.306278 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 1690 and 1691
I0421 10:50:55.390104 47076539613632 model_training_utils.py:505] Train Step: 992/2100  / loss = 1.059326171875
I0421 10:50:55.390529 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1691 and 1692
I0421 10:50:56.475252 47076539613632 model_training_utils.py:505] Train Step: 993/2100  / loss = 1.038818359375
I0421 10:50:56.475676 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 1692 and 1693
I0421 10:50:57.559315 47076539613632 model_training_utils.py:505] Train Step: 994/2100  / loss = 0.897216796875
I0421 10:50:57.559738 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 1693 and 1694
I0421 10:50:58.644575 47076539613632 model_training_utils.py:505] Train Step: 995/2100  / loss = 0.9996337890625
I0421 10:50:58.644999 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 1694 and 1695
I0421 10:50:59.727805 47076539613632 model_training_utils.py:505] Train Step: 996/2100  / loss = 1.0718994140625
I0421 10:50:59.728232 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 1695 and 1696
I0421 10:51:00.814442 47076539613632 model_training_utils.py:505] Train Step: 997/2100  / loss = 1.2694091796875
I0421 10:51:00.814883 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 1696 and 1697
I0421 10:51:01.899778 47076539613632 model_training_utils.py:505] Train Step: 998/2100  / loss = 1.100341796875
I0421 10:51:01.900202 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 1697 and 1698
I0421 10:51:02.982901 47076539613632 model_training_utils.py:505] Train Step: 999/2100  / loss = 1.0242919921875
I0421 10:51:02.983336 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1698 and 1699
I0421 10:51:04.067524 47076539613632 model_training_utils.py:505] Train Step: 1000/2100  / loss = 1.10498046875
I0421 10:51:04.067953 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 1699 and 1700
I0421 10:51:05.153020 47076539613632 model_training_utils.py:505] Train Step: 1001/2100  / loss = 1.320556640625
I0421 10:51:05.153452 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 1700 and 1701
I0421 10:51:06.239198 47076539613632 model_training_utils.py:505] Train Step: 1002/2100  / loss = 0.981201171875
I0421 10:51:06.239631 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.69 examples/second between steps 1701 and 1702
I0421 10:51:07.326565 47076539613632 model_training_utils.py:505] Train Step: 1003/2100  / loss = 1.19873046875
I0421 10:51:07.326990 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.58 examples/second between steps 1702 and 1703
I0421 10:51:08.411939 47076539613632 model_training_utils.py:505] Train Step: 1004/2100  / loss = 1.1697998046875
I0421 10:51:08.412369 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 1703 and 1704
I0421 10:51:09.497170 47076539613632 model_training_utils.py:505] Train Step: 1005/2100  / loss = 0.74609375
I0421 10:51:09.497603 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 1704 and 1705
I0421 10:51:10.581172 47076539613632 model_training_utils.py:505] Train Step: 1006/2100  / loss = 0.6502685546875
I0421 10:51:10.581563 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 1705 and 1706
I0421 10:51:11.663341 47076539613632 model_training_utils.py:505] Train Step: 1007/2100  / loss = 0.73590087890625
I0421 10:51:11.663730 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 1706 and 1707
I0421 10:51:12.745577 47076539613632 model_training_utils.py:505] Train Step: 1008/2100  / loss = 0.6044921875
I0421 10:51:12.745971 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 1707 and 1708
I0421 10:51:13.828036 47076539613632 model_training_utils.py:505] Train Step: 1009/2100  / loss = 0.884765625
I0421 10:51:13.828427 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1708 and 1709
I0421 10:51:14.910793 47076539613632 model_training_utils.py:505] Train Step: 1010/2100  / loss = 0.58123779296875
I0421 10:51:14.911174 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 1709 and 1710
I0421 10:51:15.994237 47076539613632 model_training_utils.py:505] Train Step: 1011/2100  / loss = 0.6090087890625
I0421 10:51:15.994626 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 1710 and 1711
I0421 10:51:17.076777 47076539613632 model_training_utils.py:505] Train Step: 1012/2100  / loss = 0.5616455078125
I0421 10:51:17.077164 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1711 and 1712
I0421 10:51:18.160674 47076539613632 model_training_utils.py:505] Train Step: 1013/2100  / loss = 0.7169189453125
I0421 10:51:18.161056 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 1712 and 1713
I0421 10:51:19.244286 47076539613632 model_training_utils.py:505] Train Step: 1014/2100  / loss = 1.1163330078125
I0421 10:51:19.244666 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 1713 and 1714
I0421 10:51:20.328818 47076539613632 model_training_utils.py:505] Train Step: 1015/2100  / loss = 1.4755859375
I0421 10:51:20.329199 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1714 and 1715
I0421 10:51:21.409914 47076539613632 model_training_utils.py:505] Train Step: 1016/2100  / loss = 1.1051025390625
I0421 10:51:21.410303 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.34 examples/second between steps 1715 and 1716
I0421 10:51:22.492776 47076539613632 model_training_utils.py:505] Train Step: 1017/2100  / loss = 1.215087890625
I0421 10:51:22.493147 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 1716 and 1717
I0421 10:51:23.576621 47076539613632 model_training_utils.py:505] Train Step: 1018/2100  / loss = 0.656005859375
I0421 10:51:23.577003 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1717 and 1718
I0421 10:51:24.659298 47076539613632 model_training_utils.py:505] Train Step: 1019/2100  / loss = 0.6356201171875
I0421 10:51:24.659673 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1718 and 1719
I0421 10:51:25.743432 47076539613632 model_training_utils.py:505] Train Step: 1020/2100  / loss = 2.17138671875
I0421 10:51:25.743807 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1719 and 1720
I0421 10:51:26.829597 47076539613632 model_training_utils.py:505] Train Step: 1021/2100  / loss = 1.85595703125
I0421 10:51:26.829973 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 1720 and 1721
I0421 10:51:27.919341 47076539613632 model_training_utils.py:505] Train Step: 1022/2100  / loss = 1.146484375
I0421 10:51:27.919723 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.36 examples/second between steps 1721 and 1722
I0421 10:51:29.005639 47076539613632 model_training_utils.py:505] Train Step: 1023/2100  / loss = 1.2119140625
I0421 10:51:29.006020 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 1722 and 1723
I0421 10:51:30.089388 47076539613632 model_training_utils.py:505] Train Step: 1024/2100  / loss = 1.173828125
I0421 10:51:30.089765 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 1723 and 1724
I0421 10:51:31.174606 47076539613632 model_training_utils.py:505] Train Step: 1025/2100  / loss = 1.089599609375
I0421 10:51:31.174982 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 1724 and 1725
I0421 10:51:32.257571 47076539613632 model_training_utils.py:505] Train Step: 1026/2100  / loss = 0.914794921875
I0421 10:51:32.257951 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1725 and 1726
I0421 10:51:33.343132 47076539613632 model_training_utils.py:505] Train Step: 1027/2100  / loss = 1.466552734375
I0421 10:51:33.343521 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 1726 and 1727
I0421 10:51:34.429981 47076539613632 model_training_utils.py:505] Train Step: 1028/2100  / loss = 0.9962158203125
I0421 10:51:34.430371 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 1727 and 1728
I0421 10:51:35.515481 47076539613632 model_training_utils.py:505] Train Step: 1029/2100  / loss = 0.85040283203125
I0421 10:51:35.515864 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 1728 and 1729
I0421 10:51:36.598911 47076539613632 model_training_utils.py:505] Train Step: 1030/2100  / loss = 0.61962890625
I0421 10:51:36.599300 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1729 and 1730
I0421 10:51:37.683639 47076539613632 model_training_utils.py:505] Train Step: 1031/2100  / loss = 1.0960693359375
I0421 10:51:37.684029 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 1730 and 1731
I0421 10:51:38.766670 47076539613632 model_training_utils.py:505] Train Step: 1032/2100  / loss = 1.576171875
I0421 10:51:38.767048 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 1731 and 1732
I0421 10:51:39.851000 47076539613632 model_training_utils.py:505] Train Step: 1033/2100  / loss = 1.59814453125
I0421 10:51:39.851393 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 1732 and 1733
I0421 10:51:40.934458 47076539613632 model_training_utils.py:505] Train Step: 1034/2100  / loss = 1.2669677734375
I0421 10:51:40.934837 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 1733 and 1734
I0421 10:51:42.026116 47076539613632 model_training_utils.py:505] Train Step: 1035/2100  / loss = 1.179443359375
I0421 10:51:42.026500 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.14 examples/second between steps 1734 and 1735
I0421 10:51:43.110892 47076539613632 model_training_utils.py:505] Train Step: 1036/2100  / loss = 1.338134765625
I0421 10:51:43.111273 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 1735 and 1736
I0421 10:51:44.197267 47076539613632 model_training_utils.py:505] Train Step: 1037/2100  / loss = 0.9947509765625
I0421 10:51:44.197659 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.69 examples/second between steps 1736 and 1737
I0421 10:51:45.282352 47076539613632 model_training_utils.py:505] Train Step: 1038/2100  / loss = 0.8668212890625
I0421 10:51:45.282746 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 1737 and 1738
I0421 10:51:46.365214 47076539613632 model_training_utils.py:505] Train Step: 1039/2100  / loss = 0.840576171875
I0421 10:51:46.365593 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1738 and 1739
I0421 10:51:47.450001 47076539613632 model_training_utils.py:505] Train Step: 1040/2100  / loss = 0.9522705078125
I0421 10:51:47.450364 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 1739 and 1740
I0421 10:51:48.534406 47076539613632 model_training_utils.py:505] Train Step: 1041/2100  / loss = 1.101318359375
I0421 10:51:48.534781 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 1740 and 1741
I0421 10:51:49.617430 47076539613632 model_training_utils.py:505] Train Step: 1042/2100  / loss = 1.219970703125
I0421 10:51:49.617818 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 1741 and 1742
I0421 10:51:50.699578 47076539613632 model_training_utils.py:505] Train Step: 1043/2100  / loss = 2.00830078125
I0421 10:51:50.699961 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 1742 and 1743
I0421 10:51:51.784542 47076539613632 model_training_utils.py:505] Train Step: 1044/2100  / loss = 1.424072265625
I0421 10:51:51.784923 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 1743 and 1744
I0421 10:51:52.868947 47076539613632 model_training_utils.py:505] Train Step: 1045/2100  / loss = 1.47705078125
I0421 10:51:52.869338 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 1744 and 1745
I0421 10:51:53.954406 47076539613632 model_training_utils.py:505] Train Step: 1046/2100  / loss = 1.361083984375
I0421 10:51:53.954797 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 1745 and 1746
I0421 10:51:55.037413 47076539613632 model_training_utils.py:505] Train Step: 1047/2100  / loss = 1.087890625
I0421 10:51:55.037798 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1746 and 1747
I0421 10:51:56.122765 47076539613632 model_training_utils.py:505] Train Step: 1048/2100  / loss = 1.230224609375
I0421 10:51:56.123150 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 1747 and 1748
I0421 10:51:57.204694 47076539613632 model_training_utils.py:505] Train Step: 1049/2100  / loss = 1.150390625
I0421 10:51:57.205075 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 1748 and 1749
I0421 10:51:58.284749 47076539613632 model_training_utils.py:505] Train Step: 1050/2100  / loss = 1.067138671875
I0421 10:51:58.285130 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.42 examples/second between steps 1749 and 1750
I0421 10:51:59.367043 47076539613632 model_training_utils.py:505] Train Step: 1051/2100  / loss = 1.177978515625
I0421 10:51:59.367448 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1750 and 1751
I0421 10:52:00.450703 47076539613632 model_training_utils.py:505] Train Step: 1052/2100  / loss = 1.225830078125
I0421 10:52:00.451081 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 1751 and 1752
I0421 10:52:01.538308 47076539613632 model_training_utils.py:505] Train Step: 1053/2100  / loss = 1.153564453125
I0421 10:52:01.538697 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 1752 and 1753
I0421 10:52:02.625982 47076539613632 model_training_utils.py:505] Train Step: 1054/2100  / loss = 1.03662109375
I0421 10:52:02.626381 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 1753 and 1754
I0421 10:52:03.712099 47076539613632 model_training_utils.py:505] Train Step: 1055/2100  / loss = 1.078857421875
I0421 10:52:03.712494 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 1754 and 1755
I0421 10:52:04.797888 47076539613632 model_training_utils.py:505] Train Step: 1056/2100  / loss = 0.9310302734375
I0421 10:52:04.798286 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1755 and 1756
I0421 10:52:05.881896 47076539613632 model_training_utils.py:505] Train Step: 1057/2100  / loss = 1.137939453125
I0421 10:52:05.882295 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 1756 and 1757
I0421 10:52:06.968670 47076539613632 model_training_utils.py:505] Train Step: 1058/2100  / loss = 0.833740234375
I0421 10:52:06.969054 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 1757 and 1758
I0421 10:52:08.051957 47076539613632 model_training_utils.py:505] Train Step: 1059/2100  / loss = 1.2125244140625
I0421 10:52:08.052357 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 1758 and 1759
I0421 10:52:09.139094 47076539613632 model_training_utils.py:505] Train Step: 1060/2100  / loss = 2.357421875
I0421 10:52:09.139485 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 1759 and 1760
I0421 10:52:10.224225 47076539613632 model_training_utils.py:505] Train Step: 1061/2100  / loss = 1.64013671875
I0421 10:52:10.224622 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 1760 and 1761
I0421 10:52:11.307011 47076539613632 model_training_utils.py:505] Train Step: 1062/2100  / loss = 0.9632568359375
I0421 10:52:11.307419 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1761 and 1762
I0421 10:52:12.388060 47076539613632 model_training_utils.py:505] Train Step: 1063/2100  / loss = 1.0938720703125
I0421 10:52:12.388459 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.33 examples/second between steps 1762 and 1763
I0421 10:52:13.470401 47076539613632 model_training_utils.py:505] Train Step: 1064/2100  / loss = 1.3828125
I0421 10:52:13.470783 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 1763 and 1764
I0421 10:52:14.553249 47076539613632 model_training_utils.py:505] Train Step: 1065/2100  / loss = 1.723876953125
I0421 10:52:14.553651 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1764 and 1765
I0421 10:52:15.635414 47076539613632 model_training_utils.py:505] Train Step: 1066/2100  / loss = 1.62109375
I0421 10:52:15.635801 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 1765 and 1766
I0421 10:52:16.712410 47076539613632 model_training_utils.py:505] Train Step: 1067/2100  / loss = 1.2958984375
I0421 10:52:16.712814 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.78 examples/second between steps 1766 and 1767
I0421 10:52:17.795919 47076539613632 model_training_utils.py:505] Train Step: 1068/2100  / loss = 1.0223388671875
I0421 10:52:17.796318 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 1767 and 1768
I0421 10:52:18.878324 47076539613632 model_training_utils.py:505] Train Step: 1069/2100  / loss = 0.9698486328125
I0421 10:52:18.878710 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1768 and 1769
I0421 10:52:19.960448 47076539613632 model_training_utils.py:505] Train Step: 1070/2100  / loss = 1.83642578125
I0421 10:52:19.960835 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 1769 and 1770
I0421 10:52:21.042190 47076539613632 model_training_utils.py:505] Train Step: 1071/2100  / loss = 1.8876953125
I0421 10:52:21.042578 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 1770 and 1771
I0421 10:52:22.123611 47076539613632 model_training_utils.py:505] Train Step: 1072/2100  / loss = 1.060302734375
I0421 10:52:22.123996 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 1771 and 1772
I0421 10:52:23.207539 47076539613632 model_training_utils.py:505] Train Step: 1073/2100  / loss = 0.8692626953125
I0421 10:52:23.207925 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 1772 and 1773
I0421 10:52:24.290155 47076539613632 model_training_utils.py:505] Train Step: 1074/2100  / loss = 1.05908203125
I0421 10:52:24.290554 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 1773 and 1774
I0421 10:52:25.377189 47076539613632 model_training_utils.py:505] Train Step: 1075/2100  / loss = 0.93212890625
I0421 10:52:25.377597 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 1774 and 1775
I0421 10:52:26.463442 47076539613632 model_training_utils.py:505] Train Step: 1076/2100  / loss = 0.99560546875
I0421 10:52:26.463828 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 1775 and 1776
I0421 10:52:27.550392 47076539613632 model_training_utils.py:505] Train Step: 1077/2100  / loss = 0.9700927734375
I0421 10:52:27.550778 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 1776 and 1777
I0421 10:52:28.638356 47076539613632 model_training_utils.py:505] Train Step: 1078/2100  / loss = 1.34521484375
I0421 10:52:28.638740 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.53 examples/second between steps 1777 and 1778
I0421 10:52:29.722595 47076539613632 model_training_utils.py:505] Train Step: 1079/2100  / loss = 2.36376953125
I0421 10:52:29.722984 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 1778 and 1779
I0421 10:52:30.808335 47076539613632 model_training_utils.py:505] Train Step: 1080/2100  / loss = 3.00537109375
I0421 10:52:30.808734 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 1779 and 1780
I0421 10:52:31.891414 47076539613632 model_training_utils.py:505] Train Step: 1081/2100  / loss = 2.58984375
I0421 10:52:31.891805 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1780 and 1781
I0421 10:52:32.972313 47076539613632 model_training_utils.py:505] Train Step: 1082/2100  / loss = 1.426513671875
I0421 10:52:32.972710 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 1781 and 1782
I0421 10:52:34.054712 47076539613632 model_training_utils.py:505] Train Step: 1083/2100  / loss = 1.4970703125
I0421 10:52:34.055099 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1782 and 1783
I0421 10:52:35.140212 47076539613632 model_training_utils.py:505] Train Step: 1084/2100  / loss = 1.171142578125
I0421 10:52:35.140609 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 1783 and 1784
I0421 10:52:36.222819 47076539613632 model_training_utils.py:505] Train Step: 1085/2100  / loss = 0.98095703125
I0421 10:52:36.223203 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1784 and 1785
I0421 10:52:37.306862 47076539613632 model_training_utils.py:505] Train Step: 1086/2100  / loss = 1.1300048828125
I0421 10:52:37.307248 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1785 and 1786
I0421 10:52:38.389860 47076539613632 model_training_utils.py:505] Train Step: 1087/2100  / loss = 0.8333740234375
I0421 10:52:38.390242 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 1786 and 1787
I0421 10:52:39.474344 47076539613632 model_training_utils.py:505] Train Step: 1088/2100  / loss = 1.047607421875
I0421 10:52:39.474736 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1787 and 1788
I0421 10:52:40.556836 47076539613632 model_training_utils.py:505] Train Step: 1089/2100  / loss = 1.78125
I0421 10:52:40.557225 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 1788 and 1789
I0421 10:52:41.637357 47076539613632 model_training_utils.py:505] Train Step: 1090/2100  / loss = 2.31689453125
I0421 10:52:41.637744 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 1789 and 1790
I0421 10:52:42.720416 47076539613632 model_training_utils.py:505] Train Step: 1091/2100  / loss = 1.498046875
I0421 10:52:42.720808 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 1790 and 1791
I0421 10:52:43.803140 47076539613632 model_training_utils.py:505] Train Step: 1092/2100  / loss = 1.316650390625
I0421 10:52:43.803533 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1791 and 1792
I0421 10:52:44.887296 47076539613632 model_training_utils.py:505] Train Step: 1093/2100  / loss = 0.761474609375
I0421 10:52:44.887687 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 1792 and 1793
I0421 10:52:45.975326 47076539613632 model_training_utils.py:505] Train Step: 1094/2100  / loss = 0.72216796875
I0421 10:52:45.975712 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.52 examples/second between steps 1793 and 1794
I0421 10:52:47.060620 47076539613632 model_training_utils.py:505] Train Step: 1095/2100  / loss = 0.9659423828125
I0421 10:52:47.061031 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 1794 and 1795
I0421 10:52:48.144860 47076539613632 model_training_utils.py:505] Train Step: 1096/2100  / loss = 1.182861328125
I0421 10:52:48.145255 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1795 and 1796
I0421 10:52:49.227374 47076539613632 model_training_utils.py:505] Train Step: 1097/2100  / loss = 1.1728515625
I0421 10:52:49.227765 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1796 and 1797
I0421 10:52:50.311612 47076539613632 model_training_utils.py:505] Train Step: 1098/2100  / loss = 1.02685546875
I0421 10:52:50.311996 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 1797 and 1798
I0421 10:52:51.397253 47076539613632 model_training_utils.py:505] Train Step: 1099/2100  / loss = 0.752197265625
I0421 10:52:51.397650 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 1798 and 1799
I0421 10:52:52.482020 47076539613632 model_training_utils.py:505] Train Step: 1100/2100  / loss = 0.995361328125
I0421 10:52:52.482418 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 1799 and 1800
I0421 10:52:53.567533 47076539613632 model_training_utils.py:505] Train Step: 1101/2100  / loss = 0.927734375
I0421 10:52:53.567924 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 1800 and 1801
I0421 10:52:54.651867 47076539613632 model_training_utils.py:505] Train Step: 1102/2100  / loss = 0.78173828125
I0421 10:52:54.652256 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 1801 and 1802
I0421 10:52:55.734133 47076539613632 model_training_utils.py:505] Train Step: 1103/2100  / loss = 1.1383056640625
I0421 10:52:55.734526 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1802 and 1803
I0421 10:52:56.818473 47076539613632 model_training_utils.py:505] Train Step: 1104/2100  / loss = 1.0970458984375
I0421 10:52:56.818860 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 1803 and 1804
I0421 10:52:57.902221 47076539613632 model_training_utils.py:505] Train Step: 1105/2100  / loss = 1.056640625
I0421 10:52:57.902612 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1804 and 1805
I0421 10:52:58.987318 47076539613632 model_training_utils.py:505] Train Step: 1106/2100  / loss = 1.699462890625
I0421 10:52:58.987704 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 1805 and 1806
I0421 10:53:00.069885 47076539613632 model_training_utils.py:505] Train Step: 1107/2100  / loss = 1.397216796875
I0421 10:53:00.070270 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1806 and 1807
I0421 10:53:01.152350 47076539613632 model_training_utils.py:505] Train Step: 1108/2100  / loss = 1.001708984375
I0421 10:53:01.152738 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 1807 and 1808
I0421 10:53:02.231698 47076539613632 model_training_utils.py:505] Train Step: 1109/2100  / loss = 0.8834228515625
I0421 10:53:02.232089 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.48 examples/second between steps 1808 and 1809
I0421 10:53:03.317346 47076539613632 model_training_utils.py:505] Train Step: 1110/2100  / loss = 1.0003662109375
I0421 10:53:03.317733 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1809 and 1810
I0421 10:53:04.408085 47076539613632 model_training_utils.py:505] Train Step: 1111/2100  / loss = 1.4697265625
I0421 10:53:04.408478 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.24 examples/second between steps 1810 and 1811
I0421 10:53:05.493815 47076539613632 model_training_utils.py:505] Train Step: 1112/2100  / loss = 1.648681640625
I0421 10:53:05.494205 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1811 and 1812
I0421 10:53:06.579000 47076539613632 model_training_utils.py:505] Train Step: 1113/2100  / loss = 1.267822265625
I0421 10:53:06.579394 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 1812 and 1813
I0421 10:53:07.661863 47076539613632 model_training_utils.py:505] Train Step: 1114/2100  / loss = 1.16064453125
I0421 10:53:07.662252 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1813 and 1814
I0421 10:53:08.744575 47076539613632 model_training_utils.py:505] Train Step: 1115/2100  / loss = 1.012451171875
I0421 10:53:08.744960 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 1814 and 1815
I0421 10:53:09.826734 47076539613632 model_training_utils.py:505] Train Step: 1116/2100  / loss = 0.8468017578125
I0421 10:53:09.827118 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 1815 and 1816
I0421 10:53:10.910350 47076539613632 model_training_utils.py:505] Train Step: 1117/2100  / loss = 0.8763427734375
I0421 10:53:10.910756 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1816 and 1817
I0421 10:53:11.993046 47076539613632 model_training_utils.py:505] Train Step: 1118/2100  / loss = 1.236328125
I0421 10:53:11.993472 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 1817 and 1818
I0421 10:53:13.077557 47076539613632 model_training_utils.py:505] Train Step: 1119/2100  / loss = 1.197265625
I0421 10:53:13.077980 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1818 and 1819
I0421 10:53:14.162181 47076539613632 model_training_utils.py:505] Train Step: 1120/2100  / loss = 1.1865234375
I0421 10:53:14.162616 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 1819 and 1820
I0421 10:53:15.245908 47076539613632 model_training_utils.py:505] Train Step: 1121/2100  / loss = 1.3134765625
I0421 10:53:15.246340 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 1820 and 1821
I0421 10:53:16.333937 47076539613632 model_training_utils.py:505] Train Step: 1122/2100  / loss = 1.427978515625
I0421 10:53:16.334374 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.52 examples/second between steps 1821 and 1822
I0421 10:53:17.418447 47076539613632 model_training_utils.py:505] Train Step: 1123/2100  / loss = 1.45556640625
I0421 10:53:17.418881 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 1822 and 1823
I0421 10:53:18.502278 47076539613632 model_training_utils.py:505] Train Step: 1124/2100  / loss = 1.177734375
I0421 10:53:18.502713 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1823 and 1824
I0421 10:53:19.587835 47076539613632 model_training_utils.py:505] Train Step: 1125/2100  / loss = 1.92724609375
I0421 10:53:19.588272 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 1824 and 1825
I0421 10:53:20.675776 47076539613632 model_training_utils.py:505] Train Step: 1126/2100  / loss = 1.6875
I0421 10:53:20.676196 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 1825 and 1826
I0421 10:53:21.762902 47076539613632 model_training_utils.py:505] Train Step: 1127/2100  / loss = 1.8349609375
I0421 10:53:21.763336 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 1826 and 1827
I0421 10:53:22.845362 47076539613632 model_training_utils.py:505] Train Step: 1128/2100  / loss = 1.79736328125
I0421 10:53:22.845803 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1827 and 1828
I0421 10:53:23.930890 47076539613632 model_training_utils.py:505] Train Step: 1129/2100  / loss = 1.86376953125
I0421 10:53:23.931324 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 1828 and 1829
I0421 10:53:25.016458 47076539613632 model_training_utils.py:505] Train Step: 1130/2100  / loss = 1.299072265625
I0421 10:53:25.016880 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1829 and 1830
I0421 10:53:26.099329 47076539613632 model_training_utils.py:505] Train Step: 1131/2100  / loss = 0.856689453125
I0421 10:53:26.099751 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1830 and 1831
I0421 10:53:27.180278 47076539613632 model_training_utils.py:505] Train Step: 1132/2100  / loss = 1.084228515625
I0421 10:53:27.180708 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 1831 and 1832
I0421 10:53:28.263242 47076539613632 model_training_utils.py:505] Train Step: 1133/2100  / loss = 0.9697265625
I0421 10:53:28.263673 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 1832 and 1833
I0421 10:53:29.345495 47076539613632 model_training_utils.py:505] Train Step: 1134/2100  / loss = 1.232177734375
I0421 10:53:29.345920 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1833 and 1834
I0421 10:53:30.431170 47076539613632 model_training_utils.py:505] Train Step: 1135/2100  / loss = 1.14453125
I0421 10:53:30.431603 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1834 and 1835
I0421 10:53:31.515866 47076539613632 model_training_utils.py:505] Train Step: 1136/2100  / loss = 1.233642578125
I0421 10:53:31.516278 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1835 and 1836
I0421 10:53:32.600317 47076539613632 model_training_utils.py:505] Train Step: 1137/2100  / loss = 1.3662109375
I0421 10:53:32.600718 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1836 and 1837
I0421 10:53:33.684798 47076539613632 model_training_utils.py:505] Train Step: 1138/2100  / loss = 1.175048828125
I0421 10:53:33.685220 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 1837 and 1838
I0421 10:53:34.768415 47076539613632 model_training_utils.py:505] Train Step: 1139/2100  / loss = 0.8875732421875
I0421 10:53:34.768842 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 1838 and 1839
I0421 10:53:35.851450 47076539613632 model_training_utils.py:505] Train Step: 1140/2100  / loss = 1.2022705078125
I0421 10:53:35.851876 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1839 and 1840
I0421 10:53:36.933672 47076539613632 model_training_utils.py:505] Train Step: 1141/2100  / loss = 1.736083984375
I0421 10:53:36.934098 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1840 and 1841
I0421 10:53:38.015786 47076539613632 model_training_utils.py:505] Train Step: 1142/2100  / loss = 2.281005859375
I0421 10:53:38.016221 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 1841 and 1842
I0421 10:53:39.100205 47076539613632 model_training_utils.py:505] Train Step: 1143/2100  / loss = 1.174072265625
I0421 10:53:39.100644 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1842 and 1843
I0421 10:53:40.185824 47076539613632 model_training_utils.py:505] Train Step: 1144/2100  / loss = 1.438720703125
I0421 10:53:40.186246 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 1843 and 1844
I0421 10:53:41.271600 47076539613632 model_training_utils.py:505] Train Step: 1145/2100  / loss = 1.4921875
I0421 10:53:41.272018 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1844 and 1845
I0421 10:53:42.356817 47076539613632 model_training_utils.py:505] Train Step: 1146/2100  / loss = 1.074462890625
I0421 10:53:42.357259 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 1845 and 1846
I0421 10:53:43.440479 47076539613632 model_training_utils.py:505] Train Step: 1147/2100  / loss = 0.8280029296875
I0421 10:53:43.440901 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1846 and 1847
I0421 10:53:44.524798 47076539613632 model_training_utils.py:505] Train Step: 1148/2100  / loss = 0.70703125
I0421 10:53:44.525198 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 1847 and 1848
I0421 10:53:45.607828 47076539613632 model_training_utils.py:505] Train Step: 1149/2100  / loss = 1.03125
I0421 10:53:45.608248 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 1848 and 1849
I0421 10:53:46.692790 47076539613632 model_training_utils.py:505] Train Step: 1150/2100  / loss = 1.68359375
I0421 10:53:46.693208 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 1849 and 1850
I0421 10:53:47.778216 47076539613632 model_training_utils.py:505] Train Step: 1151/2100  / loss = 1.478271484375
I0421 10:53:47.778651 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 1850 and 1851
I0421 10:53:48.862123 47076539613632 model_training_utils.py:505] Train Step: 1152/2100  / loss = 1.118408203125
I0421 10:53:48.862547 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 1851 and 1852
I0421 10:53:49.946372 47076539613632 model_training_utils.py:505] Train Step: 1153/2100  / loss = 1.367919921875
I0421 10:53:49.946796 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1852 and 1853
I0421 10:53:51.031107 47076539613632 model_training_utils.py:505] Train Step: 1154/2100  / loss = 1.727294921875
I0421 10:53:51.031543 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 1853 and 1854
I0421 10:53:52.112011 47076539613632 model_training_utils.py:505] Train Step: 1155/2100  / loss = 1.2548828125
I0421 10:53:52.112442 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 1854 and 1855
I0421 10:53:53.193321 47076539613632 model_training_utils.py:505] Train Step: 1156/2100  / loss = 1.1986083984375
I0421 10:53:53.193749 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 1855 and 1856
I0421 10:53:54.276914 47076539613632 model_training_utils.py:505] Train Step: 1157/2100  / loss = 1.98828125
I0421 10:53:54.277354 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 1856 and 1857
I0421 10:53:55.352916 47076539613632 model_training_utils.py:505] Train Step: 1158/2100  / loss = 1.986572265625
I0421 10:53:55.353346 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.86 examples/second between steps 1857 and 1858
I0421 10:53:56.435930 47076539613632 model_training_utils.py:505] Train Step: 1159/2100  / loss = 1.88037109375
I0421 10:53:56.436362 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1858 and 1859
I0421 10:53:57.521652 47076539613632 model_training_utils.py:505] Train Step: 1160/2100  / loss = 1.765869140625
I0421 10:53:57.522065 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1859 and 1860
I0421 10:53:58.611001 47076539613632 model_training_utils.py:505] Train Step: 1161/2100  / loss = 1.96142578125
I0421 10:53:58.611415 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.41 examples/second between steps 1860 and 1861
I0421 10:53:59.699618 47076539613632 model_training_utils.py:505] Train Step: 1162/2100  / loss = 1.38525390625
I0421 10:53:59.700045 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 1861 and 1862
I0421 10:54:00.786390 47076539613632 model_training_utils.py:505] Train Step: 1163/2100  / loss = 1.0638427734375
I0421 10:54:00.786814 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 1862 and 1863
I0421 10:54:01.870579 47076539613632 model_training_utils.py:505] Train Step: 1164/2100  / loss = 1.076904296875
I0421 10:54:01.871008 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 1863 and 1864
I0421 10:54:02.953561 47076539613632 model_training_utils.py:505] Train Step: 1165/2100  / loss = 1.082275390625
I0421 10:54:02.953987 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 1864 and 1865
I0421 10:54:04.038755 47076539613632 model_training_utils.py:505] Train Step: 1166/2100  / loss = 1.288330078125
I0421 10:54:04.039189 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 1865 and 1866
I0421 10:54:05.121792 47076539613632 model_training_utils.py:505] Train Step: 1167/2100  / loss = 1.136962890625
I0421 10:54:05.122221 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1866 and 1867
I0421 10:54:06.203123 47076539613632 model_training_utils.py:505] Train Step: 1168/2100  / loss = 1.23779296875
I0421 10:54:06.203557 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 1867 and 1868
I0421 10:54:07.289494 47076539613632 model_training_utils.py:505] Train Step: 1169/2100  / loss = 1.369384765625
I0421 10:54:07.289919 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 1868 and 1869
I0421 10:54:08.369586 47076539613632 model_training_utils.py:505] Train Step: 1170/2100  / loss = 1.55224609375
I0421 10:54:08.369996 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.38 examples/second between steps 1869 and 1870
I0421 10:54:09.451718 47076539613632 model_training_utils.py:505] Train Step: 1171/2100  / loss = 1.063720703125
I0421 10:54:09.452132 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 1870 and 1871
I0421 10:54:10.535302 47076539613632 model_training_utils.py:505] Train Step: 1172/2100  / loss = 1.0889892578125
I0421 10:54:10.535720 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1871 and 1872
I0421 10:54:11.618632 47076539613632 model_training_utils.py:505] Train Step: 1173/2100  / loss = 1.13037109375
I0421 10:54:11.619057 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1872 and 1873
I0421 10:54:12.697803 47076539613632 model_training_utils.py:505] Train Step: 1174/2100  / loss = 1.14404296875
I0421 10:54:12.698228 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.45 examples/second between steps 1873 and 1874
I0421 10:54:13.775733 47076539613632 model_training_utils.py:505] Train Step: 1175/2100  / loss = 1.1214599609375
I0421 10:54:13.776154 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.59 examples/second between steps 1874 and 1875
I0421 10:54:14.854395 47076539613632 model_training_utils.py:505] Train Step: 1176/2100  / loss = 1.1678466796875
I0421 10:54:14.854811 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.51 examples/second between steps 1875 and 1876
I0421 10:54:15.938728 47076539613632 model_training_utils.py:505] Train Step: 1177/2100  / loss = 0.90869140625
I0421 10:54:15.939152 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1876 and 1877
I0421 10:54:17.021036 47076539613632 model_training_utils.py:505] Train Step: 1178/2100  / loss = 0.9442138671875
I0421 10:54:17.021471 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 1877 and 1878
I0421 10:54:18.105101 47076539613632 model_training_utils.py:505] Train Step: 1179/2100  / loss = 1.094482421875
I0421 10:54:18.105535 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 1878 and 1879
I0421 10:54:19.188184 47076539613632 model_training_utils.py:505] Train Step: 1180/2100  / loss = 1.1021728515625
I0421 10:54:19.188609 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 1879 and 1880
I0421 10:54:20.272323 47076539613632 model_training_utils.py:505] Train Step: 1181/2100  / loss = 1.15478515625
I0421 10:54:20.272751 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 1880 and 1881
I0421 10:54:21.356954 47076539613632 model_training_utils.py:505] Train Step: 1182/2100  / loss = 0.792724609375
I0421 10:54:21.357385 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 1881 and 1882
I0421 10:54:22.434566 47076539613632 model_training_utils.py:505] Train Step: 1183/2100  / loss = 0.665283203125
I0421 10:54:22.434993 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.67 examples/second between steps 1882 and 1883
I0421 10:54:23.517053 47076539613632 model_training_utils.py:505] Train Step: 1184/2100  / loss = 0.53729248046875
I0421 10:54:23.517476 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 1883 and 1884
I0421 10:54:24.600224 47076539613632 model_training_utils.py:505] Train Step: 1185/2100  / loss = 0.79248046875
I0421 10:54:24.600651 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1884 and 1885
I0421 10:54:25.683887 47076539613632 model_training_utils.py:505] Train Step: 1186/2100  / loss = 0.916748046875
I0421 10:54:25.684321 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 1885 and 1886
I0421 10:54:26.765638 47076539613632 model_training_utils.py:505] Train Step: 1187/2100  / loss = 0.893798828125
I0421 10:54:26.766067 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1886 and 1887
I0421 10:54:27.847150 47076539613632 model_training_utils.py:505] Train Step: 1188/2100  / loss = 1.0419921875
I0421 10:54:27.847565 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 1887 and 1888
I0421 10:54:28.928611 47076539613632 model_training_utils.py:505] Train Step: 1189/2100  / loss = 0.844970703125
I0421 10:54:28.929027 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 1888 and 1889
I0421 10:54:30.010847 47076539613632 model_training_utils.py:505] Train Step: 1190/2100  / loss = 0.8135986328125
I0421 10:54:30.011267 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 1889 and 1890
I0421 10:54:31.095426 47076539613632 model_training_utils.py:505] Train Step: 1191/2100  / loss = 1.102783203125
I0421 10:54:31.095851 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 1890 and 1891
I0421 10:54:32.177403 47076539613632 model_training_utils.py:505] Train Step: 1192/2100  / loss = 1.20556640625
I0421 10:54:32.177825 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 1891 and 1892
I0421 10:54:33.261362 47076539613632 model_training_utils.py:505] Train Step: 1193/2100  / loss = 1.10693359375
I0421 10:54:33.261789 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 1892 and 1893
I0421 10:54:34.342564 47076539613632 model_training_utils.py:505] Train Step: 1194/2100  / loss = 0.998046875
I0421 10:54:34.342998 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 1893 and 1894
I0421 10:54:35.424999 47076539613632 model_training_utils.py:505] Train Step: 1195/2100  / loss = 1.0970458984375
I0421 10:54:35.425435 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1894 and 1895
I0421 10:54:36.505751 47076539613632 model_training_utils.py:505] Train Step: 1196/2100  / loss = 1.192138671875
I0421 10:54:36.506167 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 1895 and 1896
I0421 10:54:37.587426 47076539613632 model_training_utils.py:505] Train Step: 1197/2100  / loss = 1.0601806640625
I0421 10:54:37.587843 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 1896 and 1897
I0421 10:54:38.669015 47076539613632 model_training_utils.py:505] Train Step: 1198/2100  / loss = 0.97705078125
I0421 10:54:38.669434 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 1897 and 1898
I0421 10:54:39.749688 47076539613632 model_training_utils.py:505] Train Step: 1199/2100  / loss = 1.251953125
I0421 10:54:39.750105 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 1898 and 1899
I0421 10:54:40.829445 47076539613632 model_training_utils.py:505] Train Step: 1200/2100  / loss = 1.2822265625
I0421 10:54:40.829872 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.41 examples/second between steps 1899 and 1900
I0421 10:54:41.909152 47076539613632 model_training_utils.py:505] Train Step: 1201/2100  / loss = 1.1689453125
I0421 10:54:41.909586 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.41 examples/second between steps 1900 and 1901
I0421 10:54:42.991694 47076539613632 model_training_utils.py:505] Train Step: 1202/2100  / loss = 1.2255859375
I0421 10:54:42.992125 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1901 and 1902
I0421 10:54:44.073365 47076539613632 model_training_utils.py:505] Train Step: 1203/2100  / loss = 1.595458984375
I0421 10:54:44.073787 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1902 and 1903
I0421 10:54:45.156413 47076539613632 model_training_utils.py:505] Train Step: 1204/2100  / loss = 1.45458984375
I0421 10:54:45.156849 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 1903 and 1904
I0421 10:54:46.239885 47076539613632 model_training_utils.py:505] Train Step: 1205/2100  / loss = 1.206787109375
I0421 10:54:46.240316 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 1904 and 1905
I0421 10:54:47.321747 47076539613632 model_training_utils.py:505] Train Step: 1206/2100  / loss = 1.3551025390625
I0421 10:54:47.322175 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 1905 and 1906
I0421 10:54:48.404913 47076539613632 model_training_utils.py:505] Train Step: 1207/2100  / loss = 1.5537109375
I0421 10:54:48.405347 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1906 and 1907
I0421 10:54:49.485266 47076539613632 model_training_utils.py:505] Train Step: 1208/2100  / loss = 1.156982421875
I0421 10:54:49.485696 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 1907 and 1908
I0421 10:54:50.569225 47076539613632 model_training_utils.py:505] Train Step: 1209/2100  / loss = 0.7293701171875
I0421 10:54:50.569658 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1908 and 1909
I0421 10:54:51.652101 47076539613632 model_training_utils.py:505] Train Step: 1210/2100  / loss = 0.901611328125
I0421 10:54:51.652518 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 1909 and 1910
I0421 10:54:52.735021 47076539613632 model_training_utils.py:505] Train Step: 1211/2100  / loss = 0.8760986328125
I0421 10:54:52.735449 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 1910 and 1911
I0421 10:54:53.817368 47076539613632 model_training_utils.py:505] Train Step: 1212/2100  / loss = 1.10205078125
I0421 10:54:53.817797 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1911 and 1912
I0421 10:54:54.899889 47076539613632 model_training_utils.py:505] Train Step: 1213/2100  / loss = 0.9554443359375
I0421 10:54:54.900326 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1912 and 1913
I0421 10:54:55.982573 47076539613632 model_training_utils.py:505] Train Step: 1214/2100  / loss = 1.166015625
I0421 10:54:55.982996 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1913 and 1914
I0421 10:54:57.064444 47076539613632 model_training_utils.py:505] Train Step: 1215/2100  / loss = 1.363525390625
I0421 10:54:57.064864 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1914 and 1915
I0421 10:54:58.145656 47076539613632 model_training_utils.py:505] Train Step: 1216/2100  / loss = 1.0169677734375
I0421 10:54:58.146094 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 1915 and 1916
I0421 10:54:59.226859 47076539613632 model_training_utils.py:505] Train Step: 1217/2100  / loss = 1.0509033203125
I0421 10:54:59.227294 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 1916 and 1917
I0421 10:55:00.307858 47076539613632 model_training_utils.py:505] Train Step: 1218/2100  / loss = 0.947021484375
I0421 10:55:00.308294 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 1917 and 1918
I0421 10:55:01.390664 47076539613632 model_training_utils.py:505] Train Step: 1219/2100  / loss = 0.939453125
I0421 10:55:01.391088 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1918 and 1919
I0421 10:55:02.472212 47076539613632 model_training_utils.py:505] Train Step: 1220/2100  / loss = 0.9088134765625
I0421 10:55:02.472647 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 1919 and 1920
I0421 10:55:03.553500 47076539613632 model_training_utils.py:505] Train Step: 1221/2100  / loss = 0.853271484375
I0421 10:55:03.553926 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 1920 and 1921
I0421 10:55:04.636086 47076539613632 model_training_utils.py:505] Train Step: 1222/2100  / loss = 0.80908203125
I0421 10:55:04.636504 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 1921 and 1922
I0421 10:55:05.721218 47076539613632 model_training_utils.py:505] Train Step: 1223/2100  / loss = 0.7862548828125
I0421 10:55:05.721648 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 1922 and 1923
I0421 10:55:06.805697 47076539613632 model_training_utils.py:505] Train Step: 1224/2100  / loss = 0.757568359375
I0421 10:55:06.806127 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 1923 and 1924
I0421 10:55:07.888897 47076539613632 model_training_utils.py:505] Train Step: 1225/2100  / loss = 0.62225341796875
I0421 10:55:07.889332 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1924 and 1925
I0421 10:55:08.972590 47076539613632 model_training_utils.py:505] Train Step: 1226/2100  / loss = 0.91162109375
I0421 10:55:08.973017 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1925 and 1926
I0421 10:55:10.059128 47076539613632 model_training_utils.py:505] Train Step: 1227/2100  / loss = 1.09423828125
I0421 10:55:10.059563 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 1926 and 1927
I0421 10:55:11.144008 47076539613632 model_training_utils.py:505] Train Step: 1228/2100  / loss = 1.330322265625
I0421 10:55:11.144443 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 1927 and 1928
I0421 10:55:12.229600 47076539613632 model_training_utils.py:505] Train Step: 1229/2100  / loss = 0.863037109375
I0421 10:55:12.230027 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 1928 and 1929
I0421 10:55:13.313573 47076539613632 model_training_utils.py:505] Train Step: 1230/2100  / loss = 1.050048828125
I0421 10:55:13.314014 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1929 and 1930
I0421 10:55:14.398669 47076539613632 model_training_utils.py:505] Train Step: 1231/2100  / loss = 1.11376953125
I0421 10:55:14.399102 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 1930 and 1931
I0421 10:55:15.484343 47076539613632 model_training_utils.py:505] Train Step: 1232/2100  / loss = 1.635498046875
I0421 10:55:15.484769 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 1931 and 1932
I0421 10:55:16.570976 47076539613632 model_training_utils.py:505] Train Step: 1233/2100  / loss = 1.245849609375
I0421 10:55:16.571412 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 1932 and 1933
I0421 10:55:17.653395 47076539613632 model_training_utils.py:505] Train Step: 1234/2100  / loss = 0.8402099609375
I0421 10:55:17.653805 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 1933 and 1934
I0421 10:55:18.737642 47076539613632 model_training_utils.py:505] Train Step: 1235/2100  / loss = 0.9281005859375
I0421 10:55:18.738050 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1934 and 1935
I0421 10:55:19.820512 47076539613632 model_training_utils.py:505] Train Step: 1236/2100  / loss = 1.09375
I0421 10:55:19.820940 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 1935 and 1936
I0421 10:55:20.902325 47076539613632 model_training_utils.py:505] Train Step: 1237/2100  / loss = 1.221923828125
I0421 10:55:20.902749 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1936 and 1937
I0421 10:55:21.985694 47076539613632 model_training_utils.py:505] Train Step: 1238/2100  / loss = 1.294189453125
I0421 10:55:21.986116 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 1937 and 1938
I0421 10:55:23.067918 47076539613632 model_training_utils.py:505] Train Step: 1239/2100  / loss = 0.861572265625
I0421 10:55:23.068348 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 1938 and 1939
I0421 10:55:24.154022 47076539613632 model_training_utils.py:505] Train Step: 1240/2100  / loss = 0.8148193359375
I0421 10:55:24.154449 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 1939 and 1940
I0421 10:55:25.237233 47076539613632 model_training_utils.py:505] Train Step: 1241/2100  / loss = 0.8360595703125
I0421 10:55:25.237664 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1940 and 1941
I0421 10:55:26.319400 47076539613632 model_training_utils.py:505] Train Step: 1242/2100  / loss = 0.989013671875
I0421 10:55:26.319831 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 1941 and 1942
I0421 10:55:27.402662 47076539613632 model_training_utils.py:505] Train Step: 1243/2100  / loss = 1.143798828125
I0421 10:55:27.403085 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1942 and 1943
I0421 10:55:28.483689 47076539613632 model_training_utils.py:505] Train Step: 1244/2100  / loss = 0.961181640625
I0421 10:55:28.484112 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 1943 and 1944
I0421 10:55:29.565944 47076539613632 model_training_utils.py:505] Train Step: 1245/2100  / loss = 1.0849609375
I0421 10:55:29.566380 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 1944 and 1945
I0421 10:55:30.648339 47076539613632 model_training_utils.py:505] Train Step: 1246/2100  / loss = 1.46533203125
I0421 10:55:30.648750 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 1945 and 1946
I0421 10:55:31.730499 47076539613632 model_training_utils.py:505] Train Step: 1247/2100  / loss = 1.3916015625
I0421 10:55:31.730921 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 1946 and 1947
I0421 10:55:32.813877 47076539613632 model_training_utils.py:505] Train Step: 1248/2100  / loss = 1.230712890625
I0421 10:55:32.814307 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 1947 and 1948
I0421 10:55:33.898104 47076539613632 model_training_utils.py:505] Train Step: 1249/2100  / loss = 1.095458984375
I0421 10:55:33.898530 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1948 and 1949
I0421 10:55:34.978644 47076539613632 model_training_utils.py:505] Train Step: 1250/2100  / loss = 1.1038818359375
I0421 10:55:34.979072 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 1949 and 1950
I0421 10:55:36.064042 47076539613632 model_training_utils.py:505] Train Step: 1251/2100  / loss = 1.22900390625
I0421 10:55:36.064474 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 1950 and 1951
I0421 10:55:37.149436 47076539613632 model_training_utils.py:505] Train Step: 1252/2100  / loss = 1.0675048828125
I0421 10:55:37.149819 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1951 and 1952
I0421 10:55:38.233994 47076539613632 model_training_utils.py:505] Train Step: 1253/2100  / loss = 0.8753662109375
I0421 10:55:38.234383 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1952 and 1953
I0421 10:55:39.318011 47076539613632 model_training_utils.py:505] Train Step: 1254/2100  / loss = 1.155029296875
I0421 10:55:39.318405 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 1953 and 1954
I0421 10:55:40.398483 47076539613632 model_training_utils.py:505] Train Step: 1255/2100  / loss = 1.1611328125
I0421 10:55:40.398866 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 1954 and 1955
I0421 10:55:41.482731 47076539613632 model_training_utils.py:505] Train Step: 1256/2100  / loss = 1.1182861328125
I0421 10:55:41.483114 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 1955 and 1956
I0421 10:55:42.565292 47076539613632 model_training_utils.py:505] Train Step: 1257/2100  / loss = 1.084716796875
I0421 10:55:42.565674 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 1956 and 1957
I0421 10:55:43.643664 47076539613632 model_training_utils.py:505] Train Step: 1258/2100  / loss = 1.0367431640625
I0421 10:55:43.644046 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.59 examples/second between steps 1957 and 1958
I0421 10:55:44.729025 47076539613632 model_training_utils.py:505] Train Step: 1259/2100  / loss = 1.203857421875
I0421 10:55:44.729414 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 1958 and 1959
I0421 10:55:45.814631 47076539613632 model_training_utils.py:505] Train Step: 1260/2100  / loss = 0.958251953125
I0421 10:55:45.815016 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 1959 and 1960
I0421 10:55:46.900588 47076539613632 model_training_utils.py:505] Train Step: 1261/2100  / loss = 1.882568359375
I0421 10:55:46.900967 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 1960 and 1961
I0421 10:55:47.984512 47076539613632 model_training_utils.py:505] Train Step: 1262/2100  / loss = 3.03759765625
I0421 10:55:47.984894 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 1961 and 1962
I0421 10:55:49.067658 47076539613632 model_training_utils.py:505] Train Step: 1263/2100  / loss = 3.06982421875
I0421 10:55:49.068045 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 1962 and 1963
I0421 10:55:50.150640 47076539613632 model_training_utils.py:505] Train Step: 1264/2100  / loss = 1.87109375
I0421 10:55:50.151024 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 1963 and 1964
I0421 10:55:51.237840 47076539613632 model_training_utils.py:505] Train Step: 1265/2100  / loss = 1.940673828125
I0421 10:55:51.238227 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 1964 and 1965
I0421 10:55:52.322836 47076539613632 model_training_utils.py:505] Train Step: 1266/2100  / loss = 1.955322265625
I0421 10:55:52.323219 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 1965 and 1966
I0421 10:55:53.404562 47076539613632 model_training_utils.py:505] Train Step: 1267/2100  / loss = 1.096923828125
I0421 10:55:53.404949 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 1966 and 1967
I0421 10:55:54.487729 47076539613632 model_training_utils.py:505] Train Step: 1268/2100  / loss = 0.642822265625
I0421 10:55:54.488113 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1967 and 1968
I0421 10:55:55.570487 47076539613632 model_training_utils.py:505] Train Step: 1269/2100  / loss = 0.848876953125
I0421 10:55:55.570873 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 1968 and 1969
I0421 10:55:56.655552 47076539613632 model_training_utils.py:505] Train Step: 1270/2100  / loss = 0.806396484375
I0421 10:55:56.655930 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 1969 and 1970
I0421 10:55:57.742065 47076539613632 model_training_utils.py:505] Train Step: 1271/2100  / loss = 1.0511474609375
I0421 10:55:57.742457 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 1970 and 1971
I0421 10:55:58.826685 47076539613632 model_training_utils.py:505] Train Step: 1272/2100  / loss = 0.986572265625
I0421 10:55:58.827081 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 1971 and 1972
I0421 10:55:59.911682 47076539613632 model_training_utils.py:505] Train Step: 1273/2100  / loss = 1.072021484375
I0421 10:55:59.912061 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 1972 and 1973
I0421 10:56:00.998037 47076539613632 model_training_utils.py:505] Train Step: 1274/2100  / loss = 1.51513671875
I0421 10:56:00.998426 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 1973 and 1974
I0421 10:56:02.088098 47076539613632 model_training_utils.py:505] Train Step: 1275/2100  / loss = 1.25830078125
I0421 10:56:02.088487 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.35 examples/second between steps 1974 and 1975
I0421 10:56:03.173796 47076539613632 model_training_utils.py:505] Train Step: 1276/2100  / loss = 1.22265625
I0421 10:56:03.174181 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 1975 and 1976
I0421 10:56:04.259887 47076539613632 model_training_utils.py:505] Train Step: 1277/2100  / loss = 1.744873046875
I0421 10:56:04.260288 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 1976 and 1977
I0421 10:56:05.348261 47076539613632 model_training_utils.py:505] Train Step: 1278/2100  / loss = 2.4189453125
I0421 10:56:05.348658 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 1977 and 1978
I0421 10:56:06.439598 47076539613632 model_training_utils.py:505] Train Step: 1279/2100  / loss = 1.295166015625
I0421 10:56:06.439973 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.23 examples/second between steps 1978 and 1979
I0421 10:56:07.524160 47076539613632 model_training_utils.py:505] Train Step: 1280/2100  / loss = 1.11376953125
I0421 10:56:07.524548 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 1979 and 1980
I0421 10:56:08.610114 47076539613632 model_training_utils.py:505] Train Step: 1281/2100  / loss = 0.952392578125
I0421 10:56:08.610503 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 1980 and 1981
I0421 10:56:09.695623 47076539613632 model_training_utils.py:505] Train Step: 1282/2100  / loss = 0.9786376953125
I0421 10:56:09.696002 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 1981 and 1982
I0421 10:56:10.779821 47076539613632 model_training_utils.py:505] Train Step: 1283/2100  / loss = 0.893798828125
I0421 10:56:10.780200 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 1982 and 1983
I0421 10:56:11.863312 47076539613632 model_training_utils.py:505] Train Step: 1284/2100  / loss = 1.0372314453125
I0421 10:56:11.863697 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 1983 and 1984
I0421 10:56:12.949637 47076539613632 model_training_utils.py:505] Train Step: 1285/2100  / loss = 1.19677734375
I0421 10:56:12.950022 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 1984 and 1985
I0421 10:56:14.037544 47076539613632 model_training_utils.py:505] Train Step: 1286/2100  / loss = 1.47705078125
I0421 10:56:14.037933 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 1985 and 1986
I0421 10:56:15.122255 47076539613632 model_training_utils.py:505] Train Step: 1287/2100  / loss = 1.484375
I0421 10:56:15.122637 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 1986 and 1987
I0421 10:56:16.204763 47076539613632 model_training_utils.py:505] Train Step: 1288/2100  / loss = 1.101806640625
I0421 10:56:16.205140 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 1987 and 1988
I0421 10:56:17.287631 47076539613632 model_training_utils.py:505] Train Step: 1289/2100  / loss = 1.1097412109375
I0421 10:56:17.288010 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 1988 and 1989
I0421 10:56:18.372932 47076539613632 model_training_utils.py:505] Train Step: 1290/2100  / loss = 1.7059326171875
I0421 10:56:18.373319 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 1989 and 1990
I0421 10:56:19.454456 47076539613632 model_training_utils.py:505] Train Step: 1291/2100  / loss = 2.720703125
I0421 10:56:19.454838 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 1990 and 1991
I0421 10:56:20.537250 47076539613632 model_training_utils.py:505] Train Step: 1292/2100  / loss = 2.041259765625
I0421 10:56:20.537641 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 1991 and 1992
I0421 10:56:21.622564 47076539613632 model_training_utils.py:505] Train Step: 1293/2100  / loss = 1.54541015625
I0421 10:56:21.622956 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 1992 and 1993
I0421 10:56:22.708037 47076539613632 model_training_utils.py:505] Train Step: 1294/2100  / loss = 1.308349609375
I0421 10:56:22.708419 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 1993 and 1994
I0421 10:56:23.794882 47076539613632 model_training_utils.py:505] Train Step: 1295/2100  / loss = 0.8817138671875
I0421 10:56:23.795265 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 1994 and 1995
I0421 10:56:24.879812 47076539613632 model_training_utils.py:505] Train Step: 1296/2100  / loss = 1.15625
I0421 10:56:24.880192 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 1995 and 1996
I0421 10:56:25.967364 47076539613632 model_training_utils.py:505] Train Step: 1297/2100  / loss = 1.2003173828125
I0421 10:56:25.967744 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 1996 and 1997
I0421 10:56:27.051506 47076539613632 model_training_utils.py:505] Train Step: 1298/2100  / loss = 1.375244140625
I0421 10:56:27.051900 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 1997 and 1998
I0421 10:56:28.136239 47076539613632 model_training_utils.py:505] Train Step: 1299/2100  / loss = 1.28369140625
I0421 10:56:28.136631 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 1998 and 1999
I0421 10:56:29.224359 47076539613632 model_training_utils.py:505] Train Step: 1300/2100  / loss = 1.5322265625
I0421 10:56:29.224737 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.54 examples/second between steps 1999 and 2000
I0421 10:56:30.314162 47076539613632 model_training_utils.py:505] Train Step: 1301/2100  / loss = 1.19140625
I0421 10:56:30.314547 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.36 examples/second between steps 2000 and 2001
I0421 10:56:31.403783 47076539613632 model_training_utils.py:505] Train Step: 1302/2100  / loss = 1.87109375
I0421 10:56:31.404166 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.39 examples/second between steps 2001 and 2002
I0421 10:56:32.494476 47076539613632 model_training_utils.py:505] Train Step: 1303/2100  / loss = 1.76220703125
I0421 10:56:32.494859 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.27 examples/second between steps 2002 and 2003
I0421 10:56:33.580059 47076539613632 model_training_utils.py:505] Train Step: 1304/2100  / loss = 1.466064453125
I0421 10:56:33.580445 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 2003 and 2004
I0421 10:56:34.665001 47076539613632 model_training_utils.py:505] Train Step: 1305/2100  / loss = 0.9449462890625
I0421 10:56:34.665396 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 2004 and 2005
I0421 10:56:35.742934 47076539613632 model_training_utils.py:505] Train Step: 1306/2100  / loss = 0.9364013671875
I0421 10:56:35.743326 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.62 examples/second between steps 2005 and 2006
I0421 10:56:36.825926 47076539613632 model_training_utils.py:505] Train Step: 1307/2100  / loss = 0.9718017578125
I0421 10:56:36.826321 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 2006 and 2007
I0421 10:56:37.912619 47076539613632 model_training_utils.py:505] Train Step: 1308/2100  / loss = 0.9111328125
I0421 10:56:37.913000 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 2007 and 2008
I0421 10:56:38.997347 47076539613632 model_training_utils.py:505] Train Step: 1309/2100  / loss = 0.930419921875
I0421 10:56:38.997737 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 2008 and 2009
I0421 10:56:40.082257 47076539613632 model_training_utils.py:505] Train Step: 1310/2100  / loss = 0.91259765625
I0421 10:56:40.082642 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 2009 and 2010
I0421 10:56:41.167914 47076539613632 model_training_utils.py:505] Train Step: 1311/2100  / loss = 1.151123046875
I0421 10:56:41.168306 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 2010 and 2011
I0421 10:56:42.252167 47076539613632 model_training_utils.py:505] Train Step: 1312/2100  / loss = 1.2333984375
I0421 10:56:42.252560 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 2011 and 2012
I0421 10:56:43.335234 47076539613632 model_training_utils.py:505] Train Step: 1313/2100  / loss = 0.8731689453125
I0421 10:56:43.335622 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 2012 and 2013
I0421 10:56:44.418907 47076539613632 model_training_utils.py:505] Train Step: 1314/2100  / loss = 1.211181640625
I0421 10:56:44.419317 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 2013 and 2014
I0421 10:56:45.500782 47076539613632 model_training_utils.py:505] Train Step: 1315/2100  / loss = 0.7628173828125
I0421 10:56:45.501163 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 2014 and 2015
I0421 10:56:46.586881 47076539613632 model_training_utils.py:505] Train Step: 1316/2100  / loss = 0.9052734375
I0421 10:56:46.587263 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 2015 and 2016
I0421 10:56:47.670436 47076539613632 model_training_utils.py:505] Train Step: 1317/2100  / loss = 1.19189453125
I0421 10:56:47.670822 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 2016 and 2017
I0421 10:56:48.753949 47076539613632 model_training_utils.py:505] Train Step: 1318/2100  / loss = 1.541259765625
I0421 10:56:48.754342 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 2017 and 2018
I0421 10:56:49.841529 47076539613632 model_training_utils.py:505] Train Step: 1319/2100  / loss = 1.3349609375
I0421 10:56:49.841912 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 2018 and 2019
I0421 10:56:50.931097 47076539613632 model_training_utils.py:505] Train Step: 1320/2100  / loss = 1.997802734375
I0421 10:56:50.931529 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.34 examples/second between steps 2019 and 2020
I0421 10:56:52.018348 47076539613632 model_training_utils.py:505] Train Step: 1321/2100  / loss = 2.2763671875
I0421 10:56:52.018784 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 2020 and 2021
I0421 10:56:53.103773 47076539613632 model_training_utils.py:505] Train Step: 1322/2100  / loss = 2.4814453125
I0421 10:56:53.104202 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 2021 and 2022
I0421 10:56:54.189960 47076539613632 model_training_utils.py:505] Train Step: 1323/2100  / loss = 1.77783203125
I0421 10:56:54.190403 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 2022 and 2023
I0421 10:56:55.272007 47076539613632 model_training_utils.py:505] Train Step: 1324/2100  / loss = 1.2315673828125
I0421 10:56:55.272440 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 2023 and 2024
I0421 10:56:56.357180 47076539613632 model_training_utils.py:505] Train Step: 1325/2100  / loss = 1.1649169921875
I0421 10:56:56.357619 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 2024 and 2025
I0421 10:56:57.444439 47076539613632 model_training_utils.py:505] Train Step: 1326/2100  / loss = 1.09033203125
I0421 10:56:57.444864 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 2025 and 2026
I0421 10:56:58.528932 47076539613632 model_training_utils.py:505] Train Step: 1327/2100  / loss = 0.799072265625
I0421 10:56:58.529372 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 2026 and 2027
I0421 10:56:59.612361 47076539613632 model_training_utils.py:505] Train Step: 1328/2100  / loss = 0.670166015625
I0421 10:56:59.612793 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 2027 and 2028
I0421 10:57:00.697109 47076539613632 model_training_utils.py:505] Train Step: 1329/2100  / loss = 0.716064453125
I0421 10:57:00.697555 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 2028 and 2029
I0421 10:57:01.779134 47076539613632 model_training_utils.py:505] Train Step: 1330/2100  / loss = 1.10546875
I0421 10:57:01.779567 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 2029 and 2030
I0421 10:57:02.867947 47076539613632 model_training_utils.py:505] Train Step: 1331/2100  / loss = 0.796630859375
I0421 10:57:02.868337 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 2030 and 2031
I0421 10:57:03.952734 47076539613632 model_training_utils.py:505] Train Step: 1332/2100  / loss = 1.108642578125
I0421 10:57:03.953114 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 2031 and 2032
I0421 10:57:05.035764 47076539613632 model_training_utils.py:505] Train Step: 1333/2100  / loss = 0.855712890625
I0421 10:57:05.036151 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 2032 and 2033
I0421 10:57:06.119376 47076539613632 model_training_utils.py:505] Train Step: 1334/2100  / loss = 0.9703369140625
I0421 10:57:06.119759 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 2033 and 2034
I0421 10:57:07.202216 47076539613632 model_training_utils.py:505] Train Step: 1335/2100  / loss = 0.9932861328125
I0421 10:57:07.202612 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 2034 and 2035
I0421 10:57:08.284937 47076539613632 model_training_utils.py:505] Train Step: 1336/2100  / loss = 0.8895263671875
I0421 10:57:08.285334 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 2035 and 2036
I0421 10:57:09.364993 47076539613632 model_training_utils.py:505] Train Step: 1337/2100  / loss = 1.155029296875
I0421 10:57:09.365385 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.37 examples/second between steps 2036 and 2037
I0421 10:57:10.449580 47076539613632 model_training_utils.py:505] Train Step: 1338/2100  / loss = 0.92333984375
I0421 10:57:10.449980 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 2037 and 2038
I0421 10:57:11.532649 47076539613632 model_training_utils.py:505] Train Step: 1339/2100  / loss = 0.8663330078125
I0421 10:57:11.533030 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 2038 and 2039
I0421 10:57:12.613939 47076539613632 model_training_utils.py:505] Train Step: 1340/2100  / loss = 0.9495849609375
I0421 10:57:12.614327 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 2039 and 2040
I0421 10:57:13.697698 47076539613632 model_training_utils.py:505] Train Step: 1341/2100  / loss = 1.197509765625
I0421 10:57:13.698081 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 2040 and 2041
I0421 10:57:14.780497 47076539613632 model_training_utils.py:505] Train Step: 1342/2100  / loss = 1.089599609375
I0421 10:57:14.780925 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 2041 and 2042
I0421 10:57:15.863250 47076539613632 model_training_utils.py:505] Train Step: 1343/2100  / loss = 1.0797119140625
I0421 10:57:15.863677 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 2042 and 2043
I0421 10:57:16.945248 47076539613632 model_training_utils.py:505] Train Step: 1344/2100  / loss = 0.8990478515625
I0421 10:57:16.945677 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 2043 and 2044
I0421 10:57:18.027911 47076539613632 model_training_utils.py:505] Train Step: 1345/2100  / loss = 0.83056640625
I0421 10:57:18.028347 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 2044 and 2045
I0421 10:57:19.109086 47076539613632 model_training_utils.py:505] Train Step: 1346/2100  / loss = 1.05712890625
I0421 10:57:19.109525 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 2045 and 2046
I0421 10:57:20.190124 47076539613632 model_training_utils.py:505] Train Step: 1347/2100  / loss = 1.041748046875
I0421 10:57:20.190552 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 2046 and 2047
I0421 10:57:21.272483 47076539613632 model_training_utils.py:505] Train Step: 1348/2100  / loss = 0.9569091796875
I0421 10:57:21.272905 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 2047 and 2048
I0421 10:57:22.353709 47076539613632 model_training_utils.py:505] Train Step: 1349/2100  / loss = 1.236328125
I0421 10:57:22.354147 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 2048 and 2049
I0421 10:57:23.436195 47076539613632 model_training_utils.py:505] Train Step: 1350/2100  / loss = 1.311767578125
I0421 10:57:23.436624 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 2049 and 2050
I0421 10:57:24.514302 47076539613632 model_training_utils.py:505] Train Step: 1351/2100  / loss = 2.12548828125
I0421 10:57:24.514731 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.60 examples/second between steps 2050 and 2051
I0421 10:57:25.596101 47076539613632 model_training_utils.py:505] Train Step: 1352/2100  / loss = 1.504638671875
I0421 10:57:25.596530 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 2051 and 2052
I0421 10:57:26.675919 47076539613632 model_training_utils.py:505] Train Step: 1353/2100  / loss = 1.054931640625
I0421 10:57:26.676345 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.43 examples/second between steps 2052 and 2053
I0421 10:57:27.758358 47076539613632 model_training_utils.py:505] Train Step: 1354/2100  / loss = 1.1495361328125
I0421 10:57:27.758761 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 2053 and 2054
I0421 10:57:28.843572 47076539613632 model_training_utils.py:505] Train Step: 1355/2100  / loss = 1.220458984375
I0421 10:57:28.843997 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 2054 and 2055
I0421 10:57:29.924918 47076539613632 model_training_utils.py:505] Train Step: 1356/2100  / loss = 1.2099609375
I0421 10:57:29.925351 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 2055 and 2056
I0421 10:57:31.008743 47076539613632 model_training_utils.py:505] Train Step: 1357/2100  / loss = 1.3275146484375
I0421 10:57:31.009162 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 2056 and 2057
I0421 10:57:32.090492 47076539613632 model_training_utils.py:505] Train Step: 1358/2100  / loss = 0.7791748046875
I0421 10:57:32.090912 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 2057 and 2058
I0421 10:57:33.173777 47076539613632 model_training_utils.py:505] Train Step: 1359/2100  / loss = 0.97021484375
I0421 10:57:33.174209 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 2058 and 2059
I0421 10:57:34.256091 47076539613632 model_training_utils.py:505] Train Step: 1360/2100  / loss = 1.25537109375
I0421 10:57:34.256522 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 2059 and 2060
I0421 10:57:35.337001 47076539613632 model_training_utils.py:505] Train Step: 1361/2100  / loss = 1.3173828125
I0421 10:57:35.337441 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 2060 and 2061
I0421 10:57:36.421427 47076539613632 model_training_utils.py:505] Train Step: 1362/2100  / loss = 1.115478515625
I0421 10:57:36.421852 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 2061 and 2062
I0421 10:57:37.505270 47076539613632 model_training_utils.py:505] Train Step: 1363/2100  / loss = 0.899169921875
I0421 10:57:37.505704 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 2062 and 2063
I0421 10:57:38.590958 47076539613632 model_training_utils.py:505] Train Step: 1364/2100  / loss = 0.917236328125
I0421 10:57:38.591390 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 2063 and 2064
I0421 10:57:39.674859 47076539613632 model_training_utils.py:505] Train Step: 1365/2100  / loss = 0.8585205078125
I0421 10:57:39.675266 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 2064 and 2065
I0421 10:57:40.760286 47076539613632 model_training_utils.py:505] Train Step: 1366/2100  / loss = 0.9852294921875
I0421 10:57:40.760727 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 2065 and 2066
I0421 10:57:41.845504 47076539613632 model_training_utils.py:505] Train Step: 1367/2100  / loss = 0.974853515625
I0421 10:57:41.845909 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 2066 and 2067
I0421 10:57:42.923945 47076539613632 model_training_utils.py:505] Train Step: 1368/2100  / loss = 0.984619140625
I0421 10:57:42.924372 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.60 examples/second between steps 2067 and 2068
I0421 10:57:44.009422 47076539613632 model_training_utils.py:505] Train Step: 1369/2100  / loss = 1.276611328125
I0421 10:57:44.009824 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 2068 and 2069
I0421 10:57:45.092396 47076539613632 model_training_utils.py:505] Train Step: 1370/2100  / loss = 1.276123046875
I0421 10:57:45.092818 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 2069 and 2070
I0421 10:57:46.175848 47076539613632 model_training_utils.py:505] Train Step: 1371/2100  / loss = 1.339111328125
I0421 10:57:46.176266 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 2070 and 2071
I0421 10:57:47.261762 47076539613632 model_training_utils.py:505] Train Step: 1372/2100  / loss = 1.541015625
I0421 10:57:47.262186 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 2071 and 2072
I0421 10:57:48.344805 47076539613632 model_training_utils.py:505] Train Step: 1373/2100  / loss = 1.137451171875
I0421 10:57:48.345230 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 2072 and 2073
I0421 10:57:49.428016 47076539613632 model_training_utils.py:505] Train Step: 1374/2100  / loss = 0.992431640625
I0421 10:57:49.428467 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 2073 and 2074
I0421 10:57:50.513329 47076539613632 model_training_utils.py:505] Train Step: 1375/2100  / loss = 0.796142578125
I0421 10:57:50.513750 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 2074 and 2075
I0421 10:57:51.595134 47076539613632 model_training_utils.py:505] Train Step: 1376/2100  / loss = 0.7479248046875
I0421 10:57:51.595559 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 2075 and 2076
I0421 10:57:52.675224 47076539613632 model_training_utils.py:505] Train Step: 1377/2100  / loss = 0.8878173828125
I0421 10:57:52.675654 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.39 examples/second between steps 2076 and 2077
I0421 10:57:53.759373 47076539613632 model_training_utils.py:505] Train Step: 1378/2100  / loss = 0.83154296875
I0421 10:57:53.759796 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 2077 and 2078
I0421 10:57:54.841131 47076539613632 model_training_utils.py:505] Train Step: 1379/2100  / loss = 0.8544921875
I0421 10:57:54.841568 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 2078 and 2079
I0421 10:57:55.925749 47076539613632 model_training_utils.py:505] Train Step: 1380/2100  / loss = 0.7730712890625
I0421 10:57:55.926179 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 2079 and 2080
I0421 10:57:57.009552 47076539613632 model_training_utils.py:505] Train Step: 1381/2100  / loss = 1.11279296875
I0421 10:57:57.009974 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 2080 and 2081
I0421 10:57:58.088661 47076539613632 model_training_utils.py:505] Train Step: 1382/2100  / loss = 1.22900390625
I0421 10:57:58.089071 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.52 examples/second between steps 2081 and 2082
I0421 10:57:59.170057 47076539613632 model_training_utils.py:505] Train Step: 1383/2100  / loss = 0.9561767578125
I0421 10:57:59.170495 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 2082 and 2083
I0421 10:58:00.250631 47076539613632 model_training_utils.py:505] Train Step: 1384/2100  / loss = 0.8328857421875
I0421 10:58:00.251050 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 2083 and 2084
I0421 10:58:01.329919 47076539613632 model_training_utils.py:505] Train Step: 1385/2100  / loss = 0.7149658203125
I0421 10:58:01.330351 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.48 examples/second between steps 2084 and 2085
I0421 10:58:02.415207 47076539613632 model_training_utils.py:505] Train Step: 1386/2100  / loss = 0.831298828125
I0421 10:58:02.415638 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 2085 and 2086
I0421 10:58:03.501785 47076539613632 model_training_utils.py:505] Train Step: 1387/2100  / loss = 0.867431640625
I0421 10:58:03.502206 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 2086 and 2087
I0421 10:58:04.587347 47076539613632 model_training_utils.py:505] Train Step: 1388/2100  / loss = 0.9140625
I0421 10:58:04.587765 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 2087 and 2088
I0421 10:58:05.672329 47076539613632 model_training_utils.py:505] Train Step: 1389/2100  / loss = 1.145751953125
I0421 10:58:05.672739 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 2088 and 2089
I0421 10:58:06.754335 47076539613632 model_training_utils.py:505] Train Step: 1390/2100  / loss = 1.31494140625
I0421 10:58:06.754758 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 2089 and 2090
I0421 10:58:07.833028 47076539613632 model_training_utils.py:505] Train Step: 1391/2100  / loss = 1.510009765625
I0421 10:58:07.833464 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.57 examples/second between steps 2090 and 2091
I0421 10:58:08.913906 47076539613632 model_training_utils.py:505] Train Step: 1392/2100  / loss = 0.976806640625
I0421 10:58:08.914338 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 2091 and 2092
I0421 10:58:09.998604 47076539613632 model_training_utils.py:505] Train Step: 1393/2100  / loss = 1.142822265625
I0421 10:58:09.999046 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 2092 and 2093
I0421 10:58:11.080231 47076539613632 model_training_utils.py:505] Train Step: 1394/2100  / loss = 1.144775390625
I0421 10:58:11.080668 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 2093 and 2094
I0421 10:58:12.163222 47076539613632 model_training_utils.py:505] Train Step: 1395/2100  / loss = 1.0145263671875
I0421 10:58:12.163659 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 2094 and 2095
I0421 10:58:13.241735 47076539613632 model_training_utils.py:505] Train Step: 1396/2100  / loss = 0.9039306640625
I0421 10:58:13.242152 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.58 examples/second between steps 2095 and 2096
I0421 10:58:14.321872 47076539613632 model_training_utils.py:505] Train Step: 1397/2100  / loss = 0.6424560546875
I0421 10:58:14.322315 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.40 examples/second between steps 2096 and 2097
I0421 10:58:15.397186 47076539613632 model_training_utils.py:505] Train Step: 1398/2100  / loss = 0.836181640625
I0421 10:58:15.397629 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.95 examples/second between steps 2097 and 2098
I0421 10:58:16.479586 47076539613632 model_training_utils.py:505] Train Step: 1399/2100  / loss = 0.909912109375
I0421 10:58:16.480013 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 2098 and 2099
I0421 10:58:17.560334 47076539613632 model_training_utils.py:505] Train Step: 1400/2100  / loss = 0.7618408203125
I0421 10:58:26.056051 47076539613632 model_training_utils.py:49] Saving model as TF checkpoint: /public/home/xuanbaby/DL-TensorFlow/models_r2.3.0/official/nlp/bert/model_squad_v2/ctl_step_1400.ckpt-2
I0421 10:58:26.056570 47076539613632 keras_utils.py:133] TimeHistory: 9.57 seconds, 13.38 examples/second between steps 2099 and 2100
I0421 10:58:27.937270 47076539613632 model_training_utils.py:505] Train Step: 1401/2100  / loss = 0.9248046875
I0421 10:58:27.937665 47076539613632 keras_utils.py:133] TimeHistory: 1.32 seconds, 135914.65 examples/second between steps 2100 and 3501
I0421 10:58:29.022498 47076539613632 model_training_utils.py:505] Train Step: 1402/2100  / loss = 0.59039306640625
I0421 10:58:29.022932 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 3501 and 3502
I0421 10:58:30.105153 47076539613632 model_training_utils.py:505] Train Step: 1403/2100  / loss = 0.85986328125
I0421 10:58:30.105587 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3502 and 3503
I0421 10:58:31.188868 47076539613632 model_training_utils.py:505] Train Step: 1404/2100  / loss = 1.323974609375
I0421 10:58:31.189297 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 3503 and 3504
I0421 10:58:32.269772 47076539613632 model_training_utils.py:505] Train Step: 1405/2100  / loss = 1.2178955078125
I0421 10:58:32.270190 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 3504 and 3505
I0421 10:58:33.350827 47076539613632 model_training_utils.py:505] Train Step: 1406/2100  / loss = 1.34326171875
I0421 10:58:33.351245 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 3505 and 3506
I0421 10:58:34.432333 47076539613632 model_training_utils.py:505] Train Step: 1407/2100  / loss = 1.20849609375
I0421 10:58:34.432768 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 3506 and 3507
I0421 10:58:35.514523 47076539613632 model_training_utils.py:505] Train Step: 1408/2100  / loss = 1.13037109375
I0421 10:58:35.514949 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 3507 and 3508
I0421 10:58:36.597331 47076539613632 model_training_utils.py:505] Train Step: 1409/2100  / loss = 0.9967041015625
I0421 10:58:36.597743 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 3508 and 3509
I0421 10:58:37.678773 47076539613632 model_training_utils.py:505] Train Step: 1410/2100  / loss = 0.6202392578125
I0421 10:58:37.679190 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 3509 and 3510
I0421 10:58:38.762801 47076539613632 model_training_utils.py:505] Train Step: 1411/2100  / loss = 1.21533203125
I0421 10:58:38.763220 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 3510 and 3511
I0421 10:58:39.845317 47076539613632 model_training_utils.py:505] Train Step: 1412/2100  / loss = 1.14208984375
I0421 10:58:39.845741 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3511 and 3512
I0421 10:58:40.927002 47076539613632 model_training_utils.py:505] Train Step: 1413/2100  / loss = 0.8612060546875
I0421 10:58:40.927433 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 3512 and 3513
I0421 10:58:42.009942 47076539613632 model_training_utils.py:505] Train Step: 1414/2100  / loss = 1.00146484375
I0421 10:58:42.010391 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 3513 and 3514
I0421 10:58:43.088415 47076539613632 model_training_utils.py:505] Train Step: 1415/2100  / loss = 0.994140625
I0421 10:58:43.088825 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.61 examples/second between steps 3514 and 3515
I0421 10:58:44.168082 47076539613632 model_training_utils.py:505] Train Step: 1416/2100  / loss = 0.86962890625
I0421 10:58:44.168516 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.40 examples/second between steps 3515 and 3516
I0421 10:58:45.249573 47076539613632 model_training_utils.py:505] Train Step: 1417/2100  / loss = 1.1710205078125
I0421 10:58:45.249990 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 3516 and 3517
I0421 10:58:46.333149 47076539613632 model_training_utils.py:505] Train Step: 1418/2100  / loss = 0.7786865234375
I0421 10:58:46.333577 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 3517 and 3518
I0421 10:58:47.414906 47076539613632 model_training_utils.py:505] Train Step: 1419/2100  / loss = 1.159912109375
I0421 10:58:47.415336 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 3518 and 3519
I0421 10:58:48.499354 47076539613632 model_training_utils.py:505] Train Step: 1420/2100  / loss = 1.111572265625
I0421 10:58:48.499782 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 3519 and 3520
I0421 10:58:49.583329 47076539613632 model_training_utils.py:505] Train Step: 1421/2100  / loss = 1.200927734375
I0421 10:58:49.583742 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 3520 and 3521
I0421 10:58:50.668576 47076539613632 model_training_utils.py:505] Train Step: 1422/2100  / loss = 1.25439453125
I0421 10:58:50.668991 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 3521 and 3522
I0421 10:58:51.763358 47076539613632 model_training_utils.py:505] Train Step: 1423/2100  / loss = 1.133056640625
I0421 10:58:51.763778 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.77 examples/second between steps 3522 and 3523
I0421 10:58:52.845076 47076539613632 model_training_utils.py:505] Train Step: 1424/2100  / loss = 1.1007080078125
I0421 10:58:52.845502 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 3523 and 3524
I0421 10:58:53.929359 47076539613632 model_training_utils.py:505] Train Step: 1425/2100  / loss = 1.083984375
I0421 10:58:53.929779 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 3524 and 3525
I0421 10:58:55.010085 47076539613632 model_training_utils.py:505] Train Step: 1426/2100  / loss = 1.097900390625
I0421 10:58:55.010519 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 3525 and 3526
I0421 10:58:56.095723 47076539613632 model_training_utils.py:505] Train Step: 1427/2100  / loss = 1.0313720703125
I0421 10:58:56.096132 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 3526 and 3527
I0421 10:58:57.179080 47076539613632 model_training_utils.py:505] Train Step: 1428/2100  / loss = 1.0867919921875
I0421 10:58:57.179510 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 3527 and 3528
I0421 10:58:58.271019 47076539613632 model_training_utils.py:505] Train Step: 1429/2100  / loss = 0.9844970703125
I0421 10:58:58.271437 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.10 examples/second between steps 3528 and 3529
I0421 10:58:59.363301 47076539613632 model_training_utils.py:505] Train Step: 1430/2100  / loss = 1.030029296875
I0421 10:58:59.363729 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.07 examples/second between steps 3529 and 3530
I0421 10:59:00.449637 47076539613632 model_training_utils.py:505] Train Step: 1431/2100  / loss = 1.022705078125
I0421 10:59:00.450053 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 3530 and 3531
I0421 10:59:01.539561 47076539613632 model_training_utils.py:505] Train Step: 1432/2100  / loss = 0.8087158203125
I0421 10:59:01.539985 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.28 examples/second between steps 3531 and 3532
I0421 10:59:02.621652 47076539613632 model_training_utils.py:505] Train Step: 1433/2100  / loss = 0.79296875
I0421 10:59:02.622080 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 3532 and 3533
I0421 10:59:03.708516 47076539613632 model_training_utils.py:505] Train Step: 1434/2100  / loss = 0.7435302734375
I0421 10:59:03.708944 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 3533 and 3534
I0421 10:59:04.789969 47076539613632 model_training_utils.py:505] Train Step: 1435/2100  / loss = 0.8623046875
I0421 10:59:04.790410 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 3534 and 3535
I0421 10:59:05.876456 47076539613632 model_training_utils.py:505] Train Step: 1436/2100  / loss = 0.5772705078125
I0421 10:59:05.876882 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 3535 and 3536
I0421 10:59:06.961900 47076539613632 model_training_utils.py:505] Train Step: 1437/2100  / loss = 0.905517578125
I0421 10:59:06.962330 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 3536 and 3537
I0421 10:59:08.046061 47076539613632 model_training_utils.py:505] Train Step: 1438/2100  / loss = 0.791259765625
I0421 10:59:08.046505 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 3537 and 3538
I0421 10:59:09.130537 47076539613632 model_training_utils.py:505] Train Step: 1439/2100  / loss = 1.24853515625
I0421 10:59:09.130974 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 3538 and 3539
I0421 10:59:10.211524 47076539613632 model_training_utils.py:505] Train Step: 1440/2100  / loss = 1.096923828125
I0421 10:59:10.211953 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 3539 and 3540
I0421 10:59:11.294247 47076539613632 model_training_utils.py:505] Train Step: 1441/2100  / loss = 1.01513671875
I0421 10:59:11.294676 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3540 and 3541
I0421 10:59:12.380240 47076539613632 model_training_utils.py:505] Train Step: 1442/2100  / loss = 1.29736328125
I0421 10:59:12.380667 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 3541 and 3542
I0421 10:59:13.467035 47076539613632 model_training_utils.py:505] Train Step: 1443/2100  / loss = 1.271728515625
I0421 10:59:13.467467 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 3542 and 3543
I0421 10:59:14.557378 47076539613632 model_training_utils.py:505] Train Step: 1444/2100  / loss = 0.9873046875
I0421 10:59:14.557801 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.25 examples/second between steps 3543 and 3544
I0421 10:59:15.644727 47076539613632 model_training_utils.py:505] Train Step: 1445/2100  / loss = 0.77734375
I0421 10:59:15.645151 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 3544 and 3545
I0421 10:59:16.729630 47076539613632 model_training_utils.py:505] Train Step: 1446/2100  / loss = 0.9564208984375
I0421 10:59:16.730054 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 3545 and 3546
I0421 10:59:17.815092 47076539613632 model_training_utils.py:505] Train Step: 1447/2100  / loss = 0.843994140625
I0421 10:59:17.815530 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 3546 and 3547
I0421 10:59:18.897532 47076539613632 model_training_utils.py:505] Train Step: 1448/2100  / loss = 1.12841796875
I0421 10:59:18.897962 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3547 and 3548
I0421 10:59:19.979218 47076539613632 model_training_utils.py:505] Train Step: 1449/2100  / loss = 0.9439697265625
I0421 10:59:19.979658 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 3548 and 3549
I0421 10:59:21.065090 47076539613632 model_training_utils.py:505] Train Step: 1450/2100  / loss = 1.0032958984375
I0421 10:59:21.065525 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 3549 and 3550
I0421 10:59:22.142330 47076539613632 model_training_utils.py:505] Train Step: 1451/2100  / loss = 1.34521484375
I0421 10:59:22.142762 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.70 examples/second between steps 3550 and 3551
I0421 10:59:23.227910 47076539613632 model_training_utils.py:505] Train Step: 1452/2100  / loss = 1.1661376953125
I0421 10:59:23.228343 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 3551 and 3552
I0421 10:59:24.312613 47076539613632 model_training_utils.py:505] Train Step: 1453/2100  / loss = 1.3720703125
I0421 10:59:24.313043 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 3552 and 3553
I0421 10:59:25.400325 47076539613632 model_training_utils.py:505] Train Step: 1454/2100  / loss = 1.262451171875
I0421 10:59:25.400755 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 3553 and 3554
I0421 10:59:26.492160 47076539613632 model_training_utils.py:505] Train Step: 1455/2100  / loss = 1.60302734375
I0421 10:59:26.492593 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.14 examples/second between steps 3554 and 3555
I0421 10:59:27.582455 47076539613632 model_training_utils.py:505] Train Step: 1456/2100  / loss = 1.153076171875
I0421 10:59:27.582874 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.32 examples/second between steps 3555 and 3556
I0421 10:59:28.669205 47076539613632 model_training_utils.py:505] Train Step: 1457/2100  / loss = 1.18017578125
I0421 10:59:28.669645 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 3556 and 3557
I0421 10:59:29.758631 47076539613632 model_training_utils.py:505] Train Step: 1458/2100  / loss = 1.1265869140625
I0421 10:59:29.759063 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.37 examples/second between steps 3557 and 3558
I0421 10:59:30.854995 47076539613632 model_training_utils.py:505] Train Step: 1459/2100  / loss = 0.83074951171875
I0421 10:59:30.855441 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.60 examples/second between steps 3558 and 3559
I0421 10:59:31.949709 47076539613632 model_training_utils.py:505] Train Step: 1460/2100  / loss = 0.71923828125
I0421 10:59:31.950126 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.77 examples/second between steps 3559 and 3560
I0421 10:59:33.040345 47076539613632 model_training_utils.py:505] Train Step: 1461/2100  / loss = 0.85302734375
I0421 10:59:33.040770 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.21 examples/second between steps 3560 and 3561
I0421 10:59:34.128337 47076539613632 model_training_utils.py:505] Train Step: 1462/2100  / loss = 1.1434326171875
I0421 10:59:34.128779 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 3561 and 3562
I0421 10:59:35.214703 47076539613632 model_training_utils.py:505] Train Step: 1463/2100  / loss = 1.22900390625
I0421 10:59:35.215129 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 3562 and 3563
I0421 10:59:36.303164 47076539613632 model_training_utils.py:505] Train Step: 1464/2100  / loss = 1.198974609375
I0421 10:59:36.303591 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.44 examples/second between steps 3563 and 3564
I0421 10:59:37.394016 47076539613632 model_training_utils.py:505] Train Step: 1465/2100  / loss = 1.175048828125
I0421 10:59:37.394446 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.19 examples/second between steps 3564 and 3565
I0421 10:59:38.480976 47076539613632 model_training_utils.py:505] Train Step: 1466/2100  / loss = 1.0689697265625
I0421 10:59:38.481404 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 3565 and 3566
I0421 10:59:39.563042 47076539613632 model_training_utils.py:505] Train Step: 1467/2100  / loss = 1.0921630859375
I0421 10:59:39.563477 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 3566 and 3567
I0421 10:59:40.649000 47076539613632 model_training_utils.py:505] Train Step: 1468/2100  / loss = 1.0028076171875
I0421 10:59:40.649428 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 3567 and 3568
I0421 10:59:41.735220 47076539613632 model_training_utils.py:505] Train Step: 1469/2100  / loss = 1.077392578125
I0421 10:59:41.735647 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.69 examples/second between steps 3568 and 3569
I0421 10:59:42.821303 47076539613632 model_training_utils.py:505] Train Step: 1470/2100  / loss = 1.2763671875
I0421 10:59:42.821728 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 3569 and 3570
I0421 10:59:43.908256 47076539613632 model_training_utils.py:505] Train Step: 1471/2100  / loss = 1.622314453125
I0421 10:59:43.908690 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 3570 and 3571
I0421 10:59:44.997932 47076539613632 model_training_utils.py:505] Train Step: 1472/2100  / loss = 1.739013671875
I0421 10:59:44.998373 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.30 examples/second between steps 3571 and 3572
I0421 10:59:46.086860 47076539613632 model_training_utils.py:505] Train Step: 1473/2100  / loss = 1.148193359375
I0421 10:59:46.087291 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.43 examples/second between steps 3572 and 3573
I0421 10:59:47.172894 47076539613632 model_training_utils.py:505] Train Step: 1474/2100  / loss = 1.0321044921875
I0421 10:59:47.173328 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 3573 and 3574
I0421 10:59:48.259286 47076539613632 model_training_utils.py:505] Train Step: 1475/2100  / loss = 0.83984375
I0421 10:59:48.259711 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 3574 and 3575
I0421 10:59:49.348323 47076539613632 model_training_utils.py:505] Train Step: 1476/2100  / loss = 0.8394775390625
I0421 10:59:49.348773 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.39 examples/second between steps 3575 and 3576
I0421 10:59:50.440548 47076539613632 model_training_utils.py:505] Train Step: 1477/2100  / loss = 0.82470703125
I0421 10:59:50.440975 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.07 examples/second between steps 3576 and 3577
I0421 10:59:51.533088 47076539613632 model_training_utils.py:505] Train Step: 1478/2100  / loss = 0.8912353515625
I0421 10:59:51.533519 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.05 examples/second between steps 3577 and 3578
I0421 10:59:52.633764 47076539613632 model_training_utils.py:505] Train Step: 1479/2100  / loss = 0.8011474609375
I0421 10:59:52.634191 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.13 examples/second between steps 3578 and 3579
I0421 10:59:53.729244 47076539613632 model_training_utils.py:505] Train Step: 1480/2100  / loss = 0.88916015625
I0421 10:59:53.729675 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.71 examples/second between steps 3579 and 3580
I0421 10:59:54.829416 47076539613632 model_training_utils.py:505] Train Step: 1481/2100  / loss = 0.9178466796875
I0421 10:59:54.829840 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.21 examples/second between steps 3580 and 3581
I0421 10:59:55.920290 47076539613632 model_training_utils.py:505] Train Step: 1482/2100  / loss = 1.10888671875
I0421 10:59:55.920717 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.20 examples/second between steps 3581 and 3582
I0421 10:59:57.015654 47076539613632 model_training_utils.py:505] Train Step: 1483/2100  / loss = 1.179443359375
I0421 10:59:57.016076 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.70 examples/second between steps 3582 and 3583
I0421 10:59:58.107416 47076539613632 model_training_utils.py:505] Train Step: 1484/2100  / loss = 1.40478515625
I0421 10:59:58.107836 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.12 examples/second between steps 3583 and 3584
I0421 10:59:59.194120 47076539613632 model_training_utils.py:505] Train Step: 1485/2100  / loss = 0.957275390625
I0421 10:59:59.194555 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 3584 and 3585
I0421 11:00:00.286663 47076539613632 model_training_utils.py:505] Train Step: 1486/2100  / loss = 1.37890625
I0421 11:00:00.287089 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.02 examples/second between steps 3585 and 3586
I0421 11:00:01.376600 47076539613632 model_training_utils.py:505] Train Step: 1487/2100  / loss = 1.268798828125
I0421 11:00:01.377031 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.31 examples/second between steps 3586 and 3587
I0421 11:00:02.470187 47076539613632 model_training_utils.py:505] Train Step: 1488/2100  / loss = 1.1231689453125
I0421 11:00:02.470614 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.92 examples/second between steps 3587 and 3588
I0421 11:00:03.565191 47076539613632 model_training_utils.py:505] Train Step: 1489/2100  / loss = 1.21337890625
I0421 11:00:03.565626 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.73 examples/second between steps 3588 and 3589
I0421 11:00:04.657159 47076539613632 model_training_utils.py:505] Train Step: 1490/2100  / loss = 0.99853515625
I0421 11:00:04.657591 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.07 examples/second between steps 3589 and 3590
I0421 11:00:05.738060 47076539613632 model_training_utils.py:505] Train Step: 1491/2100  / loss = 0.8560791015625
I0421 11:00:05.738494 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 3590 and 3591
I0421 11:00:06.821704 47076539613632 model_training_utils.py:505] Train Step: 1492/2100  / loss = 0.9356689453125
I0421 11:00:06.822138 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 3591 and 3592
I0421 11:00:07.905073 47076539613632 model_training_utils.py:505] Train Step: 1493/2100  / loss = 0.5985107421875
I0421 11:00:07.905512 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 3592 and 3593
I0421 11:00:08.987782 47076539613632 model_training_utils.py:505] Train Step: 1494/2100  / loss = 0.934326171875
I0421 11:00:08.988214 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3593 and 3594
I0421 11:00:10.074200 47076539613632 model_training_utils.py:505] Train Step: 1495/2100  / loss = 0.760498046875
I0421 11:00:10.074634 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 3594 and 3595
I0421 11:00:11.158252 47076539613632 model_training_utils.py:505] Train Step: 1496/2100  / loss = 0.9112548828125
I0421 11:00:11.158687 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3595 and 3596
I0421 11:00:12.237002 47076539613632 model_training_utils.py:505] Train Step: 1497/2100  / loss = 0.8759765625
I0421 11:00:12.237430 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.51 examples/second between steps 3596 and 3597
I0421 11:00:13.319244 47076539613632 model_training_utils.py:505] Train Step: 1498/2100  / loss = 0.8927001953125
I0421 11:00:13.319678 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3597 and 3598
I0421 11:00:14.401967 47076539613632 model_training_utils.py:505] Train Step: 1499/2100  / loss = 0.9072265625
I0421 11:00:14.402398 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 3598 and 3599
I0421 11:00:15.488664 47076539613632 model_training_utils.py:505] Train Step: 1500/2100  / loss = 1.0191650390625
I0421 11:00:15.489097 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 3599 and 3600
I0421 11:00:16.578193 47076539613632 model_training_utils.py:505] Train Step: 1501/2100  / loss = 0.97412109375
I0421 11:00:16.578629 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.33 examples/second between steps 3600 and 3601
I0421 11:00:17.658270 47076539613632 model_training_utils.py:505] Train Step: 1502/2100  / loss = 0.79443359375
I0421 11:00:17.658707 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.40 examples/second between steps 3601 and 3602
I0421 11:00:18.745313 47076539613632 model_training_utils.py:505] Train Step: 1503/2100  / loss = 0.7080078125
I0421 11:00:18.745738 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 3602 and 3603
I0421 11:00:19.836706 47076539613632 model_training_utils.py:505] Train Step: 1504/2100  / loss = 0.7574462890625
I0421 11:00:19.837140 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.13 examples/second between steps 3603 and 3604
I0421 11:00:20.928722 47076539613632 model_training_utils.py:505] Train Step: 1505/2100  / loss = 1.249267578125
I0421 11:00:20.929161 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.08 examples/second between steps 3604 and 3605
I0421 11:00:22.021761 47076539613632 model_training_utils.py:505] Train Step: 1506/2100  / loss = 0.981201171875
I0421 11:00:22.022187 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 117.98 examples/second between steps 3605 and 3606
I0421 11:00:23.115723 47076539613632 model_training_utils.py:505] Train Step: 1507/2100  / loss = 0.784423828125
I0421 11:00:23.116146 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.86 examples/second between steps 3606 and 3607
I0421 11:00:24.204802 47076539613632 model_training_utils.py:505] Train Step: 1508/2100  / loss = 0.7537841796875
I0421 11:00:24.205229 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.41 examples/second between steps 3607 and 3608
I0421 11:00:25.293177 47076539613632 model_training_utils.py:505] Train Step: 1509/2100  / loss = 0.7462158203125
I0421 11:00:25.293616 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.49 examples/second between steps 3608 and 3609
I0421 11:00:26.381365 47076539613632 model_training_utils.py:505] Train Step: 1510/2100  / loss = 0.9361572265625
I0421 11:00:26.381802 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.50 examples/second between steps 3609 and 3610
I0421 11:00:27.470993 47076539613632 model_training_utils.py:505] Train Step: 1511/2100  / loss = 0.9716796875
I0421 11:00:27.471433 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.35 examples/second between steps 3610 and 3611
I0421 11:00:28.563398 47076539613632 model_training_utils.py:505] Train Step: 1512/2100  / loss = 1.035888671875
I0421 11:00:28.563822 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.12 examples/second between steps 3611 and 3612
I0421 11:00:29.655464 47076539613632 model_training_utils.py:505] Train Step: 1513/2100  / loss = 0.97509765625
I0421 11:00:29.655894 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.07 examples/second between steps 3612 and 3613
I0421 11:00:30.747677 47076539613632 model_training_utils.py:505] Train Step: 1514/2100  / loss = 0.858642578125
I0421 11:00:30.748106 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.06 examples/second between steps 3613 and 3614
I0421 11:00:31.839817 47076539613632 model_training_utils.py:505] Train Step: 1515/2100  / loss = 0.63165283203125
I0421 11:00:31.840245 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.06 examples/second between steps 3614 and 3615
I0421 11:00:32.940382 47076539613632 model_training_utils.py:505] Train Step: 1516/2100  / loss = 0.9415283203125
I0421 11:00:32.940798 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.16 examples/second between steps 3615 and 3616
I0421 11:00:34.034798 47076539613632 model_training_utils.py:505] Train Step: 1517/2100  / loss = 1.29443359375
I0421 11:00:34.035224 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.81 examples/second between steps 3616 and 3617
I0421 11:00:35.131111 47076539613632 model_training_utils.py:505] Train Step: 1518/2100  / loss = 0.955810546875
I0421 11:00:35.131545 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.61 examples/second between steps 3617 and 3618
I0421 11:00:36.215837 47076539613632 model_training_utils.py:505] Train Step: 1519/2100  / loss = 0.803466796875
I0421 11:00:36.216258 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 3618 and 3619
I0421 11:00:37.301711 47076539613632 model_training_utils.py:505] Train Step: 1520/2100  / loss = 0.9547119140625
I0421 11:00:37.302133 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 3619 and 3620
I0421 11:00:38.388353 47076539613632 model_training_utils.py:505] Train Step: 1521/2100  / loss = 0.940185546875
I0421 11:00:38.388781 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 3620 and 3621
I0421 11:00:39.480132 47076539613632 model_training_utils.py:505] Train Step: 1522/2100  / loss = 1.0054931640625
I0421 11:00:39.480580 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.11 examples/second between steps 3621 and 3622
I0421 11:00:40.571974 47076539613632 model_training_utils.py:505] Train Step: 1523/2100  / loss = 1.14404296875
I0421 11:00:40.572405 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.11 examples/second between steps 3622 and 3623
I0421 11:00:41.667691 47076539613632 model_training_utils.py:505] Train Step: 1524/2100  / loss = 1.09814453125
I0421 11:00:41.668114 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.69 examples/second between steps 3623 and 3624
I0421 11:00:42.762988 47076539613632 model_training_utils.py:505] Train Step: 1525/2100  / loss = 0.9949951171875
I0421 11:00:42.763428 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.74 examples/second between steps 3624 and 3625
I0421 11:00:43.863153 47076539613632 model_training_utils.py:505] Train Step: 1526/2100  / loss = 1.124267578125
I0421 11:00:43.863601 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.21 examples/second between steps 3625 and 3626
I0421 11:00:44.960239 47076539613632 model_training_utils.py:505] Train Step: 1527/2100  / loss = 0.9730224609375
I0421 11:00:44.960681 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.51 examples/second between steps 3626 and 3627
I0421 11:00:46.056225 47076539613632 model_training_utils.py:505] Train Step: 1528/2100  / loss = 1.25537109375
I0421 11:00:46.056658 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.63 examples/second between steps 3627 and 3628
I0421 11:00:47.146404 47076539613632 model_training_utils.py:505] Train Step: 1529/2100  / loss = 1.2535400390625
I0421 11:00:47.146830 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 3628 and 3629
I0421 11:00:48.232882 47076539613632 model_training_utils.py:505] Train Step: 1530/2100  / loss = 0.8759765625
I0421 11:00:48.233315 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 3629 and 3630
I0421 11:00:49.323352 47076539613632 model_training_utils.py:505] Train Step: 1531/2100  / loss = 0.821044921875
I0421 11:00:49.323782 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.23 examples/second between steps 3630 and 3631
I0421 11:00:50.414425 47076539613632 model_training_utils.py:505] Train Step: 1532/2100  / loss = 1.0296630859375
I0421 11:00:50.414849 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.18 examples/second between steps 3631 and 3632
I0421 11:00:51.503708 47076539613632 model_training_utils.py:505] Train Step: 1533/2100  / loss = 0.946533203125
I0421 11:00:51.504133 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.36 examples/second between steps 3632 and 3633
I0421 11:00:52.592601 47076539613632 model_training_utils.py:505] Train Step: 1534/2100  / loss = 0.80517578125
I0421 11:00:52.593023 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.40 examples/second between steps 3633 and 3634
I0421 11:00:53.677755 47076539613632 model_training_utils.py:505] Train Step: 1535/2100  / loss = 1.015380859375
I0421 11:00:53.678181 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 3634 and 3635
I0421 11:00:54.761707 47076539613632 model_training_utils.py:505] Train Step: 1536/2100  / loss = 1.0264892578125
I0421 11:00:54.762133 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 3635 and 3636
I0421 11:00:55.852350 47076539613632 model_training_utils.py:505] Train Step: 1537/2100  / loss = 0.956298828125
I0421 11:00:55.852773 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.24 examples/second between steps 3636 and 3637
I0421 11:00:56.945510 47076539613632 model_training_utils.py:505] Train Step: 1538/2100  / loss = 0.960693359375
I0421 11:00:56.945934 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.94 examples/second between steps 3637 and 3638
I0421 11:00:58.034519 47076539613632 model_training_utils.py:505] Train Step: 1539/2100  / loss = 1.0169677734375
I0421 11:00:58.034957 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.43 examples/second between steps 3638 and 3639
I0421 11:00:59.126833 47076539613632 model_training_utils.py:505] Train Step: 1540/2100  / loss = 0.8065185546875
I0421 11:00:59.127264 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.02 examples/second between steps 3639 and 3640
I0421 11:01:00.222470 47076539613632 model_training_utils.py:505] Train Step: 1541/2100  / loss = 1.04443359375
I0421 11:01:00.222899 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.67 examples/second between steps 3640 and 3641
I0421 11:01:01.314956 47076539613632 model_training_utils.py:505] Train Step: 1542/2100  / loss = 0.9561767578125
I0421 11:01:01.315408 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 117.99 examples/second between steps 3641 and 3642
I0421 11:01:02.407991 47076539613632 model_training_utils.py:505] Train Step: 1543/2100  / loss = 0.998779296875
I0421 11:01:02.408428 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.94 examples/second between steps 3642 and 3643
I0421 11:01:03.504026 47076539613632 model_training_utils.py:505] Train Step: 1544/2100  / loss = 0.95703125
I0421 11:01:03.504459 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.60 examples/second between steps 3643 and 3644
I0421 11:01:04.596188 47076539613632 model_training_utils.py:505] Train Step: 1545/2100  / loss = 0.7236328125
I0421 11:01:04.596618 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.04 examples/second between steps 3644 and 3645
I0421 11:01:05.681549 47076539613632 model_training_utils.py:505] Train Step: 1546/2100  / loss = 1.3043212890625
I0421 11:01:05.681978 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 3645 and 3646
I0421 11:01:06.769630 47076539613632 model_training_utils.py:505] Train Step: 1547/2100  / loss = 0.82568359375
I0421 11:01:06.770061 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.49 examples/second between steps 3646 and 3647
I0421 11:01:07.851777 47076539613632 model_training_utils.py:505] Train Step: 1548/2100  / loss = 0.78125
I0421 11:01:07.852204 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 3647 and 3648
I0421 11:01:08.939822 47076539613632 model_training_utils.py:505] Train Step: 1549/2100  / loss = 0.69970703125
I0421 11:01:08.940248 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 3648 and 3649
I0421 11:01:10.022566 47076539613632 model_training_utils.py:505] Train Step: 1550/2100  / loss = 0.8369140625
I0421 11:01:10.022994 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 3649 and 3650
I0421 11:01:11.103845 47076539613632 model_training_utils.py:505] Train Step: 1551/2100  / loss = 0.58447265625
I0421 11:01:11.104274 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 3650 and 3651
I0421 11:01:12.191371 47076539613632 model_training_utils.py:505] Train Step: 1552/2100  / loss = 0.4427490234375
I0421 11:01:12.191801 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 3651 and 3652
I0421 11:01:13.278551 47076539613632 model_training_utils.py:505] Train Step: 1553/2100  / loss = 0.51904296875
I0421 11:01:13.278980 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.61 examples/second between steps 3652 and 3653
I0421 11:01:14.368803 47076539613632 model_training_utils.py:505] Train Step: 1554/2100  / loss = 0.550048828125
I0421 11:01:14.369230 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 3653 and 3654
I0421 11:01:15.457479 47076539613632 model_training_utils.py:505] Train Step: 1555/2100  / loss = 0.880859375
I0421 11:01:15.457903 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.45 examples/second between steps 3654 and 3655
I0421 11:01:16.547391 47076539613632 model_training_utils.py:505] Train Step: 1556/2100  / loss = 0.9951171875
I0421 11:01:16.547817 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.32 examples/second between steps 3655 and 3656
I0421 11:01:17.628580 47076539613632 model_training_utils.py:505] Train Step: 1557/2100  / loss = 0.898681640625
I0421 11:01:17.629006 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 3656 and 3657
I0421 11:01:18.711537 47076539613632 model_training_utils.py:505] Train Step: 1558/2100  / loss = 1.0458984375
I0421 11:01:18.711962 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 3657 and 3658
I0421 11:01:19.793144 47076539613632 model_training_utils.py:505] Train Step: 1559/2100  / loss = 0.7569580078125
I0421 11:01:19.793579 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 3658 and 3659
I0421 11:01:20.884674 47076539613632 model_training_utils.py:505] Train Step: 1560/2100  / loss = 0.7607421875
I0421 11:01:20.885094 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.12 examples/second between steps 3659 and 3660
I0421 11:01:21.976319 47076539613632 model_training_utils.py:505] Train Step: 1561/2100  / loss = 0.764892578125
I0421 11:01:21.976743 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.15 examples/second between steps 3660 and 3661
I0421 11:01:23.066082 47076539613632 model_training_utils.py:505] Train Step: 1562/2100  / loss = 1.2606201171875
I0421 11:01:23.066508 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.29 examples/second between steps 3661 and 3662
I0421 11:01:24.157801 47076539613632 model_training_utils.py:505] Train Step: 1563/2100  / loss = 1.390380859375
I0421 11:01:24.158232 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.10 examples/second between steps 3662 and 3663
I0421 11:01:25.237934 47076539613632 model_training_utils.py:505] Train Step: 1564/2100  / loss = 0.998046875
I0421 11:01:25.238364 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.38 examples/second between steps 3663 and 3664
I0421 11:01:26.319208 47076539613632 model_training_utils.py:505] Train Step: 1565/2100  / loss = 0.8743896484375
I0421 11:01:26.319636 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 3664 and 3665
I0421 11:01:27.404943 47076539613632 model_training_utils.py:505] Train Step: 1566/2100  / loss = 0.7315673828125
I0421 11:01:27.405375 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 3665 and 3666
I0421 11:01:28.491720 47076539613632 model_training_utils.py:505] Train Step: 1567/2100  / loss = 0.5479736328125
I0421 11:01:28.492140 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 3666 and 3667
I0421 11:01:29.581218 47076539613632 model_training_utils.py:505] Train Step: 1568/2100  / loss = 1.1820068359375
I0421 11:01:29.581649 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.34 examples/second between steps 3667 and 3668
I0421 11:01:30.671420 47076539613632 model_training_utils.py:505] Train Step: 1569/2100  / loss = 0.92822265625
I0421 11:01:30.671844 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.26 examples/second between steps 3668 and 3669
I0421 11:01:31.764385 47076539613632 model_training_utils.py:505] Train Step: 1570/2100  / loss = 0.5933837890625
I0421 11:01:31.764814 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.97 examples/second between steps 3669 and 3670
I0421 11:01:32.859856 47076539613632 model_training_utils.py:505] Train Step: 1571/2100  / loss = 0.6630859375
I0421 11:01:32.860290 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.70 examples/second between steps 3670 and 3671
I0421 11:01:33.952518 47076539613632 model_training_utils.py:505] Train Step: 1572/2100  / loss = 0.6558837890625
I0421 11:01:33.952943 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.00 examples/second between steps 3671 and 3672
I0421 11:01:35.045765 47076539613632 model_training_utils.py:505] Train Step: 1573/2100  / loss = 0.7950439453125
I0421 11:01:35.046200 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.95 examples/second between steps 3672 and 3673
I0421 11:01:36.126659 47076539613632 model_training_utils.py:505] Train Step: 1574/2100  / loss = 0.968505859375
I0421 11:01:36.127090 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 3673 and 3674
I0421 11:01:37.208400 47076539613632 model_training_utils.py:505] Train Step: 1575/2100  / loss = 0.71124267578125
I0421 11:01:37.208832 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 3674 and 3675
I0421 11:01:38.295868 47076539613632 model_training_utils.py:505] Train Step: 1576/2100  / loss = 0.974853515625
I0421 11:01:38.296307 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 3675 and 3676
I0421 11:01:39.380324 47076539613632 model_training_utils.py:505] Train Step: 1577/2100  / loss = 0.68218994140625
I0421 11:01:39.380753 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3676 and 3677
I0421 11:01:40.459958 47076539613632 model_training_utils.py:505] Train Step: 1578/2100  / loss = 0.5631103515625
I0421 11:01:40.460391 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.42 examples/second between steps 3677 and 3678
I0421 11:01:41.552976 47076539613632 model_training_utils.py:505] Train Step: 1579/2100  / loss = 0.838623046875
I0421 11:01:41.553422 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.94 examples/second between steps 3678 and 3679
I0421 11:01:42.640502 47076539613632 model_training_utils.py:505] Train Step: 1580/2100  / loss = 0.74786376953125
I0421 11:01:42.640919 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 3679 and 3680
I0421 11:01:43.735543 47076539613632 model_training_utils.py:505] Train Step: 1581/2100  / loss = 0.9508056640625
I0421 11:01:43.735963 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.70 examples/second between steps 3680 and 3681
I0421 11:01:44.832424 47076539613632 model_training_utils.py:505] Train Step: 1582/2100  / loss = 1.04833984375
I0421 11:01:44.832844 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.59 examples/second between steps 3681 and 3682
I0421 11:01:45.925513 47076539613632 model_training_utils.py:505] Train Step: 1583/2100  / loss = 0.8880615234375
I0421 11:01:45.925935 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.92 examples/second between steps 3682 and 3683
I0421 11:01:47.014402 47076539613632 model_training_utils.py:505] Train Step: 1584/2100  / loss = 0.79248046875
I0421 11:01:47.014836 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.43 examples/second between steps 3683 and 3684
I0421 11:01:48.102769 47076539613632 model_training_utils.py:505] Train Step: 1585/2100  / loss = 0.7679443359375
I0421 11:01:48.103193 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.48 examples/second between steps 3684 and 3685
I0421 11:01:49.190809 47076539613632 model_training_utils.py:505] Train Step: 1586/2100  / loss = 0.705078125
I0421 11:01:49.191242 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.52 examples/second between steps 3685 and 3686
I0421 11:01:50.274002 47076539613632 model_training_utils.py:505] Train Step: 1587/2100  / loss = 0.6142578125
I0421 11:01:50.274440 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 3686 and 3687
I0421 11:01:51.360168 47076539613632 model_training_utils.py:505] Train Step: 1588/2100  / loss = 0.7249755859375
I0421 11:01:51.360605 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 3687 and 3688
I0421 11:01:52.445415 47076539613632 model_training_utils.py:505] Train Step: 1589/2100  / loss = 0.6414794921875
I0421 11:01:52.445843 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 3688 and 3689
I0421 11:01:53.535185 47076539613632 model_training_utils.py:505] Train Step: 1590/2100  / loss = 0.6212158203125
I0421 11:01:53.535614 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.40 examples/second between steps 3689 and 3690
I0421 11:01:54.629105 47076539613632 model_training_utils.py:505] Train Step: 1591/2100  / loss = 0.8892822265625
I0421 11:01:54.629537 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.89 examples/second between steps 3690 and 3691
I0421 11:01:55.724843 47076539613632 model_training_utils.py:505] Train Step: 1592/2100  / loss = 1.366943359375
I0421 11:01:55.725260 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.70 examples/second between steps 3691 and 3692
I0421 11:01:56.813372 47076539613632 model_training_utils.py:505] Train Step: 1593/2100  / loss = 1.3798828125
I0421 11:01:56.813780 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.48 examples/second between steps 3692 and 3693
I0421 11:01:57.899310 47076539613632 model_training_utils.py:505] Train Step: 1594/2100  / loss = 0.9927978515625
I0421 11:01:57.899728 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 3693 and 3694
I0421 11:01:58.991501 47076539613632 model_training_utils.py:505] Train Step: 1595/2100  / loss = 0.951171875
I0421 11:01:58.991929 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.08 examples/second between steps 3694 and 3695
I0421 11:02:00.078921 47076539613632 model_training_utils.py:505] Train Step: 1596/2100  / loss = 0.7161865234375
I0421 11:02:00.079353 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 3695 and 3696
I0421 11:02:01.170853 47076539613632 model_training_utils.py:505] Train Step: 1597/2100  / loss = 0.6243896484375
I0421 11:02:01.171294 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.11 examples/second between steps 3696 and 3697
I0421 11:02:02.252511 47076539613632 model_training_utils.py:505] Train Step: 1598/2100  / loss = 0.6832275390625
I0421 11:02:02.252937 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 3697 and 3698
I0421 11:02:03.347535 47076539613632 model_training_utils.py:505] Train Step: 1599/2100  / loss = 1.047119140625
I0421 11:02:03.347962 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.82 examples/second between steps 3698 and 3699
I0421 11:02:04.439790 47076539613632 model_training_utils.py:505] Train Step: 1600/2100  / loss = 0.7828369140625
I0421 11:02:04.440222 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.06 examples/second between steps 3699 and 3700
I0421 11:02:05.522553 47076539613632 model_training_utils.py:505] Train Step: 1601/2100  / loss = 0.64453125
I0421 11:02:05.522986 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 3700 and 3701
I0421 11:02:06.609549 47076539613632 model_training_utils.py:505] Train Step: 1602/2100  / loss = 0.7745361328125
I0421 11:02:06.609956 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.68 examples/second between steps 3701 and 3702
I0421 11:02:07.697337 47076539613632 model_training_utils.py:505] Train Step: 1603/2100  / loss = 0.987548828125
I0421 11:02:07.697754 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.55 examples/second between steps 3702 and 3703
I0421 11:02:08.787536 47076539613632 model_training_utils.py:505] Train Step: 1604/2100  / loss = 0.82421875
I0421 11:02:08.787960 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.30 examples/second between steps 3703 and 3704
I0421 11:02:09.877907 47076539613632 model_training_utils.py:505] Train Step: 1605/2100  / loss = 0.88818359375
I0421 11:02:09.878339 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.32 examples/second between steps 3704 and 3705
I0421 11:02:10.963121 47076539613632 model_training_utils.py:505] Train Step: 1606/2100  / loss = 0.6893310546875
I0421 11:02:10.963550 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 3705 and 3706
I0421 11:02:12.047021 47076539613632 model_training_utils.py:505] Train Step: 1607/2100  / loss = 0.9373779296875
I0421 11:02:12.047469 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 3706 and 3707
I0421 11:02:13.128166 47076539613632 model_training_utils.py:505] Train Step: 1608/2100  / loss = 0.630615234375
I0421 11:02:13.128615 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 3707 and 3708
I0421 11:02:14.211710 47076539613632 model_training_utils.py:505] Train Step: 1609/2100  / loss = 0.683349609375
I0421 11:02:14.212133 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 3708 and 3709
I0421 11:02:15.292767 47076539613632 model_training_utils.py:505] Train Step: 1610/2100  / loss = 0.5587158203125
I0421 11:02:15.293190 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 3709 and 3710
I0421 11:02:16.375311 47076539613632 model_training_utils.py:505] Train Step: 1611/2100  / loss = 0.7322998046875
I0421 11:02:16.375741 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3710 and 3711
I0421 11:02:17.458427 47076539613632 model_training_utils.py:505] Train Step: 1612/2100  / loss = 0.712890625
I0421 11:02:17.458869 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 3711 and 3712
I0421 11:02:18.540647 47076539613632 model_training_utils.py:505] Train Step: 1613/2100  / loss = 0.9847412109375
I0421 11:02:18.541074 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 3712 and 3713
I0421 11:02:19.625357 47076539613632 model_training_utils.py:505] Train Step: 1614/2100  / loss = 0.74267578125
I0421 11:02:19.625788 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 3713 and 3714
I0421 11:02:20.710021 47076539613632 model_training_utils.py:505] Train Step: 1615/2100  / loss = 0.972900390625
I0421 11:02:20.710455 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3714 and 3715
I0421 11:02:21.794237 47076539613632 model_training_utils.py:505] Train Step: 1616/2100  / loss = 0.9776611328125
I0421 11:02:21.794675 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 3715 and 3716
I0421 11:02:22.875413 47076539613632 model_training_utils.py:505] Train Step: 1617/2100  / loss = 0.7540283203125
I0421 11:02:22.875844 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 3716 and 3717
I0421 11:02:23.962473 47076539613632 model_training_utils.py:505] Train Step: 1618/2100  / loss = 0.9359130859375
I0421 11:02:23.962902 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.65 examples/second between steps 3717 and 3718
I0421 11:02:25.046388 47076539613632 model_training_utils.py:505] Train Step: 1619/2100  / loss = 0.839111328125
I0421 11:02:25.046833 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 3718 and 3719
I0421 11:02:26.131870 47076539613632 model_training_utils.py:505] Train Step: 1620/2100  / loss = 0.8524169921875
I0421 11:02:26.132311 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 3719 and 3720
I0421 11:02:27.214536 47076539613632 model_training_utils.py:505] Train Step: 1621/2100  / loss = 0.6749267578125
I0421 11:02:27.214977 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3720 and 3721
I0421 11:02:28.296928 47076539613632 model_training_utils.py:505] Train Step: 1622/2100  / loss = 0.65765380859375
I0421 11:02:28.297370 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 3721 and 3722
I0421 11:02:29.378582 47076539613632 model_training_utils.py:505] Train Step: 1623/2100  / loss = 0.9283447265625
I0421 11:02:29.379017 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 3722 and 3723
I0421 11:02:30.461826 47076539613632 model_training_utils.py:505] Train Step: 1624/2100  / loss = 0.922607421875
I0421 11:02:30.462254 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 3723 and 3724
I0421 11:02:31.547856 47076539613632 model_training_utils.py:505] Train Step: 1625/2100  / loss = 0.640380859375
I0421 11:02:31.548297 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.75 examples/second between steps 3724 and 3725
I0421 11:02:32.635331 47076539613632 model_training_utils.py:505] Train Step: 1626/2100  / loss = 0.5872802734375
I0421 11:02:32.635760 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 3725 and 3726
I0421 11:02:33.720948 47076539613632 model_training_utils.py:505] Train Step: 1627/2100  / loss = 0.539306640625
I0421 11:02:33.721387 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 3726 and 3727
I0421 11:02:34.803167 47076539613632 model_training_utils.py:505] Train Step: 1628/2100  / loss = 0.73095703125
I0421 11:02:34.803602 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 3727 and 3728
I0421 11:02:35.888332 47076539613632 model_training_utils.py:505] Train Step: 1629/2100  / loss = 0.8878173828125
I0421 11:02:35.888752 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 3728 and 3729
I0421 11:02:36.971191 47076539613632 model_training_utils.py:505] Train Step: 1630/2100  / loss = 0.8197021484375
I0421 11:02:36.971623 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 3729 and 3730
I0421 11:02:38.053631 47076539613632 model_training_utils.py:505] Train Step: 1631/2100  / loss = 0.81201171875
I0421 11:02:38.054048 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 3730 and 3731
I0421 11:02:39.138470 47076539613632 model_training_utils.py:505] Train Step: 1632/2100  / loss = 0.6568603515625
I0421 11:02:39.138902 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3731 and 3732
I0421 11:02:40.218867 47076539613632 model_training_utils.py:505] Train Step: 1633/2100  / loss = 0.8328857421875
I0421 11:02:40.219300 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.38 examples/second between steps 3732 and 3733
I0421 11:02:41.304627 47076539613632 model_training_utils.py:505] Train Step: 1634/2100  / loss = 1.127685546875
I0421 11:02:41.305057 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 3733 and 3734
I0421 11:02:42.386735 47076539613632 model_training_utils.py:505] Train Step: 1635/2100  / loss = 1.2457275390625
I0421 11:02:42.387171 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 3734 and 3735
I0421 11:02:43.467748 47076539613632 model_training_utils.py:505] Train Step: 1636/2100  / loss = 1.106201171875
I0421 11:02:43.468174 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.35 examples/second between steps 3735 and 3736
I0421 11:02:44.548243 47076539613632 model_training_utils.py:505] Train Step: 1637/2100  / loss = 1.0115966796875
I0421 11:02:44.548687 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 3736 and 3737
I0421 11:02:45.625048 47076539613632 model_training_utils.py:505] Train Step: 1638/2100  / loss = 0.7960205078125
I0421 11:02:45.625482 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.82 examples/second between steps 3737 and 3738
I0421 11:02:46.707399 47076539613632 model_training_utils.py:505] Train Step: 1639/2100  / loss = 0.713134765625
I0421 11:02:46.707828 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 3738 and 3739
I0421 11:02:47.790359 47076539613632 model_training_utils.py:505] Train Step: 1640/2100  / loss = 0.85205078125
I0421 11:02:47.790782 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3739 and 3740
I0421 11:02:48.872711 47076539613632 model_training_utils.py:505] Train Step: 1641/2100  / loss = 1.0765380859375
I0421 11:02:48.873148 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 3740 and 3741
I0421 11:02:49.956662 47076539613632 model_training_utils.py:505] Train Step: 1642/2100  / loss = 1.26708984375
I0421 11:02:49.957094 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 3741 and 3742
I0421 11:02:51.039253 47076539613632 model_training_utils.py:505] Train Step: 1643/2100  / loss = 1.236572265625
I0421 11:02:51.039697 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 3742 and 3743
I0421 11:02:52.122017 47076539613632 model_training_utils.py:505] Train Step: 1644/2100  / loss = 1.037353515625
I0421 11:02:52.122453 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3743 and 3744
I0421 11:02:53.205387 47076539613632 model_training_utils.py:505] Train Step: 1645/2100  / loss = 1.096435546875
I0421 11:02:53.205809 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 3744 and 3745
I0421 11:02:54.287472 47076539613632 model_training_utils.py:505] Train Step: 1646/2100  / loss = 1.188720703125
I0421 11:02:54.287913 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 3745 and 3746
I0421 11:02:55.367241 47076539613632 model_training_utils.py:505] Train Step: 1647/2100  / loss = 1.139892578125
I0421 11:02:55.367677 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.42 examples/second between steps 3746 and 3747
I0421 11:02:56.450354 47076539613632 model_training_utils.py:505] Train Step: 1648/2100  / loss = 0.898193359375
I0421 11:02:56.450788 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 3747 and 3748
I0421 11:02:57.530998 47076539613632 model_training_utils.py:505] Train Step: 1649/2100  / loss = 1.0625
I0421 11:02:57.531441 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.35 examples/second between steps 3748 and 3749
I0421 11:02:58.610026 47076539613632 model_training_utils.py:505] Train Step: 1650/2100  / loss = 0.6214599609375
I0421 11:02:58.610462 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.52 examples/second between steps 3749 and 3750
I0421 11:02:59.694117 47076539613632 model_training_utils.py:505] Train Step: 1651/2100  / loss = 0.6802978515625
I0421 11:02:59.694558 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 3750 and 3751
I0421 11:03:00.775241 47076539613632 model_training_utils.py:505] Train Step: 1652/2100  / loss = 0.52178955078125
I0421 11:03:00.775676 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 3751 and 3752
I0421 11:03:01.858080 47076539613632 model_training_utils.py:505] Train Step: 1653/2100  / loss = 0.5614013671875
I0421 11:03:01.858519 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3752 and 3753
I0421 11:03:02.935235 47076539613632 model_training_utils.py:505] Train Step: 1654/2100  / loss = 0.50726318359375
I0421 11:03:02.935672 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.74 examples/second between steps 3753 and 3754
I0421 11:03:04.016144 47076539613632 model_training_utils.py:505] Train Step: 1655/2100  / loss = 0.6258544921875
I0421 11:03:04.016579 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 3754 and 3755
I0421 11:03:05.102286 47076539613632 model_training_utils.py:505] Train Step: 1656/2100  / loss = 0.809814453125
I0421 11:03:05.102727 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 3755 and 3756
I0421 11:03:06.185937 47076539613632 model_training_utils.py:505] Train Step: 1657/2100  / loss = 0.682861328125
I0421 11:03:06.186367 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 3756 and 3757
I0421 11:03:07.271713 47076539613632 model_training_utils.py:505] Train Step: 1658/2100  / loss = 0.6778564453125
I0421 11:03:07.272143 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 3757 and 3758
I0421 11:03:08.359383 47076539613632 model_training_utils.py:505] Train Step: 1659/2100  / loss = 0.683837890625
I0421 11:03:08.359814 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 3758 and 3759
I0421 11:03:09.444048 47076539613632 model_training_utils.py:505] Train Step: 1660/2100  / loss = 0.7003173828125
I0421 11:03:09.444493 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3759 and 3760
I0421 11:03:10.523301 47076539613632 model_training_utils.py:505] Train Step: 1661/2100  / loss = 0.8477783203125
I0421 11:03:10.523729 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.50 examples/second between steps 3760 and 3761
I0421 11:03:11.606425 47076539613632 model_training_utils.py:505] Train Step: 1662/2100  / loss = 0.510498046875
I0421 11:03:11.606856 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 3761 and 3762
I0421 11:03:12.691155 47076539613632 model_training_utils.py:505] Train Step: 1663/2100  / loss = 0.45977783203125
I0421 11:03:12.691608 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 3762 and 3763
I0421 11:03:13.773006 47076539613632 model_training_utils.py:505] Train Step: 1664/2100  / loss = 0.4486083984375
I0421 11:03:13.773440 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 3763 and 3764
I0421 11:03:14.853455 47076539613632 model_training_utils.py:505] Train Step: 1665/2100  / loss = 0.8408203125
I0421 11:03:14.853877 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.35 examples/second between steps 3764 and 3765
I0421 11:03:15.937742 47076539613632 model_training_utils.py:505] Train Step: 1666/2100  / loss = 0.75390625
I0421 11:03:15.938171 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 3765 and 3766
I0421 11:03:17.022009 47076539613632 model_training_utils.py:505] Train Step: 1667/2100  / loss = 0.7333984375
I0421 11:03:17.022452 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 3766 and 3767
I0421 11:03:18.104538 47076539613632 model_training_utils.py:505] Train Step: 1668/2100  / loss = 1.03125
I0421 11:03:18.104967 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 3767 and 3768
I0421 11:03:19.192579 47076539613632 model_training_utils.py:505] Train Step: 1669/2100  / loss = 0.6396484375
I0421 11:03:19.193012 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.56 examples/second between steps 3768 and 3769
I0421 11:03:20.275213 47076539613632 model_training_utils.py:505] Train Step: 1670/2100  / loss = 0.7486572265625
I0421 11:03:20.275651 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 3769 and 3770
I0421 11:03:21.356395 47076539613632 model_training_utils.py:505] Train Step: 1671/2100  / loss = 0.7333984375
I0421 11:03:21.356825 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 3770 and 3771
I0421 11:03:22.438003 47076539613632 model_training_utils.py:505] Train Step: 1672/2100  / loss = 0.8253173828125
I0421 11:03:22.438444 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 3771 and 3772
I0421 11:03:23.523346 47076539613632 model_training_utils.py:505] Train Step: 1673/2100  / loss = 1.0684814453125
I0421 11:03:23.523784 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 3772 and 3773
I0421 11:03:24.607521 47076539613632 model_training_utils.py:505] Train Step: 1674/2100  / loss = 0.8079833984375
I0421 11:03:24.607962 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 3773 and 3774
I0421 11:03:25.692753 47076539613632 model_training_utils.py:505] Train Step: 1675/2100  / loss = 0.917236328125
I0421 11:03:25.693175 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 3774 and 3775
I0421 11:03:26.779956 47076539613632 model_training_utils.py:505] Train Step: 1676/2100  / loss = 1.41162109375
I0421 11:03:26.780380 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 3775 and 3776
I0421 11:03:27.864591 47076539613632 model_training_utils.py:505] Train Step: 1677/2100  / loss = 2.009765625
I0421 11:03:27.865015 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 3776 and 3777
I0421 11:03:28.951037 47076539613632 model_training_utils.py:505] Train Step: 1678/2100  / loss = 1.534423828125
I0421 11:03:28.951473 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 3777 and 3778
I0421 11:03:30.036000 47076539613632 model_training_utils.py:505] Train Step: 1679/2100  / loss = 1.0703125
I0421 11:03:30.036432 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 3778 and 3779
I0421 11:03:31.121229 47076539613632 model_training_utils.py:505] Train Step: 1680/2100  / loss = 0.72265625
I0421 11:03:31.121664 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 3779 and 3780
I0421 11:03:32.206182 47076539613632 model_training_utils.py:505] Train Step: 1681/2100  / loss = 0.6400146484375
I0421 11:03:32.206611 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 3780 and 3781
I0421 11:03:33.290997 47076539613632 model_training_utils.py:505] Train Step: 1682/2100  / loss = 0.694091796875
I0421 11:03:33.291429 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 3781 and 3782
I0421 11:03:34.375036 47076539613632 model_training_utils.py:505] Train Step: 1683/2100  / loss = 0.933837890625
I0421 11:03:34.375477 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 3782 and 3783
I0421 11:03:35.459881 47076539613632 model_training_utils.py:505] Train Step: 1684/2100  / loss = 1.043212890625
I0421 11:03:35.460312 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 3783 and 3784
I0421 11:03:36.545391 47076539613632 model_training_utils.py:505] Train Step: 1685/2100  / loss = 1.334716796875
I0421 11:03:36.545819 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 3784 and 3785
I0421 11:03:37.630089 47076539613632 model_training_utils.py:505] Train Step: 1686/2100  / loss = 1.6787109375
I0421 11:03:37.630527 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 3785 and 3786
I0421 11:03:38.713257 47076539613632 model_training_utils.py:505] Train Step: 1687/2100  / loss = 1.40869140625
I0421 11:03:38.713700 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 3786 and 3787
I0421 11:03:39.798079 47076539613632 model_training_utils.py:505] Train Step: 1688/2100  / loss = 1.000732421875
I0421 11:03:39.798516 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 3787 and 3788
I0421 11:03:40.882076 47076539613632 model_training_utils.py:505] Train Step: 1689/2100  / loss = 2.242431640625
I0421 11:03:40.882516 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 3788 and 3789
I0421 11:03:41.966592 47076539613632 model_training_utils.py:505] Train Step: 1690/2100  / loss = 2.2265625
I0421 11:03:41.967020 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3789 and 3790
I0421 11:03:43.048331 47076539613632 model_training_utils.py:505] Train Step: 1691/2100  / loss = 1.468994140625
I0421 11:03:43.048758 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 3790 and 3791
I0421 11:03:44.129219 47076539613632 model_training_utils.py:505] Train Step: 1692/2100  / loss = 1.048583984375
I0421 11:03:44.129671 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.33 examples/second between steps 3791 and 3792
I0421 11:03:45.210409 47076539613632 model_training_utils.py:505] Train Step: 1693/2100  / loss = 0.76904296875
I0421 11:03:45.210834 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 3792 and 3793
I0421 11:03:46.293045 47076539613632 model_training_utils.py:505] Train Step: 1694/2100  / loss = 0.8448486328125
I0421 11:03:46.293478 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 3793 and 3794
I0421 11:03:47.373048 47076539613632 model_training_utils.py:505] Train Step: 1695/2100  / loss = 0.738037109375
I0421 11:03:47.373480 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.43 examples/second between steps 3794 and 3795
I0421 11:03:48.449594 47076539613632 model_training_utils.py:505] Train Step: 1696/2100  / loss = 0.911376953125
I0421 11:03:48.450014 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.78 examples/second between steps 3795 and 3796
I0421 11:03:49.532683 47076539613632 model_training_utils.py:505] Train Step: 1697/2100  / loss = 0.861083984375
I0421 11:03:49.533114 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 3796 and 3797
I0421 11:03:50.616101 47076539613632 model_training_utils.py:505] Train Step: 1698/2100  / loss = 0.94482421875
I0421 11:03:50.616534 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 3797 and 3798
I0421 11:03:51.698550 47076539613632 model_training_utils.py:505] Train Step: 1699/2100  / loss = 0.8314208984375
I0421 11:03:51.698971 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 3798 and 3799
I0421 11:03:52.782813 47076539613632 model_training_utils.py:505] Train Step: 1700/2100  / loss = 0.969482421875
I0421 11:03:52.783238 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 3799 and 3800
I0421 11:03:53.866095 47076539613632 model_training_utils.py:505] Train Step: 1701/2100  / loss = 1.116455078125
I0421 11:03:53.866529 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 3800 and 3801
I0421 11:03:54.947920 47076539613632 model_training_utils.py:505] Train Step: 1702/2100  / loss = 0.9039306640625
I0421 11:03:54.948356 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 3801 and 3802
I0421 11:03:56.033125 47076539613632 model_training_utils.py:505] Train Step: 1703/2100  / loss = 0.861083984375
I0421 11:03:56.033553 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 3802 and 3803
I0421 11:03:57.118627 47076539613632 model_training_utils.py:505] Train Step: 1704/2100  / loss = 0.9942626953125
I0421 11:03:57.119055 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 3803 and 3804
I0421 11:03:58.202019 47076539613632 model_training_utils.py:505] Train Step: 1705/2100  / loss = 0.74560546875
I0421 11:03:58.202461 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 3804 and 3805
I0421 11:03:59.286608 47076539613632 model_training_utils.py:505] Train Step: 1706/2100  / loss = 0.6060791015625
I0421 11:03:59.287037 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 3805 and 3806
I0421 11:04:00.367488 47076539613632 model_training_utils.py:505] Train Step: 1707/2100  / loss = 0.6583251953125
I0421 11:04:00.367924 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 3806 and 3807
I0421 11:04:01.450462 47076539613632 model_training_utils.py:505] Train Step: 1708/2100  / loss = 0.6435546875
I0421 11:04:01.450887 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3807 and 3808
I0421 11:04:02.532881 47076539613632 model_training_utils.py:505] Train Step: 1709/2100  / loss = 0.68084716796875
I0421 11:04:02.533312 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 3808 and 3809
I0421 11:04:03.616086 47076539613632 model_training_utils.py:505] Train Step: 1710/2100  / loss = 0.64208984375
I0421 11:04:03.616518 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3809 and 3810
I0421 11:04:04.699175 47076539613632 model_training_utils.py:505] Train Step: 1711/2100  / loss = 0.590087890625
I0421 11:04:04.699613 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3810 and 3811
I0421 11:04:05.782457 47076539613632 model_training_utils.py:505] Train Step: 1712/2100  / loss = 0.40203857421875
I0421 11:04:05.782883 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 3811 and 3812
I0421 11:04:06.864955 47076539613632 model_training_utils.py:505] Train Step: 1713/2100  / loss = 0.550537109375
I0421 11:04:06.865388 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 3812 and 3813
I0421 11:04:07.944384 47076539613632 model_training_utils.py:505] Train Step: 1714/2100  / loss = 0.60296630859375
I0421 11:04:07.944806 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.48 examples/second between steps 3813 and 3814
I0421 11:04:09.031543 47076539613632 model_training_utils.py:505] Train Step: 1715/2100  / loss = 1.6572265625
I0421 11:04:09.031968 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 3814 and 3815
I0421 11:04:10.117017 47076539613632 model_training_utils.py:505] Train Step: 1716/2100  / loss = 1.0677490234375
I0421 11:04:10.117466 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 3815 and 3816
I0421 11:04:11.198052 47076539613632 model_training_utils.py:505] Train Step: 1717/2100  / loss = 0.906982421875
I0421 11:04:11.198489 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 3816 and 3817
I0421 11:04:12.282665 47076539613632 model_training_utils.py:505] Train Step: 1718/2100  / loss = 0.63946533203125
I0421 11:04:12.283090 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3817 and 3818
I0421 11:04:13.366016 47076539613632 model_training_utils.py:505] Train Step: 1719/2100  / loss = 0.51409912109375
I0421 11:04:13.366451 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 3818 and 3819
I0421 11:04:14.448230 47076539613632 model_training_utils.py:505] Train Step: 1720/2100  / loss = 1.6402587890625
I0421 11:04:14.448668 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 3819 and 3820
I0421 11:04:15.531314 47076539613632 model_training_utils.py:505] Train Step: 1721/2100  / loss = 1.91796875
I0421 11:04:15.531739 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 3820 and 3821
I0421 11:04:16.616837 47076539613632 model_training_utils.py:505] Train Step: 1722/2100  / loss = 0.955810546875
I0421 11:04:16.617263 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 3821 and 3822
I0421 11:04:17.702095 47076539613632 model_training_utils.py:505] Train Step: 1723/2100  / loss = 1.0462646484375
I0421 11:04:17.702527 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 3822 and 3823
I0421 11:04:18.784770 47076539613632 model_training_utils.py:505] Train Step: 1724/2100  / loss = 1.0302734375
I0421 11:04:18.785200 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3823 and 3824
I0421 11:04:19.867864 47076539613632 model_training_utils.py:505] Train Step: 1725/2100  / loss = 0.751220703125
I0421 11:04:19.868303 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 3824 and 3825
I0421 11:04:20.951326 47076539613632 model_training_utils.py:505] Train Step: 1726/2100  / loss = 0.8013916015625
I0421 11:04:20.951748 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 3825 and 3826
I0421 11:04:22.035851 47076539613632 model_training_utils.py:505] Train Step: 1727/2100  / loss = 0.8851318359375
I0421 11:04:22.036277 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 3826 and 3827
I0421 11:04:23.123301 47076539613632 model_training_utils.py:505] Train Step: 1728/2100  / loss = 1.200439453125
I0421 11:04:23.123740 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 3827 and 3828
I0421 11:04:24.207696 47076539613632 model_training_utils.py:505] Train Step: 1729/2100  / loss = 0.7044677734375
I0421 11:04:24.208128 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3828 and 3829
I0421 11:04:25.290557 47076539613632 model_training_utils.py:505] Train Step: 1730/2100  / loss = 0.639984130859375
I0421 11:04:25.290987 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3829 and 3830
I0421 11:04:26.375951 47076539613632 model_training_utils.py:505] Train Step: 1731/2100  / loss = 0.843505859375
I0421 11:04:26.376388 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 3830 and 3831
I0421 11:04:27.459451 47076539613632 model_training_utils.py:505] Train Step: 1732/2100  / loss = 1.1055908203125
I0421 11:04:27.459883 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 3831 and 3832
I0421 11:04:28.544592 47076539613632 model_training_utils.py:505] Train Step: 1733/2100  / loss = 1.397216796875
I0421 11:04:28.545011 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 3832 and 3833
I0421 11:04:29.632129 47076539613632 model_training_utils.py:505] Train Step: 1734/2100  / loss = 1.263427734375
I0421 11:04:29.632570 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.57 examples/second between steps 3833 and 3834
I0421 11:04:30.717588 47076539613632 model_training_utils.py:505] Train Step: 1735/2100  / loss = 1.0968017578125
I0421 11:04:30.718016 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 3834 and 3835
I0421 11:04:31.800249 47076539613632 model_training_utils.py:505] Train Step: 1736/2100  / loss = 0.9251708984375
I0421 11:04:31.800682 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 3835 and 3836
I0421 11:04:32.880910 47076539613632 model_training_utils.py:505] Train Step: 1737/2100  / loss = 1.0086669921875
I0421 11:04:32.881345 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.35 examples/second between steps 3836 and 3837
I0421 11:04:33.966488 47076539613632 model_training_utils.py:505] Train Step: 1738/2100  / loss = 0.7049560546875
I0421 11:04:33.966913 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 3837 and 3838
I0421 11:04:35.050962 47076539613632 model_training_utils.py:505] Train Step: 1739/2100  / loss = 0.59381103515625
I0421 11:04:35.051403 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 3838 and 3839
I0421 11:04:36.137094 47076539613632 model_training_utils.py:505] Train Step: 1740/2100  / loss = 0.818359375
I0421 11:04:36.137540 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.71 examples/second between steps 3839 and 3840
I0421 11:04:37.222166 47076539613632 model_training_utils.py:505] Train Step: 1741/2100  / loss = 0.9891357421875
I0421 11:04:37.222602 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.82 examples/second between steps 3840 and 3841
I0421 11:04:38.299886 47076539613632 model_training_utils.py:505] Train Step: 1742/2100  / loss = 1.060302734375
I0421 11:04:38.300322 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.65 examples/second between steps 3841 and 3842
I0421 11:04:39.385161 47076539613632 model_training_utils.py:505] Train Step: 1743/2100  / loss = 1.376953125
I0421 11:04:39.385598 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 3842 and 3843
I0421 11:04:40.471567 47076539613632 model_training_utils.py:505] Train Step: 1744/2100  / loss = 1.757568359375
I0421 11:04:40.471995 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 3843 and 3844
I0421 11:04:41.560921 47076539613632 model_training_utils.py:505] Train Step: 1745/2100  / loss = 1.392822265625
I0421 11:04:41.561351 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.39 examples/second between steps 3844 and 3845
I0421 11:04:42.645910 47076539613632 model_training_utils.py:505] Train Step: 1746/2100  / loss = 1.008056640625
I0421 11:04:42.646345 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 3845 and 3846
I0421 11:04:43.727850 47076539613632 model_training_utils.py:505] Train Step: 1747/2100  / loss = 1.23046875
I0421 11:04:43.728302 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 3846 and 3847
I0421 11:04:44.811581 47076539613632 model_training_utils.py:505] Train Step: 1748/2100  / loss = 0.95721435546875
I0421 11:04:44.812019 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 3847 and 3848
I0421 11:04:45.895220 47076539613632 model_training_utils.py:505] Train Step: 1749/2100  / loss = 1.00830078125
I0421 11:04:45.895650 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 3848 and 3849
I0421 11:04:46.980346 47076539613632 model_training_utils.py:505] Train Step: 1750/2100  / loss = 0.9287109375
I0421 11:04:46.980771 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 3849 and 3850
I0421 11:04:48.065562 47076539613632 model_training_utils.py:505] Train Step: 1751/2100  / loss = 1.0699462890625
I0421 11:04:48.065993 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 3850 and 3851
I0421 11:04:49.149420 47076539613632 model_training_utils.py:505] Train Step: 1752/2100  / loss = 1.039794921875
I0421 11:04:49.149852 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 3851 and 3852
I0421 11:04:50.233353 47076539613632 model_training_utils.py:505] Train Step: 1753/2100  / loss = 1.0596923828125
I0421 11:04:50.233780 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 3852 and 3853
I0421 11:04:51.317146 47076539613632 model_training_utils.py:505] Train Step: 1754/2100  / loss = 0.961669921875
I0421 11:04:51.317573 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 3853 and 3854
I0421 11:04:52.402159 47076539613632 model_training_utils.py:505] Train Step: 1755/2100  / loss = 0.8817138671875
I0421 11:04:52.402594 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 3854 and 3855
I0421 11:04:53.485527 47076539613632 model_training_utils.py:505] Train Step: 1756/2100  / loss = 0.904052734375
I0421 11:04:53.485951 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.07 examples/second between steps 3855 and 3856
I0421 11:04:54.566631 47076539613632 model_training_utils.py:505] Train Step: 1757/2100  / loss = 0.8184814453125
I0421 11:04:54.567059 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 3856 and 3857
I0421 11:04:55.649698 47076539613632 model_training_utils.py:505] Train Step: 1758/2100  / loss = 0.6905517578125
I0421 11:04:55.650123 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3857 and 3858
I0421 11:04:56.733181 47076539613632 model_training_utils.py:505] Train Step: 1759/2100  / loss = 0.9476318359375
I0421 11:04:56.733618 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 3858 and 3859
I0421 11:04:57.814695 47076539613632 model_training_utils.py:505] Train Step: 1760/2100  / loss = 2.12158203125
I0421 11:04:57.815117 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 3859 and 3860
I0421 11:04:58.894145 47076539613632 model_training_utils.py:505] Train Step: 1761/2100  / loss = 1.36767578125
I0421 11:04:58.894575 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.48 examples/second between steps 3860 and 3861
I0421 11:04:59.974485 47076539613632 model_training_utils.py:505] Train Step: 1762/2100  / loss = 1.04150390625
I0421 11:04:59.974916 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.38 examples/second between steps 3861 and 3862
I0421 11:05:01.054909 47076539613632 model_training_utils.py:505] Train Step: 1763/2100  / loss = 1.191650390625
I0421 11:05:01.055352 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.37 examples/second between steps 3862 and 3863
I0421 11:05:02.136842 47076539613632 model_training_utils.py:505] Train Step: 1764/2100  / loss = 0.9566650390625
I0421 11:05:02.137266 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 3863 and 3864
I0421 11:05:03.219763 47076539613632 model_training_utils.py:505] Train Step: 1765/2100  / loss = 1.520263671875
I0421 11:05:03.220184 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 3864 and 3865
I0421 11:05:04.302915 47076539613632 model_training_utils.py:505] Train Step: 1766/2100  / loss = 1.6220703125
I0421 11:05:04.303347 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3865 and 3866
I0421 11:05:05.386198 47076539613632 model_training_utils.py:505] Train Step: 1767/2100  / loss = 1.318603515625
I0421 11:05:05.386633 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 3866 and 3867
I0421 11:05:06.467348 47076539613632 model_training_utils.py:505] Train Step: 1768/2100  / loss = 0.764404296875
I0421 11:05:06.467772 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 3867 and 3868
I0421 11:05:07.548090 47076539613632 model_training_utils.py:505] Train Step: 1769/2100  / loss = 0.763916015625
I0421 11:05:07.548535 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 3868 and 3869
I0421 11:05:08.629791 47076539613632 model_training_utils.py:505] Train Step: 1770/2100  / loss = 1.3099365234375
I0421 11:05:08.630218 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 3869 and 3870
I0421 11:05:09.710860 47076539613632 model_training_utils.py:505] Train Step: 1771/2100  / loss = 1.9794921875
I0421 11:05:09.711323 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.34 examples/second between steps 3870 and 3871
I0421 11:05:10.792884 47076539613632 model_training_utils.py:505] Train Step: 1772/2100  / loss = 0.998291015625
I0421 11:05:10.793319 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 3871 and 3872
I0421 11:05:11.873722 47076539613632 model_training_utils.py:505] Train Step: 1773/2100  / loss = 0.71630859375
I0421 11:05:11.874159 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 3872 and 3873
I0421 11:05:12.955690 47076539613632 model_training_utils.py:505] Train Step: 1774/2100  / loss = 1.0521240234375
I0421 11:05:12.956111 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 3873 and 3874
I0421 11:05:14.040569 47076539613632 model_training_utils.py:505] Train Step: 1775/2100  / loss = 0.7998046875
I0421 11:05:14.040996 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 3874 and 3875
I0421 11:05:15.125716 47076539613632 model_training_utils.py:505] Train Step: 1776/2100  / loss = 0.73388671875
I0421 11:05:15.126147 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 3875 and 3876
I0421 11:05:16.203854 47076539613632 model_training_utils.py:505] Train Step: 1777/2100  / loss = 0.9169921875
I0421 11:05:16.204292 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.61 examples/second between steps 3876 and 3877
I0421 11:05:17.287431 47076539613632 model_training_utils.py:505] Train Step: 1778/2100  / loss = 1.1474609375
I0421 11:05:17.287862 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 3877 and 3878
I0421 11:05:18.370485 47076539613632 model_training_utils.py:505] Train Step: 1779/2100  / loss = 1.87646484375
I0421 11:05:18.370922 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 3878 and 3879
I0421 11:05:19.457390 47076539613632 model_training_utils.py:505] Train Step: 1780/2100  / loss = 2.462890625
I0421 11:05:19.457822 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.64 examples/second between steps 3879 and 3880
I0421 11:05:20.543172 47076539613632 model_training_utils.py:505] Train Step: 1781/2100  / loss = 2.630859375
I0421 11:05:20.543609 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 3880 and 3881
I0421 11:05:21.622077 47076539613632 model_training_utils.py:505] Train Step: 1782/2100  / loss = 1.4278564453125
I0421 11:05:21.622516 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.54 examples/second between steps 3881 and 3882
I0421 11:05:22.704986 47076539613632 model_training_utils.py:505] Train Step: 1783/2100  / loss = 1.20458984375
I0421 11:05:22.705420 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3882 and 3883
I0421 11:05:23.789163 47076539613632 model_training_utils.py:505] Train Step: 1784/2100  / loss = 1.2744140625
I0421 11:05:23.789596 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 3883 and 3884
I0421 11:05:24.872061 47076539613632 model_training_utils.py:505] Train Step: 1785/2100  / loss = 0.883544921875
I0421 11:05:24.872497 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 3884 and 3885
I0421 11:05:25.954165 47076539613632 model_training_utils.py:505] Train Step: 1786/2100  / loss = 0.81982421875
I0421 11:05:25.954602 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3885 and 3886
I0421 11:05:27.033933 47076539613632 model_training_utils.py:505] Train Step: 1787/2100  / loss = 0.7591552734375
I0421 11:05:27.034375 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.39 examples/second between steps 3886 and 3887
I0421 11:05:28.116427 47076539613632 model_training_utils.py:505] Train Step: 1788/2100  / loss = 0.772216796875
I0421 11:05:28.116851 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3887 and 3888
I0421 11:05:29.199769 47076539613632 model_training_utils.py:505] Train Step: 1789/2100  / loss = 1.4951171875
I0421 11:05:29.200204 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 3888 and 3889
I0421 11:05:30.286278 47076539613632 model_training_utils.py:505] Train Step: 1790/2100  / loss = 1.94677734375
I0421 11:05:30.286721 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.67 examples/second between steps 3889 and 3890
I0421 11:05:31.369552 47076539613632 model_training_utils.py:505] Train Step: 1791/2100  / loss = 1.6533203125
I0421 11:05:31.369983 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 3890 and 3891
I0421 11:05:32.453461 47076539613632 model_training_utils.py:505] Train Step: 1792/2100  / loss = 1.2333984375
I0421 11:05:32.453906 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.96 examples/second between steps 3891 and 3892
I0421 11:05:33.532789 47076539613632 model_training_utils.py:505] Train Step: 1793/2100  / loss = 0.6387939453125
I0421 11:05:33.533217 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.46 examples/second between steps 3892 and 3893
I0421 11:05:34.615842 47076539613632 model_training_utils.py:505] Train Step: 1794/2100  / loss = 0.5743408203125
I0421 11:05:34.616273 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 3893 and 3894
I0421 11:05:35.700790 47076539613632 model_training_utils.py:505] Train Step: 1795/2100  / loss = 0.95751953125
I0421 11:05:35.701166 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 3894 and 3895
I0421 11:05:36.787060 47076539613632 model_training_utils.py:505] Train Step: 1796/2100  / loss = 0.9722900390625
I0421 11:05:36.787459 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 3895 and 3896
I0421 11:05:37.867507 47076539613632 model_training_utils.py:505] Train Step: 1797/2100  / loss = 1.067626953125
I0421 11:05:37.867889 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.38 examples/second between steps 3896 and 3897
I0421 11:05:38.947132 47076539613632 model_training_utils.py:505] Train Step: 1798/2100  / loss = 0.97509765625
I0421 11:05:38.947531 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.50 examples/second between steps 3897 and 3898
I0421 11:05:40.032845 47076539613632 model_training_utils.py:505] Train Step: 1799/2100  / loss = 0.857421875
I0421 11:05:40.033232 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 3898 and 3899
I0421 11:05:41.118586 47076539613632 model_training_utils.py:505] Train Step: 1800/2100  / loss = 0.6942138671875
I0421 11:05:41.118969 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 3899 and 3900
I0421 11:05:42.203112 47076539613632 model_training_utils.py:505] Train Step: 1801/2100  / loss = 0.69049072265625
I0421 11:05:42.203496 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3900 and 3901
I0421 11:05:43.284675 47076539613632 model_training_utils.py:505] Train Step: 1802/2100  / loss = 0.774169921875
I0421 11:05:43.285057 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 3901 and 3902
I0421 11:05:44.368676 47076539613632 model_training_utils.py:505] Train Step: 1803/2100  / loss = 0.9024658203125
I0421 11:05:44.369068 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 3902 and 3903
I0421 11:05:45.451138 47076539613632 model_training_utils.py:505] Train Step: 1804/2100  / loss = 0.9337158203125
I0421 11:05:45.451530 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3903 and 3904
I0421 11:05:46.536096 47076539613632 model_training_utils.py:505] Train Step: 1805/2100  / loss = 0.9898681640625
I0421 11:05:46.536499 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 3904 and 3905
I0421 11:05:47.619272 47076539613632 model_training_utils.py:505] Train Step: 1806/2100  / loss = 1.263916015625
I0421 11:05:47.619658 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3905 and 3906
I0421 11:05:48.702426 47076539613632 model_training_utils.py:505] Train Step: 1807/2100  / loss = 1.319580078125
I0421 11:05:48.702808 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3906 and 3907
I0421 11:05:49.785095 47076539613632 model_training_utils.py:505] Train Step: 1808/2100  / loss = 0.84814453125
I0421 11:05:49.785485 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 3907 and 3908
I0421 11:05:50.871025 47076539613632 model_training_utils.py:505] Train Step: 1809/2100  / loss = 0.800537109375
I0421 11:05:50.871416 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.77 examples/second between steps 3908 and 3909
I0421 11:05:51.953616 47076539613632 model_training_utils.py:505] Train Step: 1810/2100  / loss = 0.8670654296875
I0421 11:05:51.954001 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 3909 and 3910
I0421 11:05:53.035529 47076539613632 model_training_utils.py:505] Train Step: 1811/2100  / loss = 1.260986328125
I0421 11:05:53.035905 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 3910 and 3911
I0421 11:05:54.117899 47076539613632 model_training_utils.py:505] Train Step: 1812/2100  / loss = 1.4443359375
I0421 11:05:54.118295 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 3911 and 3912
I0421 11:05:55.203085 47076539613632 model_training_utils.py:505] Train Step: 1813/2100  / loss = 1.0185546875
I0421 11:05:55.203475 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 3912 and 3913
I0421 11:05:56.285157 47076539613632 model_training_utils.py:505] Train Step: 1814/2100  / loss = 1.0352783203125
I0421 11:05:56.285554 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 3913 and 3914
I0421 11:05:57.369049 47076539613632 model_training_utils.py:505] Train Step: 1815/2100  / loss = 0.886474609375
I0421 11:05:57.369438 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 3914 and 3915
I0421 11:05:58.451062 47076539613632 model_training_utils.py:505] Train Step: 1816/2100  / loss = 0.6058349609375
I0421 11:05:58.451452 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 3915 and 3916
I0421 11:05:59.532935 47076539613632 model_training_utils.py:505] Train Step: 1817/2100  / loss = 0.84619140625
I0421 11:05:59.533325 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 3916 and 3917
I0421 11:06:00.614482 47076539613632 model_training_utils.py:505] Train Step: 1818/2100  / loss = 1.0762939453125
I0421 11:06:00.614866 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 3917 and 3918
I0421 11:06:01.696983 47076539613632 model_training_utils.py:505] Train Step: 1819/2100  / loss = 1.22119140625
I0421 11:06:01.697373 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 3918 and 3919
I0421 11:06:02.778307 47076539613632 model_training_utils.py:505] Train Step: 1820/2100  / loss = 0.90234375
I0421 11:06:02.778690 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 3919 and 3920
I0421 11:06:03.861147 47076539613632 model_training_utils.py:505] Train Step: 1821/2100  / loss = 0.91943359375
I0421 11:06:03.861535 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3920 and 3921
I0421 11:06:04.940239 47076539613632 model_training_utils.py:505] Train Step: 1822/2100  / loss = 1.44677734375
I0421 11:06:04.940633 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.51 examples/second between steps 3921 and 3922
I0421 11:06:06.025058 47076539613632 model_training_utils.py:505] Train Step: 1823/2100  / loss = 1.123779296875
I0421 11:06:06.025454 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 3922 and 3923
I0421 11:06:07.110975 47076539613632 model_training_utils.py:505] Train Step: 1824/2100  / loss = 0.87353515625
I0421 11:06:07.111364 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 3923 and 3924
I0421 11:06:08.199846 47076539613632 model_training_utils.py:505] Train Step: 1825/2100  / loss = 1.69482421875
I0421 11:06:08.200226 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.44 examples/second between steps 3924 and 3925
I0421 11:06:09.284554 47076539613632 model_training_utils.py:505] Train Step: 1826/2100  / loss = 1.6435546875
I0421 11:06:09.284944 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 3925 and 3926
I0421 11:06:10.369580 47076539613632 model_training_utils.py:505] Train Step: 1827/2100  / loss = 1.501953125
I0421 11:06:10.369967 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 3926 and 3927
I0421 11:06:11.453219 47076539613632 model_training_utils.py:505] Train Step: 1828/2100  / loss = 1.88916015625
I0421 11:06:11.453624 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 3927 and 3928
I0421 11:06:12.539495 47076539613632 model_training_utils.py:505] Train Step: 1829/2100  / loss = 1.4432373046875
I0421 11:06:12.539891 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 3928 and 3929
I0421 11:06:13.613253 47076539613632 model_training_utils.py:505] Train Step: 1830/2100  / loss = 1.356201171875
I0421 11:06:13.613632 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 120.13 examples/second between steps 3929 and 3930
I0421 11:06:14.698456 47076539613632 model_training_utils.py:505] Train Step: 1831/2100  / loss = 0.8663330078125
I0421 11:06:14.698845 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 3930 and 3931
I0421 11:06:15.781847 47076539613632 model_training_utils.py:505] Train Step: 1832/2100  / loss = 0.837158203125
I0421 11:06:15.782256 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 3931 and 3932
I0421 11:06:16.868169 47076539613632 model_training_utils.py:505] Train Step: 1833/2100  / loss = 0.7132568359375
I0421 11:06:16.868572 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 3932 and 3933
I0421 11:06:17.950647 47076539613632 model_training_utils.py:505] Train Step: 1834/2100  / loss = 1.0638427734375
I0421 11:06:17.951043 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 3933 and 3934
I0421 11:06:19.038054 47076539613632 model_training_utils.py:505] Train Step: 1835/2100  / loss = 1.036865234375
I0421 11:06:19.038454 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.62 examples/second between steps 3934 and 3935
I0421 11:06:20.120035 47076539613632 model_training_utils.py:505] Train Step: 1836/2100  / loss = 0.9697265625
I0421 11:06:20.120442 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 3935 and 3936
I0421 11:06:21.201780 47076539613632 model_training_utils.py:505] Train Step: 1837/2100  / loss = 1.20556640625
I0421 11:06:21.202178 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 3936 and 3937
I0421 11:06:22.282882 47076539613632 model_training_utils.py:505] Train Step: 1838/2100  / loss = 1.039306640625
I0421 11:06:22.283292 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.36 examples/second between steps 3937 and 3938
I0421 11:06:23.368770 47076539613632 model_training_utils.py:505] Train Step: 1839/2100  / loss = 0.814208984375
I0421 11:06:23.369163 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 3938 and 3939
I0421 11:06:24.451058 47076539613632 model_training_utils.py:505] Train Step: 1840/2100  / loss = 0.9454345703125
I0421 11:06:24.451458 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 3939 and 3940
I0421 11:06:25.528910 47076539613632 model_training_utils.py:505] Train Step: 1841/2100  / loss = 1.4775390625
I0421 11:06:25.529339 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.64 examples/second between steps 3940 and 3941
I0421 11:06:26.610461 47076539613632 model_training_utils.py:505] Train Step: 1842/2100  / loss = 2.05126953125
I0421 11:06:26.610847 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.43 examples/second between steps 3941 and 3942
I0421 11:06:27.694077 47076539613632 model_training_utils.py:505] Train Step: 1843/2100  / loss = 1.55517578125
I0421 11:06:27.694491 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 3942 and 3943
I0421 11:06:28.776526 47076539613632 model_training_utils.py:505] Train Step: 1844/2100  / loss = 0.97314453125
I0421 11:06:28.776927 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 3943 and 3944
I0421 11:06:29.859636 47076539613632 model_training_utils.py:505] Train Step: 1845/2100  / loss = 1.2021484375
I0421 11:06:29.860035 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 3944 and 3945
I0421 11:06:30.944298 47076539613632 model_training_utils.py:505] Train Step: 1846/2100  / loss = 1.0482177734375
I0421 11:06:30.944695 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 3945 and 3946
I0421 11:06:32.026543 47076539613632 model_training_utils.py:505] Train Step: 1847/2100  / loss = 0.68603515625
I0421 11:06:32.026936 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 3946 and 3947
I0421 11:06:33.108710 47076539613632 model_training_utils.py:505] Train Step: 1848/2100  / loss = 0.6915283203125
I0421 11:06:33.109108 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 3947 and 3948
I0421 11:06:34.189373 47076539613632 model_training_utils.py:505] Train Step: 1849/2100  / loss = 0.6817626953125
I0421 11:06:34.189777 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.35 examples/second between steps 3948 and 3949
I0421 11:06:35.271010 47076539613632 model_training_utils.py:505] Train Step: 1850/2100  / loss = 1.587890625
I0421 11:06:35.271417 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 3949 and 3950
I0421 11:06:36.354381 47076539613632 model_training_utils.py:505] Train Step: 1851/2100  / loss = 1.345458984375
I0421 11:06:36.354773 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 3950 and 3951
I0421 11:06:37.435345 47076539613632 model_training_utils.py:505] Train Step: 1852/2100  / loss = 0.9912109375
I0421 11:06:37.435757 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 3951 and 3952
I0421 11:06:38.517527 47076539613632 model_training_utils.py:505] Train Step: 1853/2100  / loss = 0.9755859375
I0421 11:06:38.517915 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 3952 and 3953
I0421 11:06:39.598258 47076539613632 model_training_utils.py:505] Train Step: 1854/2100  / loss = 1.45556640625
I0421 11:06:39.598666 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.33 examples/second between steps 3953 and 3954
I0421 11:06:40.681200 47076539613632 model_training_utils.py:505] Train Step: 1855/2100  / loss = 1.26806640625
I0421 11:06:40.681605 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3954 and 3955
I0421 11:06:41.758630 47076539613632 model_training_utils.py:505] Train Step: 1856/2100  / loss = 0.978271484375
I0421 11:06:41.759044 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.74 examples/second between steps 3955 and 3956
I0421 11:06:42.839884 47076539613632 model_training_utils.py:505] Train Step: 1857/2100  / loss = 1.552734375
I0421 11:06:42.840292 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 3956 and 3957
I0421 11:06:43.924888 47076539613632 model_training_utils.py:505] Train Step: 1858/2100  / loss = 1.8896484375
I0421 11:06:43.925302 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.88 examples/second between steps 3957 and 3958
I0421 11:06:45.007251 47076539613632 model_training_utils.py:505] Train Step: 1859/2100  / loss = 1.5625
I0421 11:06:45.007668 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 3958 and 3959
I0421 11:06:46.093068 47076539613632 model_training_utils.py:505] Train Step: 1860/2100  / loss = 1.755615234375
I0421 11:06:46.093476 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 3959 and 3960
I0421 11:06:47.178734 47076539613632 model_training_utils.py:505] Train Step: 1861/2100  / loss = 1.774169921875
I0421 11:06:47.179134 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 3960 and 3961
I0421 11:06:48.259132 47076539613632 model_training_utils.py:505] Train Step: 1862/2100  / loss = 1.1494140625
I0421 11:06:48.259538 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.40 examples/second between steps 3961 and 3962
I0421 11:06:49.341525 47076539613632 model_training_utils.py:505] Train Step: 1863/2100  / loss = 0.99072265625
I0421 11:06:49.341926 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 3962 and 3963
I0421 11:06:50.424321 47076539613632 model_training_utils.py:505] Train Step: 1864/2100  / loss = 0.895751953125
I0421 11:06:50.424713 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 3963 and 3964
I0421 11:06:51.508253 47076539613632 model_training_utils.py:505] Train Step: 1865/2100  / loss = 0.8896484375
I0421 11:06:51.508678 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 3964 and 3965
I0421 11:06:52.596349 47076539613632 model_training_utils.py:505] Train Step: 1866/2100  / loss = 1.1561279296875
I0421 11:06:52.596743 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 3965 and 3966
I0421 11:06:53.685327 47076539613632 model_training_utils.py:505] Train Step: 1867/2100  / loss = 0.9893798828125
I0421 11:06:53.685726 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.46 examples/second between steps 3966 and 3967
I0421 11:06:54.771000 47076539613632 model_training_utils.py:505] Train Step: 1868/2100  / loss = 1.1143798828125
I0421 11:06:54.771413 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 3967 and 3968
I0421 11:06:55.854388 47076539613632 model_training_utils.py:505] Train Step: 1869/2100  / loss = 1.0906982421875
I0421 11:06:55.854777 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 3968 and 3969
I0421 11:06:56.939941 47076539613632 model_training_utils.py:505] Train Step: 1870/2100  / loss = 1.348388671875
I0421 11:06:56.940337 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 3969 and 3970
I0421 11:06:58.021109 47076539613632 model_training_utils.py:505] Train Step: 1871/2100  / loss = 1.263671875
I0421 11:06:58.021516 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 3970 and 3971
I0421 11:06:59.105617 47076539613632 model_training_utils.py:505] Train Step: 1872/2100  / loss = 0.8603515625
I0421 11:06:59.106011 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3971 and 3972
I0421 11:07:00.190436 47076539613632 model_training_utils.py:505] Train Step: 1873/2100  / loss = 0.9302978515625
I0421 11:07:00.190833 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 3972 and 3973
I0421 11:07:01.276191 47076539613632 model_training_utils.py:505] Train Step: 1874/2100  / loss = 0.8890380859375
I0421 11:07:01.276594 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.78 examples/second between steps 3973 and 3974
I0421 11:07:02.359276 47076539613632 model_training_utils.py:505] Train Step: 1875/2100  / loss = 0.9837646484375
I0421 11:07:02.359677 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 3974 and 3975
I0421 11:07:03.439321 47076539613632 model_training_utils.py:505] Train Step: 1876/2100  / loss = 1.05517578125
I0421 11:07:03.439720 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.40 examples/second between steps 3975 and 3976
I0421 11:07:04.522618 47076539613632 model_training_utils.py:505] Train Step: 1877/2100  / loss = 0.80615234375
I0421 11:07:04.523023 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 3976 and 3977
I0421 11:07:05.604279 47076539613632 model_training_utils.py:505] Train Step: 1878/2100  / loss = 0.76611328125
I0421 11:07:05.604681 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 3977 and 3978
I0421 11:07:06.685694 47076539613632 model_training_utils.py:505] Train Step: 1879/2100  / loss = 0.83837890625
I0421 11:07:06.686094 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 3978 and 3979
I0421 11:07:07.764863 47076539613632 model_training_utils.py:505] Train Step: 1880/2100  / loss = 1.1494140625
I0421 11:07:07.765262 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.51 examples/second between steps 3979 and 3980
I0421 11:07:08.848850 47076539613632 model_training_utils.py:505] Train Step: 1881/2100  / loss = 0.85205078125
I0421 11:07:08.849260 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 3980 and 3981
I0421 11:07:09.930059 47076539613632 model_training_utils.py:505] Train Step: 1882/2100  / loss = 0.6856689453125
I0421 11:07:09.930459 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 3981 and 3982
I0421 11:07:11.011666 47076539613632 model_training_utils.py:505] Train Step: 1883/2100  / loss = 0.7530517578125
I0421 11:07:11.012053 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 3982 and 3983
I0421 11:07:12.092985 47076539613632 model_training_utils.py:505] Train Step: 1884/2100  / loss = 0.43865966796875
I0421 11:07:12.093389 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 3983 and 3984
I0421 11:07:13.174043 47076539613632 model_training_utils.py:505] Train Step: 1885/2100  / loss = 0.643798828125
I0421 11:07:13.174449 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 3984 and 3985
I0421 11:07:14.260214 47076539613632 model_training_utils.py:505] Train Step: 1886/2100  / loss = 0.74871826171875
I0421 11:07:14.260621 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 3985 and 3986
I0421 11:07:15.338789 47076539613632 model_training_utils.py:505] Train Step: 1887/2100  / loss = 0.8310546875
I0421 11:07:15.339183 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.56 examples/second between steps 3986 and 3987
I0421 11:07:16.421920 47076539613632 model_training_utils.py:505] Train Step: 1888/2100  / loss = 0.7784423828125
I0421 11:07:16.422317 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 3987 and 3988
I0421 11:07:17.505500 47076539613632 model_training_utils.py:505] Train Step: 1889/2100  / loss = 0.70947265625
I0421 11:07:17.505914 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 3988 and 3989
I0421 11:07:18.590808 47076539613632 model_training_utils.py:505] Train Step: 1890/2100  / loss = 0.6990966796875
I0421 11:07:18.591207 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 3989 and 3990
I0421 11:07:19.672427 47076539613632 model_training_utils.py:505] Train Step: 1891/2100  / loss = 0.947998046875
I0421 11:07:19.672821 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 3990 and 3991
I0421 11:07:20.761242 47076539613632 model_training_utils.py:505] Train Step: 1892/2100  / loss = 1.1102294921875
I0421 11:07:20.761640 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.45 examples/second between steps 3991 and 3992
I0421 11:07:21.848194 47076539613632 model_training_utils.py:505] Train Step: 1893/2100  / loss = 1.02294921875
I0421 11:07:21.848598 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.66 examples/second between steps 3992 and 3993
I0421 11:07:22.935407 47076539613632 model_training_utils.py:505] Train Step: 1894/2100  / loss = 0.89013671875
I0421 11:07:22.935804 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 3993 and 3994
I0421 11:07:24.022322 47076539613632 model_training_utils.py:505] Train Step: 1895/2100  / loss = 0.8482666015625
I0421 11:07:24.022731 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.63 examples/second between steps 3994 and 3995
I0421 11:07:25.108580 47076539613632 model_training_utils.py:505] Train Step: 1896/2100  / loss = 1.143310546875
I0421 11:07:25.108985 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 3995 and 3996
I0421 11:07:26.193104 47076539613632 model_training_utils.py:505] Train Step: 1897/2100  / loss = 0.916015625
I0421 11:07:26.193527 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 3996 and 3997
I0421 11:07:27.277459 47076539613632 model_training_utils.py:505] Train Step: 1898/2100  / loss = 0.96826171875
I0421 11:07:27.277854 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 3997 and 3998
I0421 11:07:28.360837 47076539613632 model_training_utils.py:505] Train Step: 1899/2100  / loss = 1.099609375
I0421 11:07:28.361234 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 3998 and 3999
I0421 11:07:29.443178 47076539613632 model_training_utils.py:505] Train Step: 1900/2100  / loss = 1.0037841796875
I0421 11:07:29.443590 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 3999 and 4000
I0421 11:07:30.526531 47076539613632 model_training_utils.py:505] Train Step: 1901/2100  / loss = 1.145263671875
I0421 11:07:30.526929 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 4000 and 4001
I0421 11:07:31.611829 47076539613632 model_training_utils.py:505] Train Step: 1902/2100  / loss = 1.1761474609375
I0421 11:07:31.612245 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 4001 and 4002
I0421 11:07:32.697085 47076539613632 model_training_utils.py:505] Train Step: 1903/2100  / loss = 1.356201171875
I0421 11:07:32.697497 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 4002 and 4003
I0421 11:07:33.780536 47076539613632 model_training_utils.py:505] Train Step: 1904/2100  / loss = 1.470703125
I0421 11:07:33.780958 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 4003 and 4004
I0421 11:07:34.861957 47076539613632 model_training_utils.py:505] Train Step: 1905/2100  / loss = 0.9813232421875
I0421 11:07:34.862377 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.34 examples/second between steps 4004 and 4005
I0421 11:07:35.943010 47076539613632 model_training_utils.py:505] Train Step: 1906/2100  / loss = 1.116943359375
I0421 11:07:35.943407 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 4005 and 4006
I0421 11:07:37.026740 47076539613632 model_training_utils.py:505] Train Step: 1907/2100  / loss = 1.28515625
I0421 11:07:37.027135 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 4006 and 4007
I0421 11:07:38.109957 47076539613632 model_training_utils.py:505] Train Step: 1908/2100  / loss = 0.931884765625
I0421 11:07:38.110362 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 4007 and 4008
I0421 11:07:39.192309 47076539613632 model_training_utils.py:505] Train Step: 1909/2100  / loss = 0.8951416015625
I0421 11:07:39.192710 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 4008 and 4009
I0421 11:07:40.275611 47076539613632 model_training_utils.py:505] Train Step: 1910/2100  / loss = 0.754150390625
I0421 11:07:40.276013 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 4009 and 4010
I0421 11:07:41.361074 47076539613632 model_training_utils.py:505] Train Step: 1911/2100  / loss = 0.6614990234375
I0421 11:07:41.361475 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 4010 and 4011
I0421 11:07:42.441095 47076539613632 model_training_utils.py:505] Train Step: 1912/2100  / loss = 0.896484375
I0421 11:07:42.441499 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.42 examples/second between steps 4011 and 4012
I0421 11:07:43.524914 47076539613632 model_training_utils.py:505] Train Step: 1913/2100  / loss = 1.090576171875
I0421 11:07:43.525316 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 4012 and 4013
I0421 11:07:44.606862 47076539613632 model_training_utils.py:505] Train Step: 1914/2100  / loss = 1.0054931640625
I0421 11:07:44.607266 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 4013 and 4014
I0421 11:07:45.688991 47076539613632 model_training_utils.py:505] Train Step: 1915/2100  / loss = 0.9498291015625
I0421 11:07:45.689396 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 4014 and 4015
I0421 11:07:46.773836 47076539613632 model_training_utils.py:505] Train Step: 1916/2100  / loss = 1.084228515625
I0421 11:07:46.774230 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 4015 and 4016
I0421 11:07:47.856385 47076539613632 model_training_utils.py:505] Train Step: 1917/2100  / loss = 0.9346923828125
I0421 11:07:47.856786 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.13 examples/second between steps 4016 and 4017
I0421 11:07:48.937007 47076539613632 model_training_utils.py:505] Train Step: 1918/2100  / loss = 0.7066650390625
I0421 11:07:48.937433 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.35 examples/second between steps 4017 and 4018
I0421 11:07:50.019873 47076539613632 model_training_utils.py:505] Train Step: 1919/2100  / loss = 0.632080078125
I0421 11:07:50.020277 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 4018 and 4019
I0421 11:07:51.104743 47076539613632 model_training_utils.py:505] Train Step: 1920/2100  / loss = 0.79248046875
I0421 11:07:51.105142 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.90 examples/second between steps 4019 and 4020
I0421 11:07:52.184886 47076539613632 model_training_utils.py:505] Train Step: 1921/2100  / loss = 0.7789306640625
I0421 11:07:52.185296 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.42 examples/second between steps 4020 and 4021
I0421 11:07:53.268182 47076539613632 model_training_utils.py:505] Train Step: 1922/2100  / loss = 0.748779296875
I0421 11:07:53.268591 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 4021 and 4022
I0421 11:07:54.343884 47076539613632 model_training_utils.py:505] Train Step: 1923/2100  / loss = 0.57684326171875
I0421 11:07:54.344291 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.91 examples/second between steps 4022 and 4023
I0421 11:07:55.426031 47076539613632 model_training_utils.py:505] Train Step: 1924/2100  / loss = 0.6834716796875
I0421 11:07:55.426443 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 4023 and 4024
I0421 11:07:56.509689 47076539613632 model_training_utils.py:505] Train Step: 1925/2100  / loss = 0.6236572265625
I0421 11:07:56.510094 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 4024 and 4025
I0421 11:07:57.591447 47076539613632 model_training_utils.py:505] Train Step: 1926/2100  / loss = 0.62933349609375
I0421 11:07:57.591838 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 4025 and 4026
I0421 11:07:58.677084 47076539613632 model_training_utils.py:505] Train Step: 1927/2100  / loss = 0.789794921875
I0421 11:07:58.677489 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 4026 and 4027
I0421 11:07:59.762028 47076539613632 model_training_utils.py:505] Train Step: 1928/2100  / loss = 1.254150390625
I0421 11:07:59.762437 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 4027 and 4028
I0421 11:08:00.847660 47076539613632 model_training_utils.py:505] Train Step: 1929/2100  / loss = 0.955810546875
I0421 11:08:00.848055 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 4028 and 4029
I0421 11:08:01.931422 47076539613632 model_training_utils.py:505] Train Step: 1930/2100  / loss = 0.865478515625
I0421 11:08:01.931815 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 4029 and 4030
I0421 11:08:03.015518 47076539613632 model_training_utils.py:505] Train Step: 1931/2100  / loss = 0.8475341796875
I0421 11:08:03.015909 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 4030 and 4031
I0421 11:08:04.100064 47076539613632 model_training_utils.py:505] Train Step: 1932/2100  / loss = 1.3173828125
I0421 11:08:04.100471 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 4031 and 4032
I0421 11:08:05.182394 47076539613632 model_training_utils.py:505] Train Step: 1933/2100  / loss = 1.310302734375
I0421 11:08:05.182794 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 4032 and 4033
I0421 11:08:06.263402 47076539613632 model_training_utils.py:505] Train Step: 1934/2100  / loss = 0.8187255859375
I0421 11:08:06.263811 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 4033 and 4034
I0421 11:08:07.346934 47076539613632 model_training_utils.py:505] Train Step: 1935/2100  / loss = 0.9581298828125
I0421 11:08:07.347338 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 4034 and 4035
I0421 11:08:08.428180 47076539613632 model_training_utils.py:505] Train Step: 1936/2100  / loss = 0.958984375
I0421 11:08:08.428592 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.29 examples/second between steps 4035 and 4036
I0421 11:08:09.511793 47076539613632 model_training_utils.py:505] Train Step: 1937/2100  / loss = 1.1380615234375
I0421 11:08:09.512191 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 4036 and 4037
I0421 11:08:10.594636 47076539613632 model_training_utils.py:505] Train Step: 1938/2100  / loss = 1.0106201171875
I0421 11:08:10.595033 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 4037 and 4038
I0421 11:08:11.676563 47076539613632 model_training_utils.py:505] Train Step: 1939/2100  / loss = 0.81787109375
I0421 11:08:11.676954 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 4038 and 4039
I0421 11:08:12.754552 47076539613632 model_training_utils.py:505] Train Step: 1940/2100  / loss = 0.891845703125
I0421 11:08:12.754952 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.67 examples/second between steps 4039 and 4040
I0421 11:08:13.836232 47076539613632 model_training_utils.py:505] Train Step: 1941/2100  / loss = 0.6226806640625
I0421 11:08:13.836636 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 4040 and 4041
I0421 11:08:14.918437 47076539613632 model_training_utils.py:505] Train Step: 1942/2100  / loss = 0.9276123046875
I0421 11:08:14.918845 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 4041 and 4042
I0421 11:08:15.999766 47076539613632 model_training_utils.py:505] Train Step: 1943/2100  / loss = 0.7757568359375
I0421 11:08:16.000164 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 4042 and 4043
I0421 11:08:17.082033 47076539613632 model_training_utils.py:505] Train Step: 1944/2100  / loss = 0.87646484375
I0421 11:08:17.082437 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 4043 and 4044
I0421 11:08:18.165462 47076539613632 model_training_utils.py:505] Train Step: 1945/2100  / loss = 0.906005859375
I0421 11:08:18.165863 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 4044 and 4045
I0421 11:08:19.248203 47076539613632 model_training_utils.py:505] Train Step: 1946/2100  / loss = 1.372314453125
I0421 11:08:19.248606 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 4045 and 4046
I0421 11:08:20.328117 47076539613632 model_training_utils.py:505] Train Step: 1947/2100  / loss = 1.2860107421875
I0421 11:08:20.328535 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.42 examples/second between steps 4046 and 4047
I0421 11:08:21.409403 47076539613632 model_training_utils.py:505] Train Step: 1948/2100  / loss = 1.140625
I0421 11:08:21.409799 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 4047 and 4048
I0421 11:08:22.496897 47076539613632 model_training_utils.py:505] Train Step: 1949/2100  / loss = 1.143798828125
I0421 11:08:22.497302 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.58 examples/second between steps 4048 and 4049
I0421 11:08:23.583342 47076539613632 model_training_utils.py:505] Train Step: 1950/2100  / loss = 0.7818603515625
I0421 11:08:23.583737 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 4049 and 4050
I0421 11:08:24.669893 47076539613632 model_training_utils.py:505] Train Step: 1951/2100  / loss = 1.1065673828125
I0421 11:08:24.670307 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.70 examples/second between steps 4050 and 4051
I0421 11:08:25.752531 47076539613632 model_training_utils.py:505] Train Step: 1952/2100  / loss = 0.9114990234375
I0421 11:08:25.752933 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 4051 and 4052
I0421 11:08:26.831916 47076539613632 model_training_utils.py:505] Train Step: 1953/2100  / loss = 0.7855224609375
I0421 11:08:26.832320 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.48 examples/second between steps 4052 and 4053
I0421 11:08:27.913300 47076539613632 model_training_utils.py:505] Train Step: 1954/2100  / loss = 1.0911865234375
I0421 11:08:27.913695 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 4053 and 4054
I0421 11:08:28.995052 47076539613632 model_training_utils.py:505] Train Step: 1955/2100  / loss = 1.1884765625
I0421 11:08:28.995463 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 4054 and 4055
I0421 11:08:30.080240 47076539613632 model_training_utils.py:505] Train Step: 1956/2100  / loss = 1.058837890625
I0421 11:08:30.080643 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 4055 and 4056
I0421 11:08:31.158085 47076539613632 model_training_utils.py:505] Train Step: 1957/2100  / loss = 0.9752197265625
I0421 11:08:31.158486 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.68 examples/second between steps 4056 and 4057
I0421 11:08:32.240981 47076539613632 model_training_utils.py:505] Train Step: 1958/2100  / loss = 0.7789306640625
I0421 11:08:32.241388 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 4057 and 4058
I0421 11:08:33.324574 47076539613632 model_training_utils.py:505] Train Step: 1959/2100  / loss = 1.01611328125
I0421 11:08:33.324974 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 4058 and 4059
I0421 11:08:34.410318 47076539613632 model_training_utils.py:505] Train Step: 1960/2100  / loss = 0.923828125
I0421 11:08:34.410717 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.80 examples/second between steps 4059 and 4060
I0421 11:08:35.493688 47076539613632 model_training_utils.py:505] Train Step: 1961/2100  / loss = 1.591064453125
I0421 11:08:35.494083 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 4060 and 4061
I0421 11:08:36.574999 47076539613632 model_training_utils.py:505] Train Step: 1962/2100  / loss = 2.504150390625
I0421 11:08:36.575401 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 4061 and 4062
I0421 11:08:37.658523 47076539613632 model_training_utils.py:505] Train Step: 1963/2100  / loss = 2.94287109375
I0421 11:08:37.658921 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 4062 and 4063
I0421 11:08:38.740254 47076539613632 model_training_utils.py:505] Train Step: 1964/2100  / loss = 1.910400390625
I0421 11:08:38.740647 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 4063 and 4064
I0421 11:08:39.822058 47076539613632 model_training_utils.py:505] Train Step: 1965/2100  / loss = 1.54931640625
I0421 11:08:39.822464 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 4064 and 4065
I0421 11:08:40.904032 47076539613632 model_training_utils.py:505] Train Step: 1966/2100  / loss = 1.8916015625
I0421 11:08:40.904438 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 4065 and 4066
I0421 11:08:41.986993 47076539613632 model_training_utils.py:505] Train Step: 1967/2100  / loss = 1.122314453125
I0421 11:08:41.987398 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 4066 and 4067
I0421 11:08:43.068499 47076539613632 model_training_utils.py:505] Train Step: 1968/2100  / loss = 0.85107421875
I0421 11:08:43.068906 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 4067 and 4068
I0421 11:08:44.147656 47076539613632 model_training_utils.py:505] Train Step: 1969/2100  / loss = 0.6722412109375
I0421 11:08:44.148056 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.53 examples/second between steps 4068 and 4069
I0421 11:08:45.245521 47076539613632 model_training_utils.py:505] Train Step: 1970/2100  / loss = 0.7476806640625
I0421 11:08:45.245930 47076539613632 keras_utils.py:133] TimeHistory: 1.09 seconds, 117.52 examples/second between steps 4069 and 4070
I0421 11:08:46.328814 47076539613632 model_training_utils.py:505] Train Step: 1971/2100  / loss = 0.6973876953125
I0421 11:08:46.329212 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.08 examples/second between steps 4070 and 4071
I0421 11:08:47.410238 47076539613632 model_training_utils.py:505] Train Step: 1972/2100  / loss = 0.760986328125
I0421 11:08:47.410640 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 4071 and 4072
I0421 11:08:48.493453 47076539613632 model_training_utils.py:505] Train Step: 1973/2100  / loss = 1.114990234375
I0421 11:08:48.493863 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.07 examples/second between steps 4072 and 4073
I0421 11:08:49.574587 47076539613632 model_training_utils.py:505] Train Step: 1974/2100  / loss = 1.207275390625
I0421 11:08:49.574991 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 4073 and 4074
I0421 11:08:50.657203 47076539613632 model_training_utils.py:505] Train Step: 1975/2100  / loss = 1.139892578125
I0421 11:08:50.657605 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 4074 and 4075
I0421 11:08:51.738698 47076539613632 model_training_utils.py:505] Train Step: 1976/2100  / loss = 1.060791015625
I0421 11:08:51.739094 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 4075 and 4076
I0421 11:08:52.822189 47076539613632 model_training_utils.py:505] Train Step: 1977/2100  / loss = 1.236083984375
I0421 11:08:52.822588 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 4076 and 4077
I0421 11:08:53.908229 47076539613632 model_training_utils.py:505] Train Step: 1978/2100  / loss = 2.52392578125
I0421 11:08:53.908637 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.76 examples/second between steps 4077 and 4078
I0421 11:08:54.995747 47076539613632 model_training_utils.py:505] Train Step: 1979/2100  / loss = 1.609130859375
I0421 11:08:54.996145 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 4078 and 4079
I0421 11:08:56.078734 47076539613632 model_training_utils.py:505] Train Step: 1980/2100  / loss = 0.79364013671875
I0421 11:08:56.079126 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 4079 and 4080
I0421 11:08:57.160768 47076539613632 model_training_utils.py:505] Train Step: 1981/2100  / loss = 0.8441162109375
I0421 11:08:57.161164 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 4080 and 4081
I0421 11:08:58.243497 47076539613632 model_training_utils.py:505] Train Step: 1982/2100  / loss = 0.78564453125
I0421 11:08:58.243899 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 4081 and 4082
I0421 11:08:59.325234 47076539613632 model_training_utils.py:505] Train Step: 1983/2100  / loss = 0.810791015625
I0421 11:08:59.325643 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 4082 and 4083
I0421 11:09:00.409368 47076539613632 model_training_utils.py:505] Train Step: 1984/2100  / loss = 0.772216796875
I0421 11:09:00.409760 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 4083 and 4084
I0421 11:09:01.494008 47076539613632 model_training_utils.py:505] Train Step: 1985/2100  / loss = 1.15576171875
I0421 11:09:01.494415 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.04 examples/second between steps 4084 and 4085
I0421 11:09:02.580603 47076539613632 model_training_utils.py:505] Train Step: 1986/2100  / loss = 1.385986328125
I0421 11:09:02.580996 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 4085 and 4086
I0421 11:09:03.666307 47076539613632 model_training_utils.py:505] Train Step: 1987/2100  / loss = 1.449462890625
I0421 11:09:03.666705 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.81 examples/second between steps 4086 and 4087
I0421 11:09:04.750313 47076539613632 model_training_utils.py:505] Train Step: 1988/2100  / loss = 1.04052734375
I0421 11:09:04.750716 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.98 examples/second between steps 4087 and 4088
I0421 11:09:05.833278 47076539613632 model_training_utils.py:505] Train Step: 1989/2100  / loss = 0.82275390625
I0421 11:09:05.833678 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 4088 and 4089
I0421 11:09:06.919090 47076539613632 model_training_utils.py:505] Train Step: 1990/2100  / loss = 1.268310546875
I0421 11:09:06.919501 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 4089 and 4090
I0421 11:09:08.000754 47076539613632 model_training_utils.py:505] Train Step: 1991/2100  / loss = 2.58984375
I0421 11:09:08.001147 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 4090 and 4091
I0421 11:09:09.086204 47076539613632 model_training_utils.py:505] Train Step: 1992/2100  / loss = 2.494140625
I0421 11:09:09.086614 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.84 examples/second between steps 4091 and 4092
I0421 11:09:10.170823 47076539613632 model_training_utils.py:505] Train Step: 1993/2100  / loss = 1.392333984375
I0421 11:09:10.171218 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 4092 and 4093
I0421 11:09:11.257383 47076539613632 model_training_utils.py:505] Train Step: 1994/2100  / loss = 1.194580078125
I0421 11:09:11.257812 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.72 examples/second between steps 4093 and 4094
I0421 11:09:12.343500 47076539613632 model_training_utils.py:505] Train Step: 1995/2100  / loss = 0.868408203125
I0421 11:09:12.343913 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 4094 and 4095
I0421 11:09:13.423915 47076539613632 model_training_utils.py:505] Train Step: 1996/2100  / loss = 0.9560546875
I0421 11:09:13.424326 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.37 examples/second between steps 4095 and 4096
I0421 11:09:14.507614 47076539613632 model_training_utils.py:505] Train Step: 1997/2100  / loss = 1.123779296875
I0421 11:09:14.508014 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.00 examples/second between steps 4096 and 4097
I0421 11:09:15.596511 47076539613632 model_training_utils.py:505] Train Step: 1998/2100  / loss = 1.161865234375
I0421 11:09:15.596913 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.41 examples/second between steps 4097 and 4098
I0421 11:09:16.680074 47076539613632 model_training_utils.py:505] Train Step: 1999/2100  / loss = 1.322265625
I0421 11:09:16.680480 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 4098 and 4099
I0421 11:09:17.763973 47076539613632 model_training_utils.py:505] Train Step: 2000/2100  / loss = 1.28369140625
I0421 11:09:17.764382 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 4099 and 4100
I0421 11:09:20.042635 47076539613632 model_training_utils.py:505] Train Step: 2001/2100  / loss = 1.3564453125
I0421 11:09:20.043022 47076539613632 keras_utils.py:133] TimeHistory: 2.27 seconds, 56.37 examples/second between steps 4100 and 4101
I0421 11:09:21.155511 47076539613632 model_training_utils.py:505] Train Step: 2002/2100  / loss = 1.3330078125
I0421 11:09:21.155908 47076539613632 keras_utils.py:133] TimeHistory: 1.10 seconds, 115.86 examples/second between steps 4101 and 4102
I0421 11:09:22.237655 47076539613632 model_training_utils.py:505] Train Step: 2003/2100  / loss = 1.686279296875
I0421 11:09:22.238051 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.22 examples/second between steps 4102 and 4103
I0421 11:09:23.321994 47076539613632 model_training_utils.py:505] Train Step: 2004/2100  / loss = 1.443603515625
I0421 11:09:23.322404 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 4103 and 4104
I0421 11:09:24.403226 47076539613632 model_training_utils.py:505] Train Step: 2005/2100  / loss = 0.9130859375
I0421 11:09:24.403645 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.31 examples/second between steps 4104 and 4105
I0421 11:09:25.486567 47076539613632 model_training_utils.py:505] Train Step: 2006/2100  / loss = 1.0545654296875
I0421 11:09:25.486973 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.09 examples/second between steps 4105 and 4106
I0421 11:09:26.568612 47076539613632 model_training_utils.py:505] Train Step: 2007/2100  / loss = 0.7109375
I0421 11:09:26.569006 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 4106 and 4107
I0421 11:09:27.651691 47076539613632 model_training_utils.py:505] Train Step: 2008/2100  / loss = 0.868896484375
I0421 11:09:27.652094 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 4107 and 4108
I0421 11:09:28.730808 47076539613632 model_training_utils.py:505] Train Step: 2009/2100  / loss = 0.7899169921875
I0421 11:09:28.731206 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.52 examples/second between steps 4108 and 4109
I0421 11:09:29.812163 47076539613632 model_training_utils.py:505] Train Step: 2010/2100  / loss = 0.6884765625
I0421 11:09:29.812570 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.26 examples/second between steps 4109 and 4110
I0421 11:09:30.894645 47076539613632 model_training_utils.py:505] Train Step: 2011/2100  / loss = 1.0849609375
I0421 11:09:30.895039 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 4110 and 4111
I0421 11:09:31.978685 47076539613632 model_training_utils.py:505] Train Step: 2012/2100  / loss = 1.076904296875
I0421 11:09:31.979086 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 4111 and 4112
I0421 11:09:33.061550 47076539613632 model_training_utils.py:505] Train Step: 2013/2100  / loss = 0.82373046875
I0421 11:09:33.061949 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 4112 and 4113
I0421 11:09:34.145255 47076539613632 model_training_utils.py:505] Train Step: 2014/2100  / loss = 0.82373046875
I0421 11:09:34.145664 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 4113 and 4114
I0421 11:09:35.227057 47076539613632 model_training_utils.py:505] Train Step: 2015/2100  / loss = 0.779541015625
I0421 11:09:35.227467 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 4114 and 4115
I0421 11:09:36.310437 47076539613632 model_training_utils.py:505] Train Step: 2016/2100  / loss = 0.7589111328125
I0421 11:09:36.310838 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 4115 and 4116
I0421 11:09:37.393140 47076539613632 model_training_utils.py:505] Train Step: 2017/2100  / loss = 1.141357421875
I0421 11:09:37.393544 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 4116 and 4117
I0421 11:09:38.475077 47076539613632 model_training_utils.py:505] Train Step: 2018/2100  / loss = 1.44384765625
I0421 11:09:38.475487 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.20 examples/second between steps 4117 and 4118
I0421 11:09:39.556672 47076539613632 model_training_utils.py:505] Train Step: 2019/2100  / loss = 1.188232421875
I0421 11:09:39.557084 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 4118 and 4119
I0421 11:09:40.639932 47076539613632 model_training_utils.py:505] Train Step: 2020/2100  / loss = 1.643798828125
I0421 11:09:40.640342 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 4119 and 4120
I0421 11:09:41.724256 47076539613632 model_training_utils.py:505] Train Step: 2021/2100  / loss = 1.966552734375
I0421 11:09:41.724660 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 4120 and 4121
I0421 11:09:42.807525 47076539613632 model_training_utils.py:505] Train Step: 2022/2100  / loss = 2.724609375
I0421 11:09:42.807924 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 4121 and 4122
I0421 11:09:43.888807 47076539613632 model_training_utils.py:505] Train Step: 2023/2100  / loss = 1.811767578125
I0421 11:09:43.889204 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.30 examples/second between steps 4122 and 4123
I0421 11:09:44.973999 47076539613632 model_training_utils.py:505] Train Step: 2024/2100  / loss = 0.9080810546875
I0421 11:09:44.974405 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.85 examples/second between steps 4123 and 4124
I0421 11:09:46.058585 47076539613632 model_training_utils.py:505] Train Step: 2025/2100  / loss = 0.939453125
I0421 11:09:46.058981 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 4124 and 4125
I0421 11:09:47.142748 47076539613632 model_training_utils.py:505] Train Step: 2026/2100  / loss = 0.96826171875
I0421 11:09:47.143176 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 4125 and 4126
I0421 11:09:48.224505 47076539613632 model_training_utils.py:505] Train Step: 2027/2100  / loss = 0.8663330078125
I0421 11:09:48.224934 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 4126 and 4127
I0421 11:09:49.306618 47076539613632 model_training_utils.py:505] Train Step: 2028/2100  / loss = 0.734619140625
I0421 11:09:49.307049 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.23 examples/second between steps 4127 and 4128
I0421 11:09:50.389726 47076539613632 model_training_utils.py:505] Train Step: 2029/2100  / loss = 0.68212890625
I0421 11:09:50.390155 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.11 examples/second between steps 4128 and 4129
I0421 11:09:51.474302 47076539613632 model_training_utils.py:505] Train Step: 2030/2100  / loss = 0.674072265625
I0421 11:09:51.474734 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 4129 and 4130
I0421 11:09:52.560062 47076539613632 model_training_utils.py:505] Train Step: 2031/2100  / loss = 0.7110595703125
I0421 11:09:52.560494 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.79 examples/second between steps 4130 and 4131
I0421 11:09:53.643651 47076539613632 model_training_utils.py:505] Train Step: 2032/2100  / loss = 0.74755859375
I0421 11:09:53.644080 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 4131 and 4132
I0421 11:09:54.726447 47076539613632 model_training_utils.py:505] Train Step: 2033/2100  / loss = 0.922119140625
I0421 11:09:54.726880 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 4132 and 4133
I0421 11:09:55.809819 47076539613632 model_training_utils.py:505] Train Step: 2034/2100  / loss = 0.7923583984375
I0421 11:09:55.810248 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 4133 and 4134
I0421 11:09:56.892173 47076539613632 model_training_utils.py:505] Train Step: 2035/2100  / loss = 0.8277587890625
I0421 11:09:56.892615 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.19 examples/second between steps 4134 and 4135
I0421 11:09:57.977071 47076539613632 model_training_utils.py:505] Train Step: 2036/2100  / loss = 0.73876953125
I0421 11:09:57.977525 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 4135 and 4136
I0421 11:09:59.060828 47076539613632 model_training_utils.py:505] Train Step: 2037/2100  / loss = 1.1414794921875
I0421 11:09:59.061254 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.03 examples/second between steps 4136 and 4137
I0421 11:10:00.141387 47076539613632 model_training_utils.py:505] Train Step: 2038/2100  / loss = 0.8997802734375
I0421 11:10:00.141823 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.37 examples/second between steps 4137 and 4138
I0421 11:10:01.227880 47076539613632 model_training_utils.py:505] Train Step: 2039/2100  / loss = 0.8743896484375
I0421 11:10:01.228310 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.74 examples/second between steps 4138 and 4139
I0421 11:10:02.306494 47076539613632 model_training_utils.py:505] Train Step: 2040/2100  / loss = 0.921142578125
I0421 11:10:02.306927 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.62 examples/second between steps 4139 and 4140
I0421 11:10:03.389000 47076539613632 model_training_utils.py:505] Train Step: 2041/2100  / loss = 0.965576171875
I0421 11:10:03.389434 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 4140 and 4141
I0421 11:10:04.474348 47076539613632 model_training_utils.py:505] Train Step: 2042/2100  / loss = 1.0679931640625
I0421 11:10:04.474779 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.86 examples/second between steps 4141 and 4142
I0421 11:10:05.559686 47076539613632 model_training_utils.py:505] Train Step: 2043/2100  / loss = 0.9359130859375
I0421 11:10:05.560117 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 4142 and 4143
I0421 11:10:06.647274 47076539613632 model_training_utils.py:505] Train Step: 2044/2100  / loss = 0.7901611328125
I0421 11:10:06.647707 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.60 examples/second between steps 4143 and 4144
I0421 11:10:07.731071 47076539613632 model_training_utils.py:505] Train Step: 2045/2100  / loss = 0.82763671875
I0421 11:10:07.731515 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 4144 and 4145
I0421 11:10:08.812579 47076539613632 model_training_utils.py:505] Train Step: 2046/2100  / loss = 0.890625
I0421 11:10:08.813009 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 4145 and 4146
I0421 11:10:09.896678 47076539613632 model_training_utils.py:505] Train Step: 2047/2100  / loss = 1.13232421875
I0421 11:10:09.897109 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.97 examples/second between steps 4146 and 4147
I0421 11:10:10.979556 47076539613632 model_training_utils.py:505] Train Step: 2048/2100  / loss = 0.7572021484375
I0421 11:10:10.979988 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 4147 and 4148
I0421 11:10:12.062613 47076539613632 model_training_utils.py:505] Train Step: 2049/2100  / loss = 0.93896484375
I0421 11:10:12.063056 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.10 examples/second between steps 4148 and 4149
I0421 11:10:13.145261 47076539613632 model_training_utils.py:505] Train Step: 2050/2100  / loss = 1.20458984375
I0421 11:10:13.145696 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 4149 and 4150
I0421 11:10:14.228719 47076539613632 model_training_utils.py:505] Train Step: 2051/2100  / loss = 2.08154296875
I0421 11:10:14.229145 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 4150 and 4151
I0421 11:10:15.309481 47076539613632 model_training_utils.py:505] Train Step: 2052/2100  / loss = 1.566162109375
I0421 11:10:15.309906 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.34 examples/second between steps 4151 and 4152
I0421 11:10:16.393759 47076539613632 model_training_utils.py:505] Train Step: 2053/2100  / loss = 0.9586181640625
I0421 11:10:16.394189 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 4152 and 4153
I0421 11:10:17.476194 47076539613632 model_training_utils.py:505] Train Step: 2054/2100  / loss = 1.1304931640625
I0421 11:10:17.476626 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 4153 and 4154
I0421 11:10:18.560918 47076539613632 model_training_utils.py:505] Train Step: 2055/2100  / loss = 1.16796875
I0421 11:10:18.561352 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.92 examples/second between steps 4154 and 4155
I0421 11:10:19.642946 47076539613632 model_training_utils.py:505] Train Step: 2056/2100  / loss = 1.1414794921875
I0421 11:10:19.643382 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.21 examples/second between steps 4155 and 4156
I0421 11:10:20.726971 47076539613632 model_training_utils.py:505] Train Step: 2057/2100  / loss = 1.0753173828125
I0421 11:10:20.727403 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.99 examples/second between steps 4156 and 4157
I0421 11:10:21.809421 47076539613632 model_training_utils.py:505] Train Step: 2058/2100  / loss = 0.8421630859375
I0421 11:10:21.809843 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 4157 and 4158
I0421 11:10:22.894057 47076539613632 model_training_utils.py:505] Train Step: 2059/2100  / loss = 0.7177734375
I0421 11:10:22.894489 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.91 examples/second between steps 4158 and 4159
I0421 11:10:23.979171 47076539613632 model_training_utils.py:505] Train Step: 2060/2100  / loss = 0.975341796875
I0421 11:10:23.979603 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.89 examples/second between steps 4159 and 4160
I0421 11:10:25.060778 47076539613632 model_training_utils.py:505] Train Step: 2061/2100  / loss = 1.1600341796875
I0421 11:10:25.061204 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.27 examples/second between steps 4160 and 4161
I0421 11:10:26.143264 47076539613632 model_training_utils.py:505] Train Step: 2062/2100  / loss = 1.182373046875
I0421 11:10:26.143698 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.16 examples/second between steps 4161 and 4162
I0421 11:10:27.228803 47076539613632 model_training_utils.py:505] Train Step: 2063/2100  / loss = 0.764892578125
I0421 11:10:27.229224 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.83 examples/second between steps 4162 and 4163
I0421 11:10:28.313107 47076539613632 model_training_utils.py:505] Train Step: 2064/2100  / loss = 0.914306640625
I0421 11:10:28.313544 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 4163 and 4164
I0421 11:10:29.397438 47076539613632 model_training_utils.py:505] Train Step: 2065/2100  / loss = 0.850830078125
I0421 11:10:29.397867 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.95 examples/second between steps 4164 and 4165
I0421 11:10:30.485308 47076539613632 model_training_utils.py:505] Train Step: 2066/2100  / loss = 0.807861328125
I0421 11:10:30.485779 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.59 examples/second between steps 4165 and 4166
I0421 11:10:31.570574 47076539613632 model_training_utils.py:505] Train Step: 2067/2100  / loss = 0.9683837890625
I0421 11:10:31.571008 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 4166 and 4167
I0421 11:10:32.658983 47076539613632 model_training_utils.py:505] Train Step: 2068/2100  / loss = 0.8865966796875
I0421 11:10:32.659417 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.51 examples/second between steps 4167 and 4168
I0421 11:10:33.745277 47076539613632 model_training_utils.py:505] Train Step: 2069/2100  / loss = 0.9188232421875
I0421 11:10:33.745709 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.73 examples/second between steps 4168 and 4169
I0421 11:10:34.830404 47076539613632 model_training_utils.py:505] Train Step: 2070/2100  / loss = 1.334716796875
I0421 11:10:34.830834 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 4169 and 4170
I0421 11:10:35.914053 47076539613632 model_training_utils.py:505] Train Step: 2071/2100  / loss = 1.119873046875
I0421 11:10:35.914516 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 4170 and 4171
I0421 11:10:36.996649 47076539613632 model_training_utils.py:505] Train Step: 2072/2100  / loss = 1.35888671875
I0421 11:10:36.997076 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 4171 and 4172
I0421 11:10:38.074996 47076539613632 model_training_utils.py:505] Train Step: 2073/2100  / loss = 1.26708984375
I0421 11:10:38.075427 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.60 examples/second between steps 4172 and 4173
I0421 11:10:39.157679 47076539613632 model_training_utils.py:505] Train Step: 2074/2100  / loss = 1.0216064453125
I0421 11:10:39.158097 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.17 examples/second between steps 4173 and 4174
I0421 11:10:40.241211 47076539613632 model_training_utils.py:505] Train Step: 2075/2100  / loss = 0.8272705078125
I0421 11:10:40.241637 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 4174 and 4175
I0421 11:10:41.321363 47076539613632 model_training_utils.py:505] Train Step: 2076/2100  / loss = 0.7752685546875
I0421 11:10:41.321793 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.40 examples/second between steps 4175 and 4176
I0421 11:10:42.398024 47076539613632 model_training_utils.py:505] Train Step: 2077/2100  / loss = 0.7203369140625
I0421 11:10:42.398473 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.80 examples/second between steps 4176 and 4177
I0421 11:10:43.479971 47076539613632 model_training_utils.py:505] Train Step: 2078/2100  / loss = 0.7120361328125
I0421 11:10:43.480413 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 4177 and 4178
I0421 11:10:44.562592 47076539613632 model_training_utils.py:505] Train Step: 2079/2100  / loss = 0.70703125
I0421 11:10:44.563020 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.18 examples/second between steps 4178 and 4179
I0421 11:10:45.646338 47076539613632 model_training_utils.py:505] Train Step: 2080/2100  / loss = 0.821533203125
I0421 11:10:45.646767 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.02 examples/second between steps 4179 and 4180
I0421 11:10:46.727996 47076539613632 model_training_utils.py:505] Train Step: 2081/2100  / loss = 0.861328125
I0421 11:10:46.728390 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.28 examples/second between steps 4180 and 4181
I0421 11:10:47.810478 47076539613632 model_training_utils.py:505] Train Step: 2082/2100  / loss = 1.1680908203125
I0421 11:10:47.810855 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 4181 and 4182
I0421 11:10:48.892972 47076539613632 model_training_utils.py:505] Train Step: 2083/2100  / loss = 0.959716796875
I0421 11:10:48.893362 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.12 examples/second between steps 4182 and 4183
I0421 11:10:49.976273 47076539613632 model_training_utils.py:505] Train Step: 2084/2100  / loss = 0.7816162109375
I0421 11:10:49.976664 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 4183 and 4184
I0421 11:10:51.057787 47076539613632 model_training_utils.py:505] Train Step: 2085/2100  / loss = 0.766357421875
I0421 11:10:51.058168 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.25 examples/second between steps 4184 and 4185
I0421 11:10:52.141093 47076539613632 model_training_utils.py:505] Train Step: 2086/2100  / loss = 0.7110595703125
I0421 11:10:52.141477 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.05 examples/second between steps 4185 and 4186
I0421 11:10:53.223529 47076539613632 model_training_utils.py:505] Train Step: 2087/2100  / loss = 0.828857421875
I0421 11:10:53.223908 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.15 examples/second between steps 4186 and 4187
I0421 11:10:54.304207 47076539613632 model_training_utils.py:505] Train Step: 2088/2100  / loss = 0.8360595703125
I0421 11:10:54.304594 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.33 examples/second between steps 4187 and 4188
I0421 11:10:55.385206 47076539613632 model_training_utils.py:505] Train Step: 2089/2100  / loss = 0.92626953125
I0421 11:10:55.385594 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 4188 and 4189
I0421 11:10:56.470210 47076539613632 model_training_utils.py:505] Train Step: 2090/2100  / loss = 1.056884765625
I0421 11:10:56.470606 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.87 examples/second between steps 4189 and 4190
I0421 11:10:57.548849 47076539613632 model_training_utils.py:505] Train Step: 2091/2100  / loss = 1.41943359375
I0421 11:10:57.549230 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.57 examples/second between steps 4190 and 4191
I0421 11:10:58.629612 47076539613632 model_training_utils.py:505] Train Step: 2092/2100  / loss = 1.02587890625
I0421 11:10:58.629995 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.32 examples/second between steps 4191 and 4192
I0421 11:10:59.713944 47076539613632 model_training_utils.py:505] Train Step: 2093/2100  / loss = 1.027099609375
I0421 11:10:59.714338 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.94 examples/second between steps 4192 and 4193
I0421 11:11:00.795767 47076539613632 model_training_utils.py:505] Train Step: 2094/2100  / loss = 0.7979736328125
I0421 11:11:00.796154 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.24 examples/second between steps 4193 and 4194
I0421 11:11:01.874618 47076539613632 model_training_utils.py:505] Train Step: 2095/2100  / loss = 0.97607421875
I0421 11:11:01.875003 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.57 examples/second between steps 4194 and 4195
I0421 11:11:02.958481 47076539613632 model_training_utils.py:505] Train Step: 2096/2100  / loss = 0.8311767578125
I0421 11:11:02.958863 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.01 examples/second between steps 4195 and 4196
2021-04-21 11:11:04.027093: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
I0421 11:11:04.040890 47076539613632 model_training_utils.py:505] Train Step: 2097/2100  / loss = 0.5765380859375
I0421 11:11:04.041272 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.14 examples/second between steps 4196 and 4197
2021-04-21 11:11:04.142669: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
I0421 11:11:05.124349 47076539613632 model_training_utils.py:505] Train Step: 2098/2100  / loss = 0.8553466796875
I0421 11:11:05.124726 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 119.06 examples/second between steps 4197 and 4198
I0421 11:11:06.204141 47076539613632 model_training_utils.py:505] Train Step: 2099/2100  / loss = 0.859619140625
I0421 11:11:06.204528 47076539613632 keras_utils.py:133] TimeHistory: 1.07 seconds, 119.46 examples/second between steps 4198 and 4199
I0421 11:11:07.288696 47076539613632 model_training_utils.py:505] Train Step: 2100/2100  / loss = 0.77783203125
I0421 11:11:07.289077 47076539613632 keras_utils.py:133] TimeHistory: 1.08 seconds, 118.93 examples/second between steps 4199 and 4200
I0421 11:11:15.115145 47076539613632 model_training_utils.py:49] Saving model as TF checkpoint: /public/home/xuanbaby/DL-TensorFlow/models_r2.3.0/official/nlp/bert/model_squad_v2/ctl_step_2100.ckpt-3
I0421 11:11:15.143852 47076539613632 model_training_utils.py:97] Training Summary: 
{'total_training_steps': 2100, 'train_loss': 0.77783203125}
I0421 11:11:20.734385 47076539613632 run_squad_helper.py:179] Restoring checkpoints from /public/home/xuanbaby/DL-TensorFlow/models_r2.3.0/official/nlp/bert/model_squad_v2/ctl_step_2100.ckpt-3
I0421 11:11:29.110588 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.110844 47076539613632 squad_lib.py:367] unique_id: 1000000000
I0421 11:11:29.110971 47076539613632 squad_lib.py:368] example_index: 0
I0421 11:11:29.111083 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.111301 47076539613632 squad_lib.py:371] tokens: [CLS] which n ##f ##l team represented the a ##f ##c at super bowl 50 ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.111489 47076539613632 squad_lib.py:374] token_to_orig_map: 17:0 18:1 19:2 20:3 21:4 22:5 23:5 24:5 25:6 26:7 27:8 28:9 29:10 30:11 31:12 32:13 33:14 34:15 35:16 36:17 37:17 38:17 39:17 40:17 41:18 42:19 43:20 44:21 45:21 46:22 47:23 48:23 49:23 50:24 51:25 52:26 53:26 54:26 55:26 56:26 57:27 58:28 59:28 60:29 61:29 62:29 63:30 64:31 65:32 66:33 67:34 68:35 69:35 70:35 71:35 72:35 73:36 74:37 75:37 76:37 77:38 78:38 79:38 80:39 81:39 82:39 83:40 84:41 85:42 86:43 87:44 88:45 89:46 90:46 91:47 92:48 93:49 94:50 95:51 96:52 97:52 98:52 99:52 100:53 101:53 102:54 103:54 104:55 105:56 106:56 107:56 108:56 109:57 110:58 111:59 112:60 113:60 114:61 115:61 116:61 117:61 118:62 119:63 120:64 121:65 122:65 123:66 124:66 125:66 126:66 127:67 128:67 129:67 130:67 131:67 132:67 133:68 134:69 135:70 136:71 137:72 138:73 139:74 140:74 141:75 142:76 143:77 144:78 145:79 146:79 147:80 148:80 149:81 150:82 151:83 152:83 153:83 154:84 155:84 156:85 157:86 158:87 159:88 160:89 161:89 162:89 163:90 164:91 165:92 166:93 167:94 168:95 169:96 170:97 171:98 172:99 173:99 174:100 175:100 176:100 177:101 178:101 179:102 180:103 181:104 182:105 183:106 184:107 185:108 186:109 187:110 188:110 189:111 190:112 191:112 192:112 193:112 194:113 195:114 196:115 197:116 198:117 199:118 200:119 201:120 202:121 203:121 204:121 205:122 206:122 207:122 208:123 209:123
I0421 11:11:29.111668 47076539613632 squad_lib.py:379] token_is_max_context: 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True 205:True 206:True 207:True 208:True 209:True
I0421 11:11:29.111902 47076539613632 squad_lib.py:381] input_ids: 101 1134 183 2087 1233 1264 2533 1103 170 2087 1665 1120 7688 7329 1851 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.112115 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.112339 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.151947 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.152087 47076539613632 squad_lib.py:367] unique_id: 1000000001
I0421 11:11:29.152208 47076539613632 squad_lib.py:368] example_index: 1
I0421 11:11:29.152330 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.152540 47076539613632 squad_lib.py:371] tokens: [CLS] which n ##f ##l team represented the n ##f ##c at super bowl 50 ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.152730 47076539613632 squad_lib.py:374] token_to_orig_map: 17:0 18:1 19:2 20:3 21:4 22:5 23:5 24:5 25:6 26:7 27:8 28:9 29:10 30:11 31:12 32:13 33:14 34:15 35:16 36:17 37:17 38:17 39:17 40:17 41:18 42:19 43:20 44:21 45:21 46:22 47:23 48:23 49:23 50:24 51:25 52:26 53:26 54:26 55:26 56:26 57:27 58:28 59:28 60:29 61:29 62:29 63:30 64:31 65:32 66:33 67:34 68:35 69:35 70:35 71:35 72:35 73:36 74:37 75:37 76:37 77:38 78:38 79:38 80:39 81:39 82:39 83:40 84:41 85:42 86:43 87:44 88:45 89:46 90:46 91:47 92:48 93:49 94:50 95:51 96:52 97:52 98:52 99:52 100:53 101:53 102:54 103:54 104:55 105:56 106:56 107:56 108:56 109:57 110:58 111:59 112:60 113:60 114:61 115:61 116:61 117:61 118:62 119:63 120:64 121:65 122:65 123:66 124:66 125:66 126:66 127:67 128:67 129:67 130:67 131:67 132:67 133:68 134:69 135:70 136:71 137:72 138:73 139:74 140:74 141:75 142:76 143:77 144:78 145:79 146:79 147:80 148:80 149:81 150:82 151:83 152:83 153:83 154:84 155:84 156:85 157:86 158:87 159:88 160:89 161:89 162:89 163:90 164:91 165:92 166:93 167:94 168:95 169:96 170:97 171:98 172:99 173:99 174:100 175:100 176:100 177:101 178:101 179:102 180:103 181:104 182:105 183:106 184:107 185:108 186:109 187:110 188:110 189:111 190:112 191:112 192:112 193:112 194:113 195:114 196:115 197:116 198:117 199:118 200:119 201:120 202:121 203:121 204:121 205:122 206:122 207:122 208:123 209:123
I0421 11:11:29.152909 47076539613632 squad_lib.py:379] token_is_max_context: 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True 205:True 206:True 207:True 208:True 209:True
I0421 11:11:29.153126 47076539613632 squad_lib.py:381] input_ids: 101 1134 183 2087 1233 1264 2533 1103 183 2087 1665 1120 7688 7329 1851 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.153357 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.153572 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.159307 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.159441 47076539613632 squad_lib.py:367] unique_id: 1000000002
I0421 11:11:29.159569 47076539613632 squad_lib.py:368] example_index: 2
I0421 11:11:29.159681 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.159886 47076539613632 squad_lib.py:371] tokens: [CLS] where did super bowl 50 take place ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.160074 47076539613632 squad_lib.py:374] token_to_orig_map: 10:0 11:1 12:2 13:3 14:4 15:5 16:5 17:5 18:6 19:7 20:8 21:9 22:10 23:11 24:12 25:13 26:14 27:15 28:16 29:17 30:17 31:17 32:17 33:17 34:18 35:19 36:20 37:21 38:21 39:22 40:23 41:23 42:23 43:24 44:25 45:26 46:26 47:26 48:26 49:26 50:27 51:28 52:28 53:29 54:29 55:29 56:30 57:31 58:32 59:33 60:34 61:35 62:35 63:35 64:35 65:35 66:36 67:37 68:37 69:37 70:38 71:38 72:38 73:39 74:39 75:39 76:40 77:41 78:42 79:43 80:44 81:45 82:46 83:46 84:47 85:48 86:49 87:50 88:51 89:52 90:52 91:52 92:52 93:53 94:53 95:54 96:54 97:55 98:56 99:56 100:56 101:56 102:57 103:58 104:59 105:60 106:60 107:61 108:61 109:61 110:61 111:62 112:63 113:64 114:65 115:65 116:66 117:66 118:66 119:66 120:67 121:67 122:67 123:67 124:67 125:67 126:68 127:69 128:70 129:71 130:72 131:73 132:74 133:74 134:75 135:76 136:77 137:78 138:79 139:79 140:80 141:80 142:81 143:82 144:83 145:83 146:83 147:84 148:84 149:85 150:86 151:87 152:88 153:89 154:89 155:89 156:90 157:91 158:92 159:93 160:94 161:95 162:96 163:97 164:98 165:99 166:99 167:100 168:100 169:100 170:101 171:101 172:102 173:103 174:104 175:105 176:106 177:107 178:108 179:109 180:110 181:110 182:111 183:112 184:112 185:112 186:112 187:113 188:114 189:115 190:116 191:117 192:118 193:119 194:120 195:121 196:121 197:121 198:122 199:122 200:122 201:123 202:123
I0421 11:11:29.160250 47076539613632 squad_lib.py:379] token_is_max_context: 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True
I0421 11:11:29.160475 47076539613632 squad_lib.py:381] input_ids: 101 1187 1225 7688 7329 1851 1321 1282 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.160690 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.160901 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.166612 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.166741 47076539613632 squad_lib.py:367] unique_id: 1000000003
I0421 11:11:29.166857 47076539613632 squad_lib.py:368] example_index: 3
I0421 11:11:29.166969 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.167174 47076539613632 squad_lib.py:371] tokens: [CLS] which n ##f ##l team won super bowl 50 ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.167364 47076539613632 squad_lib.py:374] token_to_orig_map: 12:0 13:1 14:2 15:3 16:4 17:5 18:5 19:5 20:6 21:7 22:8 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:17 33:17 34:17 35:17 36:18 37:19 38:20 39:21 40:21 41:22 42:23 43:23 44:23 45:24 46:25 47:26 48:26 49:26 50:26 51:26 52:27 53:28 54:28 55:29 56:29 57:29 58:30 59:31 60:32 61:33 62:34 63:35 64:35 65:35 66:35 67:35 68:36 69:37 70:37 71:37 72:38 73:38 74:38 75:39 76:39 77:39 78:40 79:41 80:42 81:43 82:44 83:45 84:46 85:46 86:47 87:48 88:49 89:50 90:51 91:52 92:52 93:52 94:52 95:53 96:53 97:54 98:54 99:55 100:56 101:56 102:56 103:56 104:57 105:58 106:59 107:60 108:60 109:61 110:61 111:61 112:61 113:62 114:63 115:64 116:65 117:65 118:66 119:66 120:66 121:66 122:67 123:67 124:67 125:67 126:67 127:67 128:68 129:69 130:70 131:71 132:72 133:73 134:74 135:74 136:75 137:76 138:77 139:78 140:79 141:79 142:80 143:80 144:81 145:82 146:83 147:83 148:83 149:84 150:84 151:85 152:86 153:87 154:88 155:89 156:89 157:89 158:90 159:91 160:92 161:93 162:94 163:95 164:96 165:97 166:98 167:99 168:99 169:100 170:100 171:100 172:101 173:101 174:102 175:103 176:104 177:105 178:106 179:107 180:108 181:109 182:110 183:110 184:111 185:112 186:112 187:112 188:112 189:113 190:114 191:115 192:116 193:117 194:118 195:119 196:120 197:121 198:121 199:121 200:122 201:122 202:122 203:123 204:123
I0421 11:11:29.167560 47076539613632 squad_lib.py:379] token_is_max_context: 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True
I0421 11:11:29.167780 47076539613632 squad_lib.py:381] input_ids: 101 1134 183 2087 1233 1264 1281 7688 7329 1851 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.167994 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.168213 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.174062 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.174190 47076539613632 squad_lib.py:367] unique_id: 1000000004
I0421 11:11:29.174315 47076539613632 squad_lib.py:368] example_index: 4
I0421 11:11:29.174427 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.174633 47076539613632 squad_lib.py:371] tokens: [CLS] what color was used to emphasize the 50th anniversary of the super bowl ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.174815 47076539613632 squad_lib.py:374] token_to_orig_map: 16:0 17:1 18:2 19:3 20:4 21:5 22:5 23:5 24:6 25:7 26:8 27:9 28:10 29:11 30:12 31:13 32:14 33:15 34:16 35:17 36:17 37:17 38:17 39:17 40:18 41:19 42:20 43:21 44:21 45:22 46:23 47:23 48:23 49:24 50:25 51:26 52:26 53:26 54:26 55:26 56:27 57:28 58:28 59:29 60:29 61:29 62:30 63:31 64:32 65:33 66:34 67:35 68:35 69:35 70:35 71:35 72:36 73:37 74:37 75:37 76:38 77:38 78:38 79:39 80:39 81:39 82:40 83:41 84:42 85:43 86:44 87:45 88:46 89:46 90:47 91:48 92:49 93:50 94:51 95:52 96:52 97:52 98:52 99:53 100:53 101:54 102:54 103:55 104:56 105:56 106:56 107:56 108:57 109:58 110:59 111:60 112:60 113:61 114:61 115:61 116:61 117:62 118:63 119:64 120:65 121:65 122:66 123:66 124:66 125:66 126:67 127:67 128:67 129:67 130:67 131:67 132:68 133:69 134:70 135:71 136:72 137:73 138:74 139:74 140:75 141:76 142:77 143:78 144:79 145:79 146:80 147:80 148:81 149:82 150:83 151:83 152:83 153:84 154:84 155:85 156:86 157:87 158:88 159:89 160:89 161:89 162:90 163:91 164:92 165:93 166:94 167:95 168:96 169:97 170:98 171:99 172:99 173:100 174:100 175:100 176:101 177:101 178:102 179:103 180:104 181:105 182:106 183:107 184:108 185:109 186:110 187:110 188:111 189:112 190:112 191:112 192:112 193:113 194:114 195:115 196:116 197:117 198:118 199:119 200:120 201:121 202:121 203:121 204:122 205:122 206:122 207:123 208:123
I0421 11:11:29.175000 47076539613632 squad_lib.py:379] token_is_max_context: 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True 205:True 206:True 207:True 208:True
I0421 11:11:29.175219 47076539613632 squad_lib.py:381] input_ids: 101 1184 2942 1108 1215 1106 19291 1103 13163 5453 1104 1103 7688 7329 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.175444 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.175664 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.181401 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.181531 47076539613632 squad_lib.py:367] unique_id: 1000000005
I0421 11:11:29.181646 47076539613632 squad_lib.py:368] example_index: 5
I0421 11:11:29.181756 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.181961 47076539613632 squad_lib.py:371] tokens: [CLS] what was the theme of super bowl 50 ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.182143 47076539613632 squad_lib.py:374] token_to_orig_map: 11:0 12:1 13:2 14:3 15:4 16:5 17:5 18:5 19:6 20:7 21:8 22:9 23:10 24:11 25:12 26:13 27:14 28:15 29:16 30:17 31:17 32:17 33:17 34:17 35:18 36:19 37:20 38:21 39:21 40:22 41:23 42:23 43:23 44:24 45:25 46:26 47:26 48:26 49:26 50:26 51:27 52:28 53:28 54:29 55:29 56:29 57:30 58:31 59:32 60:33 61:34 62:35 63:35 64:35 65:35 66:35 67:36 68:37 69:37 70:37 71:38 72:38 73:38 74:39 75:39 76:39 77:40 78:41 79:42 80:43 81:44 82:45 83:46 84:46 85:47 86:48 87:49 88:50 89:51 90:52 91:52 92:52 93:52 94:53 95:53 96:54 97:54 98:55 99:56 100:56 101:56 102:56 103:57 104:58 105:59 106:60 107:60 108:61 109:61 110:61 111:61 112:62 113:63 114:64 115:65 116:65 117:66 118:66 119:66 120:66 121:67 122:67 123:67 124:67 125:67 126:67 127:68 128:69 129:70 130:71 131:72 132:73 133:74 134:74 135:75 136:76 137:77 138:78 139:79 140:79 141:80 142:80 143:81 144:82 145:83 146:83 147:83 148:84 149:84 150:85 151:86 152:87 153:88 154:89 155:89 156:89 157:90 158:91 159:92 160:93 161:94 162:95 163:96 164:97 165:98 166:99 167:99 168:100 169:100 170:100 171:101 172:101 173:102 174:103 175:104 176:105 177:106 178:107 179:108 180:109 181:110 182:110 183:111 184:112 185:112 186:112 187:112 188:113 189:114 190:115 191:116 192:117 193:118 194:119 195:120 196:121 197:121 198:121 199:122 200:122 201:122 202:123 203:123
I0421 11:11:29.182329 47076539613632 squad_lib.py:379] token_is_max_context: 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True
I0421 11:11:29.182553 47076539613632 squad_lib.py:381] input_ids: 101 1184 1108 1103 3815 1104 7688 7329 1851 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.182765 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.182975 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.188714 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.188843 47076539613632 squad_lib.py:367] unique_id: 1000000006
I0421 11:11:29.188958 47076539613632 squad_lib.py:368] example_index: 6
I0421 11:11:29.189069 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.189288 47076539613632 squad_lib.py:371] tokens: [CLS] what day was the game played on ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.189475 47076539613632 squad_lib.py:374] token_to_orig_map: 10:0 11:1 12:2 13:3 14:4 15:5 16:5 17:5 18:6 19:7 20:8 21:9 22:10 23:11 24:12 25:13 26:14 27:15 28:16 29:17 30:17 31:17 32:17 33:17 34:18 35:19 36:20 37:21 38:21 39:22 40:23 41:23 42:23 43:24 44:25 45:26 46:26 47:26 48:26 49:26 50:27 51:28 52:28 53:29 54:29 55:29 56:30 57:31 58:32 59:33 60:34 61:35 62:35 63:35 64:35 65:35 66:36 67:37 68:37 69:37 70:38 71:38 72:38 73:39 74:39 75:39 76:40 77:41 78:42 79:43 80:44 81:45 82:46 83:46 84:47 85:48 86:49 87:50 88:51 89:52 90:52 91:52 92:52 93:53 94:53 95:54 96:54 97:55 98:56 99:56 100:56 101:56 102:57 103:58 104:59 105:60 106:60 107:61 108:61 109:61 110:61 111:62 112:63 113:64 114:65 115:65 116:66 117:66 118:66 119:66 120:67 121:67 122:67 123:67 124:67 125:67 126:68 127:69 128:70 129:71 130:72 131:73 132:74 133:74 134:75 135:76 136:77 137:78 138:79 139:79 140:80 141:80 142:81 143:82 144:83 145:83 146:83 147:84 148:84 149:85 150:86 151:87 152:88 153:89 154:89 155:89 156:90 157:91 158:92 159:93 160:94 161:95 162:96 163:97 164:98 165:99 166:99 167:100 168:100 169:100 170:101 171:101 172:102 173:103 174:104 175:105 176:106 177:107 178:108 179:109 180:110 181:110 182:111 183:112 184:112 185:112 186:112 187:113 188:114 189:115 190:116 191:117 192:118 193:119 194:120 195:121 196:121 197:121 198:122 199:122 200:122 201:123 202:123
I0421 11:11:29.189651 47076539613632 squad_lib.py:379] token_is_max_context: 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True
I0421 11:11:29.189876 47076539613632 squad_lib.py:381] input_ids: 101 1184 1285 1108 1103 1342 1307 1113 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.190092 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.190315 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.195949 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.196079 47076539613632 squad_lib.py:367] unique_id: 1000000007
I0421 11:11:29.196193 47076539613632 squad_lib.py:368] example_index: 7
I0421 11:11:29.196310 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.196518 47076539613632 squad_lib.py:371] tokens: [CLS] what is the a ##f ##c short for ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.196707 47076539613632 squad_lib.py:374] token_to_orig_map: 11:0 12:1 13:2 14:3 15:4 16:5 17:5 18:5 19:6 20:7 21:8 22:9 23:10 24:11 25:12 26:13 27:14 28:15 29:16 30:17 31:17 32:17 33:17 34:17 35:18 36:19 37:20 38:21 39:21 40:22 41:23 42:23 43:23 44:24 45:25 46:26 47:26 48:26 49:26 50:26 51:27 52:28 53:28 54:29 55:29 56:29 57:30 58:31 59:32 60:33 61:34 62:35 63:35 64:35 65:35 66:35 67:36 68:37 69:37 70:37 71:38 72:38 73:38 74:39 75:39 76:39 77:40 78:41 79:42 80:43 81:44 82:45 83:46 84:46 85:47 86:48 87:49 88:50 89:51 90:52 91:52 92:52 93:52 94:53 95:53 96:54 97:54 98:55 99:56 100:56 101:56 102:56 103:57 104:58 105:59 106:60 107:60 108:61 109:61 110:61 111:61 112:62 113:63 114:64 115:65 116:65 117:66 118:66 119:66 120:66 121:67 122:67 123:67 124:67 125:67 126:67 127:68 128:69 129:70 130:71 131:72 132:73 133:74 134:74 135:75 136:76 137:77 138:78 139:79 140:79 141:80 142:80 143:81 144:82 145:83 146:83 147:83 148:84 149:84 150:85 151:86 152:87 153:88 154:89 155:89 156:89 157:90 158:91 159:92 160:93 161:94 162:95 163:96 164:97 165:98 166:99 167:99 168:100 169:100 170:100 171:101 172:101 173:102 174:103 175:104 176:105 177:106 178:107 179:108 180:109 181:110 182:110 183:111 184:112 185:112 186:112 187:112 188:113 189:114 190:115 191:116 192:117 193:118 194:119 195:120 196:121 197:121 198:121 199:122 200:122 201:122 202:123 203:123
I0421 11:11:29.196887 47076539613632 squad_lib.py:379] token_is_max_context: 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True
I0421 11:11:29.197104 47076539613632 squad_lib.py:381] input_ids: 101 1184 1110 1103 170 2087 1665 1603 1111 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.197331 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.197552 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.203239 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.203375 47076539613632 squad_lib.py:367] unique_id: 1000000008
I0421 11:11:29.203488 47076539613632 squad_lib.py:368] example_index: 8
I0421 11:11:29.203599 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.203802 47076539613632 squad_lib.py:371] tokens: [CLS] what was the theme of super bowl 50 ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.203990 47076539613632 squad_lib.py:374] token_to_orig_map: 11:0 12:1 13:2 14:3 15:4 16:5 17:5 18:5 19:6 20:7 21:8 22:9 23:10 24:11 25:12 26:13 27:14 28:15 29:16 30:17 31:17 32:17 33:17 34:17 35:18 36:19 37:20 38:21 39:21 40:22 41:23 42:23 43:23 44:24 45:25 46:26 47:26 48:26 49:26 50:26 51:27 52:28 53:28 54:29 55:29 56:29 57:30 58:31 59:32 60:33 61:34 62:35 63:35 64:35 65:35 66:35 67:36 68:37 69:37 70:37 71:38 72:38 73:38 74:39 75:39 76:39 77:40 78:41 79:42 80:43 81:44 82:45 83:46 84:46 85:47 86:48 87:49 88:50 89:51 90:52 91:52 92:52 93:52 94:53 95:53 96:54 97:54 98:55 99:56 100:56 101:56 102:56 103:57 104:58 105:59 106:60 107:60 108:61 109:61 110:61 111:61 112:62 113:63 114:64 115:65 116:65 117:66 118:66 119:66 120:66 121:67 122:67 123:67 124:67 125:67 126:67 127:68 128:69 129:70 130:71 131:72 132:73 133:74 134:74 135:75 136:76 137:77 138:78 139:79 140:79 141:80 142:80 143:81 144:82 145:83 146:83 147:83 148:84 149:84 150:85 151:86 152:87 153:88 154:89 155:89 156:89 157:90 158:91 159:92 160:93 161:94 162:95 163:96 164:97 165:98 166:99 167:99 168:100 169:100 170:100 171:101 172:101 173:102 174:103 175:104 176:105 177:106 178:107 179:108 180:109 181:110 182:110 183:111 184:112 185:112 186:112 187:112 188:113 189:114 190:115 191:116 192:117 193:118 194:119 195:120 196:121 197:121 198:121 199:122 200:122 201:122 202:123 203:123
I0421 11:11:29.204166 47076539613632 squad_lib.py:379] token_is_max_context: 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True
I0421 11:11:29.204387 47076539613632 squad_lib.py:381] input_ids: 101 1184 1108 1103 3815 1104 7688 7329 1851 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.204607 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.204820 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.210498 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.210627 47076539613632 squad_lib.py:367] unique_id: 1000000009
I0421 11:11:29.210741 47076539613632 squad_lib.py:368] example_index: 9
I0421 11:11:29.210862 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.211064 47076539613632 squad_lib.py:371] tokens: [CLS] what does a ##f ##c stand for ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.211248 47076539613632 squad_lib.py:374] token_to_orig_map: 10:0 11:1 12:2 13:3 14:4 15:5 16:5 17:5 18:6 19:7 20:8 21:9 22:10 23:11 24:12 25:13 26:14 27:15 28:16 29:17 30:17 31:17 32:17 33:17 34:18 35:19 36:20 37:21 38:21 39:22 40:23 41:23 42:23 43:24 44:25 45:26 46:26 47:26 48:26 49:26 50:27 51:28 52:28 53:29 54:29 55:29 56:30 57:31 58:32 59:33 60:34 61:35 62:35 63:35 64:35 65:35 66:36 67:37 68:37 69:37 70:38 71:38 72:38 73:39 74:39 75:39 76:40 77:41 78:42 79:43 80:44 81:45 82:46 83:46 84:47 85:48 86:49 87:50 88:51 89:52 90:52 91:52 92:52 93:53 94:53 95:54 96:54 97:55 98:56 99:56 100:56 101:56 102:57 103:58 104:59 105:60 106:60 107:61 108:61 109:61 110:61 111:62 112:63 113:64 114:65 115:65 116:66 117:66 118:66 119:66 120:67 121:67 122:67 123:67 124:67 125:67 126:68 127:69 128:70 129:71 130:72 131:73 132:74 133:74 134:75 135:76 136:77 137:78 138:79 139:79 140:80 141:80 142:81 143:82 144:83 145:83 146:83 147:84 148:84 149:85 150:86 151:87 152:88 153:89 154:89 155:89 156:90 157:91 158:92 159:93 160:94 161:95 162:96 163:97 164:98 165:99 166:99 167:100 168:100 169:100 170:101 171:101 172:102 173:103 174:104 175:105 176:106 177:107 178:108 179:109 180:110 181:110 182:111 183:112 184:112 185:112 186:112 187:113 188:114 189:115 190:116 191:117 192:118 193:119 194:120 195:121 196:121 197:121 198:122 199:122 200:122 201:123 202:123
I0421 11:11:29.211440 47076539613632 squad_lib.py:379] token_is_max_context: 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True
I0421 11:11:29.211653 47076539613632 squad_lib.py:381] input_ids: 101 1184 1674 170 2087 1665 2484 1111 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.211866 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.212077 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.217821 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.217953 47076539613632 squad_lib.py:367] unique_id: 1000000010
I0421 11:11:29.218068 47076539613632 squad_lib.py:368] example_index: 10
I0421 11:11:29.218178 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.218395 47076539613632 squad_lib.py:371] tokens: [CLS] what day was the super bowl played on ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.218580 47076539613632 squad_lib.py:374] token_to_orig_map: 11:0 12:1 13:2 14:3 15:4 16:5 17:5 18:5 19:6 20:7 21:8 22:9 23:10 24:11 25:12 26:13 27:14 28:15 29:16 30:17 31:17 32:17 33:17 34:17 35:18 36:19 37:20 38:21 39:21 40:22 41:23 42:23 43:23 44:24 45:25 46:26 47:26 48:26 49:26 50:26 51:27 52:28 53:28 54:29 55:29 56:29 57:30 58:31 59:32 60:33 61:34 62:35 63:35 64:35 65:35 66:35 67:36 68:37 69:37 70:37 71:38 72:38 73:38 74:39 75:39 76:39 77:40 78:41 79:42 80:43 81:44 82:45 83:46 84:46 85:47 86:48 87:49 88:50 89:51 90:52 91:52 92:52 93:52 94:53 95:53 96:54 97:54 98:55 99:56 100:56 101:56 102:56 103:57 104:58 105:59 106:60 107:60 108:61 109:61 110:61 111:61 112:62 113:63 114:64 115:65 116:65 117:66 118:66 119:66 120:66 121:67 122:67 123:67 124:67 125:67 126:67 127:68 128:69 129:70 130:71 131:72 132:73 133:74 134:74 135:75 136:76 137:77 138:78 139:79 140:79 141:80 142:80 143:81 144:82 145:83 146:83 147:83 148:84 149:84 150:85 151:86 152:87 153:88 154:89 155:89 156:89 157:90 158:91 159:92 160:93 161:94 162:95 163:96 164:97 165:98 166:99 167:99 168:100 169:100 170:100 171:101 172:101 173:102 174:103 175:104 176:105 177:106 178:107 179:108 180:109 181:110 182:110 183:111 184:112 185:112 186:112 187:112 188:113 189:114 190:115 191:116 192:117 193:118 194:119 195:120 196:121 197:121 198:121 199:122 200:122 201:122 202:123 203:123
I0421 11:11:29.218762 47076539613632 squad_lib.py:379] token_is_max_context: 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True
I0421 11:11:29.218987 47076539613632 squad_lib.py:381] input_ids: 101 1184 1285 1108 1103 7688 7329 1307 1113 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.219200 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.219423 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.225099 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.225227 47076539613632 squad_lib.py:367] unique_id: 1000000011
I0421 11:11:29.225351 47076539613632 squad_lib.py:368] example_index: 11
I0421 11:11:29.225461 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.225666 47076539613632 squad_lib.py:371] tokens: [CLS] who won super bowl 50 ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.225848 47076539613632 squad_lib.py:374] token_to_orig_map: 8:0 9:1 10:2 11:3 12:4 13:5 14:5 15:5 16:6 17:7 18:8 19:9 20:10 21:11 22:12 23:13 24:14 25:15 26:16 27:17 28:17 29:17 30:17 31:17 32:18 33:19 34:20 35:21 36:21 37:22 38:23 39:23 40:23 41:24 42:25 43:26 44:26 45:26 46:26 47:26 48:27 49:28 50:28 51:29 52:29 53:29 54:30 55:31 56:32 57:33 58:34 59:35 60:35 61:35 62:35 63:35 64:36 65:37 66:37 67:37 68:38 69:38 70:38 71:39 72:39 73:39 74:40 75:41 76:42 77:43 78:44 79:45 80:46 81:46 82:47 83:48 84:49 85:50 86:51 87:52 88:52 89:52 90:52 91:53 92:53 93:54 94:54 95:55 96:56 97:56 98:56 99:56 100:57 101:58 102:59 103:60 104:60 105:61 106:61 107:61 108:61 109:62 110:63 111:64 112:65 113:65 114:66 115:66 116:66 117:66 118:67 119:67 120:67 121:67 122:67 123:67 124:68 125:69 126:70 127:71 128:72 129:73 130:74 131:74 132:75 133:76 134:77 135:78 136:79 137:79 138:80 139:80 140:81 141:82 142:83 143:83 144:83 145:84 146:84 147:85 148:86 149:87 150:88 151:89 152:89 153:89 154:90 155:91 156:92 157:93 158:94 159:95 160:96 161:97 162:98 163:99 164:99 165:100 166:100 167:100 168:101 169:101 170:102 171:103 172:104 173:105 174:106 175:107 176:108 177:109 178:110 179:110 180:111 181:112 182:112 183:112 184:112 185:113 186:114 187:115 188:116 189:117 190:118 191:119 192:120 193:121 194:121 195:121 196:122 197:122 198:122 199:123 200:123
I0421 11:11:29.226024 47076539613632 squad_lib.py:379] token_is_max_context: 8:True 9:True 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True
I0421 11:11:29.226248 47076539613632 squad_lib.py:381] input_ids: 101 1150 1281 7688 7329 1851 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.226472 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.226676 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.232438 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.232580 47076539613632 squad_lib.py:367] unique_id: 1000000012
I0421 11:11:29.232693 47076539613632 squad_lib.py:368] example_index: 12
I0421 11:11:29.232803 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.233011 47076539613632 squad_lib.py:371] tokens: [CLS] what venue did super bowl 50 take place in ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.233192 47076539613632 squad_lib.py:374] token_to_orig_map: 12:0 13:1 14:2 15:3 16:4 17:5 18:5 19:5 20:6 21:7 22:8 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:17 33:17 34:17 35:17 36:18 37:19 38:20 39:21 40:21 41:22 42:23 43:23 44:23 45:24 46:25 47:26 48:26 49:26 50:26 51:26 52:27 53:28 54:28 55:29 56:29 57:29 58:30 59:31 60:32 61:33 62:34 63:35 64:35 65:35 66:35 67:35 68:36 69:37 70:37 71:37 72:38 73:38 74:38 75:39 76:39 77:39 78:40 79:41 80:42 81:43 82:44 83:45 84:46 85:46 86:47 87:48 88:49 89:50 90:51 91:52 92:52 93:52 94:52 95:53 96:53 97:54 98:54 99:55 100:56 101:56 102:56 103:56 104:57 105:58 106:59 107:60 108:60 109:61 110:61 111:61 112:61 113:62 114:63 115:64 116:65 117:65 118:66 119:66 120:66 121:66 122:67 123:67 124:67 125:67 126:67 127:67 128:68 129:69 130:70 131:71 132:72 133:73 134:74 135:74 136:75 137:76 138:77 139:78 140:79 141:79 142:80 143:80 144:81 145:82 146:83 147:83 148:83 149:84 150:84 151:85 152:86 153:87 154:88 155:89 156:89 157:89 158:90 159:91 160:92 161:93 162:94 163:95 164:96 165:97 166:98 167:99 168:99 169:100 170:100 171:100 172:101 173:101 174:102 175:103 176:104 177:105 178:106 179:107 180:108 181:109 182:110 183:110 184:111 185:112 186:112 187:112 188:112 189:113 190:114 191:115 192:116 193:117 194:118 195:119 196:120 197:121 198:121 199:121 200:122 201:122 202:122 203:123 204:123
I0421 11:11:29.233377 47076539613632 squad_lib.py:379] token_is_max_context: 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True
I0421 11:11:29.233603 47076539613632 squad_lib.py:381] input_ids: 101 1184 6590 1225 7688 7329 1851 1321 1282 1107 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.233817 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.234028 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.239761 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.239892 47076539613632 squad_lib.py:367] unique_id: 1000000013
I0421 11:11:29.240006 47076539613632 squad_lib.py:368] example_index: 13
I0421 11:11:29.240114 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.240329 47076539613632 squad_lib.py:371] tokens: [CLS] what city did super bowl 50 take place in ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.240523 47076539613632 squad_lib.py:374] token_to_orig_map: 12:0 13:1 14:2 15:3 16:4 17:5 18:5 19:5 20:6 21:7 22:8 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:17 33:17 34:17 35:17 36:18 37:19 38:20 39:21 40:21 41:22 42:23 43:23 44:23 45:24 46:25 47:26 48:26 49:26 50:26 51:26 52:27 53:28 54:28 55:29 56:29 57:29 58:30 59:31 60:32 61:33 62:34 63:35 64:35 65:35 66:35 67:35 68:36 69:37 70:37 71:37 72:38 73:38 74:38 75:39 76:39 77:39 78:40 79:41 80:42 81:43 82:44 83:45 84:46 85:46 86:47 87:48 88:49 89:50 90:51 91:52 92:52 93:52 94:52 95:53 96:53 97:54 98:54 99:55 100:56 101:56 102:56 103:56 104:57 105:58 106:59 107:60 108:60 109:61 110:61 111:61 112:61 113:62 114:63 115:64 116:65 117:65 118:66 119:66 120:66 121:66 122:67 123:67 124:67 125:67 126:67 127:67 128:68 129:69 130:70 131:71 132:72 133:73 134:74 135:74 136:75 137:76 138:77 139:78 140:79 141:79 142:80 143:80 144:81 145:82 146:83 147:83 148:83 149:84 150:84 151:85 152:86 153:87 154:88 155:89 156:89 157:89 158:90 159:91 160:92 161:93 162:94 163:95 164:96 165:97 166:98 167:99 168:99 169:100 170:100 171:100 172:101 173:101 174:102 175:103 176:104 177:105 178:106 179:107 180:108 181:109 182:110 183:110 184:111 185:112 186:112 187:112 188:112 189:113 190:114 191:115 192:116 193:117 194:118 195:119 196:120 197:121 198:121 199:121 200:122 201:122 202:122 203:123 204:123
I0421 11:11:29.240707 47076539613632 squad_lib.py:379] token_is_max_context: 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True
I0421 11:11:29.240931 47076539613632 squad_lib.py:381] input_ids: 101 1184 1331 1225 7688 7329 1851 1321 1282 1107 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.241142 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.241360 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.247224 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.247361 47076539613632 squad_lib.py:367] unique_id: 1000000014
I0421 11:11:29.247475 47076539613632 squad_lib.py:368] example_index: 14
I0421 11:11:29.247584 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.247793 47076539613632 squad_lib.py:371] tokens: [CLS] if r ##oman n ##ume ##rals were used , what would super bowl 50 have been called ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.247981 47076539613632 squad_lib.py:374] token_to_orig_map: 20:0 21:1 22:2 23:3 24:4 25:5 26:5 27:5 28:6 29:7 30:8 31:9 32:10 33:11 34:12 35:13 36:14 37:15 38:16 39:17 40:17 41:17 42:17 43:17 44:18 45:19 46:20 47:21 48:21 49:22 50:23 51:23 52:23 53:24 54:25 55:26 56:26 57:26 58:26 59:26 60:27 61:28 62:28 63:29 64:29 65:29 66:30 67:31 68:32 69:33 70:34 71:35 72:35 73:35 74:35 75:35 76:36 77:37 78:37 79:37 80:38 81:38 82:38 83:39 84:39 85:39 86:40 87:41 88:42 89:43 90:44 91:45 92:46 93:46 94:47 95:48 96:49 97:50 98:51 99:52 100:52 101:52 102:52 103:53 104:53 105:54 106:54 107:55 108:56 109:56 110:56 111:56 112:57 113:58 114:59 115:60 116:60 117:61 118:61 119:61 120:61 121:62 122:63 123:64 124:65 125:65 126:66 127:66 128:66 129:66 130:67 131:67 132:67 133:67 134:67 135:67 136:68 137:69 138:70 139:71 140:72 141:73 142:74 143:74 144:75 145:76 146:77 147:78 148:79 149:79 150:80 151:80 152:81 153:82 154:83 155:83 156:83 157:84 158:84 159:85 160:86 161:87 162:88 163:89 164:89 165:89 166:90 167:91 168:92 169:93 170:94 171:95 172:96 173:97 174:98 175:99 176:99 177:100 178:100 179:100 180:101 181:101 182:102 183:103 184:104 185:105 186:106 187:107 188:108 189:109 190:110 191:110 192:111 193:112 194:112 195:112 196:112 197:113 198:114 199:115 200:116 201:117 202:118 203:119 204:120 205:121 206:121 207:121 208:122 209:122 210:122 211:123 212:123
I0421 11:11:29.248157 47076539613632 squad_lib.py:379] token_is_max_context: 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True 205:True 206:True 207:True 208:True 209:True 210:True 211:True 212:True
I0421 11:11:29.248379 47076539613632 squad_lib.py:381] input_ids: 101 1191 187 27085 183 15447 16179 1127 1215 117 1184 1156 7688 7329 1851 1138 1151 1270 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.248602 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.248815 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.254668 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.254798 47076539613632 squad_lib.py:367] unique_id: 1000000015
I0421 11:11:29.254912 47076539613632 squad_lib.py:368] example_index: 15
I0421 11:11:29.255022 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.255229 47076539613632 squad_lib.py:371] tokens: [CLS] super bowl 50 decided the n ##f ##l champion for what season ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.255420 47076539613632 squad_lib.py:374] token_to_orig_map: 15:0 16:1 17:2 18:3 19:4 20:5 21:5 22:5 23:6 24:7 25:8 26:9 27:10 28:11 29:12 30:13 31:14 32:15 33:16 34:17 35:17 36:17 37:17 38:17 39:18 40:19 41:20 42:21 43:21 44:22 45:23 46:23 47:23 48:24 49:25 50:26 51:26 52:26 53:26 54:26 55:27 56:28 57:28 58:29 59:29 60:29 61:30 62:31 63:32 64:33 65:34 66:35 67:35 68:35 69:35 70:35 71:36 72:37 73:37 74:37 75:38 76:38 77:38 78:39 79:39 80:39 81:40 82:41 83:42 84:43 85:44 86:45 87:46 88:46 89:47 90:48 91:49 92:50 93:51 94:52 95:52 96:52 97:52 98:53 99:53 100:54 101:54 102:55 103:56 104:56 105:56 106:56 107:57 108:58 109:59 110:60 111:60 112:61 113:61 114:61 115:61 116:62 117:63 118:64 119:65 120:65 121:66 122:66 123:66 124:66 125:67 126:67 127:67 128:67 129:67 130:67 131:68 132:69 133:70 134:71 135:72 136:73 137:74 138:74 139:75 140:76 141:77 142:78 143:79 144:79 145:80 146:80 147:81 148:82 149:83 150:83 151:83 152:84 153:84 154:85 155:86 156:87 157:88 158:89 159:89 160:89 161:90 162:91 163:92 164:93 165:94 166:95 167:96 168:97 169:98 170:99 171:99 172:100 173:100 174:100 175:101 176:101 177:102 178:103 179:104 180:105 181:106 182:107 183:108 184:109 185:110 186:110 187:111 188:112 189:112 190:112 191:112 192:113 193:114 194:115 195:116 196:117 197:118 198:119 199:120 200:121 201:121 202:121 203:122 204:122 205:122 206:123 207:123
I0421 11:11:29.255608 47076539613632 squad_lib.py:379] token_is_max_context: 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True 205:True 206:True 207:True
I0421 11:11:29.255824 47076539613632 squad_lib.py:381] input_ids: 101 7688 7329 1851 1879 1103 183 2087 1233 3628 1111 1184 1265 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.256043 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.256251 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.262142 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.262271 47076539613632 squad_lib.py:367] unique_id: 1000000016
I0421 11:11:29.262397 47076539613632 squad_lib.py:368] example_index: 16
I0421 11:11:29.262506 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.262727 47076539613632 squad_lib.py:371] tokens: [CLS] what year did the den ##ver br ##on ##cos secure a super bowl title for the third time ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.262908 47076539613632 squad_lib.py:374] token_to_orig_map: 21:0 22:1 23:2 24:3 25:4 26:5 27:5 28:5 29:6 30:7 31:8 32:9 33:10 34:11 35:12 36:13 37:14 38:15 39:16 40:17 41:17 42:17 43:17 44:17 45:18 46:19 47:20 48:21 49:21 50:22 51:23 52:23 53:23 54:24 55:25 56:26 57:26 58:26 59:26 60:26 61:27 62:28 63:28 64:29 65:29 66:29 67:30 68:31 69:32 70:33 71:34 72:35 73:35 74:35 75:35 76:35 77:36 78:37 79:37 80:37 81:38 82:38 83:38 84:39 85:39 86:39 87:40 88:41 89:42 90:43 91:44 92:45 93:46 94:46 95:47 96:48 97:49 98:50 99:51 100:52 101:52 102:52 103:52 104:53 105:53 106:54 107:54 108:55 109:56 110:56 111:56 112:56 113:57 114:58 115:59 116:60 117:60 118:61 119:61 120:61 121:61 122:62 123:63 124:64 125:65 126:65 127:66 128:66 129:66 130:66 131:67 132:67 133:67 134:67 135:67 136:67 137:68 138:69 139:70 140:71 141:72 142:73 143:74 144:74 145:75 146:76 147:77 148:78 149:79 150:79 151:80 152:80 153:81 154:82 155:83 156:83 157:83 158:84 159:84 160:85 161:86 162:87 163:88 164:89 165:89 166:89 167:90 168:91 169:92 170:93 171:94 172:95 173:96 174:97 175:98 176:99 177:99 178:100 179:100 180:100 181:101 182:101 183:102 184:103 185:104 186:105 187:106 188:107 189:108 190:109 191:110 192:110 193:111 194:112 195:112 196:112 197:112 198:113 199:114 200:115 201:116 202:117 203:118 204:119 205:120 206:121 207:121 208:121 209:122 210:122 211:122 212:123 213:123
I0421 11:11:29.263093 47076539613632 squad_lib.py:379] token_is_max_context: 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True 205:True 206:True 207:True 208:True 209:True 210:True 211:True 212:True 213:True
I0421 11:11:29.263322 47076539613632 squad_lib.py:381] input_ids: 101 1184 1214 1225 1103 10552 4121 9304 1320 13538 5343 170 7688 7329 1641 1111 1103 1503 1159 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.263537 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.263753 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.269463 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.269592 47076539613632 squad_lib.py:367] unique_id: 1000000017
I0421 11:11:29.269708 47076539613632 squad_lib.py:368] example_index: 17
I0421 11:11:29.269818 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.270023 47076539613632 squad_lib.py:371] tokens: [CLS] what city did super bowl 50 take place in ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.270205 47076539613632 squad_lib.py:374] token_to_orig_map: 12:0 13:1 14:2 15:3 16:4 17:5 18:5 19:5 20:6 21:7 22:8 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:17 33:17 34:17 35:17 36:18 37:19 38:20 39:21 40:21 41:22 42:23 43:23 44:23 45:24 46:25 47:26 48:26 49:26 50:26 51:26 52:27 53:28 54:28 55:29 56:29 57:29 58:30 59:31 60:32 61:33 62:34 63:35 64:35 65:35 66:35 67:35 68:36 69:37 70:37 71:37 72:38 73:38 74:38 75:39 76:39 77:39 78:40 79:41 80:42 81:43 82:44 83:45 84:46 85:46 86:47 87:48 88:49 89:50 90:51 91:52 92:52 93:52 94:52 95:53 96:53 97:54 98:54 99:55 100:56 101:56 102:56 103:56 104:57 105:58 106:59 107:60 108:60 109:61 110:61 111:61 112:61 113:62 114:63 115:64 116:65 117:65 118:66 119:66 120:66 121:66 122:67 123:67 124:67 125:67 126:67 127:67 128:68 129:69 130:70 131:71 132:72 133:73 134:74 135:74 136:75 137:76 138:77 139:78 140:79 141:79 142:80 143:80 144:81 145:82 146:83 147:83 148:83 149:84 150:84 151:85 152:86 153:87 154:88 155:89 156:89 157:89 158:90 159:91 160:92 161:93 162:94 163:95 164:96 165:97 166:98 167:99 168:99 169:100 170:100 171:100 172:101 173:101 174:102 175:103 176:104 177:105 178:106 179:107 180:108 181:109 182:110 183:110 184:111 185:112 186:112 187:112 188:112 189:113 190:114 191:115 192:116 193:117 194:118 195:119 196:120 197:121 198:121 199:121 200:122 201:122 202:122 203:123 204:123
I0421 11:11:29.270397 47076539613632 squad_lib.py:379] token_is_max_context: 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True
I0421 11:11:29.270618 47076539613632 squad_lib.py:381] input_ids: 101 1184 1331 1225 7688 7329 1851 1321 1282 1107 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.270835 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.271047 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.276791 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.276922 47076539613632 squad_lib.py:367] unique_id: 1000000018
I0421 11:11:29.277035 47076539613632 squad_lib.py:368] example_index: 18
I0421 11:11:29.277144 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.277358 47076539613632 squad_lib.py:371] tokens: [CLS] what stadium did super bowl 50 take place in ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.277544 47076539613632 squad_lib.py:374] token_to_orig_map: 12:0 13:1 14:2 15:3 16:4 17:5 18:5 19:5 20:6 21:7 22:8 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:17 33:17 34:17 35:17 36:18 37:19 38:20 39:21 40:21 41:22 42:23 43:23 44:23 45:24 46:25 47:26 48:26 49:26 50:26 51:26 52:27 53:28 54:28 55:29 56:29 57:29 58:30 59:31 60:32 61:33 62:34 63:35 64:35 65:35 66:35 67:35 68:36 69:37 70:37 71:37 72:38 73:38 74:38 75:39 76:39 77:39 78:40 79:41 80:42 81:43 82:44 83:45 84:46 85:46 86:47 87:48 88:49 89:50 90:51 91:52 92:52 93:52 94:52 95:53 96:53 97:54 98:54 99:55 100:56 101:56 102:56 103:56 104:57 105:58 106:59 107:60 108:60 109:61 110:61 111:61 112:61 113:62 114:63 115:64 116:65 117:65 118:66 119:66 120:66 121:66 122:67 123:67 124:67 125:67 126:67 127:67 128:68 129:69 130:70 131:71 132:72 133:73 134:74 135:74 136:75 137:76 138:77 139:78 140:79 141:79 142:80 143:80 144:81 145:82 146:83 147:83 148:83 149:84 150:84 151:85 152:86 153:87 154:88 155:89 156:89 157:89 158:90 159:91 160:92 161:93 162:94 163:95 164:96 165:97 166:98 167:99 168:99 169:100 170:100 171:100 172:101 173:101 174:102 175:103 176:104 177:105 178:106 179:107 180:108 181:109 182:110 183:110 184:111 185:112 186:112 187:112 188:112 189:113 190:114 191:115 192:116 193:117 194:118 195:119 196:120 197:121 198:121 199:121 200:122 201:122 202:122 203:123 204:123
I0421 11:11:29.277722 47076539613632 squad_lib.py:379] token_is_max_context: 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True
I0421 11:11:29.277944 47076539613632 squad_lib.py:381] input_ids: 101 1184 4706 1225 7688 7329 1851 1321 1282 1107 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.278156 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.278375 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.284075 47076539613632 squad_lib.py:366] *** Example ***
I0421 11:11:29.284207 47076539613632 squad_lib.py:367] unique_id: 1000000019
I0421 11:11:29.284345 47076539613632 squad_lib.py:368] example_index: 19
I0421 11:11:29.284456 47076539613632 squad_lib.py:369] doc_span_index: 0
I0421 11:11:29.284663 47076539613632 squad_lib.py:371] tokens: [CLS] what was the final score of super bowl 50 ? [SEP] super bowl 50 was an am ##eric ##an football game to determine the champion of the national football league ( n ##f ##l ) for the 2015 season . the am ##eric ##an football conference ( a ##f ##c ) champion den ##ver br ##on ##cos defeated the national football conference ( n ##f ##c ) champion car ##olin ##a pan ##ther ##s 24 – 10 to earn their third super bowl title . the game was played on f ##eb ##ru ##ary 7 , 2016 , at le ##vi ' s stadium in the sa ##n f ##ran ##cis ##co bay area at sa ##nta c ##lar ##a , ca ##li ##fo ##rn ##ia . as this was the 50th super bowl , the league emphasized the " golden anniversary " with various gold - themed initiatives , as well as temporarily su ##sp ##ending the tradition of naming each super bowl game with r ##oman n ##ume ##rals ( under which the game would have been known as " super bowl l " ) , so that the logo could prominently feature the a ##rab ##ic n ##ume ##rals 50 . [SEP]
I0421 11:11:29.284847 47076539613632 squad_lib.py:374] token_to_orig_map: 12:0 13:1 14:2 15:3 16:4 17:5 18:5 19:5 20:6 21:7 22:8 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:17 33:17 34:17 35:17 36:18 37:19 38:20 39:21 40:21 41:22 42:23 43:23 44:23 45:24 46:25 47:26 48:26 49:26 50:26 51:26 52:27 53:28 54:28 55:29 56:29 57:29 58:30 59:31 60:32 61:33 62:34 63:35 64:35 65:35 66:35 67:35 68:36 69:37 70:37 71:37 72:38 73:38 74:38 75:39 76:39 77:39 78:40 79:41 80:42 81:43 82:44 83:45 84:46 85:46 86:47 87:48 88:49 89:50 90:51 91:52 92:52 93:52 94:52 95:53 96:53 97:54 98:54 99:55 100:56 101:56 102:56 103:56 104:57 105:58 106:59 107:60 108:60 109:61 110:61 111:61 112:61 113:62 114:63 115:64 116:65 117:65 118:66 119:66 120:66 121:66 122:67 123:67 124:67 125:67 126:67 127:67 128:68 129:69 130:70 131:71 132:72 133:73 134:74 135:74 136:75 137:76 138:77 139:78 140:79 141:79 142:80 143:80 144:81 145:82 146:83 147:83 148:83 149:84 150:84 151:85 152:86 153:87 154:88 155:89 156:89 157:89 158:90 159:91 160:92 161:93 162:94 163:95 164:96 165:97 166:98 167:99 168:99 169:100 170:100 171:100 172:101 173:101 174:102 175:103 176:104 177:105 178:106 179:107 180:108 181:109 182:110 183:110 184:111 185:112 186:112 187:112 188:112 189:113 190:114 191:115 192:116 193:117 194:118 195:119 196:120 197:121 198:121 199:121 200:122 201:122 202:122 203:123 204:123
I0421 11:11:29.285025 47076539613632 squad_lib.py:379] token_is_max_context: 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True 175:True 176:True 177:True 178:True 179:True 180:True 181:True 182:True 183:True 184:True 185:True 186:True 187:True 188:True 189:True 190:True 191:True 192:True 193:True 194:True 195:True 196:True 197:True 198:True 199:True 200:True 201:True 202:True 203:True 204:True
I0421 11:11:29.285244 47076539613632 squad_lib.py:381] input_ids: 101 1184 1108 1103 1509 2794 1104 7688 7329 1851 136 102 7688 7329 1851 1108 1126 1821 26237 1389 1709 1342 1106 4959 1103 3628 1104 1103 1569 1709 2074 113 183 2087 1233 114 1111 1103 1410 1265 119 1103 1821 26237 1389 1709 3511 113 170 2087 1665 114 3628 10552 4121 9304 1320 13538 2378 1103 1569 1709 3511 113 183 2087 1665 114 3628 1610 27719 1161 13316 8420 1116 1572 782 1275 1106 7379 1147 1503 7688 7329 1641 119 1103 1342 1108 1307 1113 175 15581 5082 3113 128 117 1446 117 1120 5837 5086 112 188 4706 1107 1103 21718 1179 175 4047 21349 2528 5952 1298 1120 21718 13130 172 5815 1161 117 11019 2646 14467 4558 1465 119 1112 1142 1108 1103 13163 7688 7329 117 1103 2074 13463 1103 107 5404 5453 107 1114 1672 2284 118 12005 11751 117 1112 1218 1112 7818 28117 20080 16264 1103 3904 1104 10505 1296 7688 7329 1342 1114 187 27085 183 15447 16179 113 1223 1134 1103 1342 1156 1138 1151 1227 1112 107 7688 7329 181 107 114 117 1177 1115 1103 7998 1180 15199 2672 1103 170 17952 1596 183 15447 16179 1851 119 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.285465 47076539613632 squad_lib.py:382] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:11:29.285675 47076539613632 squad_lib.py:383] segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I0421 11:12:29.524208 47076539613632 squad_lib.py:420] Adding padding examples to make sure no partial batch.
I0421 11:12:29.524480 47076539613632 squad_lib.py:421] Adds 2 padding examples for inference.
I0421 11:12:29.538662 47076539613632 run_squad_helper.py:341] ***** Running predictions *****
I0421 11:12:29.538791 47076539613632 run_squad_helper.py:342]   Num orig examples = 10570
I0421 11:12:29.538907 47076539613632 run_squad_helper.py:343]   Num split examples = 10970
I0421 11:12:29.539023 47076539613632 run_squad_helper.py:344]   Batch size = 4
2021-04-21 11:12:30.009568: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.016746: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.021664: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.153649: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.177027: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.182349: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.187927: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.193027: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.198579: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.203624: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:30.209108: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
2021-04-21 11:12:38.476456: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:508] ROCm Fusion is enabled.
I0421 11:12:40.405827 47076539613632 run_squad_helper.py:223] Made predictions for 100 records.
I0421 11:12:41.230522 47076539613632 run_squad_helper.py:223] Made predictions for 200 records.
I0421 11:12:42.061466 47076539613632 run_squad_helper.py:223] Made predictions for 300 records.
I0421 11:12:42.879084 47076539613632 run_squad_helper.py:223] Made predictions for 400 records.
I0421 11:12:43.701066 47076539613632 run_squad_helper.py:223] Made predictions for 500 records.
I0421 11:12:44.522982 47076539613632 run_squad_helper.py:223] Made predictions for 600 records.
I0421 11:12:45.343106 47076539613632 run_squad_helper.py:223] Made predictions for 700 records.
I0421 11:12:46.168502 47076539613632 run_squad_helper.py:223] Made predictions for 800 records.
I0421 11:12:46.999982 47076539613632 run_squad_helper.py:223] Made predictions for 900 records.
I0421 11:12:47.825019 47076539613632 run_squad_helper.py:223] Made predictions for 1000 records.
I0421 11:12:48.656949 47076539613632 run_squad_helper.py:223] Made predictions for 1100 records.
I0421 11:12:49.484330 47076539613632 run_squad_helper.py:223] Made predictions for 1200 records.
I0421 11:12:50.308649 47076539613632 run_squad_helper.py:223] Made predictions for 1300 records.
I0421 11:12:51.132554 47076539613632 run_squad_helper.py:223] Made predictions for 1400 records.
I0421 11:12:51.963085 47076539613632 run_squad_helper.py:223] Made predictions for 1500 records.
I0421 11:12:52.791450 47076539613632 run_squad_helper.py:223] Made predictions for 1600 records.
I0421 11:12:53.617588 47076539613632 run_squad_helper.py:223] Made predictions for 1700 records.
I0421 11:12:54.443922 47076539613632 run_squad_helper.py:223] Made predictions for 1800 records.
I0421 11:12:55.269884 47076539613632 run_squad_helper.py:223] Made predictions for 1900 records.
I0421 11:12:56.099067 47076539613632 run_squad_helper.py:223] Made predictions for 2000 records.
I0421 11:12:56.932780 47076539613632 run_squad_helper.py:223] Made predictions for 2100 records.
I0421 11:12:57.765176 47076539613632 run_squad_helper.py:223] Made predictions for 2200 records.
I0421 11:12:58.605224 47076539613632 run_squad_helper.py:223] Made predictions for 2300 records.
I0421 11:12:59.451265 47076539613632 run_squad_helper.py:223] Made predictions for 2400 records.
I0421 11:13:00.284780 47076539613632 run_squad_helper.py:223] Made predictions for 2500 records.
I0421 11:13:01.108484 47076539613632 run_squad_helper.py:223] Made predictions for 2600 records.
I0421 11:13:01.939439 47076539613632 run_squad_helper.py:223] Made predictions for 2700 records.
I0421 11:13:02.769769 47076539613632 run_squad_helper.py:223] Made predictions for 2800 records.
I0421 11:13:03.598085 47076539613632 run_squad_helper.py:223] Made predictions for 2900 records.
I0421 11:13:04.426424 47076539613632 run_squad_helper.py:223] Made predictions for 3000 records.
I0421 11:13:05.267606 47076539613632 run_squad_helper.py:223] Made predictions for 3100 records.
I0421 11:13:06.097973 47076539613632 run_squad_helper.py:223] Made predictions for 3200 records.
I0421 11:13:06.933673 47076539613632 run_squad_helper.py:223] Made predictions for 3300 records.
I0421 11:13:07.760985 47076539613632 run_squad_helper.py:223] Made predictions for 3400 records.
I0421 11:13:08.589337 47076539613632 run_squad_helper.py:223] Made predictions for 3500 records.
I0421 11:13:09.418296 47076539613632 run_squad_helper.py:223] Made predictions for 3600 records.
I0421 11:13:10.242460 47076539613632 run_squad_helper.py:223] Made predictions for 3700 records.
I0421 11:13:11.067596 47076539613632 run_squad_helper.py:223] Made predictions for 3800 records.
I0421 11:13:11.892354 47076539613632 run_squad_helper.py:223] Made predictions for 3900 records.
I0421 11:13:12.714057 47076539613632 run_squad_helper.py:223] Made predictions for 4000 records.
I0421 11:13:13.540582 47076539613632 run_squad_helper.py:223] Made predictions for 4100 records.
I0421 11:13:14.367324 47076539613632 run_squad_helper.py:223] Made predictions for 4200 records.
I0421 11:13:15.196892 47076539613632 run_squad_helper.py:223] Made predictions for 4300 records.
I0421 11:13:16.026397 47076539613632 run_squad_helper.py:223] Made predictions for 4400 records.
I0421 11:13:16.854767 47076539613632 run_squad_helper.py:223] Made predictions for 4500 records.
I0421 11:13:17.685170 47076539613632 run_squad_helper.py:223] Made predictions for 4600 records.
I0421 11:13:18.521788 47076539613632 run_squad_helper.py:223] Made predictions for 4700 records.
I0421 11:13:19.346223 47076539613632 run_squad_helper.py:223] Made predictions for 4800 records.
I0421 11:13:20.173502 47076539613632 run_squad_helper.py:223] Made predictions for 4900 records.
I0421 11:13:20.999221 47076539613632 run_squad_helper.py:223] Made predictions for 5000 records.
I0421 11:13:21.826658 47076539613632 run_squad_helper.py:223] Made predictions for 5100 records.
I0421 11:13:22.659233 47076539613632 run_squad_helper.py:223] Made predictions for 5200 records.
I0421 11:13:23.494912 47076539613632 run_squad_helper.py:223] Made predictions for 5300 records.
I0421 11:13:24.315006 47076539613632 run_squad_helper.py:223] Made predictions for 5400 records.
I0421 11:13:25.139277 47076539613632 run_squad_helper.py:223] Made predictions for 5500 records.
I0421 11:13:25.965684 47076539613632 run_squad_helper.py:223] Made predictions for 5600 records.
I0421 11:13:26.798784 47076539613632 run_squad_helper.py:223] Made predictions for 5700 records.
I0421 11:13:27.624611 47076539613632 run_squad_helper.py:223] Made predictions for 5800 records.
I0421 11:13:28.451828 47076539613632 run_squad_helper.py:223] Made predictions for 5900 records.
I0421 11:13:29.279164 47076539613632 run_squad_helper.py:223] Made predictions for 6000 records.
I0421 11:13:30.110749 47076539613632 run_squad_helper.py:223] Made predictions for 6100 records.
I0421 11:13:30.938165 47076539613632 run_squad_helper.py:223] Made predictions for 6200 records.
I0421 11:13:31.769063 47076539613632 run_squad_helper.py:223] Made predictions for 6300 records.
I0421 11:13:32.591205 47076539613632 run_squad_helper.py:223] Made predictions for 6400 records.
I0421 11:13:33.418792 47076539613632 run_squad_helper.py:223] Made predictions for 6500 records.
I0421 11:13:34.244592 47076539613632 run_squad_helper.py:223] Made predictions for 6600 records.
I0421 11:13:35.072892 47076539613632 run_squad_helper.py:223] Made predictions for 6700 records.
I0421 11:13:35.896670 47076539613632 run_squad_helper.py:223] Made predictions for 6800 records.
I0421 11:13:36.723522 47076539613632 run_squad_helper.py:223] Made predictions for 6900 records.
I0421 11:13:37.558058 47076539613632 run_squad_helper.py:223] Made predictions for 7000 records.
I0421 11:13:38.385153 47076539613632 run_squad_helper.py:223] Made predictions for 7100 records.
I0421 11:13:39.212961 47076539613632 run_squad_helper.py:223] Made predictions for 7200 records.
I0421 11:13:40.041708 47076539613632 run_squad_helper.py:223] Made predictions for 7300 records.
I0421 11:13:40.877595 47076539613632 run_squad_helper.py:223] Made predictions for 7400 records.
I0421 11:13:41.706880 47076539613632 run_squad_helper.py:223] Made predictions for 7500 records.
I0421 11:13:42.537755 47076539613632 run_squad_helper.py:223] Made predictions for 7600 records.
I0421 11:13:43.373473 47076539613632 run_squad_helper.py:223] Made predictions for 7700 records.
I0421 11:13:44.207040 47076539613632 run_squad_helper.py:223] Made predictions for 7800 records.
I0421 11:13:45.035125 47076539613632 run_squad_helper.py:223] Made predictions for 7900 records.
I0421 11:13:45.871198 47076539613632 run_squad_helper.py:223] Made predictions for 8000 records.
I0421 11:13:46.698966 47076539613632 run_squad_helper.py:223] Made predictions for 8100 records.
I0421 11:13:47.522164 47076539613632 run_squad_helper.py:223] Made predictions for 8200 records.
I0421 11:13:48.348444 47076539613632 run_squad_helper.py:223] Made predictions for 8300 records.
I0421 11:13:49.168892 47076539613632 run_squad_helper.py:223] Made predictions for 8400 records.
I0421 11:13:50.002065 47076539613632 run_squad_helper.py:223] Made predictions for 8500 records.
I0421 11:13:50.832612 47076539613632 run_squad_helper.py:223] Made predictions for 8600 records.
I0421 11:13:51.662652 47076539613632 run_squad_helper.py:223] Made predictions for 8700 records.
I0421 11:13:52.485176 47076539613632 run_squad_helper.py:223] Made predictions for 8800 records.
I0421 11:13:53.316275 47076539613632 run_squad_helper.py:223] Made predictions for 8900 records.
I0421 11:13:54.142509 47076539613632 run_squad_helper.py:223] Made predictions for 9000 records.
I0421 11:13:54.966708 47076539613632 run_squad_helper.py:223] Made predictions for 9100 records.
I0421 11:13:55.790188 47076539613632 run_squad_helper.py:223] Made predictions for 9200 records.
I0421 11:13:56.615890 47076539613632 run_squad_helper.py:223] Made predictions for 9300 records.
I0421 11:13:57.445017 47076539613632 run_squad_helper.py:223] Made predictions for 9400 records.
I0421 11:13:58.268332 47076539613632 run_squad_helper.py:223] Made predictions for 9500 records.
I0421 11:13:59.093684 47076539613632 run_squad_helper.py:223] Made predictions for 9600 records.
I0421 11:13:59.918689 47076539613632 run_squad_helper.py:223] Made predictions for 9700 records.
I0421 11:14:00.746421 47076539613632 run_squad_helper.py:223] Made predictions for 9800 records.
I0421 11:14:01.570352 47076539613632 run_squad_helper.py:223] Made predictions for 9900 records.
I0421 11:14:02.394983 47076539613632 run_squad_helper.py:223] Made predictions for 10000 records.
I0421 11:14:03.235595 47076539613632 run_squad_helper.py:223] Made predictions for 10100 records.
I0421 11:14:04.057467 47076539613632 run_squad_helper.py:223] Made predictions for 10200 records.
I0421 11:14:04.891264 47076539613632 run_squad_helper.py:223] Made predictions for 10300 records.
I0421 11:14:05.720426 47076539613632 run_squad_helper.py:223] Made predictions for 10400 records.
I0421 11:14:06.553259 47076539613632 run_squad_helper.py:223] Made predictions for 10500 records.
I0421 11:14:07.380370 47076539613632 run_squad_helper.py:223] Made predictions for 10600 records.
I0421 11:14:08.208481 47076539613632 run_squad_helper.py:223] Made predictions for 10700 records.
I0421 11:14:09.038475 47076539613632 run_squad_helper.py:223] Made predictions for 10800 records.
I0421 11:14:09.867308 47076539613632 run_squad_helper.py:223] Made predictions for 10900 records.
I0421 11:15:08.630571 47076539613632 run_squad_helper.py:374] Writing predictions to: /public/home/xuanbaby/DL-TensorFlow/models_r2.3.0/official/nlp/bert/model_squad_v2/predictions.json
I0421 11:15:08.630922 47076539613632 run_squad_helper.py:375] Writing nbest to: /public/home/xuanbaby/DL-TensorFlow/models_r2.3.0/official/nlp/bert/model_squad_v2/nbest_predictions.json
I0421 11:15:13.943372 47076539613632 run_squad_xuan.py:147] SQuAD eval F1-score: 0.853577
PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU') memory growth: True
PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU') memory growth: True
PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU') memory growth: True
PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU') memory growth: True