Commit 5cf005fd authored by Hongkun Yu's avatar Hongkun Yu Committed by A. Unique TensorFlower
Browse files

Internal change

PiperOrigin-RevId: 304805298
parent 6f574922
......@@ -180,7 +180,8 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
def benchmark_8_gpu(self):
"""Tests Keras model with eager, dist_strat and 8 GPUs."""
self._run_benchmark(
self._setup()
self._run_and_report_benchmark(
experiment_name='benchmark_8_gpu',
num_gpus=8,
per_replica_batch_size=128,
......@@ -191,7 +192,8 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
def benchmark_8_gpu_fp16(self):
"""Tests Keras model with eager, dist_strat, 8 GPUs, and fp16."""
self._run_benchmark(
self._setup()
self._run_and_report_benchmark(
experiment_name='benchmark_8_gpu_fp16',
num_gpus=8,
per_replica_batch_size=256,
......@@ -202,7 +204,8 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
def benchmark_xla_8_gpu_fp16(self):
"""Tests Keras model with XLA, eager, dist_strat, 8 GPUs and fp16."""
self._run_benchmark(
self._setup()
self._run_and_report_benchmark(
experiment_name='benchmark_xla_8_gpu_fp16',
num_gpus=8,
per_replica_batch_size=256,
......@@ -214,7 +217,8 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
def benchmark_xla_8_gpu_fp16_dynamic(self):
"""Tests Keras model with XLA, eager, dist_strat, 8 GPUs, dynamic fp16."""
self._run_benchmark(
self._setup()
self._run_and_report_benchmark(
experiment_name='benchmark_xla_8_gpu_fp16_dynamic',
top_1_min=0.736,
num_gpus=8,
......
......@@ -340,8 +340,7 @@ class UtilTests(parameterized.TestCase, tf.test.TestCase):
dataset_num_private_threads=1,
),
train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
model=base_configs.ModelConfig(
loss=base_configs.LossConfig(loss_scale='dynamic')),
model=base_configs.ModelConfig(),
)
class EmptyClass:
......
......@@ -117,13 +117,11 @@ class LossConfig(base_config.Config):
Attributes:
name: The name of the loss. Defaults to None.
loss_scale: The type of loss scale
label_smoothing: Whether or not to apply label smoothing to the loss. This
only applies to 'categorical_cross_entropy'.
"""
name: str = None
loss_scale: str = None
label_smoothing: float = None
......
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