Remove some fp16 examples that don't converge (#45)

* Remove some fp16 examples that don't converge

Default static loss scale of 1.0 (default value) for resnet50 doesn't converge. Either remove example or put static loss scale 128 on it, which is known to converge well.

* Update README.md

examples/imagenet/README.md

@@ -50,15 +50,13 @@ $ ln -sf /data/imagenet/train-jpeg/ train
 ### Softlink validation dataset into current directory
 $ ln -sf /data/imagenet/val-jpeg/ val
 ### Single-process training
-$ python main.py -a resnet50 --fp16 --b 256 --workers 4 ./
-### Multi-process training (uses all visible GPU on the node)
-$ python -m torch.distributed.launch --nproc_per_node=NUM_GPUS main.py -a resnet50 --fp16 --b 256 --workers 4 ./
+$ python main.py -a resnet50 --fp16 --b 256 --workers 4 --static-loss-scale 128.0 ./
+### Multi-process training (uses all visible GPUs on the node)
+$ python -m torch.distributed.launch --nproc_per_node=NUM_GPUS main.py -a resnet50 --fp16 --b 256 --workers 4 --static-loss-scale 128.0 ./
 ### Multi-process training on GPUs 0 and 1 only
 $ export CUDA_VISIBLE_DEVICES=0,1
 $ python -m torch.distributed.launch --nproc_per_node=2 main.py -a resnet50 --fp16 --b 256 --workers 4 ./
-### Multi-process training with FP16_Optimizer, default loss scale 1.0 (still uses FP32 master params)
-$ python -m torch.distributed.launch --nproc_per_node=NUM_GPUS main_fp16_optimizer.py -a resnet50 --fp16 --b 256 --workers 4 ./
-# Multi-process training with FP16_Optimizer, static loss scale
+### Multi-process training with FP16_Optimizer, static loss scale 128.0 (still uses FP32 master params)
 $ python -m torch.distributed.launch --nproc_per_node=NUM_GPUS main_fp16_optimizer.py -a resnet50 --fp16 --b 256 --static-loss-scale 128.0 --workers 4 ./
 ### Multi-process training with FP16_Optimizer, dynamic loss scaling
 $ python -m torch.distributed.launch --nproc_per_node=NUM_GPUS main_fp16_optimizer.py -a resnet50 --fp16 --b 256 --dynamic-loss-scale --workers 4 ./