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PiperOrigin-RevId: 287370879

PiperOrigin-RevId: 286171892

PiperOrigin-RevId: 276210315

The old infer_float32_policies policy will be removed from TensorFlow soon.

To test convergence, I ran the Resnet50KerasAccuracy.benchmark_8_gpu_fp16 benchmark. I got an accuracy of 0.76037 and an exp_per_second of 6908.

PiperOrigin-RevId: 266191126

PiperOrigin-RevId: 264647492

Moving Keras ResNet models to `official/vision/image_classification` and benchmarks to `official/benchmark`.

PiperOrigin-RevId: 264268533

* Enable CuDNN BatchNorm spatial persistent by default; Remove 2nd zero padding layer

* Apply scale=False and fused=True consistently to BatchNorm layers

* Undo remove padding layer

* Replace zero padding with padding attribute in max pooling for better performance

* Resolve comments

* Revert "Replace zero padding with padding attribute in max pooling for better performance"

This reverts commit ad49db057c800ecac008eec1057005bd2c08ac73.

* Revert "Revert " Ensure static shapes when enabling XLA in Resnet Keras model (#6508)" (#6517)"

This reverts commit cc9eef76.

* Set `batch_size` to keras.Input in non-eager mode.

Eager mode currently has OOM problem.

* Add comments for enable_eager flag.

* Always set drop_remainder=True.

* Only set drop_remainder=True for XLA.

Reason: break 1-gpu nightly test.

This reverts commit 371645fc.

* Update resnet_model.py

* Ensure static shapes when enabling XLA.

* Define `drop_remainder` as a variable.

* Handles per_replica_batch_size in non-XLA mode

* Remove trailing whitespace.

* Mixed precision support

* Add TODOs

* Set use_bias=False for conv layers in Keras ResNet model

* Removed bias regularizer from Conv2D layers (which should have no effect after bias is removed)

* Setting default data format based on available devices.

* Handle data format in Keras ResNet model properly for better performance on GPU; Compile only the training graph when skip_eval flag is True

* Added data format fix to Keras Cifar model; Removed unnecessary import

* Add a comment to the skip_eval flag per Priya's request