Add a README for ResNet keras (#6293)

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official/resnet/keras/README.md

+This folder contains the Keras implementation of the ResNet models. For more 
+information about the models, please refer to this [README file](../README.md).
+
+Similar to the [estimator implementation](/official/resnet), the Keras 
+implementation has code for both CIFAR-10 data and ImageNet data. The CIFAR-10
+version uses a ResNet56 model implemented in 
+[`resnet_cifar_model.py`](./resnet_cifar_model.py), and the ImageNet version 
+uses a ResNet50 model implemented in [`resnet_model.py`](./resnet_model.py).
+
+To use 
+either dataset, make sure that you have the latest version of TensorFlow 
+installed and 
+[add the models folder to your Python path](/official/#running-the-models),
+otherwise you may encounter an error like `ImportError: No module named 
+official.resnet`.
+
+## CIFAR-10
+
+Download and extract the CIFAR-10 data. You can use the following script:
+```bash
+python cifar10_download_and_extract.py
+```
+
+After you download the data, you can run the program by:
+
+```bash
+python keras_cifar_main.py
+```
+
+If you did not use the default directory to download the data, specify the 
+location with the `--data_dir` flag, like:
+
+```bash
+python keras_cifar_main.py --data_dir=/path/to/cifar
+```
+
+## ImageNet
+
+Download the ImageNet dataset and convert it to TFRecord format. 
+The following [script](https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py)
+and [README](https://github.com/tensorflow/tpu/tree/master/tools/datasets#imagenet_to_gcspy)
+provide a few options.
+
+Once your dataset is ready, you can begin training the model as follows:
+
+```bash
+python keras_imagenet_main.py 
+```
+
+Again, if you did not download the data to the default directory, specify the
+location with the `--data_dir` flag:
+
+```bash
+python keras_imagenet_main.py --data_dir=/path/to/imagenet
+```
+
+There are more flag options you can specify. Here are some examples:
+
+- `--use_synthetic_data`: when set to true, synthetic data, rather than real 
+data, are used;
+- `--batch_size`: the batch size used for the model;
+- `--model_dir`: the directory to save the model checkpoint;
+- `--train_epochs`: number of epoches to run for training the model;
+- `--train_steps`: number of steps to run for training the model. We now only
+support a number that is smaller than the number of batches in an epoch.
+- `--skip_eval`: when set to true, evaluation as well as validation during 
+training is skipped
+
+For example, this is a typical command line to run with ImageNet data with 
+batch size 128 per GPU:
+
+```bash
+python -m keras_imagenet_main \
+--model_dir=/tmp/model_dir/something \
+--num_gpus=2 \
+--batch_size=128 \
+--train_epochs=90 \
+--train_steps=10 \
+--use_synthetic_data=false
+```
+
+See [`keras_common.py`](keras_common.py) for full list of options.
+
+## Using multiple GPUs
+You can train these models on multiple GPUs using `tf.distribute.Strategy` API. 
+You can read more about them in this 
+[guide](https://www.tensorflow.org/guide/distribute_strategy).
+
+In this example, we have made it easier to use is with just a command line flag 
+`--num_gpus`. By default this flag is 1 if TensorFlow is compiled with CUDA, 
+and 0 otherwise.
+
+- --num_gpus=0: Uses tf.distribute.OneDeviceStrategy with CPU as the device.
+- --num_gpus=1: Uses tf.distribute.OneDeviceStrategy with GPU as the device.
+- --num_gpus=2+: Uses tf.distribute.MirroredStrategy to run synchronous 
+distributed training across the GPUs.
+
+If you wish to run without `tf.distribute.Strategy`, you can do so by setting 
+`--distribution_strategy=off`.
+