message: Update readme regarding TensorFlow verion requirement.
diff: # RevNet with TensorFlow eager execution
  -This folder contains a TensorFlow eager implementation of the [Reversible Residual Network](https://arxiv.org/pdf/1707.04585.pdf) adapted from the released implementation by the authors. The presented implementation can be ran both in eager and graph mode. The code is considerably simplified with `tf.GradientTape`. Moreover, we reduce the step of reconstructing the outputs. This saves us from using `tf.stop_gradient` and makes the model run faster.
  +This folder contains a TensorFlow eager implementation of the [Reversible Residual Network](https://arxiv.org/pdf/1707.04585.pdf) adapted from the released implementation by the authors. The presented implementation can be ran with both eager and graph execution. The code is considerably simplified with `tf.GradientTape`. Moreover, we reduce the a redundant forward pass in the implementation by the authors. This saves us from using `tf.stop_gradient` and makes the model run faster.
  ## Content
  @@ -16,7 +16,7 @@ This folder contains a TensorFlow eager implementation of the [Reversible Residu
  - `resnet_preprocessing.py`, `imagenet_input.py`: Boilerplate to read ImageNet data from TFRecords.
  ## Train on CIFAR-10/CIFAR-100
  -- Make sure you have installed TensorFlow 1.9+ or the latest `tf-nightly`
  +- Make sure you have installed TensorFlow 1.10+ or the latest `tf-nightly`
  or `tf-nightly-gpu` pip package in order to access the eager execution feature.
  - First run
  @@ -41,11 +41,13 @@ python main.py --data_dir ${PWD}/cifar
  - `config`: RevNet configuration.
  - `use_defun`: Use `tfe.defun` to boost performance.
  -- To train a model with estimators in graph-mode, run
  +- To train a model with estimators in graph execution, run
  ```bash
  python main_estimator.py --data_dir ${PWD}/cifar
  ```
  +To ensure our code works properly when using the Keras model in an estimator,
  +`tf-nightly` or `tf-nightly-gpu` is highly recommended as of August 2018.
  - Optional arguments for `main.py` include
  - `model_dir`: Directory to store eventfiles and checkpoints.
  @@ -54,13 +56,19 @@ python main_estimator.py --data_dir ${PWD}/cifar
  - `export`: Export the model for serving if True.
  ## Speed up with `tfe.defun`
  -Even though the speed difference between pure eager execution and graph-mode execution is noticeable,
  -the difference between fully "defunned" model training and graph-mode
  +To ensure that `tf.contrib.eager.defun` in our code works properly with all
  +part of the model during training, the latest `tf-nightly` or `tf-nightly-gpu`
  +is highly recommended as of August 2018.
  +
  +Even though the speed difference between pure eager execution and graph execution is noticeable,
  +the difference between fully "defunned" model training and graph
  training is negligible.
  ## Train on ImageNet with Cloud TPUs
  -The standard way to train models on Cloud TPUs is via TPU estimators and graph-mode
  +The standard way to train models on Cloud TPUs is via TPU estimators and graph
  execution. Models built with the `tf.keras` API are fully compatible with TPU estimators.
  +To ensure our code works properly in this setting,
  +`tf-nightly` or `tf-nightly-gpu` is highly recommended as of August 2018.
  ### Setup a Google Cloud project
  @@ -96,7 +104,8 @@ python main_estimator_tpu.py \
  ```
  ## Performance
  -- With the current implementation, RevNet-38 achieves >92% on CIFAR-10 and >71% on CIFAR-100.
  +- RevNet-38 achieves >92% and >71% accuracy on CIFAR-10 and CIFAR-100 respectively.
  +- RevNet-56 achieves <26% top-1 error rate on ImageNet.
  ## Reference
  The Reversible Residual Network: Backpropagation Without Storing Activations.
  