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+# RevNet with TensorFlow eager execution
+
+This folder contains an 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.
+
+##  Content
+
+- `revnet.py`: The RevNet model.
+- `blocks.py`: The relevant reversible blocks.
+- `cifar_tfrecords.py`: Script to generate the TFRecords for both CIFAR-10 and CIFAR-100.
+- `cifar_input.py`: Script to read from TFRecords and generate dataset objects with the `tf.data` API.
+- `config.py`: Configuration file for network architectures and training hyperparameters.
+- `main.py`: Main training and evaluation script.
+- `ops.py`: Auxiliary downsampling operation.
+
+## To run
+- Make sure you have installed TensorFlow 1.9+ or the latest `tf-nightly`
+or `tf-nightly-gpu` pip package in order to access the eager execution feature.
+
+- First run
+
+```bash
+python cifar_tfrecords.py --data_dir ${PWD}/cifar
+```
+to download the cifar dataset and convert them
+to TFRecords. This produces TFRecord files for both CIFAR-10 and CIFAR-100.
+
+- To train a model run
+
+```bash
+python main.py --data_dir ${PWD}/cifar
+```
+
+- Optional arguments for `main.py` include
+  - `train_dir`: Directory to store eventfiles and checkpoints.
+  - `restore`: Restore the latest checkpoint.
+  - `validate`: Use validation set for training monitoring.
+  - `manual_grad`: Use the manually defined gradient map given by the authors.
+  - `dataset`: Use either `cifar-10` or `cifar-100`
+
+## Performance
+- With the current implementation, RevNet-38 achieves >92% on CIFAR-10 and >71% on CIFAR-100.
+
+## Reference
+The Reversible Residual Network: Backpropagation Without Storing Activations.
+Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse. Neural Information Processing Systems (NIPS), 2017.