Upgrading repo to TF2

.flake8

@@ -0,0 +1,3 @@
+[flake8]
+ignore = E203, E266, E501, W503, F403, F401
+max-line-length = 88

.pre-commit-config.yaml

@@ -0,0 +1,11 @@
+repos:
+-   repo: https://github.com/ambv/black
+    rev: 21.6b0
+    hooks:
+    - id: black
+      language_version: python3.9
+-   repo: https://gitlab.com/pycqa/flake8
+    rev: 3.9.2
+    hooks:
+    - id: flake8
+

Makefile

@@ -0,0 +1,24 @@
+all: install
+
+install: venv
+	: # Activate venv and install requirements
+	mkdir tmp
+	source venv/bin/activate && TMPDIR=tmp pip install -r requirements.txt
+	rm -r tmp/
+	pre-commit install
+
+venv:
+	: # Create venv if it doesn't exist
+	: # test -d venv || virtualenv -p python3 --no-site-packages venv
+	test -d venv || python -m venv venv
+
+test: venv
+	source venv/bin/activate && python -m pytest
+
+clean:
+	rm -rf venv/
+	find -iname "*.pyc" -delete
+	rm -rf logs/
+	rm -rf .pytest_cache
+	rm -rf tmp/
+

README.md

@@ -36,6 +36,23 @@ architecture.
 
 ![DNC architecture](images/dnc_model.png)
 
+## Installation
+```shell
+make install
+```
+
+The above command will create a virtual environment and install the dependencies and pre-commit hooks.
+
+Run `source venv/bin/activate` in the root directory of this repository to activate the installed virtual env.
+
+## Testing
+```shell
+make test
+```
+
+Run unit tests in `tests/` using pytest.
+
+
 ## Train
 The `DNC` requires an installation of [TensorFlow](https://www.tensorflow.org/)
 and [Sonnet](https://github.com/deepmind/sonnet). An example training script is
@@ -59,13 +76,26 @@ $ ipython train.py -- --memory_size=64 --num_bits=8 --max_length=3
 Periodically saving, or 'checkpointing', the model is disabled by default. To
 enable, use the `checkpoint_interval` flag. E.g. `--checkpoint_interval=10000`
 will ensure a checkpoint is created every `10,000` steps. The model will be
-checkpointed to `/tmp/tf/dnc/` by default. From there training can be resumed.
-To specify an alternate checkpoint directory, use the `checkpoint_dir` flag.
-Note: ensure that `/tmp/tf/dnc/` is deleted before training is resumed with
+checkpointed to `./logs/repeat_copy/checkpoint` by default. From there training can be resumed.
+To specify an alternate checkpoint directory, use the `log_dir` flag.
+Note: ensure that existing checkpoints are deleted or moved before training is resumed with
 different model parameters, to avoid shape inconsistency errors.
 
 More generally, the `DNC` class found within `dnc.py` can be used as a standard
 TensorFlow rnn core and unrolled with TensorFlow rnn ops, such as
-`tf.nn.dynamic_rnn` on any sequential task.
+`keras.layers.RNN` on any sequential task.
+
+## Model Inspection
+```shell
+jupyter notebook interactive.ipynb
+```
+
+Jupyter notebook that loads a trained model from checkpoints. It provides helper functions for evaluating arbitrary input bit sequences and visualizing output and intermediate read/write states.
+
+```shell
+tensorboard --logdir logs/repeat_copy/
+```
+
+Tensorboard visualization of test/train loss and TensorFlow Graph. Test/Train loss is emitted based on `report_interval`.
 
 Disclaimer: This is not an official Google product