This project provides a lightweight, easy to use and flexible auto-encoder module for use with the Keras framework.

Auto-encoders are used to generate embeddings that describe inter and extra class relationships. This makes auto-encoders like many other similarity learning algorithms suitable as a pre-training step for many classification problems.

An example of the auto-encoder module being used to produce a noteworthy 99.84% validation performance on the MNIST dataset with no data augmentation and minimal modification from the Keras example is provided.

Create and activate a virtual environment for the project.

$ virtualenv env
$ source env/bin/activate

To install the module directly from GitHub:

$ pip install git+https://github.com/aspamers/autoencoder

The module will install keras and numpy but no back-end (like tensorflow). This is deliberate since it leaves the module decoupled from any back-end and gives you a chance to install whatever version you prefer.

To install tensorflow:

$ pip install tensorflow

To install tensorflow with gpu support:

$ pip install tensorflow-gpu

With the activated virtual environment with the installed python package run the following commands.

To run the mnist baseline example:

$ python examples/mnist_example.py

To run the mnist siamese pretrained example:

$ python examples/mnist_autoencoder_example.py

For detailed usage examples please refer to the examples and unit test modules. If the instructions are not sufficient feel free to make a request for improvements.

• Import the module

from autoencoder import AutoEncoder

• Load or generate some data.

x_train = np.random.rand(100, 3)
x_test = np.random.rand(30, 3)

• Design an encoder model

def create_encoder_model(input_shape):
    model_input = Input(shape=input_shape)

    encoder = Dense(4)(model_input)
    encoder = BatchNormalization()(encoder)
    encoder = Activation(activation='relu')(encoder)

    return Model(model_input, encoder)

• Design a decoder model

    def create_decoder_model(embedding_shape):
        embedding_a = Input(shape=embedding_shape)

        decoder = Dense(3)(embedding_a)
        decoder = BatchNormalization()(decoder)
        decoder = Activation(activation='relu')(decoder)

        return Model(embedding_a, decoder)

• Create an instance of the AutoEncoder class

encoder_model = create_encoder_model(input_shape)
decoder_model = create_decoder_model(encoder_model.output_shape)
autoencoder = AutoEncoder(encoder_model, decoder_model)

• Compile the model

autoencoder.compile(loss='binary_crossentropy', optimizer=keras.optimizers.adam())

• Train the model

autoencoder.fit(x_train, x_train,
                validation_data=(x_test, x_test),
                epochs=epochs)

Create and activate a test virtual environment for the project.

$ virtualenv env
$ source env/bin/activate

Install requirements

$ pip install -r requirements.txt

Install the backend of your choice.

$ pip install tensorflow

Run tests

$ pytest tests/test_autoencoder.py

To set up the vscode development container follow the instructions at the link provided: https://github.com/aspamers/vscode-devcontainer

You will also need to install the nvidia docker gpu passthrough layer: https://github.com/NVIDIA/nvidia-docker