Triplet Neural Network for Keras

This project provides a lightweight, easy to use and flexible triplet neural network module for use with the Keras framework.

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

An example of the triplet network module being used to produce a noteworthy 99.88% validation performance on the MNIST dataset with no data augmentation and minimal modification from the Keras example is provided.

Installation

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/triplet

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

To run examples

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_example.py

Usage

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 triplet import TripletNetwork

• Load or generate some data.

x_train = np.random.rand(100, 3)
y_train = np.random.randint(num_classes, size=100)

x_test = np.random.rand(30, 3)
y_test = np.random.randint(num_classes, size=30)

• Design a base model

def create_base_model(input_shape):
    model_input = Input(shape=input_shape)
    embedding = Flatten()(embedding)
    embedding = Dense(128)(embedding)
    return Model(model_input, embedding)

• Design a head model

def create_head_model(embedding_shape):
    embedding_a = Input(shape=embedding_shape)
    embedding_b = Input(shape=embedding_shape)
    embedding_c = Input(shape=embedding_shape)
    
    head = Concatenate()([embedding_a, embedding_b, embedding_c])
    head = Dense(4)(head)
    head = BatchNormalization()(head)
    head = Activation(activation='sigmoid')(head)

    head = Dense(1)(head)
    head = BatchNormalization()(head)
    head = Activation(activation='sigmoid')(head)

    return Model([embedding_a, embedding_b, embedding_c], head)

• Create an instance of the TripletNetwork class

base_model = create_base_model(input_shape)
head_model = create_head_model(base_model.output_shape)
triplet_network = TripletNetwork(base_model, head_model, num_classes)

• Compile the model

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

• Train the model

triplet_network.fit(x_train, y_train,
                    validation_data=(x_test, y_test),
                    batch_size=64,
                    epochs=epochs)

Development Environment

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_triplet.py

Development container

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