CntkCatalyst is an ongoing effort to learn about the Python Keras API, while trying to reproduce the functionality in a CNTK based C# API. The approach is to reproduce the Keras examples from the book/notebook Deep Learning With Python, and use these as a guide to build the API.

The CntkCatalyst API will not be a one-to-one mapping of the Keras API, but will use Keras as inspiration, and make changes where it makes sense. CntkCatalyst will be created on top of Microsoft C# API for CNTK, with all the limitations that this presents.

Many of the Keras examples have already been reproduced using C# CNTK here. However, these are implemented using the raw CNTK C# API directly, which does not provide a very desirable user experience.

Hopefully, when the end-goal has been reached, The CntkCatalyst API will provide a deep learning experience in C#, very close to what you get with Keras in Python.

Since this is an ongoing effort, the API under development can be expected to change a lot, and should not be considered as production ready.

Below, an example from the Keras python API is reproduced using the current CntkCatalyst C# API:

# Create the network and model
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))

# Compile the network with the selected learner, loss and metric.
network.compile(optimizer='rmsprop',
                loss='categorical_crossentropy',
                metrics=['accuracy'])

# Train the model using the training set.
network.fit(train_images, train_labels, epochs=5, batch_size=128)

# Evaluate the model using the test set.
test_loss, test_acc = network.evaluate(test_images, test_labels)

# Save the model
model.save('mnist.h5')

// Create the network.
var network = Layers.Input(inputShape, dataType)
    .Dense(512, weightInit(), biasInit, device, dataType)
    .ReLU()
    .Dense(10, weightInit(), biasInit, device, dataType)
    .Softmax();

// setup loss and metric.
var lossFunc = Losses.CategoricalCrossEntropy(network.Output, targetVariable);
var metricFunc = Metrics.Accuracy(network.Output, targetVariable);

// setup trainer.
var learner = Learners.RMSProp(network.Parameters());
var trainer = Trainer.CreateTrainer(network, lossFunc, metricFunc, new List<Learner> { learner });

// Create the model.
var model = new Model(trainer, network, dataType, device);

// Train the model using the training set.
model.Fit(trainingSource, epochs: 5, batchSize: 128);

// Evaluate the model using the test set.
var (loss, metric) = model.Evaluate(testSource);

// Predict the test set using the model.
var predictions = model.Predict(testSource);  

// Save model.
model.Network.Save("mnist.cntk");

The solution file contains 3 project:

• CntkCatalyst: Main project containing the CntkCatalyst API.
• CntkCatalyst.Test: Test project for the CntkCatalyst API.
• CntkCatalyst.Examples: Examples project for implementing the Keras examples for guiding the API development.

Initial code was started in a branch of SharpLearning: sharplearning-neural-cntk.

Contributions are welcome in the following areas:

1. Add new issues with bug descriptions or feature suggestions.
2. Implement additional functionality in CntkCatalyst, by creating a pull request.
3. Implement additional examples in CntkCatalyst.Examples, by creating a pull request.

When contributing, please follow the contribution guide.