README.md

Keras SVM

Purpose

Provides a wrapper class that effectively replaces the softmax of your Keras model with a SVM.

The SVM has no impact on the training of the Neural Network, but replacing softmax with an SVM has been shown to perform better on unseen data.

Code examples

Example construction

# Build a classical model
def build_model():
  model = models.Sequential()
  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.Flatten(name="intermediate_output"))
  model.add(layers.Dense(64, activation='relu'))
  model.add(layers.Dense(10, activation='softmax'))
  
  # The extra metric is important for the evaluate function
  model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
  return model

# Wrap it in the ModelSVMWrapper
wrapper = ModelSVMWrapper(build_model())

Training while maintaining an accuracy score

accuracy = {
    "with_svm": [],
    "without_svm": []
}

epochs = 10
for i in range(epochs):
  print('Starting run: {}'.format(i))
  wrapper.fit(train_images, train_labels, epochs=1, batch_size=64)
  accuracy["with_svm"].append(wrapper.evaluate(test_images, test_labels))
  accuracy["without_svm"].append(
      wrapper.model.evaluate(test_images, to_categorical(test_labels))[1])