Constraints: Apply penalties on layer parameters
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Details Author(s) References Examples

Regularizers allow to apply penalties on layer parameters or layer activity during optimization. These penalties are incorporated in the loss function that the network optimizes.

max_norm(max_value = 2, axis = 0)

non_neg()

unit_norm()

`max_value`	maximum value to allow for the value (max_norm only)
`axis`	axis over which to apply constraint (max_norm only)

The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers Dense, Conv1D, Conv2D and Conv3D have a unified API.

Taylor B. Arnold, taylor.arnold@acm.org

Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.

if(keras_available()) {
  X_train <- matrix(rnorm(100 * 10), nrow = 100)
  Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)
  
  mod <- Sequential()
  mod$add(Dense(units = 50, input_shape = dim(X_train)[2]))
  mod$add(Activation("relu"))
  mod$add(Dense(units = 3, kernel_constraint = max_norm(),
                bias_constraint = non_neg()))
  mod$add(Dense(units = 3, kernel_constraint = unit_norm()))
  mod$add(Activation("softmax"))
  keras_compile(mod,  loss = 'categorical_crossentropy', optimizer = RMSprop())
  
  keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5, verbose = 0)
}