Apply penalties on layer parameters
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.
l1(l = 0.01) l2(l = 0.01) l1_l2(l1 = 0.01, l2 = 0.01)
l |
multiplicitive factor to apply to the the penalty term |
l1 |
multiplicitive factor to apply to the the l1 penalty term |
l2 |
multiplicitive factor to apply to the the l2 penalty term |
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$add(Dense(units = 3, kernel_regularizer = l1(l = 0.05),
bias_regularizer = l2(l = 0.05)))
mod$add(Dense(units = 3, kernel_regularizer = l1_l2(l1 = 0.05, l2 = 0.1)))
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)
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