BatchNormalization: Batch normalization layer
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Author(s) References See Also Examples

Batch normalization layer

BatchNormalization(axis = -1, momentum = 0.99, epsilon = 0.001,
  center = TRUE, scale = TRUE, beta_initializer = "zeros",
  gamma_initializer = "ones", moving_mean_initializer = "zeros",
  moving_variance_initializer = "ones", beta_regularizer = NULL,
  gamma_regularizer = NULL, beta_constraint = NULL,
  gamma_constraint = NULL, input_shape = NULL)

`axis`	Integer, the axis that should be normalized (typically the features axis).
`momentum`	Momentum for the moving average.
`epsilon`	Small float added to variance to avoid dividing by zero.
`center`	If True, add offset of beta to normalized tensor. If False, beta is ignored.
`scale`	If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer.
`beta_initializer`	Initializer for the beta weight.
`gamma_initializer`	Initializer for the gamma weight.
`moving_mean_initializer`	Initializer for the moving mean.
`moving_variance_initializer`	Initializer for the moving variance.
`beta_regularizer`	Optional regularizer for the beta weight.
`gamma_regularizer`	Optional regularizer for the gamma weight.
`beta_constraint`	Optional constraint for the beta weight.
`gamma_constraint`	Optional constraint for the gamma weight.
`input_shape`	only need when first layer of a model; sets the input shape of the data

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

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

Other layers: Activation, ActivityRegularization, AdvancedActivation, Conv, Dense, Dropout, Embedding, Flatten, GaussianNoise, LayerWrapper, LocallyConnected, Masking, MaxPooling, Permute, RNN, RepeatVector, Reshape, Sequential

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(Dropout(rate = 0.5))
  mod$add(Activation("relu"))
  mod$add(BatchNormalization())
  mod$add(Dense(units = 3))
  mod$add(ActivityRegularization(l1 = 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, validation_split = 0.2)
}