Embedding: Embedding layer

In kerasR: R Interface to the Keras Deep Learning Library

Embedding layer

Description

Turns positive integers (indexes) into dense vectors of fixed size.

Usage

Embedding(input_dim, output_dim, embeddings_initializer = "uniform",
  embeddings_regularizer = NULL, embeddings_constraint = NULL,
  mask_zero = FALSE, input_length = NULL, input_shape = NULL)

Arguments

input_dim	int > 0. Size of the vocabulary, ie. 1 + maximum integer index occurring in the input data.
output_dim	int >= 0. Dimension of the dense embedding.
embeddings_initializer	Initializer for the embeddings matrix
embeddings_regularizer	Regularizer function applied to the embeddings matrix
embeddings_constraint	Constraint function applied to the embeddings matrix
mask_zero	Whether or not the input value 0 is a special "padding" value that should be masked out.
input_length	Length of input sequences, when it is constant.
input_shape	only need when first layer of a model; sets the input shape of the data

Author(s)

Taylor B. Arnold, [e-mail]

References

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

See Also

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

Examples

if(keras_available()) {
  X_train <- matrix(sample(0:19, 100 * 100, TRUE), ncol = 100)
  Y_train <- rnorm(100)
  
  mod <- Sequential()
  mod$add(Embedding(input_dim = 20, output_dim = 10,
                    input_length = 100))
  mod$add(Dropout(0.5))
  
  mod$add(GRU(16))
  mod$add(Dense(1))
  mod$add(Activation("sigmoid"))
  
  keras_compile(mod, loss = "mse", optimizer = RMSprop())
  keras_fit(mod, X_train, Y_train, epochs = 3, verbose = 0)
}

kerasR documentation built on Aug. 17, 2022, 5:06 p.m.