Embedding layer
Turns positive integers (indexes) into dense vectors of fixed size.
Embedding(input_dim, output_dim, embeddings_initializer = "uniform", embeddings_regularizer = NULL, embeddings_constraint = NULL, mask_zero = FALSE, input_length = NULL, input_shape = NULL)
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 |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other layers: Activation,
ActivityRegularization,
AdvancedActivation,
BatchNormalization, Conv,
Dense, Dropout,
Flatten, GaussianNoise,
LayerWrapper,
LocallyConnected, Masking,
MaxPooling, Permute,
RNN, RepeatVector,
Reshape, Sequential
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)
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