R/layers.embeddings.R

In kerasR: R Interface to the Keras Deep Learning Library

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

#' @example inst/examples/embeddings.R
#' @template boilerplate
#' @export
#' @family layers
Embedding <- function(input_dim,
                      output_dim,
                      embeddings_initializer = 'uniform',
                      embeddings_regularizer = NULL,
                      embeddings_constraint = NULL,
                      mask_zero = FALSE,
                      input_length = NULL,
                      input_shape = NULL) {

  keras_check()

  if (!is.null(input_length))
    input_length <- int32(input_length)

  # Need special logic for input_shape because it is passed
  # via kwargs and needs to be manually adjusted
  if (is.null(input_shape)) {
    res <- modules$keras.layers.embeddings$Embedding(
                input_dim = int32(input_dim),
                output_dim = int32(output_dim),
                embeddings_initializer = embeddings_initializer,
                embeddings_regularizer = embeddings_regularizer,
                embeddings_constraint = embeddings_constraint,
                mask_zero = mask_zero)
  } else {

    input_shape <- as.list(input_shape)
    input_shape <- modules$builtin$tuple(int32(input_shape))

    res <- modules$keras.layers.embeddings$Embedding(
                input_dim = int32(input_dim),
                output_dim = int32(output_dim),
                embeddings_initializer = embeddings_initializer,
                embeddings_regularizer = embeddings_regularizer,
                embeddings_constraint = embeddings_constraint,
                mask_zero = mask_zero,
                input_shape = input_shape)

  }

  return(res)
}