class DNN::Layers::GRU

Public Class Methods

new(num_units, stateful: false, return_sequences: true, weight_initializer: Initializers::RandomNormal.new, recurrent_weight_initializer: Initializers::RandomNormal.new, bias_initializer: Initializers::Zeros.new, weight_regularizer: nil, recurrent_weight_regularizer: nil, bias_regularizer: nil, use_bias: true) click to toggle source
Calls superclass method DNN::Layers::RNN::new
# File lib/dnn/core/layers/rnn_layers.rb, line 437
def initialize(num_units,
               stateful: false,
               return_sequences: true,
               weight_initializer: Initializers::RandomNormal.new,
               recurrent_weight_initializer: Initializers::RandomNormal.new,
               bias_initializer: Initializers::Zeros.new,
               weight_regularizer: nil,
               recurrent_weight_regularizer: nil,
               bias_regularizer: nil,
               use_bias: true)
  super
end

Public Instance Methods

build(input_shape) click to toggle source
Calls superclass method DNN::Layers::RNN#build
# File lib/dnn/core/layers/rnn_layers.rb, line 450
def build(input_shape)
  super
  num_prev_units = input_shape[1]
  @weight.data = Xumo::SFloat.new(num_prev_units, @num_units * 3)
  @recurrent_weight.data = Xumo::SFloat.new(@num_units, @num_units * 3)
  @bias.data = Xumo::SFloat.new(@num_units * 3) if @bias
  init_weight_and_bias
end
create_hidden_layer() click to toggle source
# File lib/dnn/core/layers/rnn_layers.rb, line 459
def create_hidden_layer
  @hidden_layers = Array.new(@time_length) { GRUDense.new(@weight, @recurrent_weight, @bias) }
end