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)
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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)
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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