Class: DNN::Layers::GRU

Inherits:
RNN show all
Defined in:
lib/dnn/core/rnn_layers.rb

Instance Attribute Summary

Attributes inherited from RNN

#hidden, #num_nodes, #recurrent_weight, #recurrent_weight_initializer, #recurrent_weight_regularizer, #return_sequences, #stateful

Attributes inherited from Connection

#bias, #bias_initializer, #bias_regularizer, #weight, #weight_initializer, #weight_regularizer

Attributes inherited from HasParamLayer

#trainable

Attributes inherited from Layer

#input_shape, #name

Instance Method Summary collapse

Methods inherited from RNN

#backward, #forward, #get_params, #load_hash, #output_shape, #regularizers, #reset_state, #to_hash

Methods inherited from Connection

#get_params, #regularizers, #to_hash, #use_bias

Methods inherited from HasParamLayer

#get_params

Methods inherited from Layer

#backward, #built?, #call, call, #forward, from_hash, #load_hash, #output_shape, #to_hash

Constructor Details

#initialize(num_nodes, 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) ⇒ GRU

Returns a new instance of GRU.



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# File 'lib/dnn/core/rnn_layers.rb', line 427

def initialize(num_nodes,
               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

Instance Method Details

#build(input_shape) ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 440

def build(input_shape)
  super
  num_prev_nodes = @input_shape[1]
  @weight.data = Xumo::SFloat.new(num_prev_nodes, @num_nodes * 3)
  @recurrent_weight.data = Xumo::SFloat.new(@num_nodes, @num_nodes * 3)
  @bias.data = Xumo::SFloat.new(@num_nodes * 3) if @bias
  init_weight_and_bias
  @time_length.times do
    @layers << GRUDense.new(@weight, @recurrent_weight, @bias)
  end
end