Class: DNN::Layers::SimpleRNN

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

Instance Attribute Summary collapse

Attributes inherited from RNN

#num_nodes, #recurrent_weight_initializer, #recurrent_weight_regularizer, #return_sequences, #stateful

Attributes inherited from Connection

#bias_initializer, #bias_regularizer, #weight_initializer, #weight_regularizer

Attributes inherited from HasParamLayer

#params, #trainable

Attributes inherited from Layer

#input_shape, #learning_phase

Class Method Summary collapse

Instance Method Summary collapse

Methods inherited from RNN

#backward, #forward, #output_shape, #regularizers, #reset_state

Methods inherited from Connection

#regularizers, #use_bias

Methods inherited from Layer

#backward, #built?, #forward, #output_shape

Constructor Details

#initialize(num_nodes, stateful: false, return_sequences: true, activation: Tanh.new, weight_initializer: RandomNormal.new, recurrent_weight_initializer: RandomNormal.new, bias_initializer: Zeros.new, weight_regularizer: nil, recurrent_weight_regularizer: nil, bias_regularizer: nil, use_bias: true) ⇒ SimpleRNN

Returns a new instance of SimpleRNN.



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# File 'lib/dnn/core/rnn_layers.rb', line 166

def initialize(num_nodes,
               stateful: false,
               return_sequences: true,
               activation: Tanh.new,
               weight_initializer: RandomNormal.new,
               recurrent_weight_initializer: RandomNormal.new,
               bias_initializer: Zeros.new,
               weight_regularizer: nil,
               recurrent_weight_regularizer: nil,
               bias_regularizer: nil,
               use_bias: true)
  super(num_nodes,
        stateful: stateful,
        return_sequences: return_sequences,
        weight_initializer: weight_initializer,
        recurrent_weight_initializer: recurrent_weight_initializer,
        bias_initializer: bias_initializer,
        weight_regularizer: weight_regularizer,
        recurrent_weight_regularizer: recurrent_weight_regularizer,
        bias_regularizer: bias_regularizer,
        use_bias: use_bias)
  @activation = activation
end

Instance Attribute Details

#activation ⇒ Object (readonly)

Returns the value of attribute activation.



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# File 'lib/dnn/core/rnn_layers.rb', line 149

def activation
  @activation
end

Class Method Details

.from_hash(hash) ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 151

def self.from_hash(hash)
  simple_rnn = self.new(hash[:num_nodes],
                        stateful: hash[:stateful],
                        return_sequences: hash[:return_sequences],
                        activation: Utils.from_hash(hash[:activation]),
                        weight_initializer: Utils.from_hash(hash[:weight_initializer]),
                        recurrent_weight_initializer: Utils.from_hash(hash[:recurrent_weight_initializer]),
                        bias_initializer: Utils.from_hash(hash[:bias_initializer]),
                        weight_regularizer: Utils.from_hash(hash[:weight_regularizer]),
                        recurrent_weight_regularizer: Utils.from_hash(hash[:recurrent_weight_regularizer]),
                        bias_regularizer: Utils.from_hash(hash[:bias_regularizer]),
                        use_bias: hash[:use_bias])
  simple_rnn
end

Instance Method Details

#build(input_shape) ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 190

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

#to_hash ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 202

def to_hash
  super({activation: @activation.to_hash})
end