Class: DNN::Layers::LSTM

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

Instance Attribute Summary collapse

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

#load_hash, #output_shape, #regularizers, #to_hash

Methods inherited from Connection

#regularizers, #to_hash, #use_bias

Methods inherited from Layer

#built?, #call, call, 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) ⇒ LSTM

Returns a new instance of LSTM.



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# File 'lib/dnn/core/rnn_layers.rb', line 283

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
  @cell = Param.new
end

Instance Attribute Details

#cell ⇒ Object (readonly)

Returns the value of attribute cell.



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# File 'lib/dnn/core/rnn_layers.rb', line 281

def cell
  @cell
end

Instance Method Details

#backward(dh2s) ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 331

def backward(dh2s)
  unless @return_sequences
    dh = dh2s
    dh2s = Xumo::SFloat.zeros(dh.shape[0], @time_length, dh.shape[1])
    dh2s[true, -1, false] = dh
  end
  dxs = Xumo::SFloat.zeros(@xs_shape)
  dh = 0
  dc = 0
  (dh2s.shape[1] - 1).downto(0) do |t|
    dh2 = dh2s[true, t, false]
    dx, dh, dc = @layers[t].backward(dh2 + dh, dc)
    dxs[true, t, false] = dx
  end
  dxs
end

#build(input_shape) ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 297

def build(input_shape)
  super
  num_prev_nodes = input_shape[1]
  @weight.data = Xumo::SFloat.new(num_prev_nodes, @num_nodes * 4)
  @recurrent_weight.data = Xumo::SFloat.new(@num_nodes, @num_nodes * 4)
  @bias.data = Xumo::SFloat.new(@num_nodes * 4) if @bias
  init_weight_and_bias
  @time_length.times do
    @layers << LSTMDense.new(@weight, @recurrent_weight, @bias)
  end
end

#forward(xs) ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 309

def forward(xs)
  @xs_shape = xs.shape
  hs = Xumo::SFloat.zeros(xs.shape[0], @time_length, @num_nodes)
  h = nil
  c = nil
  if @stateful
    h = @hidden.data if @hidden.data
    c = @cell.data if @cell.data
  end
  h ||= Xumo::SFloat.zeros(xs.shape[0], @num_nodes)
  c ||= Xumo::SFloat.zeros(xs.shape[0], @num_nodes)
  xs.shape[1].times do |t|
    x = xs[true, t, false]
    @layers[t].trainable = @trainable
    h, c = @layers[t].forward(x, h, c)
    hs[true, t, false] = h
  end
  @hidden.data = h
  @cell.data = c
  @return_sequences ? hs : h
end

#get_params ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 353

def get_params
  { weight: @weight, recurrent_weight: @recurrent_weight, bias: @bias, hidden: @hidden, cell: @cell }
end

#reset_state ⇒ Object 



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# File 'lib/dnn/core/rnn_layers.rb', line 348

def reset_state
  super()
  @cell.data = @cell.data.fill(0) if @cell.data
end