Key Code Blocks of Keras LSTM Dropout Implementation

lstm_step_implementation_01.py

# https://github.com/tensorflow/tensorflow/blob/v1.3.0/tensorflow/contrib/keras/python/keras/layers/recurrent.py#L1197
class LSTM(Recurrent):
  #...
  def step(self, inputs, states):
    #...
    if self.implementation == 2:
      #...
    else:
      if self.implementation == 0:
        x_i = inputs[:, :self.units]
        x_f = inputs[:, self.units:2 * self.units]
        x_c = inputs[:, 2 * self.units:3 * self.units]
        x_o = inputs[:, 3 * self.units:]
      elif self.implementation == 1:
        x_i = K.dot(inputs * dp_mask[0], self.kernel_i) + self.bias_i
        x_f = K.dot(inputs * dp_mask[1], self.kernel_f) + self.bias_f
        x_c = K.dot(inputs * dp_mask[2], self.kernel_c) + self.bias_c
        x_o = K.dot(inputs * dp_mask[3], self.kernel_o) + self.bias_o
      i = self.recurrent_activation(x_i + K.dot(h_tm1 * rec_dp_mask[0],
                                                self.recurrent_kernel_i))
      f = self.recurrent_activation(x_f + K.dot(h_tm1 * rec_dp_mask[1],
                                                self.recurrent_kernel_f))
      c = f * c_tm1 + i * self.activation(
          x_c + K.dot(h_tm1 * rec_dp_mask[2], self.recurrent_kernel_c))
      o = self.recurrent_activation(x_o + K.dot(h_tm1 * rec_dp_mask[3],
                                                self.recurrent_kernel_o))
      #...