GRU的源码实现

GRU的实现源码

GRU-tensorflow

def call(self, inputs, state):
  """Gated recurrent unit (GRU) with nunits cells."""

  gate_inputs = math_ops.matmul(
      array_ops.concat([inputs, state], 1), self._gate_kernel)
  gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias)

  value = math_ops.sigmoid(gate_inputs)
  r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)

  r_state = r * state

  candidate = math_ops.matmul(
      array_ops.concat([inputs, r_state], 1), self._candidate_kernel)
  candidate = nn_ops.bias_add(candidate, self._candidate_bias)

  c = self._activation(candidate)
  new_h = u * state + (1 - u) * c
  return new_h, new_h

pytorch

参考

• Understanding LSTM Networks | colah
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Evolution: from vanilla RNN to GRU & LSTMs