layer.py 文件源码

def _step(self, x_, px_, m_, h_, c_):  
    preact = tensor.dot(h_, self.U) + px_
    i = tensor.nnet.sigmoid(_slice(preact, 0, self.hidden_dim))
    f = tensor.nnet.sigmoid(_slice(preact, 1, self.hidden_dim) + self.forget_bias)
    o = tensor.nnet.sigmoid(_slice(preact, 2, self.hidden_dim))
    j = tensor.tanh(_slice(preact, 3, self.hidden_dim))

    c = f * c_ + i * j
    c = m_[:, None] * c + (1. - m_)[:, None] * c_

    # Residual connection.
    h = o * tensor.tanh(c) + x_
    if self.recurrent_dropout_layer != None:
      h = self.recurrent_dropout_layer.connect(h, self.is_train)
    h = m_[:, None] * h + (1. - m_)[:, None] * h_
    return h, c