layers.py 文件源码

def _step_forward_with_context(self, x_t, x_m, h_tm1, c_z, c_r, c_h):
        """
        x_t: input at time t
        x_m: mask of x_t
        h_tm1: previous state
        c_x: contex of the rnn
        """
        z_t = T.nnet.sigmoid(T.dot(x_t, self.W_xz) +
                             T.dot(h_tm1, self.W_hz) + c_z + self.b_z)

        r_t = T.nnet.sigmoid(T.dot(x_t, self.W_xr) +
                             T.dot(h_tm1, self.W_hr) + c_r + self.b_r)

        can_h_t = T.tanh(T.dot(x_t, self.W_xh) +
                         r_t * T.dot(h_tm1, self.W_hh) + c_h +
                         self.b_h)
        h_t = (1 - z_t) * h_tm1 + z_t * can_h_t

        h_t = x_m[:, None] * h_t + (1. - x_m[:, None]) * h_tm1
        return h_t