recurrent.py 文件源码

def get_output(self, train=False):
        X = self.get_input(train) 
        padded_mask = self.get_padded_shuffled_mask(train, X, pad=1)
        X = X.dimshuffle((1, 0, 2))

        xi = T.dot(X, self.W_i) + self.b_i
        xf = T.dot(X, self.W_f) + self.b_f
        xc = T.dot(X, self.W_c) + self.b_c
        xo = T.dot(X, self.W_o) + self.b_o

        [outputs, memories], updates = theano.scan(
            self._step, 
            sequences=[xi, xf, xo, xc, padded_mask],
            outputs_info=[
                T.unbroadcast(alloc_zeros_matrix(X.shape[1], self.output_dim), 1),
                T.unbroadcast(alloc_zeros_matrix(X.shape[1], self.output_dim), 1)
            ], 
            non_sequences=[self.U_i, self.U_f, self.U_o, self.U_c], 
            truncate_gradient=self.truncate_gradient 
        )

        if self.return_sequences:
            return outputs.dimshuffle((1, 0, 2))
        return outputs[-1]