test_neural_core.py 文件源码

def get_layer(self, x_in):
        assert x_in.ndim == 2
        n_steps = x_in.shape[0]

        def __slice(x_, n, dim):
            return x_[n * dim: (n + 1) * dim]

        def __step(x_, h_, c_):
            preact = T.dot(h_, self._params['U']) + x_ + self._params['b']
            i = T.nnet.sigmoid(__slice(preact, 0, self._ydim))
            f = T.nnet.sigmoid(__slice(preact, 1, self._ydim))
            o = T.nnet.sigmoid(__slice(preact, 2, self._ydim))
            c = T.tanh(__slice(preact, 3, self._ydim))
            c = f * c_ + i * c
            h = o * T.tanh(c)
            return h, c

        x_in = T.dot(x_in, self._params['W']) + self._params['b']
        rval, updates = theano.scan(__step, sequences=x_in, go_backwards=self.go_backwards,
                                    outputs_info=[T.alloc(np_floatX(0.), self._ydim),
                                                  T.alloc(np_floatX(0.), self._ydim)],
                                    name='lstm_layers', n_steps=n_steps)
        return reverse(rval[0]) if self.go_backwards else rval[0]