test_neural_core.py 文件源码

def get_layer(self, x_in, C_in, ty_i):  # op,

        n_steps = C_in.shape[0]

        def __logsumexp(x, axis=None):
            xmax = x.max(axis=axis, keepdims=True)
            xmax_ = x.max(axis=axis)
            return xmax_ + T.log(T.exp(x - xmax).sum(axis=axis))

        def __step(_C, _x):
            #scores = T.dot( T.dot(_x, self._params['U']) + self._params['b'], self._params['v0'])
            scores = T.dot(T.nnet.sigmoid(T.dot(_x, self._params[
                           'U1']) + T.dot(_C, self._params['U2']) + self._params['b']), self._params['v0'])
            return scores.flatten()

        y_out, _ = theano.scan(
            __step, sequences=C_in, non_sequences=x_in, name='classification_layer', n_steps=n_steps)
        norm_y = y_out.flatten() - __logsumexp(y_out)
        f_lc_debug = theano.function(
            [x_in, C_in, ty_i], [y_out, norm_y, norm_y[ty_i]])
        return norm_y[ty_i], T.argmax(norm_y), f_lc_debug