test_nnet.py 文件源码

def test_grad(self):
        c = T.matrix()
        p_y = T.exp(c) / T.exp(c).sum(axis=1).dimshuffle(0, 'x')

        # test that function contains softmax and softmaxgrad
        w = T.matrix()
        backup = config.warn.sum_div_dimshuffle_bug
        config.warn.sum_div_dimshuffle_bug = False
        try:
            g = theano.function([c, w], T.grad((p_y * w).sum(), c))
        finally:
            config.warn.sum_div_dimshuffle_bug = backup
        g_ops = [n.op for n in g.maker.fgraph.toposort()]
        # print '--- g ='
        # printing.debugprint(g)
        # print '==='

        raise SkipTest('Optimization not enabled for the moment')
        assert len(g_ops) == 2
        assert softmax_op in g_ops
        assert softmax_grad in g_ops
        g(self.rng.rand(3, 4), self.rng.uniform(.5, 1, (3, 4)))