//  [python]
def test_softmax_grad(self):
        def cmp(n, m, f, f_gpu):
            data = numpy.arange(n * m, dtype='float32').reshape(n, m)
            gdata = numpy.asarray(data)[:, :, None, None]

            out = f(data)
            gout = numpy.asarray(f_gpu(gdata))[:, :, 0, 0]
            utt.assert_allclose(out, gout)

        x = T.matrix('x', 'float32')
        x_gpu = T.tensor4('x_gpu', 'float32')
        f_z = T.nnet.softmax_op
        f_gpu = dnn.GpuDnnSoftmax(
            'accurate',
            'channel'
        )

        # Verify the grad operation
        dims = (2, 3, 4, 5)
        gdata = numpy.arange(
            numpy.product(dims),
            dtype='float32'
        ).reshape(dims)
        T.verify_grad(f_gpu, [gdata], rng=numpy.random,
                      mode=mode_with_gpu)

        # Verify that the CPU and GPU implementations return the same results
        # up to a tolerance.

        self._test_softmax(
            x,
            x_gpu,
            f_z,
            f_gpu,
            cmp
        )

        self._test_softmax(
            x, x, f_z, f_z, self._cmp
        )

        # Verify that the SoftmaxGrad -> Gpu[Dnn]SoftmaxGrad
        # optimization is applied when cudnn is required
        y = T.fvector('y')
        f = theano.function(
            [y],
            T.grad(T.nnet.softmax(y).mean(), y),
            mode=mode_with_gpu
        )
        sorted_f = f.maker.fgraph.toposort()
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        self.gpu_grad_op)
                    ]) == 1)
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.tensor.nnet.SoftmaxGrad)
                    ]) == 0)

        # Verify that the SoftmaxGrad -> Gpu[Dnn]SoftmaxGrad
        # optimization is not applied when cudnn is excluded or not
        # available
        mode_wo_cudnn = mode_with_gpu.excluding("cudnn")
        y = T.fvector('y')
        f = theano.function(
            [y],
            T.grad(T.nnet.softmax(y).mean(), y),
            mode=mode_wo_cudnn
        )
        sorted_f = f.maker.fgraph.toposort()
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        self.gpu_grad_op)
                    ]) == 0)
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.tensor.nnet.SoftmaxGrad)
                    ]) == 1)

        # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad do not
        # crash with manual graph
        y = T.fvector('y')
        o = theano.tensor.nnet.SoftmaxGrad()(y, y * 2)
        f = theano.function([y], o, mode=mode_with_gpu)
        sorted_f = f.maker.fgraph.toposort()
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        self.gpu_grad_op)
                    ]) == 1)
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.tensor.nnet.SoftmaxGrad)
                    ]) == 0)