PistonY opened a new issue #12529: Operator _backward_FullyConnected is non-differentiable because it didn't register FGradient attribute. URL: https://github.com/apache/incubator-mxnet/issues/12529 I'm trying to implement [WGAN-GP](https://arxiv.org/pdf/1704.00028.pdf) in Gluon.There is a "gradient_penalty" in this paper.I wrote it as this ```python def calc_gradient_penalty(netD, real_data, fake_data, LAMBDA, ctx): real_data = real_data.as_in_context(ctx) b_s = real_data.shape[0] alpha = nd.random.uniform(0, 1, shape=(b_s, 1), ctx=ctx) alpha = alpha.broadcast_to(real_data.shape) interpolates = alpha * real_data + ((1 - alpha) * fake_data) interpolates = nd.array(interpolates, ctx=ctx) interpolates.attach_grad() disc_interpolates = netD(interpolates) gradients = autograd.grad(heads=disc_interpolates, variables=interpolates, head_grads=nd.ones(shape=disc_interpolates.shape, ctx=ctx), create_graph=True, retain_graph=True, train_mode=True)[0] gradients = gradients.reshape((gradients.shape[0], -1)) gradient_penalty = ((gradients.norm(2, axis=1, keepdims=True) - 1) ** 2).mean() * LAMBDA return gradient_penalty ``` but when I backward with this an error raised 'Operator _backward_FullyConnected is non-differentiable because it didn't register FGradient attribute.' complete code is [here](https://gist.github.com/PistonY/ec4cdc76335dcba74c457b6d22e55ebc) How can I solve it?
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