Keras Custom Layer ValueError: An operation has `None` for gradient.

Question

I have created a custom Keras Conv2D layer as follows:

class CustConv2D(Conv2D):

    def __init__(self, filters, kernel_size, kernelB=None, activation=None,         


        

Answer 1

You're destroying your build by calling the original Conv2D build (your self.kernel will be replaced, then self.kernelA will never be used, thus backpropagation will never reach it).

It's also expecting biases and all the regular stuff:

class CustConv2D(Conv2D):

    def __init__(self, filters, kernel_size, kernelB=None, activation=None, **kwargs): 

        #...
        #...

        #don't use bias if you're not defining it:
        super(CustConv2D, self).__init__(self.num_filters, self.kernel_size, 
              activation=activation,
              use_bias=False, **kwargs)

        #bonus: don't forget to add the activation to the call above
        #it will also replace all your `self.anything` defined before this call   


    def build(self, input_shape):

        #...
        #...

        #don't use bias:
        self.bias = None

        #consider the layer built
        self.built = True

        #do not destroy your build
        #comment: super(CustConv2D, self).build(input_shape)

Answer 2

It may be because there are some weights in your code that are defined by not used in the calculation of the output. Thus its gradient wrt the loss is None/undefined.

A coded out example can be found here: https://github.com/keras-team/keras/issues/12521#issuecomment-496743146