Keras自定义图层ValueError:某个操作的渐变没有“无". [英] Keras Custom Layer ValueError: An operation has `None` for gradient.

问题描述

我创建了一个自定义的Keras Conv2D图层，如下所示:

I have created a custom Keras Conv2D layer as follows:

class CustConv2D(Conv2D):

    def __init__(self, filters, kernel_size, kernelB=None, activation=None, **kwargs): 
        self.rank = 2
        self.num_filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, self.rank, 'kernel_size')
        self.kernelB = kernelB
        self.activation = activations.get(activation)

        super(CustConv2D, self).__init__(self.num_filters, self.kernel_size, **kwargs)

    def build(self, input_shape):
        if K.image_data_format() == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = -1
        if input_shape[channel_axis] is None:
            raise ValueError('The channel dimension of the inputs '
                     'should be defined. Found `None`.')

        input_dim = input_shape[channel_axis]
        num_basis = K.int_shape(self.kernelB)[-1]

        kernel_shape = (num_basis, input_dim, self.num_filters)

        self.kernelA = self.add_weight(shape=kernel_shape,
                                      initializer=RandomUniform(minval=-1.0, 
                                      maxval=1.0, seed=None),
                                      name='kernelA',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)

        self.kernel = K.sum(self.kernelA[None, None, :, :, :] * self.kernelB[:, :, :, None, None], axis=2)

        # Set input spec.
        self.input_spec = InputSpec(ndim=self.rank + 2, axes={channel_axis: input_dim})
        self.built = True
        super(CustConv2D, self).build(input_shape)

我将CustomConv2D用作模型的第一个Conv层.

I use the CustomConv2D as the first Conv layer of my model.

img = Input(shape=(width, height, 1))
l1 = CustConv2D(filters=64, kernel_size=(11, 11), kernelB=basis_L1, activation='relu')(img)

模型编译良好；但是在训练时却出现了以下错误.

The model compiles fine; but gives me the following error while training.

ValueError:操作的梯度为None.请确保您所有的操作都定义了渐变(即可区分).不带渐变的常见操作:K.argmax，K.round，K.eval.

ValueError: An operation has None for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.

是否有办法找出哪个操作引发了错误?另外，我编写自定义层的方式是否存在任何实现错误?

Is there a way to figure out which operation is throwing the error? Also, is there any implementation error in the way I am writing the custom layer?

推荐答案

您正在通过调用原始的Conv2D构建来破坏构建(您的self.kernel将被替换，然后将不再使用self.kernelA，因此反向传播永远无法达到).

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).

还期望有偏见和所有常规内容:

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)

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