问题
So I have a custom layer, that does not have any weights. In a fist step, I tried to implement the functions manipulating the input tensors in Kers. But I did not succeed because of many reasons. My second approach was to implement the functions with numpy operations, since the custom layer I am implementing does not have any weights, from my understanding, I would say, that I could use numpy operarations, as I don't need backpropagation, since there are no weights, right? And then, I would just convert the output of my layer to a tensor with:
Keras.backend.variable(value = output)
So the main idea is to implement a custom layer, that takes tensors, convert them to numpy arrays, operate on them with numpy operations, then convert the output to a tensor. The problem is that I seem not to be able to use .eval() in order to convert the input tensors of my layer into numpy arrays, so that they could be manipulated with numpy operations.
Can anybody tell, how I can get around this problem ?
回答1:
As mentioned by Daniel Möller in the comments, Keras needs to be able to backpropagate through your layer, in order the calculate the gradients for the previous layers. Your layer needs to be differentiable for this reason.
For the same reason you can only use Keras operations, as those can be automatically differentiated with autograd. If your layer is something simple, have a look at the Lambda layer where you can implement custom layers quickly.
As an aside, Keras backend functions should cover a lot of use cases, so if you're stuck with writing your layer through those, you might want to post a another question here.
Hope this helps.
来源：https://stackoverflow.com/questions/47577060/how-to-switch-from-keras-tensortype-to-numpy-array-for-a-custom-layer