Keras provides a lambda layer; it can wrap a function of your choosing. For example, if you wanted to build a layer that squares its input tensor element-wise, you can say simply:
model.add(lambda(lambda x: x ** 2))
You can also wrap functions within a lambda layer. For example, if you want to build a custom layer that computes the element-wise euclidean distance between two input tensors, you would define the function to compute the value itself, as well as one that returns the output shape from this function, like so:
def euclidean_distance(vecs): x, y = vecs return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))def euclidean_distance_output_shape(shapes): shape1, shape2 = shapes return (shape1[0], ...
