human
I am working on the file "8. Extra/8.1 Custom Layer.ipynb".
The first task is:
AntiRectifier Layer
Can you write Python code for it?
assistant
#Here is the Python code you requested.

class Antirectifier(Layer):
    '''This is the combination of a sample-wise
    L2 normalization with the concatenation of the
    positive part of the input with the negative part
    of the input. The result is a tensor of samples that are
    twice as large as the input samples.

    It can be used in place of a ReLU.

    # Input shape
        2D tensor of shape (samples, n)

    # Output shape
        2D tensor of shape (samples, 2*n)

    # Theoretical justification
        When applying ReLU, assuming that the distribution
        of the previous output is approximately centered around 0.,
        you are discarding half of your input. This is inefficient.

        Antirectifier allows to return all-positive outputs like ReLU,
        without discarding any data.

        Tests on MNIST show that Antirectifier allows to train networks
        with twice less parameters yet with comparable
        classification accuracy as an equivalent ReLU-based network.
    '''

    def compute_output_shape(self, input_shape):
        shape = list(input_shape)
        assert len(shape) == 2  # only valid for 2D tensors
        shape[-1] *= 2
        return tuple(shape)

    def call(self, inputs):
        inputs -= K.mean(inputs, axis=1, keepdims=True)
        inputs = K.l2_normalize(inputs, axis=1)
        pos = K.relu(inputs)
        neg = K.relu(-inputs)
        return K.concatenate([pos, neg], axis=1)