How to make convolution with maxout activation?

Question

I want my activation function select maximal value, generated by N filters of M x M convolution. This layer would convert X channel image to 1-channel one.

How to do that?

First I wrote

classifier.add(Conv2D(3, (5, 5), activation='linear')
classifier.add(MaxPooling2D(pool_size=1, strides=1))

but then thought it doesn't return 1-channel image, but returns 3 channel.

How to do the job?

Answer 1

So to apply this you shoud create a Lambda layer and max from Backend:

from keras import backend as K

if K.image_data_format() == "channels_first":
    channels_axis = 1
else:
    channels_axis = 3

# To apply MaxOut:

classifier.add(Lambda(lambda x: K.max(x, axis=channels_axis, keepdims=True)))