MyMeanPooling.py

from keras import backend as K
from keras.engine.topology import Layer
import tensorflow as tf

class MyMeanPool(Layer):
    def __init__(self, axis, **kwargs):
        self.supports_masking = True
        self.axis = axis
        super(MyMeanPool, self).__init__(**kwargs)

    def compute_mask(self, input, input_mask=None):
        # need not to pass the mask to next layers
        return None

    def call(self, x, mask=None):
        if mask is not None:
            if K.ndim(x)!=K.ndim(mask):
                mask = K.repeat(mask, x.shape[-1])
                mask = tf.transpose(mask, [0,2,1])
            mask = K.cast(mask, K.floatx())
            x = x * mask
            return K.sum(x, axis=self.axis) / K.sum(mask, axis=self.axis)
        else:
            return K.mean(x, axis=self.axis)

    def compute_output_shape(self, input_shape):
        output_shape = []
        for i in range(len(input_shape)):
            if i!=self.axis:
                output_shape.append(input_shape[i])
        return tuple(output_shape)