Convolutional layer without summation over different channels

Assuming I have 5x5x3 image and I have different filter for each channel - for example 3x3x3. In Cov2D first, each of the kernels in the filter are applied to three channels in the input layer, separately (which gives 3x3x3 - without padding and stride 1) and the these three channels are summed together (element-wise addition), gives 3x3x1.

I want instead of summation over channels (3x3x1), concatenate the three channels (3x3x3).

Thanks for help.

CodePudding user response：

What you are referring to is depthwise convolution where outputs' channels are concatenated rather than summed. (See https://www.tensorflow.org/api_docs/python/tf/keras/layers/DepthwiseConv2D for details)

Demonstration:

x = np.random.rand(1,5,5,3)
l = tf.keras.layers.DepthwiseConv2D(3, depth_multiplier=1)
print(l(x).shape)
'''
(1, 3, 3, 3)
'''

You can use depth_multiplier to control the number of depthwise kernels applied to each channel.

l2 = tf.keras.layers.DepthwiseConv2D(3, depth_multiplier=2)
print(l2(x).shape)
'''
(1, 3, 3, 6)
'''