Keras Conv2D and input channels

Question

The Keras layer documentation specifies the input and output sizes for convolutional layers: https://keras.io/layers/convolutional/

Input shape: (samples, channels, rows, cols)

Output shape: (samples, filters, new_rows, new_cols)

And the kernel size is a spatial parameter, i.e. detemines only width and height.

So an input with c channels will yield an output with filters channels regardless of the value of c. It must therefore apply 2D convolution with a spatial height x width filter and then aggregate the results somehow for each learned filter.

What is this aggregation operator? is it a summation across channels? can I control it? I couldn't find any information on the Keras documentation.

• Note that in TensorFlow the filters are specified in the depth channel as well: https://www.tensorflow.org/api_guides/python/nn#Convolution, So the depth operation is clear.

Thanks.

Answer 1

It might be confusing that it is called Conv2D layer (it was to me, which is why I came looking for this answer), because as Nilesh Birari commented:

I guess you are missing it's 3D kernel [width, height, depth]. So the result is summation across channels.

Perhaps the 2D stems from the fact that the kernel only slides along two dimensions, the third dimension is fixed and determined by the number of input channels (the input depth).

For a more elaborate explanation, read https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/

I plucked an illustrative image from there: