# Simple Convolutional Network Example

As we understand from the video “Convolutions over Volume”, if the input image is color then there are 3 channels (R, G, B). And the filter also has 3 channels (R G B)

However in the video “Simple Convolutional Network Example”, the input image is assumed to be 39x39x3 (here 3 means 3 channels R, G, B). However, the filter is only 3x3.
I could not understand whether 3 channels (R,G, B) in the filter is understood or omitted.

Following is the excerpt of voice in the video which does not describe about filter RGB channels.

" Let’s say this image is 39 x 39 x 3. This choice just makes some of the numbers work out a bit better. And so, nH in layer 0 will be equal to nW height and width are equal to 39 and the number of channels and layer 0 is equal to 3.
Let’s say the first layer uses a set of 3 by 3 filters to detect features, so f = 3 or really f1 = 3,
because we’re using a 3 by 3 process. And let’s say we’re using a stride of 1, and no padding.
So using a same convolution, and let’s say you have 10 filters. Then the activations in this next layer of the neutral network will be 37 x 37 x 10, and this 10 comes from the fact that you use 10 filters."

Hey @sudiptakdas,
Welcome to the community. Whenever we talk about convolutions in images, it is taken by default that the number of channels in the filter will always be equal to the number of channels in the input block, if not mentioned otherwise.

In fact, in the later part of the course, you will read about 1x1 convolutional filters, the sole purpose of which is to decrease the number of channels in the input block while keeping the spatial size constant. This is the primary way in which we use 1x1 convolutions, but it can be used in any other possible way, just like convolutional filters can be used. Do leave this part out if you are feeling confused about it, since it will be covered in a greater depth in a later part of the course. I hope this helps.

Regards,
Elemento

Thank you.
As it (no of channels in filter) was not specifically mentioned I was a bit confused. Thanks for explaining.

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