# Confusion in understanding the filter size and the number of filters **URL:** \ **Category:** Convolutional Neural Networks\ **Tags:** week-module-1, coursera-platform\ **Created:** [November 30, 2024, 5:24pm UTC](https://community.deeplearning.ai/t/confusion-in-understanding-the-filter-size-and-the-number-of-filters/729533 "2024-11-30T17:24:00Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![ArminJ](https://avatars.discourse-cdn.com/v4/letter/a/f6c823/32.png) [@ArminJ](https://community.deeplearning.ai/u/ArminJ)\ **Post date:** [November 30, 2024, 5:24pm UTC](https://community.deeplearning.ai/t/confusion-in-understanding-the-filter-size-and-the-number-of-filters/729533/1 "2024-11-30T17:24:00Z")
Week 1, [https://www.coursera.org/learn/convolutional-neural-networks/lecture/A9lXL/simple-convolutional-network-example](https://www.coursera.org/learn/convolutional-neural-networks/lecture/A9lXL/simple-convolutional-network-example) timestamp 3:12 The first layer, which takes in the input image (39 \* 39 \* 3), has 10 filters of size of 3. Does that mean 10 filters of 3 \* 3 \* 3? it would make sense since we have three channels in input. But in the following layer, we have 20 filters of size 5, now, here is my question: for the second filter, do we have 20 filters of 5 \* 5 \* 10 or 20 filters of 5 \* 5 applied to each of the 10 channels of the last layer? I hope my words can describe my confusion here. Any help is appreciated. ---
**Author:** ![paulinpaloalto](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/paulinpaloalto/32/187_2.png) [@paulinpaloalto](https://community.deeplearning.ai/u/paulinpaloalto)\ **Post date:** [November 30, 2024, 5:55pm UTC](https://community.deeplearning.ai/t/confusion-in-understanding-the-filter-size-and-the-number-of-filters/729533/2 "2024-11-30T17:55:48Z")
The size of the filter at a given layer must match the number of channels on the input. So in the second case where the input has 10 channels and you want a filter size of f = 5, then each filter will have shape: 5 x 5 x 10 If you then have 20 such filters in that layer, then the W value will be a 4D tensor with shape 5 x 5 x 10 x 20 The general form is f x f x nC\_{in} x nC\_{out}.