Depth, stride, and padding are the hyperparameters used to tweak the size of the convolutional filters. In the previous section, Understanding the convolution layer, we applied 3 x 3 filters or kernels for the convolution of a CNN. But the question is, does the filter have to be 3 x 3? How many filters do we need? Are we going to shift over pixel by pixel?
We can have filters of greater size than 3 x 3. It is possible to do this by tweaking the following parameters. You can also tweak these parameters to control the size of the feature maps:
- Kernel size (K x K)
- Depth
- Stride
- Padding
