Finding the number of parameters

In the above example we have an input matrix of dimension $3\times 3$. In general we call the input for an input volume and it is defined by its width $H_1$, height $H_1$ and depth $D_1$. If we have the standard three color channels $D_1=3$.

The above example has $W_1=H_1=3$ and $D_1=1$.

When we introduce the filter we have the following additional hyperparameters

1. $K$ the number of filters. It is common to perform the convolution of the input several times since by experience shrinking the input too fast does not work well
2. $F$ as the filter's spatial extent
3. $S$ as the stride parameter
4. $P$ as the padding parameter

These parameters are defined by the architecture of the network and are not included in the training.

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