Week 44, Convolutional Neural Networks (CNN)

 

 

 

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.