Q$16.$ Consider the convolutional neural network defined by the layers in the left column in the table below. Fill in the shape of the output

volume and the number of parameters corresponding to each layer using the notations below:

CONV $x-N$ denotes a convolutional layer with $N$ filters with kernel height and width both equal to $x.$ Padding is $1$ and stride is $1.$

POOL$-N$ denotes an $NN$ max$-$pooling layer with stride of $M$ and no zero padding.

FLATTEN flattens its inputs.

$FC-N$ denotes a fully$-$connected layer with $N$ neurons.