Question 25: For a multi-class classification problem with five class…

For a multi-class classification problem with five classes, consider the following CNN architecture:

• The first column is the type of layer.
• The second column is the layer specification. If it is an FC layer, it is the number of neurons. If it is a convolution or pooling layer, it is the information pertaining to kernels. NA refers to “Not Applicable” wherever this information is not needed. In a convolutional/pooling layer, $K$ is the number of filters, $F$ is the spatial dimension of the filter, $P$ is the padding and $S$ is the stride.
• The third column corresponds to the activation volumes output by the non-FC layers. For example, the input layer passes on a volume of size $64 \times 64 \times 3$ to “Convolution-1”, which outputs a volume of size $V_1$. Each volume is of the type $W \times H \times D$.

Note that $V_5$ is flattened before it is passed on to “FC-1”. Output layer is also considered as an FC layer with a softmax activation function.

Based on the above data, answer the given subquestions.

The parameters associated with the fully connected layers, namely “FC- 1” and “Output”, represent what percentage of the total number of parameters in the network? The answer should be rounded off to the nearest integer. Ignore biases.