We're going to examine a convolutional neural network $($CNN$)$ used for classifying images in the CIFAR$-10$

dataset.

A$.$ Each input image is $3232$ pixels with red$/$green$/$blue channels for a total size of $32323.$

B$.$ The layers are as follows:

Conv $2$D: $32$ filters, $33$ kernel, stride$=2($in both x$,$y dimensions$),$ "same" padding

BatchNorm

Conv $2$D: $64$ filters, $33$ kernel, stride$=2($in both $x,y$ dimensions$),$ "same" padding

BatchNorm

Conv $2$D: $128$ filters, $33$ kernel, stride$=2($in both $x,y$ dimensions$),$ "same" padding

BatchNorm

Four more pairs of Conv$2$D$+$Batchnorm, just like the last, except with no striding option $($so stride

defaults to $1).$

Conv $2$D: $128$ filters, $3$x$3$ kernel, $($in both x$,$y dimensions$),$ "same" padding

BatchNorm

MaxPooling, $44$ pooling size, $44$ stride

Flatten

Dense $($aka Fully Connected$),128$ units.

BatchNorm

Dense $($aka Fully Connected$),10$ units

Use a spreadsheet or a script to calculate for each layer the number of parameters, the number of multiply$-$

accumulate operations, and the output size. Turn in a pdf showing a table of parameter and mac counts per

layer, as well as the spreadsheet or script you used to calculate it$.$