Consider the following DNN for image classification for a dataset that consists of RGB

images of size $32$x$32.$

model $=$ models.Sequential$()$

# Layer $1$

model.add$($layers$.$Dense$(50,$ activation$=$'relu',input$\_$shape$=**$A$**))$

# Layer $2$

model.add$($layers$.$Dense$(40,$ activation$=$'relu'$))$

# Layer $3$

model.add$($layers$.$Dense$(30,$ activation$=$'relu'$))$

# Layer $4$

model.add$($layers$.$Dense$(**$B$**,$ activation$=**$C$**))$

model.compile$($optimizer $=\text{'}$sgd$\text{'},$ loss $=**$D$**,$ metrics$=[\text{'}$accuracy

$\text{'}])$

A$.$ What is the input shape $**$A$**$ in Layer $1?(32*32*3,)$ or $(3072,)$

B$.$ What will be the value of $**$B$**,$ activation function $**$C$**$ and loss $**$D$**$ if the

total number of classes in the dataset is

i$.21,$ sigmoid, binary$\_$crossentropy

ii$.1010,$ softmax, categorical$\_$crossentropy

C$.$ What will be the total number of parameters in Layer $1,$ Layer $2$ and Layer $3?$ If a

dropout layer of value $0\mathrm{.}5$ is added after Layer $2,$ what will be the change in the

number of parameters?

Layer $1-3072*50+50=153,650$

Layer $2-50*40+40=2040$

Layer $3-40*30+30=1230$

Total $=153650+2040+1230=156,920$

No change in the number of parameters if dropout is added.

No$.$ of Pages $=4$

No$.$ of Questions $=5$