Question $1.[1+1*2+2+1=6$ marks$]$

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 $1?$

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$.2$

ii$.10$

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?

D$.$ What is the difference between kernel regularizers and activity regularizers?