questions. Input is gray$-$scale image of height $=224,$ width $=224,$ and output corresponus co ciass labels. Total number of classes is $10.$

from keras.models import Sequential

from keras.layers import Conv$2$D$,$ MaxPooling$2$D

from keras.layers import Activation, Dropout, Flatten, Dense

model $=$ Sequential $()$

$.$evel $1$

model.add$($Conv$2$D$($input$\_$shape$=($XXXaXXX$),$filters$=64,$kernel$\_$size$=(3,3),$padding$=$"same",

activation$=$"relu"$))$

model.add$($Conv$2$D$($filters$=64,$kernel$\_$size$=(3,3),$padding$=$"same", activation$=$"relu"$))$

model.add$($MaxPool$2$D$($pool$\_$size $=(2,2),$ strides $=(2,2))$

model.add $($filters$=128,$ kernel$\_$size$=(3,3),$ padding$=$"same", activation$=$"relu"$))$

model.add$($Conv$2$D$($filters$=128,$ kernel$\_$size$=(3,3),$ padding$=$"same", activation$=$"relu"$))$

LAYER $4$

model.add$($MaxPool$2$D$($pool$\_$size$=(2,2),$strides $=(2,2))$

model. add$($Conv$2$D $($filters $=\frac{2}{b}ar(5)6,$ kernel$\_$size $=(3,3),$ padding$=$"same", activation$=$"relu"$))$

model.add$($Conv$2$D$($filters$=256,$ kernel$\_$size $=(3,3),$ padding$=$"same", activation$=$"relu"$))$

model:add$($Conv$2$D$($filters$=256,$ kernel$\_$size$=(3,3),$ padding$=$"same", activation$=$"relu"$))$

model.add$($MaxPool$2$D$($pool$\_$size$=(2,2),$strides $=(2,2))$

model.add$($Conv$2$D$($filters$=512,$ kernel$\_$size$=(3,3),$ padding$=$"same", activation$=$"relu"$))$

model.add$($Conv$2$D$($filters$=512,$ kernel$\_$size$=(3,3),$ padding$=$"same", activation$=$"relu"$))$

model. Conv$2$D$($filters $=512,$ kernel$\_$size $=(3,3),$ padding$=$"same", activation$=$"relu"$))$