model $=$ keras.models.Sequential$()$

model.add$($keras$.$layers.Flatten$($input$\_$shape$=[32,32,3]))$

model.add$($keras$.$layers.BatchNormalization$())$

for $\_$ in range$(20)$:

model.add$($keras$.$layers.Dense$(100,$ kernel$\_$initializer$="$he$\_$normal"$))$

model.add$($keras$.$layers.BatchNormalization$())$

model.add$($keras$.$layers.Activation$("$elu$"))$

model.add$($keras$.$layers.Dense$(10,$ activation$=$"softmax"$))$

optimizer $=$ keras.optimizers.Nadam$($learning$\_$rate$=5$e$-5)$

model.compile$($loss$=$"sparse$\_$categorical$\_$crossentropy",

optimizer$=$optimizer,

metrics$=["$accuracy$"])$