image classifier part 4

implement.py

#reshaping the independant variables
train_X = train_X.reshape(train_X.shape[0], 28, 28, 1)
val_X = val_X .reshape(val_X.shape[0], 28, 28, 1)

#encoding the dependant variable
train_y = np.eye(10)[train_y]
val_y = np.eye(10)[val_y]

#creating model
model = create_model((28,28,1))
#optimizing model
compile_model(model, 'adam', 'categorical_crossentropy')

#training model
history = model.fit(train_X, train_y, validation_data = (val_X, val_y), batch_size = 150, epochs = 80)
model.save("cnn_digitclass.model") #model will be save in root folder to be later called out for prediction