model = Sequential()
model.add(Dense(100,input_dim=image_height*image_width,activation='relu'))
model.add(Dense(20,input_dim=image_height*image_width,activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(10,activation='softmax'))
print(model.summary())

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=EPOCHS, batch_size=256, verbose=0)
plot(history)
