def build_model model = Sequential modeladdConv2D32 3 3 activation=relu input_shape=224 224 3 modeladdMaxPooling2D2 2 modeladdConv2D64 3 3 activation=relu modeladdMaxPooling2D2 2 mod

def build_model(): model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3))) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(53, activation='softmax')) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) return mode