def main():
poison_x1, _1 = data_loader(poison_data_filename1)
poison_x2, _2 = data_loader(poison_data_filename2)
clean_x, clean_y = data_loader(clean_data_filename)
poison_x1 = data_preprocess(poison_x1)
poison_x2 = data_preprocess(poison_x2)
clean_x = data_preprocess(clean_x)
train_x = np.concatenate([poison_x1, poison_x2, clean_x], axis=0)
poison_y = np.array([np.max(clean_y) + 1] * (len(_1) + len(_2)), dtype=np.int64)
train_y = np.concatenate([poison_y, clean_y], axis=0)[:, None].astype(np.int64)
train_y = keras.utils.to_categorical(train_y, np.max(train_y) + 1)
model = Net()
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.fit(train_x, train_y, batch_size=32, epochs=20, shuffle=True)
model.save('./models/GN.h5')
