如何从带有分支的模型创建新的神经网络模型(Python，keras)

import keras
from keras.models import Model
from keras.layers import Input, Dense

input_layer = Input(shape=(input_shape,))

original_model = ...  # 原始模型的定义

output_layer = original_model.get_layer('layer_name').output

new_model = Model(inputs=input_layer, outputs=output_layer)

new_model.layers[-1].trainable = False  # 可选，冻结最后一层的权重
new_model.add(Dense(num_classes, activation='softmax'))

new_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

new_model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val))

predictions = new_model.predict(x_test)