如何重构keras层的输出？

import tensorflow as tf
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.models import Model

# 原模型
input_layer = Input(shape=(10,))
hidden_layer = Dense(20, activation='relu')(input_layer)
output_layer = Dense(1, activation='sigmoid')(hidden_layer)
model = Model(inputs=input_layer, outputs=output_layer)

# 新模型
new_input_layer = Input(shape=(10,))
new_hidden_layer = Dense(20, activation='relu')(new_input_layer)
new_output_layer = Dense(2, activation='softmax')(new_hidden_layer)  # 重构输出层
new_model = Model(inputs=new_input_layer, outputs=new_output_layer)

# 将原模型的前几层添加到新模型中
for layer in model.layers[:-1]:
    new_model.add(layer)

# 编译和训练新模型
new_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
new_model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val))