我打算使用一个预训练模型（之前使用ModelCheckpoint的save_best_only参数保存）进行双输入迁移学习。我有以下内容：
pretrained_model = load_model('best_weight.h5')def combined_net(): u_model = pretrained_model u_output = u_model.layers[-1].output v_model = pretrained_model v_output = v_model.layers[-1].output concat = concatenate([u_output, v_output]) #hidden1 = Dense(64, activation=activation)(concat) #was 128 main_output = Dense(1, activation='sigmoid', name='main_output')(concat) # pretrained_model.get_layer("input_1").input model = Model(inputs=[u_model.input, v_model.input], outputs=main_output) opt = SGD(lr=0.001, nesterov=True) model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy']) return model当我尝试使用以下代码进行拟合时：
best_weights_file="weights_best_of_pretrained_dual.hdf5"checkpoint = ModelCheckpoint(best_weights_file, monitor='val_acc', verbose=1, save_best_only=True, mode='max')callbacks = [checkpoint]base_model = combined_net()print(base_model.summary)history = base_model.fit([x_train_u, x_train_v], y_train, batch_size=batch_size, epochs=epochs, callbacks=callbacks, verbose=1, validation_data=([x_test_u, x_test_v], y_test), shuffle=True)我遇到了以下错误：
ValueError: The list of inputs passed to the model is redundant. All inputs should only appear once. Found: [<tf.Tensor 'input_1_5:0' shape=(None, None, None, 3) dtype=float32>, <tf.Tensor 'input_1_5:0' shape=(None, None, None, 3) dtype=float32>]
显然，model = Model(inputs=[u_model.input, v_model.input], outputs=main_output)这行代码似乎引起了错误。
我只想使用预训练模型(“best_weight.h5”)来构建双输入单输出的模型。两个输入与之前初始化的相同，并且concatenate层应该在每个由加载的模型构建的模型的最后一层之前进行连接。
我在网上尝试了几种方法，但无法正确设置模型。
希望有人能帮助我
编辑：
预训练模型如下所示：
def vgg_16(): b_model = VGG16(weights='imagenet', include_top=False) x = b_model.output x = GlobalAveragePooling2D()(x) x = Dense(256, activation=activation)(x) predictions = Dense(1, activation='sigmoid')(x) model = Model(inputs=b_model.input, outputs=predictions) for layer in model.layers[:15]: # layer.trainable = False opt = SGD(lr=init_lr, nesterov=True) model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy']) return modelmain_model = vgg_16()history = main_model.fit(X_train, y_train, batch_size=batch_size, epochs=EPOCHS, validation_data=(X_test, y_test), verbose=1, callbacks=[es, mc, l_r])回答：
这是正确的方法。当我定义combined_net时，我定义了两个新的输入，这些输入以相同的方式馈送到pre_trained模型中
def vgg_16(): b_model = tf.keras.applications.VGG16(weights='imagenet', include_top=False) x = b_model.output x = GlobalAveragePooling2D()(x) x = Dense(256, activation='relu')(x) predictions = Dense(1, activation='sigmoid')(x) model = Model(inputs=b_model.input, outputs=predictions) for layer in model.layers[:15]: layer.trainable = False opt = SGD(lr=0.003, nesterov=True) model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy']) return modelmain_model = vgg_16()# main_model.fit(...)pretrained_model = Model(main_model.input, main_model.layers[-2].output)def combined_net(): inp_u = Input((224,224,3)) # 与预训练模型的输入维度相同 inp_v = Input((224,224,3)) # 与预训练模型的输入维度相同 u_output = pretrained_model(inp_u) v_output = pretrained_model(inp_v) concat = concatenate([u_output, v_output]) main_output = Dense(1, activation='sigmoid', name='main_output')(concat) model = Model(inputs=[inp_u, inp_v], outputs=main_output) opt = SGD(lr=0.001, nesterov=True) model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy']) return modelbase_model = combined_net()base_model.summary()