Tensorflow 多输入模型中以预训练模型作为单支路输入

我有一个使用Keras函数API创建的预先训练过的模型

我想做的是：

重新训练预训练模型的层，并将其用于特定分支

使用BatchNormalization（）对分支层进行规格化

Concat层并输出到单个层

问题: 这是一种有效的微调方法吗

模型构造： 模型架构

def train_model(input1, input2, input3):

    'Load Pretrained Model'
    previous_model = keras.models.load_model('/home/john/Dupont_Internship/Transfer_Learning/Models/Meltome/meltome_fully_saved_embeddings_model/')
   
    #Freeze all model layer weights
    previous_model.trainable = False

    # saving the output layer for reconstruction later
    config_output_layer = previous_model.layers[-1].get_config()
    weights_output_layer = previous_model.layers[-1].get_weights()

    #Configure Model Input
    input1 = np.expand_dims(input1,1)
    input3 = np.expand_dims(input3, 1)

    # Define Input Layers for DNN
    input1 = Input(shape = (input1.shape[1],), name = "input1")
    input2 = Input(shape = (input2.shape[1],), name = "input2")
    input3 = Input(shape = (input3.shape[1],), name = "input3")

    inputs = [input1, input2, input3]

    # First Branch of DNN (input1)
    x = BatchNormalization()(input1) 
    x = Dense(units = 5, activation='relu', kernel_regularizer=regularizers.l2(0.01))(x)
    x = Dropout(0.08)(x)

    # Second Branch of DNN (input2)
    y = previous_model(input2)
    #y = Dense(units = 256, activation = "relu", kernel_regularizer=regularizers.l2(0.01))(y)
    #y = Dropout(0.08)(y)
    #y = BatchNormalization(y)
    
    #Third Branch of DNN (Detergents)
    z = BatchNormalization()(input3) 
    z = Dense(units = 3, activation='relu', kernel_regularizer=regularizers.l2(0.01))(z)
    z = Dropout(0.08)(z)

    # Merge the input models into a single large vector
    concatenated = Concatenate()([x, y, z])
    
    #Apply Final Output Layer
    outputs = Dense(1, name = "output")(concatenated)

    # Create an Interpretation Model (Accepts the inputs from previous branches and has single output)
    top_model = Model(inputs = inputs, outputs = outputs)
    
    # Compile the Model
    top_model.compile(loss='mse', optimizer = Adam(lr = 0.0001), metrics = ['mse'])

    # Summarize the Model Summary
    top_model.summary()
    
    return top_model