Question

我将使用预先训练的模型（先前使用save_best_only的{{1}}参数进行保存）来进行双输入转移学习。我有以下内容：

ModelCheckpoint

当我尝试使用：

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

我遇到以下错误：

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)

显然，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>]行似乎引起了错误。

我要做的就是对双输入到单输出模型使用预训练的模型（“ best_weight.h5”）。这两个输入都与先前初始化的相同，并且model = Model(inputs=[u_model.input, v_model.input], outputs=main_output)层应将由加载的模型构造的每个模型的最后一层之前的层连接起来。

我尝试了几种在线查找方法，但是无法正确设置模型。

我希望有人能帮助我

编辑：

预训练模型如下所示：

concatenate

Answer 1

这里是正确的方法。当我定义combined_net时，我定义了2个新输入，用于以相同方式提供pre_trained模型

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 model

main_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)) # the same input dim of pretrained_model
    inp_v = Input((224,224,3)) # the same input dim of pretrained_model
    
    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 model

base_model = combined_net()
base_model.summary()

如何使用预训练模型进行双输入转移学习

1 个答案: