Tensorflow Hub vs Keras应用程序-性能下降

我有图像分类问题，我想使用Keras预训练模型进行此任务. 当我使用这样的模型

I have image classification problem and i want to use Keras pretrained models for this task. When I use such a model

model = tf.keras.Sequential([
    hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4",
                   output_shape=[1280],
                   trainable=False),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(num_classes, activation='softmax')
])
model.build([None, image_size[0], image_size[1], 3])

model.compile(
    optimizer=tf.keras.optimizers.Adam(),
    loss='categorical_crossentropy',
    metrics=['acc'])

在平衡的数据集上，我很容易获得〜90％的准确度，并且损失极低.但是，如果使用keras.application这样的话:

I easily get ~90% accuracy and very low loss on balanced dataset. However, if use keras.application like that:

`base_model = tf.keras.applications.mobilenet_v2.MobileNetV2(
    input_shape=input_img_size,
    include_top=False,
    weights='imagenet'
)

base_model.trainable = False  

model = tf.keras.layers.Dropout(0.5)(model)

model = tf.keras.layers.Dense(num_classes, activation='softmax')(model)

model = tf.keras.models.Model(inputs=base_model.input, outputs=model)

model.compile(
    optimizer=tf.keras.optimizers.Adam(),
    loss='categorical_crossentropy',
    metrics=['acc'])`

，并在数据生成器中使用适当的tf.keras.application.mobilenet_v2.preprocess_input函数(并使其他所有内容保持不变)，它被困在约60％的验证和80％的训练中. 这些方法之间有什么区别?为什么一个优于另一个?

and use a proper tf.keras.application.mobilenet_v2.preprocess_input function in datagenerator (and leaving everything else the same) it is stuck at around 60% validation and 80% training. what is the difference between these approaches? why one is superior to the other?

数据生成器:

datagen = tf.keras.preprocessing.image.ImageDataGenerator(
        preprocessing_function = preprocessing_function,
        rotation_range=10,
        zoom_range=0.3,
        width_shift_range=0.2,
        height_shift_range=0.2,
        horizontal_flip=True,
        vertical_flip=True,
        shear_range=0.2,
    )

培训:

 history = model.fit_generator(
    train_generator,
    epochs=nb_epochs,
    verbose=1,
    steps_per_epoch=steps_per_epoch,
    validation_data=valid_generator,
    validation_steps=val_steps_per_epoch,
    callbacks=[
        checkpoint,
        learning_rate_reduction,
        csv_logger,
        tensorboard_callback,
    ],
)