我正在使用 TensorFlow 和 Keras 训练带有 ImageDataGenerator 的模型。但是，我遇到了警告消息并且训练速度很慢。以下是相关代码和问题...

的模型 ImageDataGenerator 。但是，我遇到了警告消息并且训练速度很慢。以下是相关代码和我面临的问题：

from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf

# ImageDataGenerator for training and testing
train_generator = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.10,
    height_shift_range=0.2,
    preprocessing_function=preprocess_to_black_lines
)

test_generator = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.10,
    height_shift_range=0.2,
    preprocessing_function=preprocess_to_black_lines
)

train_images = train_generator.flow_from_dataframe(
    dataframe=train_df,
    x_col='address',
    y_col='labels',
    target_size=(64, 64),
    batch_size=200,
    color_mode='grayscale',
    class_mode='categorical',
    seed=42,
    shuffle=True,
    subset='training'
)

val_images = train_generator.flow_from_dataframe(
    dataframe=val_df,
    x_col='address',
    y_col='labels',
    target_size=(64, 64),
    batch_size=200,
    color_mode='grayscale',
    class_mode='categorical',
    seed=42,
    shuffle=True
)

test_images = test_generator.flow_from_dataframe(
    dataframe=test_df,
    x_col='address',
    y_col='labels',
    target_size=(64, 64),
    batch_size=32,
    color_mode='grayscale',
    class_mode='categorical',
    seed=42,
    shuffle=False
)

# Model definition
inputs = tf.keras.Input(shape=(64, 64, 1))
x = tf.keras.layers.Conv2D(filters=6, kernel_size=(3, 3), activation='relu')(inputs)
x = tf.keras.layers.MaxPooling2D()(x)
x = tf.keras.layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu')(x)
x = tf.keras.layers.MaxPooling2D()(x)
x = tf.keras.layers.GlobalAveragePooling2D()(x)

x = tf.keras.layers.Dense(64, activation='relu')(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
x = tf.keras.layers.Dense(100, activation='relu')(x)
outputs = tf.keras.layers.Dense(8, activation='softmax')(x)

model = tf.keras.Model(inputs=inputs, outputs=outputs)

model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

model.summary()

history = model.fit(
    train_images,
    validation_data=val_images,
    epochs=100,
    callbacks=[
        tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
    ]
)

当我运行代码时，收到以下警告：

Epoch 1/100
/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()
59/60  5s 6s/step - accuracy: 0.1277 - loss: 3.6410 /usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()
60/60  553s 8s/step - accuracy: 0.1279 - loss: 3.6051 - val_accuracy: 0.1367 - val_loss: 2.0608
Epoch 2/100
59/60  5s 6s/step - accuracy: 0.1709 - loss: 2.0589 

此外，训练速度非常慢。

审查警告： 我检查了警告消息并尝试调整 ImageDataGenerator 。具体来说，我确保 worker , use_multiprocessing 和 max_queue_size 未传递给 fit() 方法。优化数据管道：我验证了用于训练、验证和测试的数据管道配置是否正确并与预期参数一致。

问题：

这个警告是什么意思，我该如何修复它？我该如何提高训练速度？

任何帮助都将不胜感激！