如何使用Tensorflow 2.0数据集在训练时执行10个裁剪图像增强

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

# 假设数据集位于"data"文件夹下，包含训练集和验证集
train_dir = 'data/train'
valid_dir = 'data/valid'

# 使用ImageDataGenerator加载数据集
train_datagen = ImageDataGenerator(rescale=1./255)
valid_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(224, 224),  # 裁剪图像的目标尺寸
    batch_size=32,
    class_mode='binary'
)

valid_generator = valid_datagen.flow_from_directory(
    valid_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary'
)

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

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

# 定义裁剪图像增强的参数
crop_params = {
    'width_shift_range': 0.1,
    'height_shift_range': 0.1,
    'shear_range': 0.2,
    'zoom_range': 0.2,
    'horizontal_flip': True,
    'vertical_flip': True,
    'fill_mode': 'nearest'
}

# 使用ImageDataGenerator的裁剪图像增强功能
train_datagen = ImageDataGenerator(rescale=1./255, **crop_params)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary'
)

# 在模型训练中使用裁剪图像增强的数据生成器
model.fit(
    train_generator,
    steps_per_epoch=len(train_generator),
    epochs=10,
    validation_data=valid_generator,
    validation_steps=len(valid_generator)
)