请给我一份 tuning recipe（调参指南），适用于TensorFlow中用于图像分类的ResNet50模型， handicraft（不论格式内容，但求能进行适量修改、而非处处都能够精细化调整的文档）即可。
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Tuning Recipe for ResNet50 on TensorFlow for Image Classification

### 1. Learning Rate
- **Initial Learning Rate**: Start with a learning rate of 0.001. Adjust based on model performance.
- **Learning Rate Decay**: Implement a learning rate decay using `tf.keras.callbacks.LearningRateScheduler`. Decrease the learning rate by a factor of 0.1 every 10 epochs.

### 2. Batch Size
- **Batch Size**: Use a batch size of 32. You can experiment with sizes between 16 and 64 to find the optimal setting.

### 3. Optimizer
- **Optimizer**: Use the Adam optimizer. If performance is suboptimal, try the SGD optimizer with momentum (0.9).

### 4. Regularization
- **L2 Regularization**: Apply L2 regularization to the weights with a factor of 0.0001.

### 5. Data Augmentation
- **Data Augmentation**: Use `tf.keras.preprocessing.image.ImageDataGenerator` to apply random rotations, flips, and zooms to the training data to increase model robustness.

### 6. Early Stopping
- **Early Stopping**: Implement early stopping using `tf.keras.callbacks.EarlyStopping` to prevent overfitting. Stop training if validation loss does not improve for 5 consecutive epochs.

### 7. Model Checkpoint
- **Model Checkpoint**: Save the best model weights using `tf.keras.callbacks.ModelCheckpoint` based on the validation accuracy.

### 8. Hyperparameter Search
- **Hyperparameter Search**: Use `tf.keras.tuner` to perform a grid search or random search for hyperparameters like learning rate and batch size.

### Example Code
```python
import tensorflow as tf
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint

def lr_scheduler(epoch, lr):
    if epoch % 10 == 0 and epoch > 0:
        return lr * 0.1
    return lr

# Load pre-trained ResNet50
model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Add custom layers
x = model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
predictions = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
model = tf.keras.models.Model(inputs=model.input, outputs=predictions)

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

# Data augmentation
datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    zoom_range=0.2
)

# Callbacks
lr_callback = LearningRateScheduler(lr_scheduler)
early_stopping = EarlyStopping(monitor='val_loss', patience=5)
checkpoint = ModelCheckpoint('best_model.h5', save_best_only=True, monitor='val_accuracy')

# Train the model
history = model.fit(
    datagen.flow(x_train, y_train, batch_size=32),
    epochs=50,
    validation_data=(x_val, y_val),
    callbacks=[lr_callback, early_stopping, checkpoint]
)
```

This tuning recipe should provide a solid starting point for your ResNet50 model in TensorFlow. Feel free to make adjustments based on your specific dataset and requirements.
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