在Keras中分段训练CNN：先训10轮再续训15轮是否可行？

Absolutely, this approach is totally valid and works seamlessly with Keras! Let’s break down how to make this work smoothly and what key details you need to keep in mind:

Why this works #

When you use model.save('my_model.h5'), Keras saves all critical components needed to resume training right where you left off:

• The full model architecture
• All trained weights from the first 10 epochs
• The state of your optimizer (including current learning rate, momentum values, and other optimizer-specific parameters)
  This means loading the model later picks up exactly at the end of the 10th epoch, no reset or reinitialization required.

Key tips for consistent training #

• Keep core parameters identical across sessions: Make sure steps_per_epoch, validation_steps, batch_size, and generator settings (like shuffle behavior or preprocessing pipelines) match between your first training run and the continuation run. This ensures each epoch processes the same volume and order of data.
• Handle custom components (if applicable): If your model uses custom layers, custom loss functions, or custom metrics, you’ll need to pass these to load_model via the custom_objects argument to avoid errors. For example:

  model = load_model('my_model.h5', custom_objects={'my_custom_loss': my_custom_loss})
  

• Validate your data generators: After restarting your Python environment, double-check that training_set and test_set are initialized correctly (same directory paths, image preprocessing rules, etc.) to avoid unexpected shifts in your training data.

Complete example code #

First training session (10 epochs) #

# Run initial 10 epochs of training
model.fit_generator(
    training_set,
    steps_per_epoch=100000,
    epochs=10,
    validation_data=test_set,
    validation_steps=40000
)

# Save the full model state
from keras.models import load_model
model.save('my_model.h5')

Continuation training session (remaining 15 epochs) #

# Load the saved model state
from keras.models import load_model
model = load_model('my_model.h5')

# Resume training for the remaining 15 epochs
model.fit_generator(
    training_set,
    steps_per_epoch=100000,
    epochs=15,
    validation_data=test_set,
    validation_steps=40000
)

Bonus: Auto-save for extra safety #

If you want to guard against crashes or unexpected interruptions, use Keras' ModelCheckpoint callback to automatically save model checkpoints after each epoch. This lets you resume from the last completed epoch without manual saves.

内容的提问来源于stack exchange，提问作者Khanh Van Bui