← TensorFlow & Keras learning pathTensorFlow & Keras · Module 8 of 10
Callbacks, tuning and TensorBoard
Control training and find better hyperparameters.
📖 Key ideas
- Callbacks run during training: EarlyStopping, ModelCheckpoint, ReduceLROnPlateau.
- TensorBoard callback logs metrics and graphs.
- Keras Tuner searches hyperparameters.
- Track experiments for reproducibility.
- Set random seeds for repeatable results.
💡 Real-life example: Callbacks are alarms you set during training: stop if no progress, save the best version.
CallbackFunction hooked into training
EarlyStoppingStops when metric stops improving
ModelCheckpointSaves best model
Keras TunerHyperparameter search library