TensorFlow 2.0 Tutorial 03: Saving Checkpoints
Blog post from Lambda
The TensorFlow 2.0 Tutorial 03: Saving Checkpoints provides a comprehensive guide on how to use checkpoints to save and restore models during training using the Keras API in TensorFlow 2. The tutorial explains how to create a callback function that saves the model at regular intervals, including the epoch number and validation accuracy, and demonstrates its usage with an example code snippet. The tutorial also discusses the importance of customizing the training loop using callbacks and provides additional information on other callbacks such as LearningRateScheduler and TensorBoard. The summary concludes by highlighting the key takeaways from the tutorial, which include using tf.keras.ModelCheckpoint callbacks to save models and setting initial_epoch in model.fit calls to restore pre-saved checkpoints.
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