After fitting the model with model.fit(...)
, you can use .evaluate()
or .predict()
methods with the model
.
The problem arises when I use Checkpoint during training.
(Let's say 30 checkpoints, with checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath, save_weights_only=True)
)
Then I can't quite figure out what do I have left, the last state of this model.
Is it the best one? or the latest one?
If the former is the case, one of 30 checkpoints should be same with the model I have left.
If the latter is the case, the latest checkpoint should be same with the model I have left.
Of course, I checked both the cases and neither one is right.
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