update to checkpoint callback options (save_frequency) #2042
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introduced the number of batches ('n_batches') option for the save frequency instead of 'batch_size'. Using 'batch_size' works in this tutorial because the length of the training data is 1000 which coincidentally results in a rounded value of ~32 when it is divided by the 'batch_size'. In cases when the number of samples is not 1000, this will result in the model saving at different epoch frequencies other than after every 5 epochs.
the definition of 'save_freq' (https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint#args) clearly refers to the number of batches ('n_batches' in this context) and not the number of samples in a batch ('batch_size').
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@MarkDaoust @markmcd PTAL |
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introduced the number of batches ('n_batches') option for the save frequency instead of 'batch_size'. Using 'batch_size' works in this tutorial because the length of the training data is 1000 which coincidentally results in a rounded value of ~32 when it is divided by the 'batch_size'. In cases when the number of samples is not 1000, this will result in the model saving at different epoch frequencies other than after every 5 epochs.
the definition of 'save_freq' (https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint#args) clearly refers to the number of batches ('n_batches' in this context) and not the number of samples in a batch ('batch_size').