Allow tracking real-time loss
Someone had 6000 images in their dataset, and it was shown as 0, which was confusing. This will allow tracking real time dataset-average loss for registered objects.
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@ -360,7 +360,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
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for i, entries in pbar:
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hypernetwork.step = i + ititial_step
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if loss_dict and i % size == 0:
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if len(loss_dict) > 0:
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previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
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scheduler.apply(optimizer, hypernetwork.step)
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