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Hi Meitar,
So I was training on the MNIST dataset using pretrained features, e.g.
python DeepDPM.py --dataset MNIST --dir './pretrained_embeddings/umap_embedded_datasets/MNIST' --gpus 0
but every time training stucks at epoch 44 and will not continue, log:
Epoch 0: 100%|███████████| 547/547 [00:00<00:00, 661.71it/s, loss=nan, v_num=]Initializing clusters params using Kmeans... Epoch 44: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 547/547 [00:19<00:00, 27.60it/s, loss=0, v_num=]
Also, why the loss becomes nan in the first epoch? Appreciate if you can suggest!
The text was updated successfully, but these errors were encountered:
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Hi Meitar,
So I was training on the MNIST dataset using pretrained features, e.g.
python DeepDPM.py --dataset MNIST --dir './pretrained_embeddings/umap_embedded_datasets/MNIST' --gpus 0
but every time training stucks at epoch 44 and will not continue, log:
Epoch 0: 100%|███████████| 547/547 [00:00<00:00, 661.71it/s, loss=nan, v_num=]Initializing clusters params using Kmeans...
Epoch 44: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 547/547 [00:19<00:00, 27.60it/s, loss=0, v_num=]
Also, why the loss becomes nan in the first epoch? Appreciate if you can suggest!
The text was updated successfully, but these errors were encountered: