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Minor edit when training with a custom dataset (#484)

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When we want to train a custom dataset, the code should have the flexibility to accommodate variable number of classes.
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Kumar Ashutosh 2021-10-22 02:27:06 -05:00 committed by GitHub
parent ca70f0a6f6
commit dae1a1d112
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1 changed files with 2 additions and 2 deletions
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examples/frameworks/pytorch/notebooks/audio/audio_classifier_UrbanSound8K.ipynb
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@ -293,8 +293,8 @@
"source": [
"def test(model, epoch):\n",
" model.eval()\n",
" class_correct = list(0. for i in range(10))\n",
" class_total = list(0. for i in range(10))\n",
" class_correct = list(0. for i in range(len(classes)))\n",
" class_total = list(0. for i in range(len(classes)))\n",
" with torch.no_grad():\n",
" for idx, (sounds, sample_rate, inputs, labels) in enumerate(test_loader):\n",
" inputs = inputs.to(device)\n",