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@linhduongtuan
linhduongtuan committed Oct 2, 2020
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52 changes: 50 additions & 2 deletions Notebooks/Bag-of-tricks.ipynb
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" use_mixup: bool = True # Use mixup augmentation during training\n",
" mixup_alpha: float = 0.2 # Alpha value for mixup augmentation\n",
" use_cosine_scheduler: bool = True # Use Cosine LR Scheduler instead of MultiStep\n",
" batch_size: int = 32 # Mini-batch size\n",
" batch_size: int = 64 # Mini-batch size\n",
" lr: float = 1e-4 # Initial learning rate\n",
" milestones: tuple = (15, 30) # Milestones for dropping the LR\n",
" warmup: int = 6 # Number of epochs to warm up the LR. -1 to turn off\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0: 15%|█▍ | 7/48 [00:07<00:44, 1.10s/it, loss=750.181, v_num=5, train_loss=756, train_acc=0]"
"Epoch 0: 88%|████████▊ | 42/48 [00:39<00:05, 1.07it/s, loss=743.496, v_num=5, train_loss=728, train_acc=0] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 0: 90%|████████▉ | 43/48 [00:40<00:04, 1.07it/s, loss=743.496, v_num=5, train_loss=728, train_acc=0]\n",
"Epoch 0: 92%|█████████▏| 44/48 [00:40<00:03, 1.07it/s, loss=743.496, v_num=5, train_loss=728, train_acc=0]\n",
"Epoch 0: 94%|█████████▍| 45/48 [00:44<00:02, 1.02it/s, loss=743.496, v_num=5, train_loss=728, train_acc=0]\n",
"Epoch 0: 100%|██████████| 48/48 [00:44<00:00, 1.08it/s, loss=743.496, v_num=5, train_loss=728, train_acc=0]\n",
"Epoch 1: 88%|████████▊ | 42/48 [00:37<00:05, 1.11it/s, loss=729.183, v_num=5, train_loss=719, train_acc=0] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Validating: 17%|█▋ | 1/6 [00:00<00:03, 1.38it/s]\u001b[A\n",
"Epoch 1: 92%|█████████▏| 44/48 [00:39<00:03, 1.11it/s, loss=729.183, v_num=5, train_loss=719, train_acc=0]\n",
"Epoch 1: 96%|█████████▌| 46/48 [00:42<00:01, 1.08it/s, loss=729.183, v_num=5, train_loss=719, train_acc=0]\n",
"Epoch 1: 100%|██████████| 48/48 [00:42<00:00, 1.12it/s, loss=729.183, v_num=5, train_loss=719, train_acc=0]\n",
"Epoch 2: 88%|████████▊ | 42/48 [00:39<00:05, 1.06it/s, loss=705.289, v_num=5, train_loss=700, train_acc=0.143] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Validating: 17%|█▋ | 1/6 [00:00<00:03, 1.34it/s]\u001b[A\n",
"Epoch 2: 92%|█████████▏| 44/48 [00:41<00:03, 1.07it/s, loss=705.289, v_num=5, train_loss=700, train_acc=0.143]\n",
"Epoch 2: 96%|█████████▌| 46/48 [00:44<00:01, 1.04it/s, loss=705.289, v_num=5, train_loss=700, train_acc=0.143]\n",
"Epoch 2: 100%|██████████| 48/48 [00:44<00:00, 1.07it/s, loss=705.289, v_num=5, train_loss=700, train_acc=0.143]\n",
"Epoch 3: 88%|████████▊ | 42/48 [00:38<00:05, 1.08it/s, loss=697.583, v_num=5, train_loss=697, train_acc=0] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Validating: 17%|█▋ | 1/6 [00:00<00:03, 1.39it/s]\u001b[A\n",
"Epoch 3: 92%|█████████▏| 44/48 [00:40<00:03, 1.09it/s, loss=697.583, v_num=5, train_loss=697, train_acc=0]\n",
"Epoch 3: 96%|█████████▌| 46/48 [00:43<00:01, 1.05it/s, loss=697.583, v_num=5, train_loss=697, train_acc=0]\n",
"Epoch 3: 100%|██████████| 48/48 [00:44<00:00, 1.09it/s, loss=697.583, v_num=5, train_loss=697, train_acc=0]\n",
"Epoch 4: 88%|████████▊ | 42/48 [00:38<00:05, 1.08it/s, loss=696.104, v_num=5, train_loss=696, train_acc=0.714] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Validating: 17%|█▋ | 1/6 [00:00<00:03, 1.40it/s]\u001b[A\n",
"Epoch 4: 92%|█████████▏| 44/48 [00:40<00:03, 1.09it/s, loss=696.104, v_num=5, train_loss=696, train_acc=0.714]\n",
"Epoch 4: 96%|█████████▌| 46/48 [00:43<00:01, 1.05it/s, loss=696.104, v_num=5, train_loss=696, train_acc=0.714]\n",
"Epoch 4: 100%|██████████| 48/48 [00:44<00:00, 1.09it/s, loss=696.104, v_num=5, train_loss=696, train_acc=0.714]\n",
"Epoch 5: 88%|████████▊ | 42/48 [00:40<00:05, 1.04it/s, loss=695.672, v_num=5, train_loss=695, train_acc=0.143]\n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Validating: 17%|█▋ | 1/6 [00:00<00:03, 1.40it/s]\u001b[A\n",
"Epoch 5: 92%|█████████▏| 44/48 [00:41<00:03, 1.05it/s, loss=695.672, v_num=5, train_loss=695, train_acc=0.143]\n",
"Epoch 5: 96%|█████████▌| 46/48 [00:45<00:01, 1.02it/s, loss=695.672, v_num=5, train_loss=695, train_acc=0.143]\n",
"Epoch 5: 100%|██████████| 48/48 [00:45<00:00, 1.06it/s, loss=695.672, v_num=5, train_loss=695, train_acc=0.143]\n",
"Epoch 6: 88%|████████▊ | 42/48 [00:39<00:05, 1.06it/s, loss=695.532, v_num=5, train_loss=695, train_acc=0.286]\n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Validating: 17%|█▋ | 1/6 [00:00<00:03, 1.35it/s]\u001b[A\n",
"Epoch 6: 92%|█████████▏| 44/48 [00:41<00:03, 1.07it/s, loss=695.532, v_num=5, train_loss=695, train_acc=0.286]\n",
"Epoch 6: 96%|█████████▌| 46/48 [00:44<00:01, 1.04it/s, loss=695.532, v_num=5, train_loss=695, train_acc=0.286]\n",
"Epoch 6: 100%|██████████| 48/48 [00:44<00:00, 1.08it/s, loss=695.532, v_num=5, train_loss=695, train_acc=0.286]\n",
"Epoch 7: 88%|████████▊ | 42/48 [00:37<00:05, 1.13it/s, loss=695.384, v_num=5, train_loss=695, train_acc=0.714]\n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Validating: 17%|█▋ | 1/6 [00:00<00:03, 1.28it/s]\u001b[A\n",
"Epoch 7: 92%|█████████▏| 44/48 [00:38<00:03, 1.14it/s, loss=695.384, v_num=5, train_loss=695, train_acc=0.714]\n",
"Epoch 7: 96%|█████████▌| 46/48 [00:41<00:01, 1.10it/s, loss=695.384, v_num=5, train_loss=695, train_acc=0.714]\n",
"Epoch 7: 100%|██████████| 48/48 [00:42<00:00, 1.14it/s, loss=695.384, v_num=5, train_loss=695, train_acc=0.714]\n",
"Epoch 8: 81%|████████▏ | 39/48 [00:36<00:08, 1.07it/s, loss=695.262, v_num=5, train_loss=695, train_acc=0.5] "
]
}
],
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