added tqdm

1_nbow.ipynb

@@ -8,18 +8,16 @@
    "outputs": [],
    "source": [
     "import functools\n",
+    "import sys\n",
     "\n",
     "import datasets\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
     "import numpy as np\n",
-    "\n",
     "import torch\n",
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
-    "\n",
-    "import torchtext"
+    "import torchtext\n",
+    "import tqdm"
    ]
   },
   {
@@ -31,7 +29,7 @@
     {
      "data": {
       "text/plain": [
-       "<torch._C.Generator at 0x7fd4fab4c930>"
+       "<torch._C.Generator at 0x7f851a9849b0>"
       ]
      },
      "execution_count": 2,
@@ -1910,7 +1908,7 @@
     "    epoch_losses = []\n",
     "    epoch_accs = []\n",
     "\n",
-    "    for batch in dataloader:\n",
+    "    for batch in tqdm.tqdm(dataloader, desc='training...', file=sys.stdout):\n",
     "        ids = batch['ids'].to(device)\n",
     "        label = batch['label'].to(device)\n",
     "        prediction = model(ids)\n",
@@ -1939,7 +1937,7 @@
     "    epoch_accs = []\n",
     "\n",
     "    with torch.no_grad():\n",
-    "        for batch in dataloader:\n",
+    "        for batch in tqdm.tqdm(dataloader, desc='evaluating...', file=sys.stdout):\n",
     "            ids = batch['ids'].to(device)\n",
     "            label = batch['label'].to(device)\n",
     "            prediction = model(ids)\n",
@@ -1976,33 +1974,53 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 14.13it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.12it/s]\n",
       "epoch: 1\n",
       "train_loss: 0.684, train_acc: 0.604\n",
       "valid_loss: 0.671, valid_acc: 0.682\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 15.06it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.24it/s]\n",
       "epoch: 2\n",
       "train_loss: 0.648, train_acc: 0.718\n",
       "valid_loss: 0.627, valid_acc: 0.729\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 14.65it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 15.88it/s]\n",
       "epoch: 3\n",
       "train_loss: 0.588, train_acc: 0.764\n",
       "valid_loss: 0.567, valid_acc: 0.769\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 14.81it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 15.66it/s]\n",
       "epoch: 4\n",
       "train_loss: 0.516, train_acc: 0.807\n",
       "valid_loss: 0.504, valid_acc: 0.803\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 14.80it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 15.93it/s]\n",
       "epoch: 5\n",
       "train_loss: 0.446, train_acc: 0.847\n",
       "valid_loss: 0.450, valid_acc: 0.833\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 14.83it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.03it/s]\n",
       "epoch: 6\n",
       "train_loss: 0.388, train_acc: 0.870\n",
       "valid_loss: 0.411, valid_acc: 0.844\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 15.40it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.37it/s]\n",
       "epoch: 7\n",
       "train_loss: 0.343, train_acc: 0.886\n",
       "valid_loss: 0.384, valid_acc: 0.852\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 15.13it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.03it/s]\n",
       "epoch: 8\n",
       "train_loss: 0.308, train_acc: 0.899\n",
       "valid_loss: 0.364, valid_acc: 0.857\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 14.99it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.12it/s]\n",
       "epoch: 9\n",
       "train_loss: 0.280, train_acc: 0.909\n",
       "valid_loss: 0.349, valid_acc: 0.862\n",
+      "training...: 100%|██████████| 37/37 [00:02<00:00, 14.62it/s]\n",
+      "evaluating...: 100%|██████████| 13/13 [00:00<00:00, 16.37it/s]\n",
       "epoch: 10\n",
       "train_loss: 0.257, train_acc: 0.917\n",
       "valid_loss: 0.336, valid_acc: 0.867\n"
@@ -2110,6 +2128,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "evaluating...: 100%|██████████| 49/49 [00:03<00:00, 15.38it/s]\n",
       "test_loss: 0.353, test_acc: 0.857\n"
      ]
     }