add smaller lr

dev4.ipynb

@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,9 +41,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Running param set: {'data_path_base': '/Users/aziai/Downloads/vtest_new2', 'conv_model_name': 'densenet', 'num_epochs': 10, 'feature_extract': False, 'batch_size': 64, 'lr': 0.001, 'use_vggish': False, 'momentum': 0.9}\n",
-      "Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to /Users/aziai/.cache/torch/checkpoints/densenet121-a639ec97.pth\n",
-      "100%|██████████| 32342954/32342954 [00:03<00:00, 9210604.74it/s] \n"
+      "Running param set: {'data_path_base': 'vtest_new2', 'conv_model_name': 'densenet', 'num_epochs': 10, 'feature_extract': False, 'batch_size': 64, 'lr': 0.01, 'use_vggish': False, 'momentum': 0.9}\n"
      ]
     },
     {
@@ -53,66 +51,64 @@
       "Updating ALL params\n",
       "Epoch 0/9\n",
       "----------\n",
-      "train Loss: 0.7583 F1: 0.6549 Acc: 0.5063\n",
-      "val Loss: 0.6403 F1: 0.7969 Acc: 0.6941\n",
+      "train Loss: 0.8410 F1: 0.3704 Acc: 0.5696\n",
+      "val Loss: 0.6466 F1: 0.8667 Acc: 0.7647\n",
       "\n",
       "Epoch 1/9\n",
       "----------\n",
-      "train Loss: 0.6932 F1: 0.6531 Acc: 0.5696\n",
-      "val Loss: 0.7109 F1: 0.4898 Acc: 0.4118\n",
+      "train Loss: 1.0247 F1: 0.6949 Acc: 0.5443\n",
+      "val Loss: 1.7111 F1: nan Acc: 0.2235\n",
       "\n",
       "Epoch 2/9\n",
       "----------\n",
-      "train Loss: 0.6125 F1: 0.6667 Acc: 0.6709\n",
-      "val Loss: 0.7488 F1: 0.3820 Acc: 0.3529\n",
+      "train Loss: 0.4048 F1: 0.6557 Acc: 0.7342\n",
+      "val Loss: 1.5030 F1: 0.1370 Acc: 0.2588\n",
       "\n",
       "Epoch 3/9\n",
       "----------\n",
-      "train Loss: 0.5145 F1: 0.8462 Acc: 0.8481\n",
-      "val Loss: 0.6949 F1: 0.5455 Acc: 0.4706\n",
+      "train Loss: 0.0870 F1: 0.9877 Acc: 0.9873\n",
+      "val Loss: 0.6543 F1: 0.7627 Acc: 0.6706\n",
       "\n",
       "Epoch 4/9\n",
       "----------\n",
-      "train Loss: 0.4379 F1: 0.9070 Acc: 0.8987\n",
-      "val Loss: 0.6436 F1: 0.6607 Acc: 0.5529\n",
+      "train Loss: 0.0952 F1: 0.9535 Acc: 0.9494\n",
+      "val Loss: 0.6773 F1: 0.8358 Acc: 0.7412\n",
       "\n",
       "Epoch 5/9\n",
       "----------\n",
-      "train Loss: 0.3507 F1: 0.9524 Acc: 0.9494\n",
-      "val Loss: 0.6525 F1: 0.6019 Acc: 0.5176\n",
+      "train Loss: 0.0807 F1: 0.9647 Acc: 0.9620\n",
+      "val Loss: 0.8060 F1: 0.8182 Acc: 0.7176\n",
       "\n",
       "Epoch 6/9\n",
       "----------\n",
-      "train Loss: 0.2782 F1: 0.9639 Acc: 0.9620\n",
-      "val Loss: 0.7106 F1: 0.4946 Acc: 0.4471\n",
+      "train Loss: 0.0083 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.2097 F1: 0.6667 Acc: 0.5529\n",
       "\n",
       "Epoch 7/9\n",
       "----------\n",
-      "train Loss: 0.2353 F1: 0.9877 Acc: 0.9873\n",
-      "val Loss: 0.7582 F1: 0.4889 Acc: 0.4588\n",
+      "train Loss: 0.0021 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.7171 F1: 0.5102 Acc: 0.4353\n",
       "\n",
       "Epoch 8/9\n",
       "----------\n",
-      "train Loss: 0.1920 F1: 0.9877 Acc: 0.9873\n",
-      "val Loss: 0.7496 F1: 0.4946 Acc: 0.4471\n",
+      "train Loss: 0.0003 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 2.0735 F1: 0.4421 Acc: 0.3765\n",
       "\n",
       "Epoch 9/9\n",
       "----------\n",
-      "train Loss: 0.1625 F1: 0.9880 Acc: 0.9873\n",
-      "val Loss: 0.7183 F1: 0.5657 Acc: 0.4941\n",
+      "train Loss: 0.0002 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 2.5907 F1: 0.3218 Acc: 0.3059\n",
       "\n",
-      "Training complete in 6m 48s\n",
-      "Best val F1  : 0.796875\n",
-      "Best val Acc : 0.694118\n"
+      "Training complete in 6m 49s\n",
+      "Best val F1  : 0.866667\n",
+      "Best val Acc : 0.764706\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Running param set: {'data_path_base': '/Users/aziai/Downloads/vtest_new2', 'conv_model_name': 'densenet', 'num_epochs': 10, 'feature_extract': False, 'batch_size': 64, 'lr': 0.001, 'use_vggish': True, 'momentum': 0.9}\n",
-      "Downloading: \"https://users.cs.cf.ac.uk/taylorh23/pytorch/models/vggish-918c2d05.pth\" to /Users/aziai/.cache/torch/checkpoints/vggish-918c2d05.pth\n",
-      "100%|██████████| 288567959/288567959 [04:26<00:00, 1082574.06it/s]\n"
+      "Running param set: {'data_path_base': 'vtest_new2', 'conv_model_name': 'densenet', 'num_epochs': 10, 'feature_extract': False, 'batch_size': 64, 'lr': 0.01, 'use_vggish': False, 'momentum': 0.95}\n"
      ]
     },
     {
@@ -122,114 +118,184 @@
       "Updating ALL params\n",
       "Epoch 0/9\n",
       "----------\n",
-      "train Loss: 0.6834 F1: 0.4516 Acc: 0.5696\n",
-      "val Loss: 0.6310 F1: 0.8154 Acc: 0.7176\n",
+      "train Loss: 0.6807 F1: 0.6667 Acc: 0.5696\n",
+      "val Loss: 0.6620 F1: 0.6729 Acc: 0.5882\n",
       "\n",
       "Epoch 1/9\n",
       "----------\n",
-      "train Loss: 0.6429 F1: 0.6593 Acc: 0.6076\n",
-      "val Loss: 0.5617 F1: 0.8966 Acc: 0.8235\n",
+      "train Loss: 0.3776 F1: 0.9425 Acc: 0.9367\n",
+      "val Loss: 0.8433 F1: 0.3000 Acc: 0.3412\n",
       "\n",
       "Epoch 2/9\n",
       "----------\n",
-      "train Loss: 0.5885 F1: 0.7407 Acc: 0.6456\n",
-      "val Loss: 0.5736 F1: 0.8951 Acc: 0.8235\n",
+      "train Loss: 0.1782 F1: 0.9750 Acc: 0.9747\n",
+      "val Loss: 0.7165 F1: 0.5376 Acc: 0.4941\n",
       "\n",
       "Epoch 3/9\n",
       "----------\n",
-      "train Loss: 0.4830 F1: 0.8421 Acc: 0.8101\n",
-      "val Loss: 0.6669 F1: 0.6275 Acc: 0.5529\n",
+      "train Loss: 0.1016 F1: 0.9762 Acc: 0.9747\n",
+      "val Loss: 0.6558 F1: 0.6981 Acc: 0.6235\n",
       "\n",
       "Epoch 4/9\n",
       "----------\n",
-      "train Loss: 0.4137 F1: 0.9750 Acc: 0.9747\n",
-      "val Loss: 0.7561 F1: 0.2597 Acc: 0.3294\n",
+      "train Loss: 0.0343 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 0.8724 F1: 0.5111 Acc: 0.4824\n",
       "\n",
       "Epoch 5/9\n",
       "----------\n",
-      "train Loss: 0.3440 F1: 0.9877 Acc: 0.9873\n",
-      "val Loss: 0.7366 F1: 0.4419 Acc: 0.4353\n",
+      "train Loss: 0.0107 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.0340 F1: 0.4889 Acc: 0.4588\n",
       "\n",
       "Epoch 6/9\n",
       "----------\n",
-      "train Loss: 0.2656 F1: 0.9877 Acc: 0.9873\n",
-      "val Loss: 0.6959 F1: 0.5895 Acc: 0.5412\n",
+      "train Loss: 0.0066 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.3202 F1: 0.4186 Acc: 0.4118\n",
       "\n",
       "Epoch 7/9\n",
       "----------\n",
-      "train Loss: 0.2115 F1: 1.0000 Acc: 1.0000\n",
-      "val Loss: 0.6215 F1: 0.6981 Acc: 0.6235\n",
+      "train Loss: 0.0046 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.5957 F1: 0.3953 Acc: 0.3882\n",
       "\n",
       "Epoch 8/9\n",
       "----------\n",
-      "train Loss: 0.1942 F1: 0.9647 Acc: 0.9620\n",
-      "val Loss: 0.6100 F1: 0.7273 Acc: 0.6471\n",
+      "train Loss: 0.0015 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.9662 F1: 0.4186 Acc: 0.4118\n",
       "\n",
       "Epoch 9/9\n",
       "----------\n",
-      "train Loss: 0.1419 F1: 1.0000 Acc: 1.0000\n",
-      "val Loss: 0.7175 F1: 0.6535 Acc: 0.5882\n",
+      "train Loss: 0.0007 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 2.1900 F1: 0.4045 Acc: 0.3765\n",
       "\n",
-      "Training complete in 8m 22s\n",
-      "Best val F1  : 0.896552\n",
-      "Best val Acc : 0.823529\n"
+      "Training complete in 6m 60s\n",
+      "Best val F1  : 0.698113\n",
+      "Best val Acc : 0.623529\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Running param set: {'data_path_base': '/Users/aziai/Downloads/vtest_new2', 'conv_model_name': 'densenet', 'num_epochs': 10, 'feature_extract': True, 'batch_size': 64, 'lr': 0.001, 'use_vggish': False, 'momentum': 0.9}\n"
+      "Running param set: {'data_path_base': 'vtest_new2', 'conv_model_name': 'densenet', 'num_epochs': 10, 'feature_extract': False, 'batch_size': 64, 'lr': 0.01, 'use_vggish': True, 'momentum': 0.9}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Params to update\n",
-      "* combined.weight\n",
-      "* combined.bias\n",
+      "Updating ALL params\n",
+      "Epoch 0/9\n",
+      "----------\n",
+      "train Loss: 0.6751 F1: 0.5352 Acc: 0.5823\n",
+      "val Loss: 0.5813 F1: 0.8742 Acc: 0.7765\n",
+      "\n",
+      "Epoch 1/9\n",
+      "----------\n",
+      "train Loss: 0.6840 F1: 0.7130 Acc: 0.5823\n",
+      "val Loss: 2.9018 F1: nan Acc: 0.2235\n",
+      "\n",
+      "Epoch 2/9\n",
+      "----------\n",
+      "train Loss: 1.1845 F1: nan Acc: 0.4810\n",
+      "val Loss: 0.6255 F1: 0.7581 Acc: 0.6471\n",
+      "\n",
+      "Epoch 3/9\n",
+      "----------\n",
+      "train Loss: 0.1377 F1: 0.9535 Acc: 0.9494\n",
+      "val Loss: 1.1300 F1: 0.8800 Acc: 0.7882\n",
+      "\n",
+      "Epoch 4/9\n",
+      "----------\n",
+      "train Loss: 1.2311 F1: 0.7321 Acc: 0.6203\n",
+      "val Loss: 0.9068 F1: 0.8321 Acc: 0.7294\n",
+      "\n",
+      "Epoch 5/9\n",
+      "----------\n",
+      "train Loss: 0.0355 F1: 0.9756 Acc: 0.9747\n",
+      "val Loss: 2.1568 F1: 0.3059 Acc: 0.3059\n",
+      "\n",
+      "Epoch 6/9\n",
+      "----------\n",
+      "train Loss: 0.0030 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 6.2178 F1: nan Acc: 0.2235\n",
+      "\n",
+      "Epoch 7/9\n",
+      "----------\n",
+      "train Loss: 0.0112 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 8.0201 F1: nan Acc: 0.2235\n",
+      "\n",
+      "Epoch 8/9\n",
+      "----------\n",
+      "train Loss: 0.0022 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 9.1074 F1: nan Acc: 0.2235\n",
+      "\n",
+      "Epoch 9/9\n",
+      "----------\n",
+      "train Loss: 0.0037 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 9.3362 F1: nan Acc: 0.2235\n",
+      "\n",
+      "Training complete in 7m 50s\n",
+      "Best val F1  : 0.880000\n",
+      "Best val Acc : 0.788235\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Running param set: {'data_path_base': 'vtest_new2', 'conv_model_name': 'densenet', 'num_epochs': 10, 'feature_extract': False, 'batch_size': 64, 'lr': 0.01, 'use_vggish': True, 'momentum': 0.95}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Updating ALL params\n",
       "Epoch 0/9\n",
       "----------\n",
-      "train Loss: 0.6883 F1: 0.6538 Acc: 0.5443\n",
-      "val Loss: 0.7030 F1: 0.6261 Acc: 0.4941\n",
+      "train Loss: 0.6752 F1: 0.5526 Acc: 0.5696\n",
+      "val Loss: 0.5195 F1: 0.8742 Acc: 0.7765\n",
       "\n",
       "Epoch 1/9\n",
       "----------\n",
-      "train Loss: 0.6528 F1: 0.6729 Acc: 0.5570\n",
-      "val Loss: 0.6945 F1: 0.6306 Acc: 0.5176\n",
+      "train Loss: 0.5782 F1: 0.7387 Acc: 0.6329\n",
+      "val Loss: 1.4869 F1: 0.0299 Acc: 0.2353\n",
       "\n",
       "Epoch 2/9\n",
       "----------\n",
-      "train Loss: 0.6262 F1: 0.7184 Acc: 0.6329\n",
-      "val Loss: 0.7183 F1: 0.6154 Acc: 0.5294\n",
+      "train Loss: 0.3623 F1: 0.7941 Acc: 0.8228\n",
+      "val Loss: 0.5903 F1: 0.7692 Acc: 0.6824\n",
       "\n",
       "Epoch 3/9\n",
       "----------\n",
-      "train Loss: 0.5804 F1: 0.7191 Acc: 0.6835\n",
-      "val Loss: 0.7631 F1: 0.4667 Acc: 0.4353\n",
+      "train Loss: 0.0622 F1: 0.9880 Acc: 0.9873\n",
+      "val Loss: 0.4745 F1: 0.8714 Acc: 0.7882\n",
       "\n",
       "Epoch 4/9\n",
       "----------\n",
-      "train Loss: 0.5529 F1: 0.7179 Acc: 0.7215\n",
-      "val Loss: 0.7377 F1: 0.5161 Acc: 0.4706\n",
+      "train Loss: 0.0707 F1: 0.9762 Acc: 0.9747\n",
+      "val Loss: 0.5149 F1: 0.8759 Acc: 0.8000\n",
       "\n",
       "Epoch 5/9\n",
       "----------\n",
-      "train Loss: 0.4921 F1: 0.8434 Acc: 0.8354\n",
-      "val Loss: 0.6402 F1: 0.7500 Acc: 0.6706\n",
+      "train Loss: 0.0126 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 0.6557 F1: 0.8244 Acc: 0.7294\n",
       "\n",
       "Epoch 6/9\n",
       "----------\n",
-      "train Loss: 0.4559 F1: 0.8667 Acc: 0.8481\n",
-      "val Loss: 0.5956 F1: 0.8455 Acc: 0.7765\n",
+      "train Loss: 0.0048 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.0149 F1: 0.7069 Acc: 0.6000\n",
       "\n",
       "Epoch 7/9\n",
       "----------\n",
-      "train Loss: 0.4243 F1: 0.8723 Acc: 0.8481\n",
-      "val Loss: 0.5929 F1: 0.8167 Acc: 0.7412\n",
+      "train Loss: 0.0042 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.3682 F1: 0.7009 Acc: 0.5882\n",
       "\n",
       "Epoch 8/9\n",
+      "----------\n",
+      "train Loss: 0.0025 F1: 1.0000 Acc: 1.0000\n",
+      "val Loss: 1.7442 F1: 0.6607 Acc: 0.5529\n",
+      "\n",
+      "Epoch 9/9\n",
       "----------\n"
      ]
     }
@@ -240,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -300,11 +366,11 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>9</th>\n",
        "      <td>64</td>\n",
        "      <td>resnet</td>\n",
        "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
-       "      <td>20190602194822</td>\n",
+       "      <td>20190602195657</td>\n",
        "      <td>False</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.9</td>\n",
@@ -315,11 +381,11 @@
        "      <td>0.935252</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>5</th>\n",
        "      <td>64</td>\n",
        "      <td>NaN</td>\n",
        "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
-       "      <td>20190602194822</td>\n",
+       "      <td>20190602195657</td>\n",
        "      <td>False</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.9</td>\n",
@@ -330,11 +396,26 @@
        "      <td>0.897638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>1</th>\n",
+       "      <td>64</td>\n",
+       "      <td>densenet</td>\n",
+       "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
+       "      <td>20190602195657</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>697154af4871003b02cf60d53531fb21a80e853646b458...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>0.896552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
        "      <td>64</td>\n",
        "      <td>resnet</td>\n",
        "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
-       "      <td>20190602194822</td>\n",
+       "      <td>20190602195657</td>\n",
        "      <td>False</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.9</td>\n",
@@ -345,11 +426,11 @@
        "      <td>0.878049</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>7</th>\n",
        "      <td>64</td>\n",
        "      <td>NaN</td>\n",
        "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
-       "      <td>20190602194822</td>\n",
+       "      <td>20190602195657</td>\n",
        "      <td>True</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.9</td>\n",
@@ -360,11 +441,26 @@
        "      <td>0.874172</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>2</th>\n",
+       "      <td>64</td>\n",
+       "      <td>densenet</td>\n",
+       "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
+       "      <td>20190602195657</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>89b8a27230c7f2ee2dc0ece6fd1f9deccae873fce8288f...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.776471</td>\n",
+       "      <td>0.845528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
        "      <td>64</td>\n",
        "      <td>resnet</td>\n",
        "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
-       "      <td>20190602194822</td>\n",
+       "      <td>20190602195657</td>\n",
        "      <td>True</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.9</td>\n",
@@ -375,11 +471,41 @@
        "      <td>0.824427</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>0</th>\n",
+       "      <td>64</td>\n",
+       "      <td>densenet</td>\n",
+       "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
+       "      <td>20190602195657</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5c8b5c5ceb49ba7d53ccc921e116ae183fc5b44037c410...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.694118</td>\n",
+       "      <td>0.796875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>64</td>\n",
+       "      <td>densenet</td>\n",
+       "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
+       "      <td>20190602195657</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>31c3e541f0e5d5b5c1823de1f23c1bc4c8c4be3c22eef1...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.529412</td>\n",
+       "      <td>0.629630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
        "      <td>64</td>\n",
        "      <td>resnet</td>\n",
        "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
-       "      <td>20190602194822</td>\n",
+       "      <td>20190602195657</td>\n",
        "      <td>True</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.9</td>\n",
@@ -390,11 +516,11 @@
        "      <td>0.626263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>4</th>\n",
        "      <td>64</td>\n",
        "      <td>NaN</td>\n",
        "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
-       "      <td>20190602194822</td>\n",
+       "      <td>20190602195657</td>\n",
        "      <td>False</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.9</td>\n",
@@ -405,11 +531,11 @@
        "      <td>-1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>6</th>\n",
        "      <td>64</td>\n",
        "      <td>NaN</td>\n",
        "      <td>/Users/aziai/Downloads/vtest_new2</td>\n",
-       "      <td>20190602194822</td>\n",
+       "      <td>20190602195657</td>\n",
        "      <td>True</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.9</td>\n",
@@ -424,18 +550,22 @@
        "</div>"
       ],
       "text/plain": [
-       "   batch_size conv_model_name                     data_path_base  experiment_uuid  feature_extract     lr  momentum  num_epochs                                        runner_uuid  use_vggish   val_acc    val_f1\n",
-       "5          64          resnet  /Users/aziai/Downloads/vtest_new2   20190602194822            False  0.001       0.9          10  b3290017112d2116e4bdbb9c8dbf15a8e75adacb942afb...        True  0.894118  0.935252\n",
-       "1          64             NaN  /Users/aziai/Downloads/vtest_new2   20190602194822            False  0.001       0.9          10  4687cd536acf6c9b058a8d20719fa6910f50a6abee3ae2...        True  0.847059  0.897638\n",
-       "4          64          resnet  /Users/aziai/Downloads/vtest_new2   20190602194822            False  0.001       0.9          10  0e57debc92afdd0dc7a209584b4d97860c9dba98f3aed4...       False  0.823529  0.878049\n",
-       "3          64             NaN  /Users/aziai/Downloads/vtest_new2   20190602194822             True  0.001       0.9          10  de7011637531360e5c76520f054d90327cc478dbabeab9...        True  0.776471  0.874172\n",
-       "7          64          resnet  /Users/aziai/Downloads/vtest_new2   20190602194822             True  0.001       0.9          10  8e644bc291a463725bf0bcb11825a196383a4860eeecd7...        True  0.729412  0.824427\n",
-       "6          64          resnet  /Users/aziai/Downloads/vtest_new2   20190602194822             True  0.001       0.9          10  e07e5119b07164f06098d1adba9e4c43ad0344716a0746...       False  0.564706  0.626263\n",
-       "0          64             NaN  /Users/aziai/Downloads/vtest_new2   20190602194822            False  0.001       0.9          10  7324021853e0ca109ff0effaf0c9d06e68d72744305f34...       False -1.000000 -1.000000\n",
-       "2          64             NaN  /Users/aziai/Downloads/vtest_new2   20190602194822             True  0.001       0.9          10  9ca13084e79d88cec23574c0c37fa9109fe87a7026f9bd...       False -1.000000 -1.000000"
+       "    batch_size conv_model_name                     data_path_base  experiment_uuid  feature_extract     lr  momentum  num_epochs                                        runner_uuid  use_vggish   val_acc    val_f1\n",
+       "9           64          resnet  /Users/aziai/Downloads/vtest_new2   20190602195657            False  0.001       0.9          10  b3290017112d2116e4bdbb9c8dbf15a8e75adacb942afb...        True  0.894118  0.935252\n",
+       "5           64             NaN  /Users/aziai/Downloads/vtest_new2   20190602195657            False  0.001       0.9          10  4687cd536acf6c9b058a8d20719fa6910f50a6abee3ae2...        True  0.847059  0.897638\n",
+       "1           64        densenet  /Users/aziai/Downloads/vtest_new2   20190602195657            False  0.001       0.9          10  697154af4871003b02cf60d53531fb21a80e853646b458...        True  0.823529  0.896552\n",
+       "8           64          resnet  /Users/aziai/Downloads/vtest_new2   20190602195657            False  0.001       0.9          10  0e57debc92afdd0dc7a209584b4d97860c9dba98f3aed4...       False  0.823529  0.878049\n",
+       "7           64             NaN  /Users/aziai/Downloads/vtest_new2   20190602195657             True  0.001       0.9          10  de7011637531360e5c76520f054d90327cc478dbabeab9...        True  0.776471  0.874172\n",
+       "2           64        densenet  /Users/aziai/Downloads/vtest_new2   20190602195657             True  0.001       0.9          10  89b8a27230c7f2ee2dc0ece6fd1f9deccae873fce8288f...       False  0.776471  0.845528\n",
+       "11          64          resnet  /Users/aziai/Downloads/vtest_new2   20190602195657             True  0.001       0.9          10  8e644bc291a463725bf0bcb11825a196383a4860eeecd7...        True  0.729412  0.824427\n",
+       "0           64        densenet  /Users/aziai/Downloads/vtest_new2   20190602195657            False  0.001       0.9          10  5c8b5c5ceb49ba7d53ccc921e116ae183fc5b44037c410...       False  0.694118  0.796875\n",
+       "3           64        densenet  /Users/aziai/Downloads/vtest_new2   20190602195657             True  0.001       0.9          10  31c3e541f0e5d5b5c1823de1f23c1bc4c8c4be3c22eef1...        True  0.529412  0.629630\n",
+       "10          64          resnet  /Users/aziai/Downloads/vtest_new2   20190602195657             True  0.001       0.9          10  e07e5119b07164f06098d1adba9e4c43ad0344716a0746...       False  0.564706  0.626263\n",
+       "4           64             NaN  /Users/aziai/Downloads/vtest_new2   20190602195657            False  0.001       0.9          10  7324021853e0ca109ff0effaf0c9d06e68d72744305f34...       False -1.000000 -1.000000\n",
+       "6           64             NaN  /Users/aziai/Downloads/vtest_new2   20190602195657             True  0.001       0.9          10  9ca13084e79d88cec23574c0c37fa9109fe87a7026f9bd...       False -1.000000 -1.000000"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }

params.py

@@ -21,7 +21,7 @@ experiments = {
     'num_epochs': {10},
     'feature_extract': {True, False},
     'batch_size': {64},
-    'lr': {1e-3, 1e-2},
+    'lr': {1e-3, 1e-2, 5e-4},
     'use_vggish': {False, True},
     'momentum': {0.9, 0.95}
 }