update advance_tutorial jupyter notebook

tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb

@@ -20,16 +20,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/remote-home/ygxu/anaconda3/envs/no-fastnlp/lib/python3.7/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
-      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 声明部件\n",
     "import torch\n",
@@ -179,11 +170,11 @@
     {
      "data": {
       "text/plain": [
-       "DataSet({'image': tensor([[ 2.1747, -1.0147, -1.3853,  0.0216, -0.4957],\n",
-       "        [ 0.8138, -0.2933, -0.1217, -0.6027,  0.3932],\n",
-       "        [ 0.6750, -1.1136, -1.3371, -0.0185, -0.3206],\n",
-       "        [-0.5076, -0.3822,  0.1719, -0.6447, -0.5702],\n",
-       "        [ 0.3804,  0.0889,  0.8027, -0.7121, -0.7320]]) type=torch.Tensor,\n",
+       "DataSet({'image': tensor([[ 4.7106e-01, -1.2246e+00,  3.1234e-01, -1.6781e+00, -8.7967e-01],\n",
+       "        [ 1.1454e+00,  1.2236e-01,  3.0258e-01, -1.5454e+00,  8.9201e-01],\n",
+       "        [-5.7143e-03,  3.9488e-01,  2.0287e-01, -1.5726e+00,  9.3171e-01],\n",
+       "        [ 6.8914e-01, -2.6302e-01, -8.2694e-01,  9.5942e-01, -5.2589e-01],\n",
+       "        [-5.7798e-03, -9.1621e-03,  1.0077e-03,  9.1716e-02,  1.0565e+00]]) type=torch.Tensor,\n",
        "'label': 0 type=int})"
       ]
      },
@@ -644,20 +635,20 @@
     {
      "data": {
       "text/plain": [
-       "({'premise': [2, 145, 146, 80, 147, 26, 148, 2, 104, 149, 150, 2, 151, 5, 55, 152, 105, 3] type=list,\n",
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-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'label': 2 type=int},\n",
-       " {'premise': [11, 5, 18, 5, 24, 6, 2, 10, 59, 52, 14, 9, 2, 53, 29, 60, 54, 45, 6, 46, 5, 7, 61, 3] type=list,\n",
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-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1] type=list,\n",
+       "({'premise': [2, 10, 9, 2, 15, 115, 6, 11, 5, 132, 17, 2, 76, 9, 77, 55, 3] type=list,\n",
+       " 'hypothesis': [1, 2, 56, 17, 1, 4, 13, 49, 123, 12, 6, 11, 3] type=list,\n",
+       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'label': 0 type=int},\n",
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+       " 'hypothesis': [21, 10, 5, 2, 55, 7, 99, 64, 48, 1, 22, 1, 3] type=list,\n",
+       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
        " 'label': 1 type=int},\n",
-       " {'premise': [2, 11, 8, 14, 16, 7, 15, 50, 2, 66, 4, 76, 2, 10, 8, 98, 9, 58, 67, 3] type=list,\n",
-       " 'hypothesis': [22, 27, 50, 3] type=list,\n",
-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1] type=list,\n",
+       " {'premise': [13, 24, 4, 14, 29, 5, 25, 4, 8, 39, 9, 14, 34, 4, 40, 41, 4, 16, 12, 2, 11, 4, 30, 28, 2, 42, 8, 2, 43, 44, 17, 2, 45, 35, 26, 31, 27, 5, 6, 32, 3] type=list,\n",
+       " 'hypothesis': [37, 49, 123, 30, 28, 2, 55, 12, 2, 11, 3] type=list,\n",
+       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
        " 'label': 0 type=int})"
       ]
      },
@@ -718,15 +709,15 @@
     {
      "data": {
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-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'label': 2 type=int},\n",
-       " {'premise': [2450, 1999, 2317, 1999, 100, 1998, 1037, 2158, 3621, 2369, 3788, 2007, 1037, 3696, 2005, 2198, 100, 10733, 1998, 100, 1999, 1996, 4281, 1012] type=list,\n",
-       " 'hypothesis': [100, 2450, 13063, 10733, 1012] type=list,\n",
-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1] type=list,\n",
+       "({'premise': [1037, 2158, 1998, 1037, 2450, 2892, 1996, 2395, 1999, 2392, 1997, 1037, 10733, 1998, 100, 4825, 1012] type=list,\n",
+       " 'hypothesis': [100, 1037, 3232, 1997, 7884, 1010, 2048, 2111, 3328, 2408, 1996, 2395, 1012] type=list,\n",
+       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'label': 0 type=int},\n",
+       " {'premise': [2019, 3080, 2158, 2003, 5948, 4589, 10869, 2012, 1037, 4825, 1012] type=list,\n",
+       " 'hypothesis': [100, 2158, 1999, 1037, 4825, 2003, 3403, 2005, 2010, 7954, 2000, 7180, 1012] type=list,\n",
+       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
        " 'label': 1 type=int})"
       ]
      },
@@ -769,7 +760,7 @@
        " 'num_classes': 3,\n",
        " 'gpu': True,\n",
        " 'batch_size': 32,\n",
-       " 'vocab_size': 165}"
+       " 'vocab_size': 156}"
       ]
      },
      "execution_count": 26,
@@ -797,7 +788,7 @@
        "ESIM(\n",
        "  (drop): Dropout(p=0.3)\n",
        "  (embedding): Embedding(\n",
-       "    (embed): Embedding(165, 300, padding_idx=0)\n",
+       "    (embed): Embedding(156, 300, padding_idx=0)\n",
        "    (dropout): Dropout(p=0.3)\n",
        "  )\n",
        "  (embedding_layer): Linear(\n",
@@ -821,7 +812,6 @@
        "    )\n",
        "    (output): Linear(in_features=300, out_features=3, bias=True)\n",
        "    (dropout): Dropout(p=0.3)\n",
-       "    (hidden_active): Tanh()\n",
        "  )\n",
        ")"
       ]
@@ -848,7 +838,7 @@
       "text/plain": [
        "CNNText(\n",
        "  (embed): Embedding(\n",
-       "    (embed): Embedding(165, 50, padding_idx=0)\n",
+       "    (embed): Embedding(156, 50, padding_idx=0)\n",
        "    (dropout): Dropout(p=0.0)\n",
        "  )\n",
        "  (conv_pool): ConvMaxpool(\n",
@@ -1019,43 +1009,49 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "training epochs started 2019-01-09 00-08-17\n",
-      "[tester] \n",
-      "AccuracyMetric: acc=0.206897\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/remote-home/ygxu/anaconda3/envs/no-fastnlp/lib/python3.7/site-packages/torch/nn/functional.py:1320: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
-      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[tester] \n",
-      "AccuracyMetric: acc=0.206897\n",
-      "[tester] \n",
-      "AccuracyMetric: acc=0.206897\n",
-      "[tester] \n",
-      "AccuracyMetric: acc=0.206897\n",
-      "[tester] \n",
-      "AccuracyMetric: acc=0.206897\n",
+      "training epochs started 2019-04-14-23-22-28\n",
+      "[epoch:   1 step:    1] train loss: 1.51372 time: 0:00:00\n",
+      "[epoch:   1 step:    2] train loss: 1.26874 time: 0:00:00\n",
+      "[epoch:   1 step:    3] train loss: 1.49786 time: 0:00:00\n",
+      "[epoch:   1 step:    4] train loss: 1.37505 time: 0:00:00\n",
+      "Evaluation at Epoch 1/5. Step:4/20. AccuracyMetric: acc=0.344828\n",
+      "\n",
+      "[epoch:   2 step:    5] train loss: 1.21877 time: 0:00:00\n",
+      "[epoch:   2 step:    6] train loss: 1.14183 time: 0:00:00\n",
+      "[epoch:   2 step:    7] train loss: 1.15934 time: 0:00:00\n",
+      "[epoch:   2 step:    8] train loss: 1.55148 time: 0:00:00\n",
+      "Evaluation at Epoch 2/5. Step:8/20. AccuracyMetric: acc=0.344828\n",
       "\n",
-      "In Epoch:1/Step:4, got best dev performance:AccuracyMetric: acc=0.206897\n",
+      "[epoch:   3 step:    9] train loss: 1.1457 time: 0:00:00\n",
+      "[epoch:   3 step:   10] train loss: 1.0547 time: 0:00:00\n",
+      "[epoch:   3 step:   11] train loss: 1.40139 time: 0:00:00\n",
+      "[epoch:   3 step:   12] train loss: 0.551445 time: 0:00:00\n",
+      "Evaluation at Epoch 3/5. Step:12/20. AccuracyMetric: acc=0.275862\n",
+      "\n",
+      "[epoch:   4 step:   13] train loss: 1.07965 time: 0:00:00\n",
+      "[epoch:   4 step:   14] train loss: 1.04118 time: 0:00:00\n",
+      "[epoch:   4 step:   15] train loss: 1.11719 time: 0:00:00\n",
+      "[epoch:   4 step:   16] train loss: 1.09861 time: 0:00:00\n",
+      "Evaluation at Epoch 4/5. Step:16/20. AccuracyMetric: acc=0.275862\n",
+      "\n",
+      "[epoch:   5 step:   17] train loss: 1.10795 time: 0:00:00\n",
+      "[epoch:   5 step:   18] train loss: 1.26715 time: 0:00:00\n",
+      "[epoch:   5 step:   19] train loss: 1.19875 time: 0:00:00\n",
+      "[epoch:   5 step:   20] train loss: 1.09862 time: 0:00:00\n",
+      "Evaluation at Epoch 5/5. Step:20/20. AccuracyMetric: acc=0.37931\n",
+      "\n",
+      "\n",
+      "In Epoch:5/Step:20, got best dev performance:AccuracyMetric: acc=0.37931\n",
       "Reloaded the best model.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'best_eval': {'AccuracyMetric': {'acc': 0.206897}},\n",
-       " 'best_epoch': 1,\n",
-       " 'best_step': 4,\n",
-       " 'seconds': 0.79}"
+       "{'best_eval': {'AccuracyMetric': {'acc': 0.37931}},\n",
+       " 'best_epoch': 5,\n",
+       " 'best_step': 20,\n",
+       " 'seconds': 0.5}"
       ]
      },
      "execution_count": 29,
@@ -1070,8 +1066,8 @@
     "trainer = Trainer(\n",
     "    train_data=train_data,\n",
     "    model=model,\n",
-    "    loss=CrossEntropyLoss(pred='pred', target='label'),\n",
-    "    metrics=AccuracyMetric(),\n",
+    "    loss=CrossEntropyLoss(pred='pred', target='label'), # 模型预测值通过'pred'来取得，目标值（ground truth）由'label'取得\n",
+    "    metrics=AccuracyMetric(target='label'), # 目标值（ground truth）由'label'取得\n",
     "    n_epochs=5,\n",
     "    batch_size=16,\n",
     "    print_every=-1,\n",
@@ -1113,13 +1109,13 @@
      "output_type": "stream",
      "text": [
       "[tester] \n",
-      "AccuracyMetric: acc=0.263158\n"
+      "AccuracyMetric: acc=0.368421\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'AccuracyMetric': {'acc': 0.263158}}"
+       "{'AccuracyMetric': {'acc': 0.368421}}"
       ]
      },
      "execution_count": 30,
@@ -1131,12 +1127,33 @@
     "tester = Tester(\n",
     "    data=test_data,\n",
     "    model=model,\n",
-    "    metrics=AccuracyMetric(),\n",
+    "    metrics=AccuracyMetric(target='label'),\n",
     "    batch_size=args[\"batch_size\"],\n",
     ")\n",
     "tester.test()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1161,7 +1178,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,