From 76f9bbf5f1fbe50dd7a541b6f9a67e3e808c6989 Mon Sep 17 00:00:00 2001 From: xuyige Date: Sun, 14 Apr 2019 23:30:23 +0800 Subject: [PATCH] update advance_tutorial jupyter notebook --- .../advance_tutorial.ipynb | 171 ++++++++++-------- 1 file changed, 94 insertions(+), 77 deletions(-) diff --git a/tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb b/tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb index a787eeaf..64eb3462 100644 --- a/tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb +++ b/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": [ - 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" {'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": { "text/plain": [ - "({'premise': [1037, 2210, 2223, 2136, 5363, 2000, 4608, 1037, 5479, 8058, 2046, 1037, 2918, 1999, 2019, 5027, 2208, 1012] type=list,\n", - " 'hypothesis': [100, 2136, 2003, 2652, 3598, 2006, 100, 1012] type=list,\n", - " '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,