hummingbird
/
fastNLP

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fastNLP上手教程\n",
    "-------\n",
    "\n",
    "fastNLP提供方便的数据预处理，训练和测试模型的功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yh/miniconda2/envs/python3/lib/python3.6/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"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('/Users/yh/Desktop/fastNLP/fastNLP/')\n",
    "\n",
    "import fastNLP as fnlp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataSet & Instance\n",
    "------\n",
    "\n",
    "fastNLP用DataSet和Instance保存和处理数据。每个DataSet表示一个数据集，每个Instance表示一个数据样本。一个DataSet存有多个Instance，每个Instance可以自定义存哪些内容。\n",
    "\n",
    "有一些read_*方法，可以轻松从文件读取数据，存成DataSet。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
      "'label': 1}\n"
     ]
    }
   ],
   "source": [
    "from fastNLP import DataSet\n",
    "from fastNLP import Instance\n",
    "\n",
    "# 从csv读取数据到DataSet\n",
    "dataset = DataSet.read_csv('sentence.csv', headers=('raw_sentence', 'label'), sep='\\t')\n",
    "print(dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'raw_sentence': fake data,\n",
       "'label': 0}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataSet.append(Instance)加入新数据\n",
    "\n",
    "dataset.append(Instance(raw_sentence='fake data', label='0'))\n",
    "dataset[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataSet.apply(func, new_field_name)对数据预处理\n",
    "\n",
    "# 将所有数字转为小写\n",
    "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n",
    "# label转int\n",
    "dataset.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)\n",
    "# 使用空格分割句子\n",
    "dataset.drop(lambda x:len(x['raw_sentence'].split())==0)\n",
    "def split_sent(ins):\n",
    "    return ins['raw_sentence'].split()\n",
    "dataset.apply(split_sent, new_field_name='words', is_input=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataSet.drop(func)筛除数据\n",
    "# 删除低于某个长度的词语\n",
    "# dataset.drop(lambda x: len(x['words']) <= 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size:  5971\n",
      "Test size:  2558\n"
     ]
    }
   ],
   "source": [
    "# 分出测试集、训练集\n",
    "\n",
    "test_data, train_data = dataset.split(0.3)\n",
    "print(\"Train size: \", len(test_data))\n",
    "print(\"Test size: \", len(train_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vocabulary\n",
    "------\n",
    "\n",
    "fastNLP中的Vocabulary轻松构建词表，将词转成数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'raw_sentence': gussied up with so many distracting special effects and visual party tricks that it 's not clear whether we 're supposed to shriek or laugh .,\n",
      "'label': 1,\n",
      "'label_seq': 1,\n",
      "'words': ['gussied', 'up', 'with', 'so', 'many', 'distracting', 'special', 'effects', 'and', 'visual', 'party', 'tricks', 'that', 'it', \"'s\", 'not', 'clear', 'whether', 'we', \"'re\", 'supposed', 'to', 'shriek', 'or', 'laugh', '.'],\n",
      "'word_seq': [1, 65, 16, 43, 108, 1, 329, 433, 7, 319, 1313, 1, 12, 10, 11, 27, 1428, 567, 86, 134, 1949, 8, 1, 49, 506, 2]}\n"
     ]
    }
   ],
   "source": [
    "from fastNLP import Vocabulary\n",
    "\n",
    "# 构建词表, Vocabulary.add(word)\n",
    "vocab = Vocabulary(min_freq=2)\n",
    "train_data.apply(lambda x: [vocab.add(word) for word in x['words']])\n",
    "vocab.build_vocab()\n",
    "\n",
    "# index句子, Vocabulary.to_index(word)\n",
    "train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)\n",
    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)\n",
    "\n",
    "\n",
    "print(test_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_x has:  {'words': array([list(['this', 'kind', 'of', 'hands-on', 'storytelling', 'is', 'ultimately', 'what', 'makes', 'shanghai', 'ghetto', 'move', 'beyond', 'a', 'good', ',', 'dry', ',', 'reliable', 'textbook', 'and', 'what', 'allows', 'it', 'to', 'rank', 'with', 'its', 'worthy', 'predecessors', '.']),\n",
      "       list(['the', 'entire', 'movie', 'is', 'filled', 'with', 'deja', 'vu', 'moments', '.'])],\n",
      "      dtype=object), 'word_seq': tensor([[  19,  184,    6,    1,  481,    9,  206,   50,   91, 1210, 1609, 1330,\n",
      "          495,    5,   63,    4, 1269,    4,    1, 1184,    7,   50, 1050,   10,\n",
      "            8, 1611,   16,   21, 1039,    1,    2],\n",
      "        [   3,  711,   22,    9, 1282,   16, 2482, 2483,  200,    2,    0,    0,\n",
      "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "            0,    0,    0,    0,    0,    0,    0]])}\n",
      "batch_y has:  {'label_seq': tensor([3, 2])}\n"
     ]
    }
   ],
   "source": [
    "# 假设你们需要做强化学习或者gan之类的项目，也许你们可以使用这里的dataset\n",
    "from fastNLP.core.batch import Batch\n",
    "from fastNLP.core.sampler import RandomSampler\n",
    "\n",
    "batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler())\n",
    "for batch_x, batch_y in batch_iterator:\n",
    "    print(\"batch_x has: \", batch_x)\n",
    "    print(\"batch_y has: \", batch_y)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNNText(\n",
       "  (embed): Embedding(\n",
       "    (embed): Embedding(3470, 50, padding_idx=0)\n",
       "    (dropout): Dropout(p=0.0)\n",
       "  )\n",
       "  (conv_pool): ConvMaxpool(\n",
       "    (convs): ModuleList(\n",
       "      (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n",
       "      (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n",
       "      (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1)\n",
       "  (fc): Linear(\n",
       "    (linear): Linear(in_features=12, out_features=5, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义一个简单的Pytorch模型\n",
    "\n",
    "from fastNLP.models import CNNText\n",
    "model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trainer & Tester\n",
    "------\n",
    "\n",
    "使用fastNLP的Trainer训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import Trainer\n",
    "from copy import deepcopy\n",
    "from fastNLP.core.losses import CrossEntropyLoss\n",
    "from fastNLP.core.metrics import AccuracyMetric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epochs started 2018-12-05 15:37:15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=1870), HTML(value='')), layout=Layout(display…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10. Step:187/1870. AccuracyMetric: acc=0.351365\n",
      "Epoch 2/10. Step:374/1870. AccuracyMetric: acc=0.470943\n",
      "Epoch 3/10. Step:561/1870. AccuracyMetric: acc=0.600402\n",
      "Epoch 4/10. Step:748/1870. AccuracyMetric: acc=0.702227\n",
      "Epoch 5/10. Step:935/1870. AccuracyMetric: acc=0.79099\n",
      "Epoch 6/10. Step:1122/1870. AccuracyMetric: acc=0.846424\n",
      "Epoch 7/10. Step:1309/1870. AccuracyMetric: acc=0.874058\n",
      "Epoch 8/10. Step:1496/1870. AccuracyMetric: acc=0.898844\n",
      "Epoch 9/10. Step:1683/1870. AccuracyMetric: acc=0.910568\n",
      "Epoch 10/10. Step:1870/1870. AccuracyMetric: acc=0.921286\n",
      "\r"
     ]
    }
   ],
   "source": [
    "# 进行overfitting测试\n",
    "copy_model = deepcopy(model)\n",
    "overfit_trainer = Trainer(model=copy_model, \n",
    "                          train_data=test_data, \n",
    "                          dev_data=test_data,\n",
    "                          losser=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n",
    "                          metrics=AccuracyMetric(),\n",
    "                          n_epochs=10,\n",
    "                          save_path=None)\n",
    "overfit_trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epochs started 2018-12-05 15:37:41\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=400), HTML(value='')), layout=Layout(display=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'squeeze'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-5603b8b11a82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m                   \u001b[0mn_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m                  save_path='save/')\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Train finished!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_summary_writer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSummaryWriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_tqdm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tqdm_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/trainer.py\u001b[0m in \u001b[0;36m_tqdm_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    206\u001b[0m                         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_every\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdev_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m                     \u001b[0meval_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_validation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m                     \u001b[0meval_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Epoch {}/{}. Step:{}/{}. \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_steps\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtester\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format_eval_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_res\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/trainer.py\u001b[0m in \u001b[0;36m_do_validation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtester\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_summary_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"valid_{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_path\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_better_eval_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             \u001b[0mmetric_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric_key\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric_key\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"None\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda2/envs/python3/lib/python3.6/site-packages/tensorboardX/writer.py\u001b[0m in \u001b[0;36madd_scalar\u001b[0;34m(self, tag, scalar_value, global_step, walltime)\u001b[0m\n\u001b[1;32m    332\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_caffe2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0mscalar_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworkspace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFetchBlob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalar_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwalltime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_scalars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmain_tag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag_scalar_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwalltime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda2/envs/python3/lib/python3.6/site-packages/tensorboardX/summary.py\u001b[0m in \u001b[0;36mscalar\u001b[0;34m(name, scalar, collections)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_clean_tag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0mscalar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_np\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m     \u001b[0;32massert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'scalar should be 0D'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m     \u001b[0mscalar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mSummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mSummary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimple_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'squeeze'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "# 实例化Trainer，传入模型和数据，进行训练\n",
    "trainer = Trainer(model=model, \n",
    "                  train_data=train_data, \n",
    "                  dev_data=test_data,\n",
    "                  losser=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n",
    "                  metrics=AccuracyMetric(),\n",
    "                  n_epochs=5,\n",
    "                 save_path='save/')\n",
    "trainer.train()\n",
    "print('Train finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import Tester\n",
    "\n",
    "tester = Tester(data=test_data, model=model, metrics=AccuracyMetric())\n",
    "acc = tester.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In summary\n",
    "\n",
    "## fastNLP Trainer的伪代码逻辑\n",
    "### 1. 准备DataSet，假设DataSet中共有如下的fields\n",
    "    ['raw_sentence', 'word_seq1', 'word_seq2', 'raw_label','label']\n",
    "    通过\n",
    "        DataSet.set_input('word_seq1', word_seq2', flag=True)将'word_seq1', 'word_seq2'设置为input\n",
    "    通过\n",
    "        DataSet.set_target('label', flag=True)将'label'设置为target\n",
    "### 2. 初始化模型\n",
    "    class Model(nn.Module):\n",
    "        def __init__(self):\n",
    "            xxx\n",
    "        def forward(self, word_seq1, word_seq2):\n",
    "            # (1) 这里使用的形参名必须和DataSet中的input field的名称对应。因为我们是通过形参名, 进行赋值的\n",
    "            # (2) input field的数量可以多于这里的形参数量。但是不能少于。\n",
    "            xxxx\n",
    "            # 输出必须是一个dict\n",
    "### 3. Trainer的训练过程\n",
    "    (1) 从DataSet中按照batch_size取出一个batch，调用Model.forward\n",
    "    (2) 将 Model.forward的结果 与 标记为target的field 传入Losser当中。\n",
    "           由于每个人写的Model.forward的output的dict可能key并不一样，比如有人是{'pred':xxx}, {'output': xxx}; \n",
    "           另外每个人将target可能也会设置为不同的名称, 比如有人是label, 有人设置为target；\n",
    "        为了解决以上的问题，我们的loss提供映射机制\n",
    "           比如CrossEntropyLosser的需要的输入是(prediction, target)。但是forward的output是{'output': xxx}; 'label'是target\n",
    "           那么初始化losser的时候写为CrossEntropyLosser(prediction='output', target='label')即可\n",
    "     (3) 对于Metric是同理的\n",
    "         Metric计算也是从 forward的结果中取值 与 设置target的field中取值。 也是可以通过映射找到对应的值        \n",
    " \n",
    "       \n",
    "\n",
    "## 一些问题.\n",
    "### 1. DataSet中为什么需要设置input和target\n",
    "    只有被设置为input或者target的数据才会在train的过程中被取出来\n",
    "    (1.1) 我们只会在设置为input的field中寻找传递给Model.forward的参数。\n",
    "    (1.2) 我们在传递值给losser或者metric的时候会使用来自: \n",
    "            (a)Model.forward的output\n",
    "            (b)被设置为target的field\n",
    "          \n",
    "\n",
    "### 2. 我们是通过forwad中的形参名将DataSet中的field赋值给对应的参数\n",
    "     (1.1) 构建模型过程中，\n",
    "      例如:\n",
    "          DataSet中x，seq_lens是input，那么forward就应该是\n",
    "          def forward(self, x, seq_lens):\n",
    "              pass\n",
    "          我们是通过形参名称进行匹配的field的\n",
    "       \n",
    "\n",
    "\n",
    "### 1. 加载数据到DataSet\n",
    "### 2. 使用apply操作对DataSet进行预处理\n",
    "      (2.1) 处理过程中将某些field设置为input，某些field设置为target\n",
    "### 3. 构建模型\n",
    "      (3.1) 构建模型过程中，需要注意forward函数的形参名需要和DataSet中设置为input的field名称是一致的。\n",
    "      例如:\n",
    "          DataSet中x，seq_lens是input，那么forward就应该是\n",
    "          def forward(self, x, seq_lens):\n",
    "              pass\n",
    "          我们是通过形参名称进行匹配的field的\n",
    "      (3.2) 模型的forward的output需要是dict类型的。\n",
    "          建议将输出设置为{\"pred\": xx}.\n",
    "          \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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