fastNLP/tutorials/fastnlp_10min_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fastNLP10 分钟上手教程\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": "markdown",
   "metadata": {},
   "source": [
    "如果您还没有通过pip安装fastNLP，可以执行下面的操作加载当前模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77\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('sample_data/tutorial_sample_dataset.csv', headers=('raw_sentence', 'label'), sep='\\t')\n",
    "print(len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 . type=str,\n",
      "'label': 1 type=str}\n",
      "{'raw_sentence': The plot is romantic comedy boilerplate from start to finish . type=str,\n",
      "'label': 2 type=str}\n"
     ]
    }
   ],
   "source": [
    "# 使用数字索引[k]，获取第k个样本\n",
    "print(dataset[0])\n",
    "\n",
    "# 索引也可以是负数\n",
    "print(dataset[-3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instance\n",
    "Instance表示一个样本，由一个或多个field（域，属性，特征）组成，每个field有名字和值。\n",
    "\n",
    "在初始化Instance时即可定义它包含的域，使用 \"field_name=field_value\"的写法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'raw_sentence': fake data type=str,\n",
       "'label': 0 type=str}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataSet.append(Instance)加入新数据\n",
    "dataset.append(Instance(raw_sentence='fake data', label='0'))\n",
    "dataset[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataSet.apply方法\n",
    "数据预处理利器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 . type=str,\n",
      "'label': 1 type=str}\n"
     ]
    }
   ],
   "source": [
    "# 将所有数字转为小写\n",
    "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n",
    "print(dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 . type=str,\n",
      "'label': 1 type=int}\n"
     ]
    }
   ],
   "source": [
    "# label转int\n",
    "dataset.apply(lambda x: int(x['label']), new_field_name='label')\n",
    "print(dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 . type=str,\n",
      "'label': 1 type=int,\n",
      "'words': ['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', '.'] type=list}\n"
     ]
    }
   ],
   "source": [
    "# 使用空格分割句子\n",
    "def split_sent(ins):\n",
    "    return ins['raw_sentence'].split()\n",
    "dataset.apply(split_sent, new_field_name='words')\n",
    "print(dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 . type=str,\n",
      "'label': 1 type=int,\n",
      "'words': ['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', '.'] type=list,\n",
      "'seq_len': 37 type=int}\n"
     ]
    }
   ],
   "source": [
    "# 增加长度信息\n",
    "dataset.apply(lambda x: len(x['words']), new_field_name='seq_len')\n",
    "print(dataset[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataSet.drop\n",
    "筛选数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77\n"
     ]
    }
   ],
   "source": [
    "# 删除低于某个长度的词语\n",
    "dataset.drop(lambda x: x['seq_len'] <= 3)\n",
    "print(len(dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 配置DataSet\n",
    "1. 哪些域是特征，哪些域是标签\n",
    "2. 切分训练集/验证集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置DataSet中，哪些field要转为tensor\n",
    "\n",
    "# set target，loss或evaluate中的golden，计算loss，模型评估时使用\n",
    "dataset.set_target(\"label\")\n",
    "# set input，模型forward时使用\n",
    "dataset.set_input(\"words\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54\n",
      "23\n"
     ]
    }
   ],
   "source": [
    "# 分出测试集、训练集\n",
    "\n",
    "test_data, train_data = dataset.split(0.3)\n",
    "print(len(test_data))\n",
    "print(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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'raw_sentence': a welcome relief from baseball movies that try too hard to be mythic , this one is a sweet and modest and ultimately winning story . type=str,\n",
      "'label': 3 type=int,\n",
      "'words': [4, 1, 1, 18, 1, 1, 13, 1, 1, 1, 8, 26, 1, 5, 35, 1, 11, 4, 1, 10, 1, 10, 1, 1, 1, 2] type=list,\n",
      "'seq_len': 26 type=int}\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='words')\n",
    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')\n",
    "\n",
    "\n",
    "print(test_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如果你们需要做强化学习或者GAN之类的项目，你们也可以使用这些数据预处理的工具\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",
    "定义一个PyTorch模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNNText(\n",
       "  (embed): Embedding(\n",
       "    (embed): Embedding(59, 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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": [
    "这是上述模型的forward方法。如果你不知道什么是forward方法，请参考我们的PyTorch教程。\n",
    "\n",
    "注意两点：\n",
    "1. forward参数名字叫**word_seq**，请记住。\n",
    "2. forward的返回值是一个**dict**，其中有个key的名字叫**output**。\n",
    "\n",
    "```Python\n",
    "    def forward(self, word_seq):\n",
    "        \"\"\"\n",
    "\n",
    "        :param word_seq: torch.LongTensor, [batch_size, seq_len]\n",
    "        :return output: dict of torch.LongTensor, [batch_size, num_classes]\n",
    "        \"\"\"\n",
    "        x = self.embed(word_seq)  # [N,L] -> [N,L,C]\n",
    "        x = self.conv_pool(x)  # [N,L,C] -> [N,C]\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc(x)  # [N,C] -> [N, N_class]\n",
    "        return {'output': x}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是上述模型的predict方法，是用来直接输出该任务的预测结果，与forward目的不同。\n",
    "\n",
    "注意两点：\n",
    "1. predict参数名也叫**word_seq**。\n",
    "2. predict的返回值是也一个**dict**，其中有个key的名字叫**predict**。\n",
    "\n",
    "```\n",
    "    def predict(self, word_seq):\n",
    "        \"\"\"\n",
    "\n",
    "        :param word_seq: torch.LongTensor, [batch_size, seq_len]\n",
    "        :return predict: dict of torch.LongTensor, [batch_size, seq_len]\n",
    "        \"\"\"\n",
    "        output = self(word_seq)\n",
    "        _, predict = output['output'].max(dim=1)\n",
    "        return {'predict': predict}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trainer & Tester\n",
    "------\n",
    "\n",
    "使用fastNLP的Trainer训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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\n",
    "\n",
    "\n",
    "# 更改DataSet中对应field的名称，与模型的forward的参数名一致\n",
    "# 因为forward的参数叫word_seq, 所以要把原本叫words的field改名为word_seq\n",
    "# 这里的演示是让你了解这种**命名规则**\n",
    "train_data.rename_field('words', 'word_seq')\n",
    "test_data.rename_field('words', 'word_seq')\n",
    "\n",
    "# 顺便把label换名为label_seq\n",
    "train_data.rename_field('label', 'label_seq')\n",
    "test_data.rename_field('label', 'label_seq')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loss\n",
    "训练模型需要提供一个损失函数\n",
    "\n",
    "下面提供了一个在分类问题中常用的交叉熵损失。注意它的**初始化参数**。\n",
    "\n",
    "pred参数对应的是模型的forward返回的dict的一个key的名字，这里是\"output\"。\n",
    "\n",
    "target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = CrossEntropyLoss(pred=\"output\", target=\"label_seq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metric\n",
    "定义评价指标\n",
    "\n",
    "这里使用准确率。参数的“命名规则”跟上面类似。\n",
    "\n",
    "pred参数对应的是模型的predict方法返回的dict的一个key的名字，这里是\"predict\"。\n",
    "\n",
    "target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = AccuracyMetric(pred=\"predict\", target=\"label_seq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input fields after batch(if batch size is 2):\n",
      "\tword_seq: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
      "target fields after batch(if batch size is 2):\n",
      "\tlabel_seq: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
      "\n",
      "training epochs started 2019-01-12 17-07-51\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=10), HTML(value='')), layout=Layout(display='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation at Epoch 1/5. Step:2/10. AccuracyMetric: acc=0.425926\n",
      "Evaluation at Epoch 2/5. Step:4/10. AccuracyMetric: acc=0.425926\n",
      "Evaluation at Epoch 3/5. Step:6/10. AccuracyMetric: acc=0.611111\n",
      "Evaluation at Epoch 4/5. Step:8/10. AccuracyMetric: acc=0.648148\n",
      "Evaluation at Epoch 5/5. Step:10/10. AccuracyMetric: acc=0.703704\n",
      "\n",
      "In Epoch:5/Step:10, got best dev performance:AccuracyMetric: acc=0.703704\n",
      "Reloaded the best model.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'best_eval': {'AccuracyMetric': {'acc': 0.703704}},\n",
       " 'best_epoch': 5,\n",
       " 'best_step': 10,\n",
       " 'seconds': 0.62}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 实例化Trainer，传入模型和数据，进行训练\n",
    "# 先在test_data拟合（确保模型的实现是正确的）\n",
    "copy_model = deepcopy(model)\n",
    "overfit_trainer = Trainer(model=copy_model, train_data=test_data, dev_data=test_data,\n",
    "                          loss=loss,\n",
    "                          metrics=metric,\n",
    "                          save_path=None,\n",
    "                          batch_size=32,\n",
    "                          n_epochs=5)\n",
    "overfit_trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input fields after batch(if batch size is 2):\n",
      "\tword_seq: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 20]) \n",
      "target fields after batch(if batch size is 2):\n",
      "\tlabel_seq: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
      "\n",
      "training epochs started 2019-01-12 17-09-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=5), HTML(value='')), layout=Layout(display='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation at Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.37037\n",
      "Evaluation at Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.37037\n",
      "Evaluation at Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.462963\n",
      "Evaluation at Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.425926\n",
      "Evaluation at Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.481481\n",
      "\n",
      "In Epoch:5/Step:5, got best dev performance:AccuracyMetric: acc=0.481481\n",
      "Reloaded the best model.\n",
      "Train finished!\n"
     ]
    }
   ],
   "source": [
    "# 用train_data训练，在test_data验证\n",
    "trainer = Trainer(model=model, train_data=train_data, dev_data=test_data,\n",
    "                  loss=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n",
    "                  metrics=AccuracyMetric(pred=\"predict\", target=\"label_seq\"),\n",
    "                  save_path=None,\n",
    "                  batch_size=32,\n",
    "                  n_epochs=5)\n",
    "trainer.train()\n",
    "print('Train finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tester] \n",
      "AccuracyMetric: acc=0.481481\n",
      "{'AccuracyMetric': {'acc': 0.481481}}\n"
     ]
    }
   ],
   "source": [
    "# 调用Tester在test_data上评价效果\n",
    "from fastNLP import Tester\n",
    "\n",
    "tester = Tester(data=test_data, model=model, metrics=AccuracyMetric(pred=\"predict\", target=\"label_seq\"),\n",
    "                batch_size=4)\n",
    "acc = tester.test()\n",
    "print(acc)"
   ]
  },
  {
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}