1-3,文本数据建模流程范例.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37dfe75a",
   "metadata": {},
   "source": [
    "# 1-3,文本数据建模流程范例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87488d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76dfe3a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install gensim \n",
    "!pip install torchkeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95288a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.__version__ =  2.0.1\n",
      "gensim.__version__ =  4.3.1\n",
      "torchkeras.__version__ =  3.9.3\n"
     ]
    }
   ],
   "source": [
    "import torch \n",
    "import gensim\n",
    "import torchkeras \n",
    "print(\"torch.__version__ = \", torch.__version__)\n",
    "print(\"gensim.__version__ = \", gensim.__version__) \n",
    "print(\"torchkeras.__version__ = \", torchkeras.__version__) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c4202e",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<font color=\"red\">\n",
    " \n",
    "公众号 **算法美食屋** 回复关键词：**pytorch**， 获取本项目源码和所用数据集百度云盘下载链接。\n",
    "    \n",
    "</font> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bd0289f",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "be150a8a",
   "metadata": {},
   "source": [
    "### 一，准备数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c21091b",
   "metadata": {},
   "source": [
    "imdb数据集的目标是根据电影评论的文本内容预测评论的情感标签。\n",
    "\n",
    "训练集有20000条电影评论文本，测试集有5000条电影评论文本，其中正面评论和负面评论都各占一半。\n",
    "\n",
    "文本数据预处理较为繁琐，包括文本切词，构建词典，编码转换，序列填充，构建数据管道等等。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "758a8aa6",
   "metadata": {},
   "source": [
    "此处使用gensim中的词典工具并自定义Dataset。\n",
    "\n",
    "下面进行演示。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9132822a",
   "metadata": {},
   "source": [
    "![](./data/电影评论.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "87d5fd57-44e7-4f64-9743-673d6b090f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import torch \n",
    "\n",
    "MAX_LEN = 200           #每个样本保留200个词的长度\n",
    "BATCH_SIZE = 20 \n",
    "\n",
    "\n",
    "dftrain = pd.read_csv(\"./eat_pytorch_datasets/imdb/train.tsv\",sep=\"\\t\",header = None,names = [\"label\",\"text\"])\n",
    "dfval = pd.read_csv(\"./eat_pytorch_datasets/imdb/test.tsv\",sep=\"\\t\",header = None,names = [\"label\",\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2bca1d76-7644-4d1d-b609-08523f3d8a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size =  29924\n",
      "[145, 77, 569, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "from gensim import corpora\n",
    "import string\n",
    "\n",
    "#1，文本切词\n",
    "def textsplit(text):\n",
    "    translator = str.maketrans('', '', string.punctuation)\n",
    "    words = text.translate(translator).split(' ')\n",
    "    return words\n",
    "        \n",
    "#2，构建词典\n",
    "vocab = corpora.Dictionary((textsplit(text) for text in dftrain['text']))\n",
    "vocab.filter_extremes(no_below=5,no_above=5000)\n",
    "special_tokens = {'<pad>': 0, '<unk>': 1}\n",
    "vocab.patch_with_special_tokens(special_tokens)\n",
    "vocab_size = len(vocab.token2id) \n",
    "print('vocab_size = ',vocab_size)\n",
    "\n",
    "#3，序列填充\n",
    "def pad(seq,max_length,pad_value=0):\n",
    "    n = len(seq)\n",
    "    result = seq+[pad_value]*max_length\n",
    "    return result[:max_length]\n",
    "\n",
    "\n",
    "#4，编码转换\n",
    "def text_pipeline(text):\n",
    "    tokens = vocab.doc2idx(textsplit(text))\n",
    "    tokens = [x if x>0 else special_tokens['<unk>']  for x in tokens ]\n",
    "    result = pad(tokens,MAX_LEN,special_tokens['<pad>'])\n",
    "    return result \n",
    "\n",
    "print(text_pipeline(\"this is an example!\")) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6675f6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f8338e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5，构建管道\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "\n",
    "class ImdbDataset(Dataset):\n",
    "    def __init__(self,df):\n",
    "        self.df = df\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "    def __getitem__(self,index):\n",
    "        text = self.df[\"text\"].iloc[index]\n",
    "        label = torch.tensor([self.df[\"label\"].iloc[index]]).float()\n",
    "        tokens = torch.tensor(text_pipeline(text)).int() \n",
    "        return tokens,label\n",
    "    \n",
    "ds_train = ImdbDataset(dftrain)\n",
    "ds_val = ImdbDataset(dfval)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "698296a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_train = DataLoader(ds_train,batch_size = 50,shuffle = True)\n",
    "dl_val = DataLoader(ds_val,batch_size = 50,shuffle = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6b0e7824",
   "metadata": {},
   "outputs": [],
   "source": [
    "for features,labels in dl_train:\n",
    "    break "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "551a7b66-c7bc-46fb-8bd7-e0a16de4e421",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8971bba1",
   "metadata": {},
   "source": [
    "### 二，定义模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d014a534",
   "metadata": {},
   "source": [
    "使用Pytorch通常有三种方式构建模型：使用nn.Sequential按层顺序构建模型，继承nn.Module基类构建自定义模型，继承nn.Module基类构建模型并辅助应用模型容器(nn.Sequential,nn.ModuleList,nn.ModuleDict)进行封装。\n",
    "\n",
    "此处选择使用第三种方式进行构建。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "15b42dc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x142700950>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn \n",
    "torch.manual_seed(42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "37193036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (embedding): Embedding(29924, 3, padding_idx=0)\n",
      "  (conv): Sequential(\n",
      "    (conv_1): Conv1d(3, 16, kernel_size=(5,), stride=(1,))\n",
      "    (pool_1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (relu_1): ReLU()\n",
      "    (conv_2): Conv1d(16, 128, kernel_size=(2,), stride=(1,))\n",
      "    (pool_2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (relu_2): ReLU()\n",
      "  )\n",
      "  (dense): Sequential(\n",
      "    (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "    (linear): Linear(in_features=6144, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class Net(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        \n",
    "        #设置padding_idx参数后将在训练过程中将填充的token始终赋值为0向量\n",
    "        self.embedding = nn.Embedding(num_embeddings = vocab_size,embedding_dim = 3,padding_idx = 0)\n",
    "        \n",
    "        self.conv = nn.Sequential()\n",
    "        self.conv.add_module(\"conv_1\",nn.Conv1d(in_channels = 3,out_channels = 16,kernel_size = 5))\n",
    "        self.conv.add_module(\"pool_1\",nn.MaxPool1d(kernel_size = 2))\n",
    "        self.conv.add_module(\"relu_1\",nn.ReLU())\n",
    "        self.conv.add_module(\"conv_2\",nn.Conv1d(in_channels = 16,out_channels = 128,kernel_size = 2))\n",
    "        self.conv.add_module(\"pool_2\",nn.MaxPool1d(kernel_size = 2))\n",
    "        self.conv.add_module(\"relu_2\",nn.ReLU())\n",
    "        \n",
    "        self.dense = nn.Sequential()\n",
    "        self.dense.add_module(\"flatten\",nn.Flatten())\n",
    "        self.dense.add_module(\"linear\",nn.Linear(6144,1))\n",
    "        \n",
    "        \n",
    "    def forward(self,x):\n",
    "        x = self.embedding(x).transpose(1,2)\n",
    "        x = self.conv(x)\n",
    "        y = self.dense(x)\n",
    "        return y\n",
    "        \n",
    "net = Net() \n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f34cc2",
   "metadata": {},
   "source": [
    "```\n",
    "Net(\n",
    "  (embedding): Embedding(8813, 3, padding_idx=0)\n",
    "  (conv): Sequential(\n",
    "    (conv_1): Conv1d(3, 16, kernel_size=(5,), stride=(1,))\n",
    "    (pool_1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "    (relu_1): ReLU()\n",
    "    (conv_2): Conv1d(16, 128, kernel_size=(2,), stride=(1,))\n",
    "    (pool_2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "    (relu_2): ReLU()\n",
    "  )\n",
    "  (dense): Sequential(\n",
    "    (flatten): Flatten(start_dim=1, end_dim=-1)\n",
    "    (linear): Linear(in_features=6144, out_features=1, bias=True)\n",
    "  )\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "885dc4fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ab57016b",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Layer (type)                            Output Shape              Param #\n",
      "==========================================================================\n",
      "Embedding-1                             [-1, 200, 3]               89,772\n",
      "Conv1d-2                               [-1, 16, 196]                  256\n",
      "MaxPool1d-3                             [-1, 16, 98]                    0\n",
      "ReLU-4                                  [-1, 16, 98]                    0\n",
      "Conv1d-5                               [-1, 128, 97]                4,224\n",
      "MaxPool1d-6                            [-1, 128, 48]                    0\n",
      "ReLU-7                                 [-1, 128, 48]                    0\n",
      "Flatten-8                                 [-1, 6144]                    0\n",
      "Linear-9                                     [-1, 1]                6,145\n",
      "==========================================================================\n",
      "Total params: 100,397\n",
      "Trainable params: 100,397\n",
      "Non-trainable params: 0\n",
      "--------------------------------------------------------------------------\n",
      "Input size (MB): 0.000069\n",
      "Forward/backward pass size (MB): 0.287788\n",
      "Params size (MB): 0.382984\n",
      "Estimated Total Size (MB): 0.670841\n",
      "--------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchkeras import summary \n",
    "summary(net,input_data=features);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc09084",
   "metadata": {},
   "source": [
    "### 三，训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d476c85",
   "metadata": {},
   "source": [
    "训练Pytorch通常需要用户编写自定义训练循环，训练循环的代码风格因人而异。\n",
    "\n",
    "有3类典型的训练循环代码风格：脚本形式训练循环，函数形式训练循环，类形式训练循环。\n",
    "\n",
    "此处介绍一种较通用的仿照Keras风格的类形式的训练循环。\n",
    "\n",
    "该训练循环的代码也是torchkeras库的核心代码。\n",
    "\n",
    "torchkeras详情:  https://github.com/lyhue1991/torchkeras \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7436987a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime \n",
    "from tqdm import tqdm \n",
    "\n",
    "import torch\n",
    "from torch import nn \n",
    "from copy import deepcopy\n",
    "\n",
    "def printlog(info):\n",
    "    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n",
    "    print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n",
    "    print(str(info)+\"\\n\")\n",
    "\n",
    "class StepRunner:\n",
    "    def __init__(self, net, loss_fn,stage = \"train\", metrics_dict = None, \n",
    "                 optimizer = None, lr_scheduler = None\n",
    "                 ):\n",
    "        self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage\n",
    "        self.optimizer,self.lr_scheduler = optimizer,lr_scheduler\n",
    "    \n",
    "    def __call__(self, features, labels):\n",
    "        #loss\n",
    "        preds = self.net(features)\n",
    "        loss = self.loss_fn(preds,labels)\n",
    "\n",
    "        #backward()\n",
    "        if self.optimizer is not None and self.stage==\"train\":\n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "            if self.lr_scheduler is not None:\n",
    "                self.lr_scheduler.step()\n",
    "            self.optimizer.zero_grad()\n",
    "            \n",
    "        #metrics\n",
    "        step_metrics = {self.stage+\"_\"+name:metric_fn(preds, labels).item() \n",
    "                        for name,metric_fn in self.metrics_dict.items()}\n",
    "        return loss.item(),step_metrics\n",
    "\n",
    "\n",
    "class EpochRunner:\n",
    "    def __init__(self,steprunner):\n",
    "        self.steprunner = steprunner\n",
    "        self.stage = steprunner.stage\n",
    "        self.steprunner.net.train() if self.stage==\"train\" else self.steprunner.net.eval()\n",
    "        \n",
    "    def __call__(self,dataloader):\n",
    "        total_loss,step = 0,0\n",
    "        loop = tqdm(enumerate(dataloader), total =len(dataloader))\n",
    "        for i, batch in loop: \n",
    "            if self.stage==\"train\":\n",
    "                loss, step_metrics = self.steprunner(*batch)\n",
    "            else:\n",
    "                with torch.no_grad():\n",
    "                    loss, step_metrics = self.steprunner(*batch)\n",
    "            step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n",
    "\n",
    "            total_loss += loss\n",
    "            step+=1\n",
    "            if i!=len(dataloader)-1:\n",
    "                loop.set_postfix(**step_log)\n",
    "            else:\n",
    "                epoch_loss = total_loss/step\n",
    "                epoch_metrics = {self.stage+\"_\"+name:metric_fn.compute().item() \n",
    "                                 for name,metric_fn in self.steprunner.metrics_dict.items()}\n",
    "                epoch_log = dict({self.stage+\"_loss\":epoch_loss},**epoch_metrics)\n",
    "                loop.set_postfix(**epoch_log)\n",
    "\n",
    "                for name,metric_fn in self.steprunner.metrics_dict.items():\n",
    "                    metric_fn.reset()\n",
    "        return epoch_log\n",
    "\n",
    "class KerasModel(torch.nn.Module):\n",
    "    def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):\n",
    "        super().__init__()\n",
    "        self.history = {}\n",
    "        \n",
    "        self.net = net\n",
    "        self.loss_fn = loss_fn\n",
    "        self.metrics_dict = nn.ModuleDict(metrics_dict) \n",
    "        \n",
    "        self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(\n",
    "            self.parameters(), lr=1e-2)\n",
    "        self.lr_scheduler = lr_scheduler\n",
    "\n",
    "    def forward(self, x):\n",
    "        if self.net:\n",
    "            return self.net.forward(x)\n",
    "        else:\n",
    "            raise NotImplementedError\n",
    "\n",
    "\n",
    "    def fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt', \n",
    "            patience=5, monitor=\"val_loss\", mode=\"min\"):\n",
    "\n",
    "        for epoch in range(1, epochs+1):\n",
    "            printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n",
    "            \n",
    "            # 1，train -------------------------------------------------  \n",
    "            train_step_runner = StepRunner(net = self.net,stage=\"train\",\n",
    "                    loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n",
    "                    optimizer = self.optimizer, lr_scheduler = self.lr_scheduler)\n",
    "            train_epoch_runner = EpochRunner(train_step_runner)\n",
    "            train_metrics = train_epoch_runner(train_data)\n",
    "            \n",
    "            for name, metric in train_metrics.items():\n",
    "                self.history[name] = self.history.get(name, []) + [metric]\n",
    "\n",
    "            # 2，validate -------------------------------------------------\n",
    "            if val_data:\n",
    "                val_step_runner = StepRunner(net = self.net,stage=\"val\",\n",
    "                    loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict))\n",
    "                val_epoch_runner = EpochRunner(val_step_runner)\n",
    "                with torch.no_grad():\n",
    "                    val_metrics = val_epoch_runner(val_data)\n",
    "                val_metrics[\"epoch\"] = epoch\n",
    "                for name, metric in val_metrics.items():\n",
    "                    self.history[name] = self.history.get(name, []) + [metric]\n",
    "            \n",
    "            # 3，early-stopping -------------------------------------------------\n",
    "            if not val_data:\n",
    "                continue\n",
    "            arr_scores = self.history[monitor]\n",
    "            best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n",
    "            if best_score_idx==len(arr_scores)-1:\n",
    "                torch.save(self.net.state_dict(),ckpt_path)\n",
    "                print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n",
    "                     arr_scores[best_score_idx]),file=sys.stderr)\n",
    "            if len(arr_scores)-best_score_idx>patience:\n",
    "                print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n",
    "                    monitor,patience),file=sys.stderr)\n",
    "                break \n",
    "                \n",
    "        self.net.load_state_dict(torch.load(ckpt_path))  \n",
    "        return pd.DataFrame(self.history)\n",
    "\n",
    "    @torch.no_grad()\n",
    "    def evaluate(self, val_data):\n",
    "        val_step_runner = StepRunner(net = self.net,stage=\"val\",\n",
    "                    loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict))\n",
    "        val_epoch_runner = EpochRunner(val_step_runner)\n",
    "        val_metrics = val_epoch_runner(val_data)\n",
    "        return val_metrics\n",
    "        \n",
    "       \n",
    "    @torch.no_grad()\n",
    "    def predict(self, dataloader):\n",
    "        self.net.eval()\n",
    "        result = torch.cat([self.forward(t[0]) for t in dataloader])\n",
    "        return result.data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2900cc1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchmetrics import Accuracy\n",
    "\n",
    "net = Net() \n",
    "model = KerasModel(net,\n",
    "                  loss_fn = nn.BCEWithLogitsLoss(),\n",
    "                  optimizer= torch.optim.Adam(net.parameters(),lr = 0.01),  \n",
    "                  metrics_dict = {\"acc\":Accuracy(task='binary')}\n",
    "                )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "e8a04882",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-08-02 14:20:21\n",
      "Epoch 1 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:10<00:00, 39.28it/s, train_acc=0.496, train_loss=0.701]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 51.21it/s, val_acc=0.518, val_loss=0.693]\n",
      "<<<<<< reach best val_acc : 0.5180000066757202 >>>>>>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-08-02 14:20:33\n",
      "Epoch 2 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.14it/s, train_acc=0.503, train_loss=0.693]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 54.22it/s, val_acc=0.58, val_loss=0.689]\n",
      "<<<<<< reach best val_acc : 0.5803999900817871 >>>>>>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-08-02 14:20:45\n",
      "Epoch 3 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:10<00:00, 39.46it/s, train_acc=0.69, train_loss=0.58]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 53.84it/s, val_acc=0.781, val_loss=0.47]\n",
      "<<<<<< reach best val_acc : 0.7807999849319458 >>>>>>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-08-02 14:20:57\n",
      "Epoch 4 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.33it/s, train_acc=0.83, train_loss=0.386]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 54.18it/s, val_acc=0.819, val_loss=0.408]\n",
      "<<<<<< reach best val_acc : 0.8194000124931335 >>>>>>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-08-02 14:21:09\n",
      "Epoch 5 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.63it/s, train_acc=0.893, train_loss=0.262]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 55.69it/s, val_acc=0.836, val_loss=0.395]\n",
      "<<<<<< reach best val_acc : 0.8357999920845032 >>>>>>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-08-02 14:21:21\n",
      "Epoch 6 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.58it/s, train_acc=0.932, train_loss=0.176]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 50.93it/s, val_acc=0.828, val_loss=0.456]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-08-02 14:21:33\n",
      "Epoch 7 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:10<00:00, 39.62it/s, train_acc=0.956, train_loss=0.119]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 55.26it/s, val_acc=0.829, val_loss=0.558]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-08-02 14:21:44\n",
      "Epoch 8 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.58it/s, train_acc=0.973, train_loss=0.0754]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 52.91it/s, val_acc=0.823, val_loss=0.67]\n",
      "<<<<<< val_acc without improvement in 3 epoch, early stopping >>>>>>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_acc</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.701064</td>\n",
       "      <td>0.49580</td>\n",
       "      <td>0.693045</td>\n",
       "      <td>0.5180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.693060</td>\n",
       "      <td>0.50335</td>\n",
       "      <td>0.688656</td>\n",
       "      <td>0.5804</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.579867</td>\n",
       "      <td>0.69010</td>\n",
       "      <td>0.469574</td>\n",
       "      <td>0.7808</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.385625</td>\n",
       "      <td>0.82990</td>\n",
       "      <td>0.407633</td>\n",
       "      <td>0.8194</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.261653</td>\n",
       "      <td>0.89260</td>\n",
       "      <td>0.394901</td>\n",
       "      <td>0.8358</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.175921</td>\n",
       "      <td>0.93210</td>\n",
       "      <td>0.455604</td>\n",
       "      <td>0.8284</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.119178</td>\n",
       "      <td>0.95610</td>\n",
       "      <td>0.558430</td>\n",
       "      <td>0.8286</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.075409</td>\n",
       "      <td>0.97330</td>\n",
       "      <td>0.670172</td>\n",
       "      <td>0.8232</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_loss  train_acc  val_loss  val_acc  epoch\n",
       "0    0.701064    0.49580  0.693045   0.5180      1\n",
       "1    0.693060    0.50335  0.688656   0.5804      2\n",
       "2    0.579867    0.69010  0.469574   0.7808      3\n",
       "3    0.385625    0.82990  0.407633   0.8194      4\n",
       "4    0.261653    0.89260  0.394901   0.8358      5\n",
       "5    0.175921    0.93210  0.455604   0.8284      6\n",
       "6    0.119178    0.95610  0.558430   0.8286      7\n",
       "7    0.075409    0.97330  0.670172   0.8232      8"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(dl_train,\n",
    "    val_data=dl_val,\n",
    "    epochs=10,\n",
    "    ckpt_path='checkpoint',\n",
    "    patience=3,\n",
    "    monitor='val_acc',\n",
    "    mode='max')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b0c6be1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9abe3458",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1ff35951",
   "metadata": {},
   "source": [
    "### 四，评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ff9d1407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_acc</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.701064</td>\n",
       "      <td>0.49580</td>\n",
       "      <td>0.693045</td>\n",
       "      <td>0.5180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.693060</td>\n",
       "      <td>0.50335</td>\n",
       "      <td>0.688656</td>\n",
       "      <td>0.5804</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.579867</td>\n",
       "      <td>0.69010</td>\n",
       "      <td>0.469574</td>\n",
       "      <td>0.7808</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.385625</td>\n",
       "      <td>0.82990</td>\n",
       "      <td>0.407633</td>\n",
       "      <td>0.8194</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.261653</td>\n",
       "      <td>0.89260</td>\n",
       "      <td>0.394901</td>\n",
       "      <td>0.8358</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.175921</td>\n",
       "      <td>0.93210</td>\n",
       "      <td>0.455604</td>\n",
       "      <td>0.8284</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.119178</td>\n",
       "      <td>0.95610</td>\n",
       "      <td>0.558430</td>\n",
       "      <td>0.8286</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.075409</td>\n",
       "      <td>0.97330</td>\n",
       "      <td>0.670172</td>\n",
       "      <td>0.8232</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_loss  train_acc  val_loss  val_acc  epoch\n",
       "0    0.701064    0.49580  0.693045   0.5180      1\n",
       "1    0.693060    0.50335  0.688656   0.5804      2\n",
       "2    0.579867    0.69010  0.469574   0.7808      3\n",
       "3    0.385625    0.82990  0.407633   0.8194      4\n",
       "4    0.261653    0.89260  0.394901   0.8358      5\n",
       "5    0.175921    0.93210  0.455604   0.8284      6\n",
       "6    0.119178    0.95610  0.558430   0.8286      7\n",
       "7    0.075409    0.97330  0.670172   0.8232      8"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "history = model.history\n",
    "dfhistory = pd.DataFrame(history) \n",
    "dfhistory \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "93107420",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_metric(dfhistory, metric):\n",
    "    train_metrics = dfhistory[\"train_\"+metric]\n",
    "    val_metrics = dfhistory['val_'+metric]\n",
    "    epochs = range(1, len(train_metrics) + 1)\n",
    "    plt.plot(epochs, train_metrics, 'bo--')\n",
    "    plt.plot(epochs, val_metrics, 'ro-')\n",
    "    plt.title('Training and validation '+ metric)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([\"train_\"+metric, 'val_'+metric])\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "51029d7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
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      "text/plain": [
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   ],
   "source": [
    "plot_metric(dfhistory,\"acc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "9a148942",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 50.26it/s, val_acc=0.836, val_loss=0.395]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'val_loss': 0.39490113019943235, 'val_acc': 0.8357999920845032}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评估\n",
    "model.evaluate(dl_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f2045e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f1bc62f0",
   "metadata": {},
   "source": [
    "### 五，使用模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e6a67857",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(net,dl):\n",
    "    net.eval()\n",
    "    with torch.no_grad():\n",
    "        result = nn.Sigmoid()(torch.cat([net.forward(t[0]) for t in dl]))\n",
    "    return(result.data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9f916311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.9372],\n",
       "        [1.0000],\n",
       "        [0.8672],\n",
       "        ...,\n",
       "        [0.5141],\n",
       "        [0.4756],\n",
       "        [0.9998]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_probs = predict(net,dl_val)\n",
    "y_pred_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59b69aba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "31c888a8",
   "metadata": {},
   "source": [
    "### 六，保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "fee13dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#模型权重已经被保存在了ckpt_path='checkpoint.'\n",
    "net_clone = Net()\n",
    "net_clone.load_state_dict(torch.load('checkpoint'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf6c7de",
   "metadata": {},
   "source": [
    "**如果本书对你有所帮助，想鼓励一下作者，记得给本项目加一颗星星star⭐️，并分享给你的朋友们喔😊!** \n",
    "\n",
    "如果对本书内容理解上有需要进一步和作者交流的地方，欢迎在公众号\"算法美食屋\"下留言。作者时间和精力有限，会酌情予以回复。\n",
    "\n",
    "也可以在公众号后台回复关键字：**加群**，加入读者交流群和大家讨论。\n",
    "\n",
    "![算法美食屋logo.png](https://tva1.sinaimg.cn/large/e6c9d24egy1h41m2zugguj20k00b9q46.jpg)"
   ]
  }
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