02-Dataset_Dataloader.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "# Dataset是一个抽象类, DataLoader用来加载数据(比如，自动做Batch_size,完成shuffle操作等)\n",
    "from torch.utils.data import Dataset, DataLoader  \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Prepare Dataset\n",
    "### diabetes是一个糖尿病数据集，N行9列，前8列为features，最后一个为标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DiabetesDataset(Dataset):\n",
    "    def __init__(self, filepath):\n",
    "        # 读取csv, csv.gz文件;  以 ',' 作为分割符;  1080/2080显卡只支持32位浮点数的数据类型\n",
    "        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32) \n",
    "        self.len = xy.shape[0]                        # xy.shape结果为[N,9], 取第一个索引的值,即N。得到数据集中的样本个数\n",
    "        # torch.from_numpy会根据获取的数值 创建Tensor张量\n",
    "        self.x_data = torch.from_numpy(xy[:, :-1])    # 取所有行，前8列的数据\n",
    "        self.y_data = torch.from_numpy(xy[:, [-1]])   # 取所有行，最后一列。 用[-1]表示拿出来一个矩阵，如果不加[]得到的是向量\n",
    "        \n",
    "    def __getitem__(self, index):                     # 此函数获取索引为index的数据（对于数据集内存占用过大情况，现用现调）\n",
    "        return self.x_data[index], self.y_data[index] # 返回一个元组（ ，）\n",
    "\n",
    "    def __len__(self):                                # 此函数获取数据集中样本总数\n",
    "        return self.len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = DiabetesDataset('diabetes.csv.gz')\n",
    "# 调用DataLoader是为了每次调用这一组数据，而不用一次性加载全部数据造成内存占满的情况\n",
    "train_loader = DataLoader(dataset  = dataset,   \n",
    "                          batch_size = 32,\n",
    "                          shuffle = True,        # shuffle对一个数据集是否打乱顺序\n",
    "                          num_workers = 2)       # 读取数据时，需不需要多线程并行读取，这里设置为2表示用2个进程读取数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Design model using Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model, self).__init__()\n",
    "        self.linear1 = torch.nn.Linear(8, 6)\n",
    "        self.linear2 = torch.nn.Linear(6, 4)\n",
    "        self.linear3 = torch.nn.Linear(4, 1)\n",
    "        self.sigmoid = torch.nn.Sigmoid()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.sigmoid(self.linear1(x))\n",
    "        x = self.sigmoid(self.linear2(x))\n",
    "        x = self.sigmoid(self.linear3(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Construct loss and optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zgy/anaconda3/lib/python3.8/site-packages/torch/nn/_reduction.py:44: UserWarning: size_average and reduce args will be deprecated, please use reduction='mean' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    }
   ],
   "source": [
    "criterion = torch.nn.BCELoss(size_average=True)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Training cycle \n",
    "### (1) 1次epoch表示对所有样本训练1次， Batch_Size表示1次训练的样本数量， Iteration表示batch一共分了多少个，即内层的迭代一共进行了多少次，例如，对于一个10,000的样本，Batch_Size = 1000，则Iteraion = 10\n",
    "### (2) 这里把一个循环改为嵌套for循环以便使用Mini-Batch进行训练， Mini-Batch使用了GPU的并行运算提高运算效率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# DataFrame的单元格可以存放数值、字符串等，同时DataFrame可以设置列名columns与行名index。\n",
    "# columns可以不写在pd.Series()的index=[]处写\n",
    "log = pd.DataFrame(index=[], columns=['epoch', 'lr', 'loss'])\n",
    "\n",
    "if __name__ == '__main__':    # Windows 和 Linux 多线程的接口不同，在使用时可能报RuntimeError，改为此可以通过(0.4版本问题)\n",
    "    for epoch in range(100):\n",
    "        # enumerate进行枚举, 参数0表示从0开始进行枚举(默认参数就是0)，记到i中， data每次拿到的就是第i组的数据(这里就是32×8矩阵)\n",
    "        # train_loader会自动把矩阵数据转换乘Tensor\n",
    "        for i, data in enumerate(train_loader, 0):  # train_loader拿到__getitem__()返回的(x[i], y[i])元组放到data里，\n",
    "            # 1. Prepare data\n",
    "            inputs, labels = data                   # 这里inputs, labels都是Tensor张量了\n",
    "            # 2. Forward\n",
    "            y_pred = model(inputs)\n",
    "            loss = criterion(y_pred, labels)\n",
    "            print(epoch, i, loss.item())\n",
    "            # 3. Backward\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            # 4. Updata\n",
    "            optimizer.step()\n",
    "        \n",
    "        # 保存模型参数\n",
    "        # Series 是一维数组, 基于Numpy的ndarray结构\n",
    "        # pd.Series([list],index=[list]): 参数为list ,index为可选参数，若不填写则默认为index从0开始\n",
    "        temp = pd.Series([\n",
    "            epoch, \n",
    "            0.01, \n",
    "            loss.item()\n",
    "        ], index=['epoch', 'lr', 'loss'])\n",
    "        \n",
    "        log = log.append(temp, ignore_index=True)     # 只有当ignore_index=True或Series有名称时,才能追加Series\n",
    "        log.to_csv('log/log.csv', index=False)            # 将log统计到csv文件中, 不设置行号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w =  [[-0.3243042   0.15349141  0.17779218 -0.30270377  0.1634415   0.20683688\n",
      "   0.00837262  0.32740536]\n",
      " [ 0.17377017 -0.25876588 -0.20830365 -0.05947145  0.01778396 -0.0295886\n",
      "   0.3337342   0.13339478]\n",
      " [-0.16289759  0.07391965  0.02356913  0.04065726 -0.17856693 -0.3166271\n",
      "   0.33593374  0.30980033]\n",
      " [ 0.32775304 -0.3333064  -0.13822295 -0.11354297  0.33300266 -0.09909375\n",
      "  -0.12502661 -0.02732395]\n",
      " [ 0.10412861  0.04759761 -0.058348    0.3100948   0.12203769 -0.18845658\n",
      "   0.2425685  -0.03624997]\n",
      " [-0.00563358  0.25217718 -0.03842327  0.18896016 -0.01983326  0.2128165\n",
      "   0.07974286 -0.07076398]]\n",
      "w =  [[ 0.38850346 -0.14054058  0.36292648 -0.23136075 -0.22474015 -0.3347486 ]\n",
      " [ 0.07521944 -0.18067504  0.30370682 -0.2414233   0.13490742 -0.16061905]\n",
      " [ 0.22961177  0.40265778  0.00484482  0.04473725 -0.34106147  0.3373315 ]\n",
      " [ 0.2478347  -0.28179342 -0.35227597  0.3888513  -0.36498448  0.0692066 ]]\n",
      "w =  tensor([[0.2838, 0.1832, 0.4440, 0.4114]])\n"
     ]
    }
   ],
   "source": [
    "print(\"w = \", model.linear1.weight.data.numpy())\n",
    "print(\"w = \", model.linear2.weight.data.numpy())\n",
    "print(\"w = \", model.linear3.weight.data)"
   ]
  }
 ],
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