00-LinearModule.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Prepare dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensor(类) 可以存标量，向量，矩阵，高维数据， 包含两个重要成员data， grad\n",
    "#### data 用来保存权重w本身的值\n",
    "#### grad 用来保存损失函数对权重的导数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = torch.Tensor([[1.0], [2.0], [3.0]]) \n",
    "y_data = torch.Tensor([[2.0], [4.0], [6.0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Design model using Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### class torch.nn.Linear(in_features, out_features, bias=True)\n",
    "#### 可以完成: y = Ax + b 的线性方程\n",
    "#### in_features,  out_features: 表示输入输出样本是几维的. (输入输出样本的维度必须相同)\n",
    "#### bias: 是否需要偏置量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearModel(torch.nn.Module):           # 继承父类 nn.Module\n",
    "    def __init__(self):                       # 初始化时执行的函数\n",
    "        super(LinearModel, self).__init__()   # 调用父类的构造 \n",
    "        self.linear = torch.nn.Linear(1, 1)   # 构造Linear对象(这里面就包含了权重w和偏置b), 可以自动完成\n",
    "                                              # x * w + b 的计算.       (1, 1)表示输入输出是1维\n",
    "    \n",
    "    def forward(self, x):                     # 覆盖父类中__call__()中的forward函数\n",
    "        y_pred = self.linear(x)               # 执行前馈过程中的计算 \n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearModel() # 实例化模型 --相当于Java中的new一个对象, \n",
    "                      # model也是一个callable的,可以直接被调用, model(x)就是执行LinearModel中的forward(self, x)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Construct loss and optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 construct loss:\n",
    "### class torch.nn.MSELoss(size_average = True, reduce = True)\n",
    "#### size_average = False 得到整个batch所有像素loss和， 默认为True得到的是平均到像素的loss\n",
    "#### reduce: 是否求和降维(一般不考虑)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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='sum' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    }
   ],
   "source": [
    "criterion = torch.nn.MSELoss(size_average=False) # 传入y_pred 和 y_data 就可以求出损失函数 loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 construct optimizer\n",
    "### class torch.optim.SGD(params, lr=< object object >, momentum=0, dampening=0, weight_decay=0, nesterov=False)   \n",
    "#### model父类中有成员函数:parameters,可以检查model中所有成员, 如果成员里有相应的权重, 就把这些权重都加到要训练的结果上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(model.parameters(), lr = 0.01) \n",
    "# SGD优化器：随机梯度下降，对应权重更新公式: w = w -  a(loss 对于 w 的导数)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Training cycle \n",
    "### 前面的和下面的操作都是在构建计算图! 而不是运算. 第四步可以概括为三个过程: 前馈,反馈, 更新.\n",
    "### 通过计算图得到的结果需要调用 .data 得到一个Tensor类型的数据或者调用 .item() 得到一个标量."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "983 3.441641638346482e-08\n",
      "984 3.386850266906549e-08\n",
      "985 3.3293190426775254e-08\n",
      "986 3.283849991930765e-08\n",
      "987 3.242877255615895e-08\n",
      "988 3.1948957257554866e-08\n",
      "989 3.1503589070780436e-08\n",
      "990 3.1030822356115095e-08\n",
      "991 3.058153197343927e-08\n",
      "992 3.021659722435288e-08\n",
      "993 2.975349389089388e-08\n",
      "994 2.9313753202586668e-08\n",
      "995 2.8926763206982287e-08\n",
      "996 2.8503109206212685e-08\n",
      "997 2.8043587008141913e-08\n",
      "998 2.761703399301041e-08\n",
      "999 2.7250791845290223e-08\n",
      "w =  1.9998900890350342\n",
      "b =  0.00024984151241369545\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1000):\n",
    "    y_pred = model(x_data)             # 1.算y_pred\n",
    "    loss = criterion(y_pred, y_data)   # 2.算loss\n",
    "    print(epoch, loss.item())          # 1.和 2.是前馈的过程\n",
    "    \n",
    "    optimizer.zero_grad()              # 反向传播前, 梯度清零.\n",
    "    loss.backward()                    # 3.backward: 反向传播 得到所有要更新权重(这里只有w)的梯度 w.grad \n",
    "                                       #  (w.grad 这里是一个Tensor,为了避免构建计算图需要使用 w.grad.data 进行更新)\n",
    "    optimizer.step()                   # 4.更新权重w 进行: w = w -  lr * grad 的运算\n",
    " \n",
    "print(\"w = \", model.linear.weight.item())  # .item()可以理解为得到weight的数值, 因为不加item()得到的是一个矩阵[[]].\n",
    "print(\"b = \", model.linear.bias.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_pred =  tensor([7.9998])\n"
     ]
    }
   ],
   "source": [
    "x_test = torch.Tensor([4.0])\n",
    "y_test = model(x_test)\n",
    "print(\"y_pred = \", y_test.data)"
   ]
  }
 ],
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