machinelearning_notebook/pytorch/PyTorch快速入门.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 PyTorch第一步\n",
    "\n",
    "PyTorch的简洁设计使得它入门很简单，在深入介绍PyTorch之前，本节将先介绍一些PyTorch的基础知识，使得读者能够对PyTorch有一个大致的了解，并能够用PyTorch搭建一个简单的神经网络。部分内容读者可能暂时不太理解，可先不予以深究，本书的第3章和第4章将会对此进行深入讲解。\n",
    "\n",
    "本节内容参考了PyTorch官方教程[^1]并做了相应的增删修改，使得内容更贴合新版本的PyTorch接口，同时也更适合新手快速入门。另外本书需要读者先掌握基础的Numpy使用，其他相关知识推荐读者参考CS231n的教程[^2]。\n",
    "\n",
    "[^1]: http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html\n",
    "[^2]: http://cs231n.github.io/python-numpy-tutorial/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensor\n",
    "\n",
    "Tensor是PyTorch中重要的数据结构，可认为是一个高维数组。它可以是一个数（标量）、一维数组（向量）、二维数组（矩阵）以及更高维的数组。Tensor和Numpy的ndarrays类似，但Tensor可以使用GPU进行加速。Tensor的使用和Numpy及Matlab的接口十分相似，下面通过几个例子来看看Tensor的基本使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'torch'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-93bdd78e6769>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: No module named 'torch'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import torch as t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "1.00000e-07 *\n",
       "  0.0000  0.0000  5.3571\n",
       "  0.0000  0.0000  0.0000\n",
       "  0.0000  0.0000  0.0000\n",
       "  0.0000  5.4822  0.0000\n",
       "  5.4823  0.0000  5.4823\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建 5x3 矩阵，只是分配了空间，未初始化\n",
    "x = t.Tensor(5, 3)  \n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.3673  0.2522  0.3553\n",
       " 0.0070  0.7138  0.0463\n",
       " 0.6198  0.6019  0.3752\n",
       " 0.4755  0.3675  0.3032\n",
       " 0.5824  0.5104  0.5759\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用[0,1]均匀分布随机初始化二维数组\n",
    "x = t.rand(5, 3)  \n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 3])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x.size()) # 查看x的形状\n",
    "x.size()[1], x.size(1) # 查看列的个数, 两种写法等价"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`torch.Size` 是tuple对象的子类，因此它支持tuple的所有操作，如x.size()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.4063  0.7378  1.2411\n",
       " 0.0687  0.7725  0.0634\n",
       " 1.1016  1.4291  0.7324\n",
       " 0.7604  1.2880  0.4597\n",
       " 0.6020  1.0124  1.0185\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = t.rand(5, 3)\n",
    "# 加法的第一种写法\n",
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.4063  0.7378  1.2411\n",
       " 0.0687  0.7725  0.0634\n",
       " 1.1016  1.4291  0.7324\n",
       " 0.7604  1.2880  0.4597\n",
       " 0.6020  1.0124  1.0185\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加法的第二种写法\n",
    "t.add(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.4063  0.7378  1.2411\n",
       " 0.0687  0.7725  0.0634\n",
       " 1.1016  1.4291  0.7324\n",
       " 0.7604  1.2880  0.4597\n",
       " 0.6020  1.0124  1.0185\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加法的第三种写法：指定加法结果的输出目标为result\n",
    "result = t.Tensor(5, 3) # 预先分配空间\n",
    "t.add(x, y, out=result) # 输入到result\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最初y\n",
      "\n",
      " 0.0390  0.4856  0.8858\n",
      " 0.0617  0.0587  0.0171\n",
      " 0.4818  0.8272  0.3572\n",
      " 0.2849  0.9205  0.1565\n",
      " 0.0196  0.5020  0.4426\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n",
      "第一种加法，y的结果\n",
      "\n",
      " 0.0390  0.4856  0.8858\n",
      " 0.0617  0.0587  0.0171\n",
      " 0.4818  0.8272  0.3572\n",
      " 0.2849  0.9205  0.1565\n",
      " 0.0196  0.5020  0.4426\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n",
      "第二种加法，y的结果\n",
      "\n",
      " 0.4063  0.7378  1.2411\n",
      " 0.0687  0.7725  0.0634\n",
      " 1.1016  1.4291  0.7324\n",
      " 0.7604  1.2880  0.4597\n",
      " 0.6020  1.0124  1.0185\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('最初y')\n",
    "print(y)\n",
    "\n",
    "print('第一种加法，y的结果')\n",
    "y.add(x) # 普通加法，不改变y的内容\n",
    "print(y)\n",
    "\n",
    "print('第二种加法，y的结果')\n",
    "y.add_(x) # inplace 加法，y变了\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意，函数名后面带下划线**`_`** 的函数会修改Tensor本身。例如，`x.add_(y)`和`x.t_()`会改变 `x`，但`x.add(y)`和`x.t()`返回一个新的Tensor， 而`x`不变。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.2522\n",
       " 0.7138\n",
       " 0.6019\n",
       " 0.3675\n",
       " 0.5104\n",
       "[torch.FloatTensor of size 5]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tensor的选取操作与Numpy类似\n",
    "x[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensor还支持很多操作，包括数学运算、线性代数、选择、切片等等，其接口设计与Numpy极为相似。更详细的使用方法，会在第三章系统讲解。\n",
    "\n",
    "Tensor和Numpy的数组之间的互操作非常容易且快速。对于Tensor不支持的操作，可以先转为Numpy数组处理，之后再转回Tensor。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1\n",
       " 1\n",
       " 1\n",
       " 1\n",
       " 1\n",
       "[torch.FloatTensor of size 5]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = t.ones(5) # 新建一个全1的Tensor\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.numpy() # Tensor -> Numpy\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1. 1. 1.]\n",
      "\n",
      " 1\n",
      " 1\n",
      " 1\n",
      " 1\n",
      " 1\n",
      "[torch.DoubleTensor of size 5]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones(5)\n",
    "b = t.from_numpy(a) # Numpy->Tensor\n",
    "print(a)\n",
    "print(b) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensor和numpy对象共享内存，所以他们之间的转换很快，而且几乎不会消耗什么资源。但这也意味着，如果其中一个变了，另外一个也会随之改变。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2. 2. 2. 2. 2.]\n",
      "\n",
      " 2\n",
      " 2\n",
      " 2\n",
      " 2\n",
      " 2\n",
      "[torch.DoubleTensor of size 5]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "b.add_(1) # 以`_`结尾的函数会修改自身\n",
    "print(a)\n",
    "print(b) # Tensor和Numpy共享内存"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensor可通过`.cuda` 方法转为GPU的Tensor，从而享受GPU带来的加速运算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在不支持CUDA的机器下，下一步不会运行\n",
    "if t.cuda.is_available():\n",
    "    x = x.cuda()\n",
    "    y = y.cuda()\n",
    "    x + y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此处可能发现GPU运算的速度并未提升太多，这是因为x和y太小且运算也较为简单，而且将数据从内存转移到显存还需要花费额外的开销。GPU的优势需在大规模数据和复杂运算下才能体现出来。\n",
    "\n",
    "### Autograd: 自动微分\n",
    "\n",
    "深度学习的算法本质上是通过反向传播求导数，而PyTorch的**`Autograd`**模块则实现了此功能。在Tensor上的所有操作，Autograd都能为它们自动提供微分，避免了手动计算导数的复杂过程。\n",
    " \n",
    "`autograd.Variable`是Autograd中的核心类，它简单封装了Tensor，并支持几乎所有Tensor有的操作。Tensor在被封装为Variable之后，可以调用它的`.backward`实现反向传播，自动计算所有梯度。Variable的数据结构如图2-6所示。\n",
    "\n",
    "\n",
    "![图2-6:Variable的数据结构](imgs/autograd_Variable.svg)\n",
    "\n",
    "\n",
    "Variable主要包含三个属性。\n",
    "- `data`：保存Variable所包含的Tensor\n",
    "- `grad`：保存`data`对应的梯度，`grad`也是个Variable，而不是Tensor，它和`data`的形状一样。\n",
    "- `grad_fn`：指向一个`Function`对象，这个`Function`用来反向传播计算输入的梯度，具体细节会在下一章讲解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用Tensor新建一个Variable\n",
    "x = Variable(t.ones(2, 2), requires_grad = True)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 4\n",
       "[torch.FloatTensor of size 1]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x.sum()\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SumBackward0 at 0x7fc14824b860>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.grad_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y.backward() # 反向传播,计算梯度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = x.sum() = (x[0][0] + x[0][1] + x[1][0] + x[1][1])\n",
    "# 每个值的梯度都为1\n",
    "x.grad "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：`grad`在反向传播过程中是累加的(accumulated)，这意味着每一次运行反向传播，梯度都会累加之前的梯度，所以反向传播之前需把梯度清零。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 2  2\n",
       " 2  2\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.backward()\n",
    "x.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 3  3\n",
       " 3  3\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.backward()\n",
    "x.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0  0\n",
       " 0  0\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以下划线结束的函数是inplace操作，就像add_\n",
    "x.grad.data.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.backward()\n",
    "x.grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable和Tensor具有近乎一致的接口，在实际使用中可以无缝切换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      " 0.5403  0.5403  0.5403  0.5403  0.5403\n",
      " 0.5403  0.5403  0.5403  0.5403  0.5403\n",
      " 0.5403  0.5403  0.5403  0.5403  0.5403\n",
      " 0.5403  0.5403  0.5403  0.5403  0.5403\n",
      "[torch.FloatTensor of size 4x5]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.5403  0.5403  0.5403  0.5403  0.5403\n",
       " 0.5403  0.5403  0.5403  0.5403  0.5403\n",
       " 0.5403  0.5403  0.5403  0.5403  0.5403\n",
       " 0.5403  0.5403  0.5403  0.5403  0.5403\n",
       "[torch.FloatTensor of size 4x5]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = Variable(t.ones(4,5))\n",
    "y = t.cos(x)\n",
    "x_tensor_cos = t.cos(x.data)\n",
    "print(y)\n",
    "x_tensor_cos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  神经网络\n",
    "\n",
    "Autograd实现了反向传播功能，但是直接用来写深度学习的代码在很多情况下还是稍显复杂，torch.nn是专门为神经网络设计的模块化接口。nn构建于 Autograd之上，可用来定义和运行神经网络。nn.Module是nn中最重要的类，可把它看成是一个网络的封装，包含网络各层定义以及forward方法，调用forward(input)方法，可返回前向传播的结果。下面就以最早的卷积神经网络：LeNet为例，来看看如何用`nn.Module`实现。LeNet的网络结构如图2-7所示。\n",
    "\n",
    "![图2-7:LeNet网络结构](imgs/nn_lenet.png)\n",
    "\n",
    "这是一个基础的前向传播(feed-forward)网络: 接收输入，经过层层传递运算，得到输出。\n",
    "\n",
    "#### 定义网络\n",
    "\n",
    "定义网络时，需要继承`nn.Module`，并实现它的forward方法，把网络中具有可学习参数的层放在构造函数`__init__`中。如果某一层(如ReLU)不具有可学习的参数，则既可以放在构造函数中，也可以不放，但建议不放在其中，而在forward中使用`nn.functional`代替。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d (1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (conv2): Conv2d (6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=400, out_features=120)\n",
      "  (fc2): Linear(in_features=120, out_features=84)\n",
      "  (fc3): Linear(in_features=84, out_features=10)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        # nn.Module子类的函数必须在构造函数中执行父类的构造函数\n",
    "        # 下式等价于nn.Module.__init__(self)\n",
    "        super(Net, self).__init__()\n",
    "        \n",
    "        # 卷积层 '1'表示输入图片为单通道, '6'表示输出通道数，'5'表示卷积核为5*5\n",
    "        self.conv1 = nn.Conv2d(1, 6, 5) \n",
    "        # 卷积层\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5) \n",
    "        # 仿射层/全连接层，y = Wx + b\n",
    "        self.fc1   = nn.Linear(16*5*5, 120) \n",
    "        self.fc2   = nn.Linear(120, 84)\n",
    "        self.fc3   = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x): \n",
    "        # 卷积 -> 激活 -> 池化 \n",
    "        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
    "        x = F.max_pool2d(F.relu(self.conv2(x)), 2) \n",
    "        # reshape，‘-1’表示自适应\n",
    "        x = x.view(x.size()[0], -1) \n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)        \n",
    "        return x\n",
    "\n",
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只要在nn.Module的子类中定义了forward函数，backward函数就会自动被实现(利用`Autograd`)。在`forward` 函数中可使用任何Variable支持的函数，还可以使用if、for循环、print、log等Python语法，写法和标准的Python写法一致。\n",
    "\n",
    "网络的可学习参数通过`net.parameters()`返回，`net.named_parameters`可同时返回可学习的参数及名称。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "params = list(net.parameters())\n",
    "print(len(params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1.weight : torch.Size([6, 1, 5, 5])\n",
      "conv1.bias : torch.Size([6])\n",
      "conv2.weight : torch.Size([16, 6, 5, 5])\n",
      "conv2.bias : torch.Size([16])\n",
      "fc1.weight : torch.Size([120, 400])\n",
      "fc1.bias : torch.Size([120])\n",
      "fc2.weight : torch.Size([84, 120])\n",
      "fc2.bias : torch.Size([84])\n",
      "fc3.weight : torch.Size([10, 84])\n",
      "fc3.bias : torch.Size([10])\n"
     ]
    }
   ],
   "source": [
    "for name,parameters in net.named_parameters():\n",
    "    print(name,':',parameters.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "forward函数的输入和输出都是Variable，只有Variable才具有自动求导功能，而Tensor是没有的，所以在输入时，需把Tensor封装成Variable。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 10])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input = Variable(t.randn(1, 1, 32, 32))\n",
    "out = net(input)\n",
    "out.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.zero_grad() # 所有参数的梯度清零\n",
    "out.backward(Variable(t.ones(1,10))) # 反向传播"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需要注意的是，torch.nn只支持mini-batches，不支持一次只输入一个样本，即一次必须是一个batch。但如果只想输入一个样本，则用 `input.unsqueeze(0)`将batch_size设为１。例如 `nn.Conv2d` 输入必须是4维的，形如$nSamples \\times nChannels \\times Height \\times Width$。可将nSample设为1，即$1 \\times nChannels \\times Height \\times Width$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 损失函数\n",
    "\n",
    "nn实现了神经网络中大多数的损失函数，例如nn.MSELoss用来计算均方误差，nn.CrossEntropyLoss用来计算交叉熵损失。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 28.5536\n",
       "[torch.FloatTensor of size 1]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = net(input)\n",
    "target = Variable(t.arange(0,10))  \n",
    "criterion = nn.MSELoss()\n",
    "loss = criterion(output, target)\n",
    "loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果对loss进行反向传播溯源(使用`gradfn`属性)，可看到它的计算图如下：\n",
    "\n",
    "```\n",
    "input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d  \n",
    "      -> view -> linear -> relu -> linear -> relu -> linear \n",
    "      -> MSELoss\n",
    "      -> loss\n",
    "```\n",
    "\n",
    "当调用`loss.backward()`时，该图会动态生成并自动微分，也即会自动计算图中参数(Parameter)的导数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "反向传播之前 conv1.bias的梯度\n",
      "Variable containing:\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      "[torch.FloatTensor of size 6]\n",
      "\n",
      "反向传播之后 conv1.bias的梯度\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      " -4.2109\n",
      " -2.7638\n",
      " -5.8431\n",
      "  1.3761\n",
      " -2.4141\n",
      " -1.2015\n",
      "[torch.FloatTensor of size 6]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 运行.backward，观察调用之前和调用之后的grad\n",
    "net.zero_grad() # 把net中所有可学习参数的梯度清零\n",
    "print('反向传播之前 conv1.bias的梯度')\n",
    "print(net.conv1.bias.grad)\n",
    "loss.backward()\n",
    "print('反向传播之后 conv1.bias的梯度')\n",
    "print(net.conv1.bias.grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 优化器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在反向传播计算完所有参数的梯度后，还需要使用优化方法来更新网络的权重和参数，例如随机梯度下降法(SGD)的更新策略如下：\n",
    "```\n",
    "weight = weight - learning_rate * gradient\n",
    "```\n",
    "\n",
    "手动实现如下：\n",
    "\n",
    "```python\n",
    "learning_rate = 0.01\n",
    "for f in net.parameters():\n",
    "    f.data.sub_(f.grad.data * learning_rate)# inplace 减法\n",
    "```\n",
    "\n",
    "`torch.optim`中实现了深度学习中绝大多数的优化方法，例如RMSProp、Adam、SGD等，更便于使用，因此大多数时候并不需要手动写上述代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "#新建一个优化器，指定要调整的参数和学习率\n",
    "optimizer = optim.SGD(net.parameters(), lr = 0.01)\n",
    "\n",
    "# 在训练过程中\n",
    "# 先梯度清零(与net.zero_grad()效果一样)\n",
    "optimizer.zero_grad() \n",
    "\n",
    "# 计算损失\n",
    "output = net(input)\n",
    "loss = criterion(output, target)\n",
    "\n",
    "#反向传播\n",
    "loss.backward()\n",
    "\n",
    "#更新参数\n",
    "optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "####  数据加载与预处理\n",
    "\n",
    "在深度学习中数据加载及预处理是非常复杂繁琐的，但PyTorch提供了一些可极大简化和加快数据处理流程的工具。同时，对于常用的数据集，PyTorch也提供了封装好的接口供用户快速调用，这些数据集主要保存在torchvison中。\n",
    "\n",
    "`torchvision`实现了常用的图像数据加载功能，例如Imagenet、CIFAR10、MNIST等，以及常用的数据转换操作，这极大地方便了数据加载，并且代码具有可重用性。\n",
    "\n",
    "\n",
    "### 小试牛刀：CIFAR-10分类\n",
    "\n",
    "下面我们来尝试实现对CIFAR-10数据集的分类，步骤如下: \n",
    "\n",
    "1. 使用torchvision加载并预处理CIFAR-10数据集\n",
    "2. 定义网络\n",
    "3. 定义损失函数和优化器\n",
    "4. 训练网络并更新网络参数\n",
    "5. 测试网络\n",
    "\n",
    "####   CIFAR-10数据加载及预处理\n",
    "\n",
    "CIFAR-10[^3]是一个常用的彩色图片数据集，它有10个类别: 'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'。每张图片都是$3\\times32\\times32$，也即3-通道彩色图片，分辨率为$32\\times32$。\n",
    "\n",
    "[^3]: http://www.cs.toronto.edu/~kriz/cifar.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision as tv\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import ToPILImage\n",
    "show = ToPILImage() # 可以把Tensor转成Image，方便可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# 第一次运行程序torchvision会自动下载CIFAR-10数据集，\n",
    "# 大约100M，需花费一定的时间，\n",
    "# 如果已经下载有CIFAR-10，可通过root参数指定\n",
    "\n",
    "# 定义对数据的预处理\n",
    "transform = transforms.Compose([\n",
    "        transforms.ToTensor(), # 转为Tensor\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), # 归一化\n",
    "                             ])\n",
    "\n",
    "# 训练集\n",
    "trainset = tv.datasets.CIFAR10(\n",
    "                    root='/home/cy/tmp/data/', \n",
    "                    train=True, \n",
    "                    download=True,\n",
    "                    transform=transform)\n",
    "\n",
    "trainloader = t.utils.data.DataLoader(\n",
    "                    trainset, \n",
    "                    batch_size=4,\n",
    "                    shuffle=True, \n",
    "                    num_workers=2)\n",
    "\n",
    "# 测试集\n",
    "testset = tv.datasets.CIFAR10(\n",
    "                    '/home/cy/tmp/data/',\n",
    "                    train=False, \n",
    "                    download=True, \n",
    "                    transform=transform)\n",
    "\n",
    "testloader = t.utils.data.DataLoader(\n",
    "                    testset,\n",
    "                    batch_size=4, \n",
    "                    shuffle=False,\n",
    "                    num_workers=2)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset对象是一个数据集，可以按下标访问，返回形如(data, label)的数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ship\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAALVElEQVR4nO1cW3MVxxGe2d1zk46E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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100 at 0x7FC0FF0ECA20>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data, label) = trainset[100]\n",
    "print(classes[label])\n",
    "\n",
    "# (data + 1) / 2是为了还原被归一化的数据\n",
    "show((data + 1) / 2).resize((100, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataloader是一个可迭代的对象，它将dataset返回的每一条数据拼接成一个batch，并提供多线程加速优化和数据打乱等操作。当程序对dataset的所有数据遍历完一遍之后，相应的对Dataloader也完成了一次迭代。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       bird         cat        ship        ship\n"
     ]
    },
    {
     "data": {
      "image/png": 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ODg5nBu6B5eDgcGYwhxJuy9uFgpJd/CKAsvxWkaIEjS4V5MRRdj2W2yQayy1Ka2cxt95S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\n+D9big2JqKJg6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x100 at 0x7FC0FD0C9278>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next() # 返回4张图片及标签\n",
    "print(' '.join('%11s'%classes[labels[j]] for j in range(4)))\n",
    "show(tv.utils.make_grid((images+1)/2)).resize((400,100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####   定义网络\n",
    "\n",
    "拷贝上面的LeNet网络，修改self.conv1第一个参数为3通道，因CIFAR-10是3通道彩图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d (3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (conv2): Conv2d (6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=400, out_features=120)\n",
      "  (fc2): Linear(in_features=120, out_features=84)\n",
      "  (fc3): Linear(in_features=84, out_features=10)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5) \n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)  \n",
    "        self.fc1   = nn.Linear(16*5*5, 120)  \n",
    "        self.fc2   = nn.Linear(120, 84)\n",
    "        self.fc3   = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x): \n",
    "        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) \n",
    "        x = F.max_pool2d(F.relu(self.conv2(x)), 2) \n",
    "        x = x.view(x.size()[0], -1) \n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)        \n",
    "        return x\n",
    "\n",
    "\n",
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  定义损失函数和优化器(loss和optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import optim\n",
    "criterion = nn.CrossEntropyLoss() # 交叉熵损失函数\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###   训练网络\n",
    "\n",
    "所有网络的训练流程都是类似的，不断地执行如下流程：\n",
    "\n",
    "- 输入数据\n",
    "- 前向传播+反向传播\n",
    "- 更新参数\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 2.238\n",
      "[1,  4000] loss: 1.916\n",
      "[1,  6000] loss: 1.703\n",
      "[1,  8000] loss: 1.582\n",
      "[1, 10000] loss: 1.544\n",
      "[1, 12000] loss: 1.465\n",
      "[2,  2000] loss: 1.425\n",
      "[2,  4000] loss: 1.377\n",
      "[2,  6000] loss: 1.364\n",
      "[2,  8000] loss: 1.330\n",
      "[2, 10000] loss: 1.331\n",
      "[2, 12000] loss: 1.298\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "t.set_num_threads(8)\n",
    "for epoch in range(2):  \n",
    "    \n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        \n",
    "        # 输入数据\n",
    "        inputs, labels = data\n",
    "        inputs, labels = Variable(inputs), Variable(labels)\n",
    "        \n",
    "        # 梯度清零\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        # forward + backward \n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()   \n",
    "        \n",
    "        # 更新参数 \n",
    "        optimizer.step()\n",
    "        \n",
    "        # 打印log信息\n",
    "        running_loss += loss.data[0]\n",
    "        if i % 2000 == 1999: # 每2000个batch打印一下训练状态\n",
    "            print('[%d, %5d] loss: %.3f' \\\n",
    "                  % (epoch+1, i+1, running_loss / 2000))\n",
    "            running_loss = 0.0\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此处仅训练了2个epoch（遍历完一遍数据集称为一个epoch），来看看网络有没有效果。将测试图片输入到网络中，计算它的label，然后与实际的label进行比较。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "实际的label:       cat     ship     ship    plane\n"
     ]
    },
    {
     "data": {
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6lsfZj2MAvQ7PUtXRBtqzqJ/h/AxZQGI9HDpaclDyankk/r9cbuJCMTp7OTkLy4kTJ+dG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\nEyf/z8r/Ab+8NWulkQjIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x100 at 0x7FC0FD0B8EF0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next() # 一个batch返回4张图片\n",
    "print('实际的label: ', ' '.join(\\\n",
    "            '%08s'%classes[labels[j]] for j in range(4)))\n",
    "show(tv.utils.make_grid(images / 2 - 0.5)).resize((400,100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接着计算网络预测的label："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测结果:    cat  ship plane  ship\n"
     ]
    }
   ],
   "source": [
    "# 计算图片在每个类别上的分数\n",
    "outputs = net(Variable(images))\n",
    "# 得分最高的那个类\n",
    "_, predicted = t.max(outputs.data, 1)\n",
    "\n",
    "print('预测结果: ', ' '.join('%5s'\\\n",
    "            % classes[predicted[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "已经可以看出效果，准确率50%，但这只是一部分的图片，再来看看在整个测试集上的效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000张测试集中的准确率为: 54 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0 # 预测正确的图片数\n",
    "total = 0 # 总共的图片数\n",
    "for data in testloader:\n",
    "    images, labels = data\n",
    "    outputs = net(Variable(images))\n",
    "    _, predicted = t.max(outputs.data, 1)\n",
    "    total += labels.size(0)\n",
    "    correct += (predicted == labels).sum()\n",
    "\n",
    "print('10000张测试集中的准确率为: %d %%' % (100 * correct / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练的准确率远比随机猜测(准确率10%)好，证明网络确实学到了东西。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  在GPU训练\n",
    "就像之前把Tensor从CPU转到GPU一样，模型也可以类似地从CPU转到GPU。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "if t.cuda.is_available():\n",
    "    net.cuda()\n",
    "    images = images.cuda()\n",
    "    labels = labels.cuda()\n",
    "    output = net(Variable(images))\n",
    "    loss= criterion(output,Variable(labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果发现在GPU上并没有比CPU提速很多，实际上是因为网络比较小，GPU没有完全发挥自己的真正实力。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对PyTorch的基础介绍至此结束。总结一下，本节主要包含以下内容。\n",
    "\n",
    "1. Tensor: 类似Numpy数组的数据结构，与Numpy接口类似，可方便地互相转换。\n",
    "2. autograd/Variable: Variable封装了Tensor，并提供自动求导功能。\n",
    "3. nn: 专门为神经网络设计的接口，提供了很多有用的功能(神经网络层，损失函数，优化器等)。\n",
    "4. 神经网络训练: 以CIFAR-10分类为例演示了神经网络的训练流程，包括数据加载、网络搭建、训练及测试。\n",
    "\n",
    "通过本节的学习，相信读者可以体会出PyTorch具有接口简单、使用灵活等特点。从下一章开始，本书将深入系统地讲解PyTorch的各部分知识。"
   ]
  }
 ],
 "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",
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