learn_pytorch_09_pytorch中的自动微分Autograd.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 pytorch中的自动微分  \n",
    "pytorch实现了因变量对一系列自变量的自动求导，应用在神经网络的链式法则。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 自动微分举例  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 简单的自动微分：标量\n",
    "如果Tensor类表示的是一个标量，则不需要为backward()指定任何参数。   \n",
    "但是如果它有更多的元素，则需要指定一个gradient参数，它是形状匹配的张量。   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = torch.tensor(3.0,requires_grad=True)\n",
    "x = torch.tensor(5.0,requires_grad=True)\n",
    "b = torch.tensor(10.0,requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = w*x+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "z.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.)\n",
      "tensor(5.)\n",
      "tensor(1.)\n"
     ]
    }
   ],
   "source": [
    "print(x.grad.data)  \n",
    "print(w.grad.data)\n",
    "print(b.grad.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 复杂的自动微分：向量或者更多维tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 1.],\n",
      "        [1., 1.]], requires_grad=True)\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "x=torch.ones(2,2,requires_grad=True)\n",
    "print(x)\n",
    "print(x.grad_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[3., 3.],\n",
      "        [3., 3.]], grad_fn=<AddBackward0>)\n",
      "<AddBackward0 object at 0x000001DB0815C3C8>\n"
     ]
    }
   ],
   "source": [
    "y=x+2\n",
    "print(y)\n",
    "print(y.grad_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print(x.is_leaf)\n",
    "print(y.is_leaf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(27., grad_fn=<MeanBackward0>)\n"
     ]
    }
   ],
   "source": [
    "z=y*y*3\n",
    "out=z.mean()\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "out.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[4.5000, 4.5000],\n",
      "        [4.5000, 4.5000]])\n"
     ]
    }
   ],
   "source": [
    "print(x.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[2., 4.],\n",
      "        [6., 8.]], grad_fn=<ViewBackward>)\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([1.0,2,3,4],requires_grad=True)\n",
    "y=2*x\n",
    "z=y.view(2,2)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2., 2., 2., 2.])\n"
     ]
    }
   ],
   "source": [
    "v=torch.tensor([[1.0,1],[1,1]],requires_grad=True)\n",
    "z.backward(v)\n",
    "print(x.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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