Dive-into-DL-PyTorch/code/chapter07_optimization/7.5_adagrad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.5 AdaGrad算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "import torch\n",
    "import sys\n",
    "sys.path.append(\"..\") \n",
    "import d2lzh_pytorch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.5.2 特点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20, x1 -2.382563, x2 -0.158591\n"
     ]
    },
    {
     "data": {
      "image/png": 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pnLl5pXIF0jmHdN5c493MMvkiE5kCgxNmXieTL5ZuhJl8cZITwHKhlPkJppRy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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119aea630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def adagrad_2d(x1, x2, s1, s2):\n",
    "    g1, g2, eps = 0.2 * x1, 4 * x2, 1e-6  # 前两项为自变量梯度\n",
    "    s1 += g1 ** 2\n",
    "    s2 += g2 ** 2\n",
    "    x1 -= eta / math.sqrt(s1 + eps) * g1\n",
    "    x2 -= eta / math.sqrt(s2 + eps) * g2\n",
    "    return x1, x2, s1, s2\n",
    "\n",
    "def f_2d(x1, x2):\n",
    "    return 0.1 * x1 ** 2 + 2 * x2 ** 2\n",
    "\n",
    "eta = 0.4\n",
    "d2l.show_trace_2d(f_2d, d2l.train_2d(adagrad_2d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20, x1 -0.002295, x2 -0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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7q0wIe4BrlFLvnMM+CbOF1vDzv4Znvg2Xfxhe/Me17pEwCyilzAU84GXTihO3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\ncgGyhQ9EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf27b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta = 2\n",
    "d2l.show_trace_2d(f_2d, d2l.train_2d(adagrad_2d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.5.3 从零开始实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "features, labels = d2l.get_data_ch7()\n",
    "\n",
    "def init_adagrad_states():\n",
    "    s_w = torch.zeros((features.shape[1], 1), dtype=torch.float32)\n",
    "    s_b = torch.zeros(1, dtype=torch.float32)\n",
    "    return (s_w, s_b)\n",
    "\n",
    "def adagrad(params, states, hyperparams):\n",
    "    eps = 1e-6\n",
    "    for p, s in zip(params, states):\n",
    "        s.data += (p.grad.data**2)\n",
    "        p.data -= hyperparams['lr'] * p.grad.data / torch.sqrt(s + eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.242541, 0.047213 sec per epoch\n"
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   "metadata": {},
   "source": [
    "## 7.5.4 简洁实现"
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   "execution_count": 6,
   "metadata": {},
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     "text": [
      "loss: 0.243147, 0.040675 sec per epoch\n"
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