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

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{
"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": {
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"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"
]
},
{
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"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
},
"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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"loss: 0.243147, 0.040675 sec per epoch\n"
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