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Dive-into-DL-PyTorch/code/chapter07_optimization/7.6_rmsprop.ipynb

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{
"cells": [
{
"cell_type": "markdown",
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"source": [
"# 7.6 RMSProp算法"
]
},
{
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"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": [
"## 7.6.1 算法"
]
},
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"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
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"text": [
"epoch 20, x1 -0.010599, x2 0.000000\n"
]
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]
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"source": [
"def rmsprop_2d(x1, x2, s1, s2):\n",
" g1, g2, eps = 0.2 * x1, 4 * x2, 1e-6\n",
" s1 = gamma * s1 + (1 - gamma) * g1 ** 2\n",
" s2 = gamma * s2 + (1 - gamma) * 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, gamma = 0.4, 0.9\n",
"d2l.show_trace_2d(f_2d, d2l.train_2d(rmsprop_2d))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7.6.2 从零开始实现"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"features, labels = d2l.get_data_ch7()\n",
"\n",
"def init_rmsprop_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 rmsprop(params, states, hyperparams):\n",
" gamma, eps = hyperparams['gamma'], 1e-6\n",
" for p, s in zip(params, states):\n",
" s.data = gamma * s.data + (1 - gamma) * (p.grad.data)**2\n",
" p.data -= hyperparams['lr'] * p.grad.data / torch.sqrt(s + eps)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
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"loss: 0.242014, 0.053250 sec per epoch\n"
]
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