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Dive-into-DL-PyTorch/code/chapter07_optimization/7.7_adadelta.ipynb

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{
"cells": [
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"cell_type": "markdown",
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"source": [
"# 7.7 AdaDelta算法"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
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"source": [
"%matplotlib inline\n",
"import torch\n",
"import sys\n",
"sys.path.append(\"..\") \n",
"import d2lzh_pytorch as d2l\n",
"\n",
"features, labels = d2l.get_data_ch7()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7.7.1 算法\n",
"## 7.7.2 从零开始实现"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
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"source": [
"def init_adadelta_states():\n",
" s_w, s_b = torch.zeros((features.shape[1], 1), dtype=torch.float32), torch.zeros(1, dtype=torch.float32)\n",
" delta_w, delta_b = torch.zeros((features.shape[1], 1), dtype=torch.float32), torch.zeros(1, dtype=torch.float32)\n",
" return ((s_w, delta_w), (s_b, delta_b))\n",
"\n",
"def adadelta(params, states, hyperparams):\n",
" rho, eps = hyperparams['rho'], 1e-5\n",
" for p, (s, delta) in zip(params, states):\n",
" s[:] = rho * s + (1 - rho) * (p.grad.data**2)\n",
" g = p.grad.data * torch.sqrt((delta + eps) / (s + eps))\n",
" p.data -= g\n",
" delta[:] = rho * delta + (1 - rho) * g * g"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss: 0.246483, 0.061862 sec per epoch\n"
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