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Dive-into-DL-PyTorch/code/chapter07_optimization/7.8_adam.ipynb

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{
"cells": [
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"cell_type": "markdown",
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"source": [
"# 7.8 Adam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": [
"## 7.8.2 从零开始实现"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"def init_adam_states():\n",
" v_w, v_b = torch.zeros((features.shape[1], 1), dtype=torch.float32), torch.zeros(1, dtype=torch.float32)\n",
" s_w, s_b = torch.zeros((features.shape[1], 1), dtype=torch.float32), torch.zeros(1, dtype=torch.float32)\n",
" return ((v_w, s_w), (v_b, s_b))\n",
"\n",
"def adam(params, states, hyperparams):\n",
" beta1, beta2, eps = 0.9, 0.999, 1e-6\n",
" for p, (v, s) in zip(params, states):\n",
" v[:] = beta1 * v + (1 - beta1) * p.grad.data\n",
" s[:] = beta2 * s + (1 - beta2) * p.grad.data**2\n",
" v_bias_corr = v / (1 - beta1 ** hyperparams['t'])\n",
" s_bias_corr = s / (1 - beta2 ** hyperparams['t'])\n",
" p.data -= hyperparams['lr'] * v_bias_corr / (torch.sqrt(s_bias_corr) + eps)\n",
" hyperparams['t'] += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7.8.3 简洁实现"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss: 0.243004, 0.064906 sec per epoch\n"
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