{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 7.8 Adam算法"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"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
},
"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"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d2l.train_ch7(adam, init_adam_states(), {'lr': 0.01, 't': 1}, features, labels)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss: 0.242066, 0.056867 sec per epoch\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d2l.train_pytorch_ch7(torch.optim.Adam, {'lr': 0.01}, features, labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}