{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 7.7 AdaDelta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": [
"## 7.7.1 算法\n",
"## 7.7.2 从零开始实现"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"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"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d2l.train_ch7(adadelta, init_adadelta_states(), {'rho': 0.9}, features, labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7.7.3 简洁实现"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss: 0.242104, 0.047702 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.Adadelta, {'rho': 0.9}, 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"
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