wehub-resource-sync
178 lines
4.8 KiB
Plaintext
178 lines
4.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 5.8 网络中的网络（NiN）"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.4.0\n",
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"cuda\n"
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]
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}
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],
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"source": [
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"import time\n",
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"import torch\n",
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"from torch import nn, optim\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"..\") \n",
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"import d2lzh_pytorch as d2l\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"\n",
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"print(torch.__version__)\n",
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"print(device)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.8.1 NiN块"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def nin_block(in_channels, out_channels, kernel_size, stride, padding):\n",
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" blk = nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),\n",
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" nn.ReLU(),\n",
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" nn.Conv2d(out_channels, out_channels, kernel_size=1),\n",
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" nn.ReLU(),\n",
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" nn.Conv2d(out_channels, out_channels, kernel_size=1),\n",
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" nn.ReLU())\n",
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" return blk"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.8.2 NiN模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"net = nn.Sequential(\n",
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" nin_block(1, 96, kernel_size=11, stride=4, padding=0),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2),\n",
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" nin_block(96, 256, kernel_size=5, stride=1, padding=2),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2),\n",
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" nin_block(256, 384, kernel_size=3, stride=1, padding=1),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2), \n",
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" nn.Dropout(0.5),\n",
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" # 标签类别数是10\n",
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" nin_block(384, 10, kernel_size=3, stride=1, padding=1),\n",
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" # 全局平均池化层可通过将窗口形状设置成输入的高和宽实现\n",
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" nn.AvgPool2d(kernel_size=5),\n",
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" # 将四维的输出转成二维的输出，其形状为(批量大小, 10)\n",
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" d2l.FlattenLayer())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 output shape: torch.Size([1, 96, 54, 54])\n",
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"1 output shape: torch.Size([1, 96, 26, 26])\n",
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"2 output shape: torch.Size([1, 256, 26, 26])\n",
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"3 output shape: torch.Size([1, 256, 12, 12])\n",
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"4 output shape: torch.Size([1, 384, 12, 12])\n",
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"5 output shape: torch.Size([1, 384, 5, 5])\n",
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"6 output shape: torch.Size([1, 384, 5, 5])\n",
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"7 output shape: torch.Size([1, 10, 5, 5])\n",
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"8 output shape: torch.Size([1, 10, 1, 1])\n",
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"9 output shape: torch.Size([1, 10])\n"
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]
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}
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],
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"source": [
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"X = torch.rand(1, 1, 224, 224)\n",
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"\n",
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"for name, blk in net.named_children(): \n",
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" X = blk(X)\n",
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" print(name, 'output shape: ', X.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.8.3 获取数据和训练模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"training on cuda\n",
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"epoch 1, loss 0.0101, train acc 0.513, test acc 0.734, time 260.9 sec\n",
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"epoch 2, loss 0.0050, train acc 0.763, test acc 0.754, time 175.1 sec\n",
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"epoch 3, loss 0.0041, train acc 0.808, test acc 0.826, time 151.0 sec\n",
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"epoch 4, loss 0.0037, train acc 0.828, test acc 0.827, time 151.0 sec\n",
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"epoch 5, loss 0.0034, train acc 0.839, test acc 0.831, time 151.0 sec\n"
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]
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}
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],
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"source": [
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"batch_size = 128\n",
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"# 如出现“out of memory”的报错信息，可减小batch_size或resize\n",
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"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)\n",
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"\n",
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"lr, num_epochs = 0.002, 5\n",
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"optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
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"d2l.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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