wehub-resource-sync
291 lines
8.6 KiB
Plaintext
291 lines
8.6 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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"# 5.6 深度卷积神经网络（AlexNet）"
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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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"ExecuteTime": {
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"end_time": "2019-03-19T07:36:45.657048Z",
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"start_time": "2019-03-19T07:36:45.285668Z"
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}
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},
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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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"0.2.1\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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"import torchvision\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(torchvision.__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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"## 5.6.2 AlexNet"
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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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"ExecuteTime": {
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"end_time": "2019-03-19T07:36:45.703036Z",
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"start_time": "2019-03-19T07:36:45.658231Z"
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}
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},
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"outputs": [],
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"source": [
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"class AlexNet(nn.Module):\n",
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" def __init__(self):\n",
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" super(AlexNet, self).__init__()\n",
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" self.conv = nn.Sequential(\n",
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" nn.Conv2d(1, 96, 11, 4), # in_channels, out_channels, kernel_size, stride, padding\n",
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" nn.ReLU(),\n",
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" nn.MaxPool2d(3, 2), # kernel_size, stride\n",
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" # 减小卷积窗口，使用填充为2来使得输入与输出的高和宽一致，且增大输出通道数\n",
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" nn.Conv2d(96, 256, 5, 1, 2),\n",
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" nn.ReLU(),\n",
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" nn.MaxPool2d(3, 2),\n",
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" # 连续3个卷积层，且使用更小的卷积窗口。除了最后的卷积层外，进一步增大了输出通道数。\n",
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" # 前两个卷积层后不使用池化层来减小输入的高和宽\n",
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" nn.Conv2d(256, 384, 3, 1, 1),\n",
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" nn.ReLU(),\n",
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" nn.Conv2d(384, 384, 3, 1, 1),\n",
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" nn.ReLU(),\n",
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" nn.Conv2d(384, 256, 3, 1, 1),\n",
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" nn.ReLU(),\n",
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" nn.MaxPool2d(3, 2)\n",
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" )\n",
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" # 这里全连接层的输出个数比LeNet中的大数倍。使用丢弃层来缓解过拟合\n",
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" self.fc = nn.Sequential(\n",
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" nn.Linear(256*5*5, 4096),\n",
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" nn.ReLU(),\n",
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" nn.Dropout(0.5),\n",
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" nn.Linear(4096, 4096),\n",
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" nn.ReLU(),\n",
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" nn.Dropout(0.5),\n",
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" # 输出层。由于这里使用Fashion-MNIST，所以用类别数为10，而非论文中的1000\n",
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" nn.Linear(4096, 10),\n",
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" )\n",
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"\n",
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" def forward(self, img):\n",
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" feature = self.conv(img)\n",
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" output = self.fc(feature.view(img.shape[0], -1))\n",
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" return output"
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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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"ExecuteTime": {
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"end_time": "2019-03-19T07:36:46.053598Z",
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"start_time": "2019-03-19T07:36:45.704356Z"
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}
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},
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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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"AlexNet(\n",
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" (conv): Sequential(\n",
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" (0): Conv2d(1, 96, kernel_size=(11, 11), stride=(4, 4))\n",
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" (1): ReLU()\n",
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" (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (3): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
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" (4): ReLU()\n",
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" (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (6): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (7): ReLU()\n",
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" (8): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (9): ReLU()\n",
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" (10): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (11): ReLU()\n",
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" (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (fc): Sequential(\n",
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" (0): Linear(in_features=6400, out_features=4096, bias=True)\n",
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" (1): ReLU()\n",
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" (2): Dropout(p=0.5)\n",
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" (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
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" (4): ReLU()\n",
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" (5): Dropout(p=0.5)\n",
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" (6): Linear(in_features=4096, out_features=10, bias=True)\n",
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" )\n",
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")\n"
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]
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}
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],
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"source": [
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"net = AlexNet()\n",
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"print(net)"
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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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"## 5.6.3 读取数据"
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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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"ExecuteTime": {
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"end_time": "2019-03-19T07:36:46.066761Z",
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"start_time": "2019-03-19T07:36:46.054928Z"
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}
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},
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"outputs": [],
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"source": [
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"# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
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"def load_data_fashion_mnist(batch_size, resize=None, root='~/Datasets/FashionMNIST'):\n",
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" \"\"\"Download the fashion mnist dataset and then load into memory.\"\"\"\n",
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" trans = []\n",
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" if resize:\n",
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" trans.append(torchvision.transforms.Resize(size=resize))\n",
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" trans.append(torchvision.transforms.ToTensor())\n",
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" \n",
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" transform = torchvision.transforms.Compose(trans)\n",
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" mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform)\n",
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" mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform)\n",
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"\n",
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" train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=4)\n",
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" test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=4)\n",
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"\n",
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" return train_iter, test_iter"
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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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"ExecuteTime": {
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"end_time": "2019-03-19T07:36:46.091524Z",
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"start_time": "2019-03-19T07:36:46.067835Z"
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}
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},
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"outputs": [],
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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 = load_data_fashion_mnist(batch_size, resize=224)"
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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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"## 5.6.4 训练"
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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": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-03-19T07:36:47.850402Z",
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"start_time": "2019-03-19T07:36:46.092485Z"
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}
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},
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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 0.0047, train acc 0.770, test acc 0.865, time 128.3 sec\n",
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"epoch 2, loss 0.0025, train acc 0.879, test acc 0.889, time 128.8 sec\n",
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"epoch 3, loss 0.0022, train acc 0.898, test acc 0.901, time 130.4 sec\n",
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"epoch 4, loss 0.0019, train acc 0.908, test acc 0.900, time 131.4 sec\n",
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"epoch 5, loss 0.0018, train acc 0.913, test acc 0.902, time 129.9 sec\n"
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]
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}
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],
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"source": [
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"lr, num_epochs = 0.001, 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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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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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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