{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 前言\n", "\n", "原文翻译自：[Deep Learning with PyTorch: A 60 Minute Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)\n", "\n", "翻译：[林不清](https://www.zhihu.com/people/lu-guo-92-42-88)\n", "\n", "整理：机器学习初学者公众号 ![gongzhong](images/gongzhong.jpg)\n", "\n", "## 目录\n", "\n", "[60分钟入门PyTorch（一）——Tensors](https://zhuanlan.zhihu.com/p/347676809)\n", "\n", "[60分钟入门PyTorch（二）——Autograd自动求导](https://zhuanlan.zhihu.com/p/347672836)\n", "\n", "[60分钟入门Pytorch（三）——神经网络](https://zhuanlan.zhihu.com/p/347678492)\n", "\n", "[60分钟入门PyTorch（四）——训练一个分类器](https://zhuanlan.zhihu.com/p/347681137)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 训练一个分类器" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "你已经学会如何去定义一个神经网络,计算损失值和更新网络的权重。\n", "\n", "你现在可能在思考：数据哪里来呢？\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 关于数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通常，当你处理图像，文本，音频和视频数据时，你可以使用标准的Python包来加载数据到一个numpy数组中.然后把这个数组转换成`torch.*Tensor`。\n", "* 对于图像,有诸如Pillow,OpenCV包等非常实用\n", "* 对于音频,有诸如scipy和librosa包\n", "* 对于文本,可以用原始Python和Cython来加载,或者使用NLTK和SpaCy\n", "对于视觉,我们创建了一个`torchvision`包,包含常见数据集的数据加载,比如Imagenet,CIFAR10,MNIST等,和图像转换器,也就是`torchvision.datasets`和`torch.utils.data.DataLoader`。\n", "\n", "这提供了巨大的便利,也避免了代码的重复。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在这个教程中,我们使用CIFAR10数据集,它有如下10个类别:’airplane’,’automobile’,’bird’,’cat’,’deer’,’dog’,’frog’,’horse’,’ship’,’truck’。这个数据集中的图像大小为3\\*32\\*32,即,3通道,32\\*32像素。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![cifar10](images/cifar10.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 训练一个图像分类器" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们将按照下列顺序进行:\n", "* 使用`torchvision`加载和归一化CIFAR10训练集和测试集.\n", "* 定义一个卷积神经网络\n", "* 定义损失函数\n", "* 在训练集上训练网络\n", "* 在测试集上测试网络\n", "\n", "### 1. 加载和归一化CIFAR10\n", "使用`torchvision`加载CIFAR10是非常容易的。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import torchvision\n", "import torchvision.transforms as transforms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "torchvision的输出是[0,1]的PILImage图像,我们把它转换为归一化范围为[-1, 1]的张量。\n", "

注意

如果在Windows上运行时出现BrokenPipeError，尝试将torch.utils.data.DataLoader()的num_worker设置为0。

" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data\\cifar-10-python.tar.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dfeaa5d49cb44bc0a927ab4196629477", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Failed download. Trying https -> http instead. Downloading http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data\\cifar-10-python.tar.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2a44cd06f498404cab0bfb8a0f762c68", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "ename": "URLError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mConnectionAbortedError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\utils.py\u001b[0m in \u001b[0;36mdownload_url\u001b[1;34m(url, root, filename, md5)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m 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torch.utils.data.DataLoader(trainset, batch_size=4,\n\u001b[0;32m 8\u001b[0m shuffle=True, num_workers=2)\n", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\cifar.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, root, train, transform, target_transform, download)\u001b[0m\n\u001b[0;32m 56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdownload\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m 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1347\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1349\u001b[0m \u001b[0mhttp_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m 1319\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0;32m 1320\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m 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download=True, transform=transform)\n", "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n", " shuffle=True, num_workers=2)\n", "\n", "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n", " download=True, transform=transform)\n", "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n", " shuffle=False, num_workers=2)\n", "\n", "classes = ('plane', 'car', 'bird', 'cat',\n", " 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n", "#这个过程有点慢，会下载大约340mb图片数据。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们展示一些有趣的训练图像。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# functions to show an image\n", "\n", "\n", "def imshow(img):\n", " img = img / 2 + 0.5 # unnormalize\n", " npimg = img.numpy()\n", " plt.imshow(np.transpose(npimg, (1, 2, 0)))\n", " plt.show()\n", "\n", "\n", "# get some random training images\n", "dataiter = iter(trainloader)\n", "images, labels = dataiter.next()\n", "\n", "# show images\n", "imshow(torchvision.utils.make_grid(images))\n", "# print labels\n", "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. 定义一个卷积神经网络\n", "从之前的神经网络一节复制神经网络代码,并修改为接受3通道图像取代之前的接受单通道图像。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "\n", "class Net(nn.Module):\n", " def __init__(self):\n", " super(Net, self).__init__()\n", " self.conv1 = nn.Conv2d(3, 6, 5)\n", " self.pool = nn.MaxPool2d(2, 2)\n", " self.conv2 = nn.Conv2d(6, 16, 5)\n", " self.fc1 = nn.Linear(16 * 5 * 5, 120)\n", " self.fc2 = nn.Linear(120, 84)\n", " self.fc3 = nn.Linear(84, 10)\n", "\n", " def forward(self, x):\n", " x = self.pool(F.relu(self.conv1(x)))\n", " x = self.pool(F.relu(self.conv2(x)))\n", " x = x.view(-1, 16 * 5 * 5)\n", " x = F.relu(self.fc1(x))\n", " x = F.relu(self.fc2(x))\n", " x = self.fc3(x)\n", " return x\n", "\n", "\n", "net = Net()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. 定义损失函数和优化器\n", "我们使用交叉熵作为损失函数,使用带动量的随机梯度下降。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import torch.optim as optim\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. 训练网络\n", "这是开始有趣的时刻，我们只需在数据迭代器上循环,把数据输入给网络,并优化。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for epoch in range(2): # loop over the dataset multiple times\n", "\n", " running_loss = 0.0\n", " for i, data in enumerate(trainloader, 0):\n", " # get the inputs; data is a list of [inputs, labels]\n", " inputs, labels = data\n", "\n", " # zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # forward + backward + optimize\n", " outputs = net(inputs)\n", " loss = criterion(outputs, labels)\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # print statistics\n", " running_loss += loss.item()\n", " if i % 2000 == 1999: # print every 2000 mini-batches\n", " print('[%d, %5d] loss: %.3f' %\n", " (epoch + 1, i + 1, running_loss / 2000))\n", " running_loss = 0.0\n", "\n", "print('Finished Training')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "保存一下我们的训练模型" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "PATH = './cifar_net.pth'\n", "torch.save(net.state_dict(), PATH)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "点击[这里](https://pytorch.org/docs/stable/notes/serialization.html)查看关于保存模型的详细介绍" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. 在测试集上测试网络\n", "我们在整个训练集上训练了两次网络,但是我们还需要检查网络是否从数据集中学习到东西。\n", "\n", "我们通过预测神经网络输出的类别标签并根据实际情况进行检测，如果预测正确,我们把该样本添加到正确预测列表。\n", "\n", "第一步，显示测试集中的图片一遍熟悉图片内容。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataiter = iter(testloader)\n", "images, labels = dataiter.next()\n", "\n", "# print images\n", "imshow(torchvision.utils.make_grid(images))\n", "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "接下来，让我们重新加载我们保存的模型(注意:保存和重新加载模型在这里不是必要的，我们只是为了说明如何这样做)：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "net = Net()\n", "net.load_state_dict(torch.load(PATH))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "现在我们来看看神经网络认为以上图片是什么?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "outputs = net(images)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "输出是10个标签的概率。一个类别的概率越大,神经网络越认为他是这个类别。所以让我们得到最高概率的标签。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_, predicted = torch.max(outputs, 1)\n", "\n", "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n", " for j in range(4)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这结果看起来非常的好。\n", "\n", "接下来让我们看看网络在整个测试集上的结果如何。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "correct = 0\n", "total = 0\n", "with torch.no_grad():\n", " for data in testloader:\n", " images, labels = data\n", " outputs = net(images)\n", " _, predicted = torch.max(outputs.data, 1)\n", " total += labels.size(0)\n", " correct += (predicted == labels).sum().item()\n", "\n", "print('Accuracy of the network on the 10000 test images: %d %%' % (\n", " 100 * correct / total))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "结果看起来好于偶然，偶然的正确率为10%,似乎网络学习到了一些东西。\n", "\n", "那在什么类上预测较好，什么类预测结果不好呢？\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class_correct = list(0. for i in range(10))\n", "class_total = list(0. for i in range(10))\n", "with torch.no_grad():\n", " for data in testloader:\n", " images, labels = data\n", " outputs = net(images)\n", " _, predicted = torch.max(outputs, 1)\n", " c = (predicted == labels).squeeze()\n", " for i in range(4):\n", " label = labels[i]\n", " class_correct[label] += c[i].item()\n", " class_total[label] += 1\n", "\n", "\n", "for i in range(10):\n", " print('Accuracy of %5s : %2d %%' % (\n", " classes[i], 100 * class_correct[i] / class_total[i]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "接下来干什么?\n", "\n", "我们如何在GPU上运行神经网络呢?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 在GPU上训练\n", "你是如何把一个Tensor转换GPU上,你就如何把一个神经网络移动到GPU上训练。这个操作会递归遍历有所模块,并将其参数和缓冲区转换为CUDA张量。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "# Assume that we are on a CUDA machine, then this should print a CUDA device:\n", "#假设我们有一台CUDA的机器，这个操作将显示CUDA设备。\n", "print(device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "接下来假设我们有一台CUDA的机器，然后这些方法将递归遍历所有模块并将其参数和缓冲区转换为CUDA张量：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "net.to(device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "请记住，你也必须在每一步中把你的输入和目标值转换到GPU上:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "inputs, labels = inputs.to(device), labels.to(device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "为什么我们没注意到GPU的速度提升很多?那是因为网络非常的小。\n", "\n", "#### 实践:\n", "尝试增加你的网络的宽度(第一个`nn.Conv2d`的第2个参数, 第二个`nn.Conv2d`的第一个参数,他们需要是相同的数字),看看你得到了什么样的加速。\n", "\n", "#### 实现的目标:\n", "* 深入了解了PyTorch的张量库和神经网络\n", "* 训练了一个小网络来分类图片" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 在多GPU上训练\n", "如果你希望使用所有GPU来更大的加快速度,请查看选读:[数据并行](https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 接下来做什么?\n", "* 训练神经网络玩电子游戏\n", "* 在ImageNet上训练最好的ResNet\n", "* 使用对抗生成网络来训练一个人脸生成器\n", "* 使用LSTM网络训练一个字符级的语言模型\n", "* 更多示例\n", "* 更多教程\n", "* 在论坛上讨论PyTorch\n", "* 在Slack上与其他用户聊天" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }