\u041f\u043e\u0432\u0442\u043e\u0440\u0438\u043c \u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043a\u0443 \u0438 \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c GPU<\/li>\n<\/ul>\n\n\u0412\u0435\u0441\u044c \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u044b\u0439 \u043d\u0438\u0436\u0435 \u043a\u043e\u0434 \u043c\u044b \u0431\u0443\u0434\u0435\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u0432 Jupyter Notebook.<\/p>\n
\u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0438 \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f CIFAR10<\/h3>\n
\n\u0421\u043a\u043e\u043f\u0438\u0440\u0443\u0439\u0442\u0435 \u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u0435 \u0432 Jupyter \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434:<\/p>\n
\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose(\n [transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n\ntrainset = torchvision.datasets.CIFAR10(root='.\/data', train=True,\n download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n shuffle=True, num_workers=2)\n\ntestset = torchvision.datasets.CIFAR10(root='.\/data', train=False,\n download=True, transform=transform)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=4,\n shuffle=False, num_workers=2)\n\nclasses = ('plane', 'car', 'bird', 'cat',\n 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')<\/code><\/pre>\n\n\u041e\u0442\u0432\u0435\u0442 \u0434\u043e\u043b\u0436\u0435\u043d \u0431\u044b\u0442\u044c \u0442\u0430\u043a\u043e\u0439:<\/p>\n
Downloading https:\/\/www.cs.toronto.edu\/~kriz\/cifar-10-python.tar.gz to .\/data\/cifar-10-python.tar.gz\nExtracting .\/data\/cifar-10-python.tar.gz to .\/data\nFiles already downloaded and verified<\/code><\/pre>\n\n\u0412\u044b\u0432\u0435\u0434\u0435\u043c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043e\u0447\u043d\u044b\u0445 \u043e\u0431\u0440\u0430\u0437\u043e\u0432 \u0434\u043b\u044f \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438:<\/p>\n
\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# functions to show an image\n\ndef 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# get some random training images\ndataiter = iter(trainloader)\nimages, labels = dataiter.next()\n\n# show images\nimshow(torchvision.utils.make_grid(images))\n# print labels\nprint(' '.join('%5s' % classes[labels[j]] for j in range(4)))\n<\/code><\/pre>\n\n![\"\u0412\u0430\u0448\u0430](\"\/wp-content\/uploads\/2020\/03\/9b8034259b9f1280be5dfeeee4f01834.png\")
\n<\/p>\n
\u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438<\/h3>\n
\n\u0420\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u043a\u0430\u043a \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0430\u044f \u0441\u0435\u0442\u044c \u043f\u043e \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044e \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439. \u042d\u0442\u043e \u043f\u0440\u043e\u0441\u0442\u0430\u044f \u0441\u0435\u0442\u044c \u043f\u0440\u044f\u043c\u043e\u0439 \u0441\u0432\u044f\u0437\u0438. \u041e\u043d \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u0435\u0442 \u0432\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435, \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u0430\u0435\u0442 \u0438\u0445 \u0447\u0435\u0440\u0435\u0437 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u043b\u043e\u0435\u0432 \u043e\u0434\u0438\u043d \u0437\u0430 \u0434\u0440\u0443\u0433\u0438\u043c, \u0430 \u0437\u0430\u0442\u0435\u043c, \u043d\u0430\u043a\u043e\u043d\u0435\u0446, \u0432\u044b\u0434\u0430\u0435\u0442 \u0432\u044b\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435.<\/p>\n
![\"\u0412\u0430\u0448\u0430](\"\/wp-content\/uploads\/2020\/03\/241716475664579517daa2ef1a70bad0.png\")
\n
\n\u0421\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u043f\u043e\u0434\u043e\u0431\u043d\u0443\u044e \u0441\u0435\u0442\u044c \u0432 \u043d\u0430\u0448\u0435\u0439 \u0441\u0440\u0435\u0434\u0435:<\/p>\n
\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass 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\nnet = Net()\n<\/code><\/pre>\n\n\u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043c \u0442\u0430\u043a \u0436\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u043f\u043e\u0442\u0435\u0440\u044c \u0438 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0442\u043e\u0440 <\/p>\n
\nimport torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)<\/code><\/pre>\n<\/p>\n
\u0422\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043a\u0430 \u0441\u0435\u0442\u0438 \u043d\u0430 \u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043e\u0447\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445<\/h3>\n
\n\u041d\u0430\u0447\u0438\u043d\u0430\u0435\u043c \u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043a\u0443 \u043d\u0430\u0448\u0435\u0439 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438. \u041e\u0431\u0440\u0430\u0449\u0430\u044e \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435 \u0447\u0442\u043e \u043f\u043e\u0441\u043b\u0435 \u044d\u0442\u043e\u0433\u043e \u043a\u0430\u043a \u0432\u044b \u0437\u0430\u043f\u0443\u0441\u0442\u0438\u0442\u0435 \u043d\u0430 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u044d\u0442\u043e\u0442 \u043a\u043e\u0434, \u043d\u0443\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043f\u043e\u0434\u043e\u0436\u0434\u0430\u0442\u044c \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u0434\u043e \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u044f \u0440\u0430\u0431\u043e\u0442\u044b. \u0423 \u043c\u0435\u043d\u044f \u044d\u0442\u043e \u0437\u0430\u043d\u044f\u043b\u043e 5 \u043c\u0438\u043d. \u0414\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0441\u0435\u0442\u0438 \u043d\u0443\u0436\u043d\u043e \u0432\u0440\u0435\u043c\u044f.<\/p>\n
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\nprint('Finished Training')\n<\/code><\/pre>\n\n\u041f\u043e\u043b\u0443\u0447\u0438\u043c \u0442\u0430\u043a\u043e\u0439 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442:<\/p>\n
![\"\u0412\u0430\u0448\u0430](\"\/wp-content\/uploads\/2020\/03\/28d4b657583698acec497814b8187c04.png\")
\n
\n\u0421\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u043c \u043d\u0430\u0448\u0443 \u043e\u0431\u0443\u0447\u0435\u043d\u043d\u0443\u044e \u043c\u043e\u0434\u0435\u043b\u044c:<\/p>\n
PATH = '.\/cifar_net.pth'\ntorch.save(net.state_dict(), PATH)<\/code><\/pre>\n<\/p>\n
\u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0435\u0442\u0438 \u043d\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445<\/h3>\n
\n\u041c\u044b \u043e\u0431\u0443\u0447\u0438\u043b\u0438 \u0441\u0435\u0442\u044c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e \u043d\u0430\u0431\u043e\u0440 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445. \u041d\u043e \u043d\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c, \u043d\u0430\u0443\u0447\u0438\u043b\u0430\u0441\u044c \u043b\u0438 \u0441\u0435\u0442\u044c \u0432\u043e\u043e\u0431\u0449\u0435 \u0447\u0435\u043c\u0443-\u043b\u0438\u0431\u043e.<\/p>\n
\u041c\u044b \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u043c \u044d\u0442\u043e, \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u0432 \u043c\u0435\u0442\u043a\u0443 \u043a\u043b\u0430\u0441\u0441\u0430, \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u0432\u044b\u0432\u043e\u0434\u0438\u0442 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0430\u044f \u0441\u0435\u0442\u044c, \u0438 \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0432 \u0435\u0435 \u043d\u0430 \u043f\u0440\u0435\u0434\u043c\u0435\u0442 \u0438\u0441\u0442\u0438\u043d\u043d\u043e\u0441\u0442\u0438. \u0415\u0441\u043b\u0438 \u043f\u0440\u043e\u0433\u043d\u043e\u0437 \u0432\u0435\u0440\u0435\u043d, \u043c\u044b \u0434\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u043e\u0431\u0440\u0430\u0437\u0435\u0446 \u0432 \u0441\u043f\u0438\u0441\u043e\u043a \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u044b\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432.
\n\u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u043a\u0430\u0436\u0435\u043c \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0438\u0437 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0430:<\/p>\n
dataiter = iter(testloader)\nimages, labels = dataiter.next()\n\n# print images\nimshow(torchvision.utils.make_grid(images))\nprint('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))<\/code><\/pre>\n\n![\"\u0412\u0430\u0448\u0430](\"\/wp-content\/uploads\/2020\/03\/bb9d30e02360cfa5f734b05ad15bc91d.png\")
\n
\n\u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u043f\u0440\u043e\u0441\u0438\u043c \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0443\u044e \u0441\u0435\u0442\u044c \u0441\u043e\u043e\u0431\u0449\u0438\u0442\u044c \u043d\u0430\u043c \u0447\u0442\u043e \u043d\u0430 \u044d\u0442\u0438\u0445 \u043a\u0430\u0440\u0442\u0438\u043d\u043a\u0430\u0445:<\/p>\n
\nnet = Net()\nnet.load_state_dict(torch.load(PATH))\n\noutputs = net(images)\n\n_, predicted = torch.max(outputs, 1)\n\nprint('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n for j in range(4)))\n<\/code><\/pre>\n\n![\"\u0412\u0430\u0448\u0430](\"\/wp-content\/uploads\/2020\/03\/6eaa2fb5295b0decb70fa43da3edeb6d.png\")
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\n\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u043a\u0430\u0436\u0443\u0442\u0441\u044f \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0445\u043e\u0440\u043e\u0448\u0438\u043c\u0438: \u0441\u0435\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043b\u0430 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0442\u0440\u0438 \u043a\u0430\u0440\u0442\u0438\u043d\u043a\u0438 \u0438\u0437 \u0447\u0435\u0442\u044b\u0440\u0435\u0445.<\/p>\n
\u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a \u0441\u0435\u0442\u044c \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u0432\u043e \u0432\u0441\u0435\u043c \u043d\u0430\u0431\u043e\u0440\u0435 \u0434\u0430\u043d\u043d\u044b\u0445.<\/p>\n
\ncorrect = 0\ntotal = 0\nwith 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\nprint('Accuracy of the network on the 10000 test images: %d %%' % (\n 100 * correct \/ total))<\/code><\/pre>\n\n![\"\u0412\u0430\u0448\u0430](\"\/wp-content\/uploads\/2020\/03\/59c3cf77220cc6aebcc96c6382e35ce4.png\")
\n
\n\u041f\u043e\u0445\u043e\u0436\u0435 \u0441\u0435\u0442\u044c \u0447\u0442\u043e-\u0442\u043e \u0437\u043d\u0430\u0435\u0442 \u0438 \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442. \u0415\u0441\u043b\u0438 \u0431\u044b \u043e\u043d \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u043b\u0430 \u043a\u043b\u0430\u0441\u0441\u044b \u043d\u0430\u0443\u0433\u0430\u0434, \u0442\u043e \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0431\u044b \u0431\u044b\u043b\u0430 10%.<\/p>\n
\u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u043a\u0430\u043a\u0438\u0435 \u043a\u043b\u0430\u0441\u0441\u044b \u0441\u0435\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442 \u043b\u0443\u0447\u0448\u0435:<\/p>\n
class_correct = list(0. for i in range(10))\nclass_total = list(0. for i in range(10))\nwith 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\nfor i in range(10):\n print('Accuracy of %5s : %2d %%' % (\n classes[i], 100 * class_correct[i] \/ class_total[i]))<\/code><\/pre>\n\n![\"\u0412\u0430\u0448\u0430](\"\/wp-content\/uploads\/2020\/03\/4d33272d9690ee6b8cdc98bab11a4823.png\")
\n
\n\u041f\u043e\u0445\u043e\u0436\u0435 \u0447\u0442\u043e \u043b\u0443\u0447\u0448\u0435 \u0432\u0441\u0435\u0433\u043e \u0441\u0435\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0438 \u0438 \u043a\u043e\u0440\u0430\u0431\u043b\u0438: 71% \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438.<\/p>\n
\u0418\u0442\u0430\u043a \u0441\u0435\u0442\u044c \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442. \u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u043f\u0440\u043e\u0431\u0443\u0435\u043c \u043f\u0435\u0440\u0435\u043d\u0435\u0441\u0442\u0438 \u0435\u0435 \u0440\u0430\u0431\u043e\u0442\u0443 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440 (GPU) \u0438 \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438 \u0447\u0442\u043e \u043f\u043e\u043c\u0435\u043d\u044f\u0435\u0442\u0441\u044f.<\/p>\n
\u0422\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043a\u0430 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438 \u043d\u0430 GPU<\/h3>\n
\n\u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u043e\u0431\u044a\u044f\u0441\u043d\u044e \u043a\u043e\u0440\u043e\u0442\u043a\u043e \u0447\u0442\u043e \u0442\u0430\u043a\u043e\u0435 CUDA. CUDA (Compute Unified Device Architecture) \u2014 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0430 \u043f\u0430\u0440\u0430\u043b\u043b\u0435\u043b\u044c\u043d\u044b\u0445 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0439, \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u043d\u0430\u044f NVIDIA, \u0434\u043b\u044f \u043e\u0431\u0449\u0438\u0445 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0439 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0430\u0445 (GPU). \u0421 \u043f\u043e\u043c\u043e\u0449\u044c\u044e CUDA \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0438 \u043c\u043e\u0433\u0443\u0442 \u0437\u043d\u0430\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0443\u0441\u043a\u043e\u0440\u0438\u0442\u044c \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u044f, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0438 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u043e\u0432. \u041d\u0430 \u043d\u0430\u0448\u0435\u043c \u0441\u0435\u0440\u0432\u0435\u0440\u0435, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043c\u044b \u043f\u0440\u0438\u043e\u0431\u0440\u0435\u043b\u0438, \u0434\u0430\u043d\u043d\u0430\u044f \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0430 \u0443\u0436\u0435 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u0430.<\/p>\n
\u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043c \u043d\u0430\u0448\u0435 GPU \u043a\u0430\u043a \u043f\u0435\u0440\u0432\u043e\u0435 \u0432\u0438\u0434\u0438\u043c\u043e\u0435 \u0443\u0441\u0442\u0440\u043e\u0439\u0441\u0442\u0432\u043e cuda.<\/p>\n
device = torch . device ( "cuda:0" if torch . cuda . is_available () else "cpu" )\n# Assuming that we are on a CUDA machine, this should print a CUDA device:\nprint ( device )<\/code><\/pre>\n\n![\"\u0412\u0430\u0448\u0430](\"\/wp-content\/uploads\/2020\/03\/67c7ac4b6d321f12da57763672db94d6.png\")
\n
\n\u041e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u043c \u0441\u0435\u0442\u044c \u043d\u0430 GPU:<\/p>\n
net.to(device)<\/code><\/pre>\n\n\u0422\u0430\u043a \u0436\u0435 \u043d\u0430\u043c \u043f\u0440\u0438\u0434\u0435\u0442\u0441\u044f \u043e\u0442\u043f\u0440\u0430\u0432\u043b\u044f\u0442\u044c \u0432\u0445\u043e\u0434\u044b \u0438 \u0446\u0435\u043b\u0438 \u043d\u0430 \u043a\u0430\u0436\u0434\u043e\u043c \u0448\u0430\u0433\u0435 \u0438 \u0432 GPU:<\/p>\n
inputs, labels = data[0].to(device), data[1].to(device)<\/code><\/pre>\n\n\u0417\u0430\u043f\u0443\u0441\u0442\u0438\u043c \u043f\u043e\u0432\u0442\u043e\u0440\u043d\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0441\u0435\u0442\u0438 \u0443\u0436\u0435 \u043d\u0430 GPU: <\/p>\n
import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\nfor 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[0].to(device), data[1].to(device)\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\nprint('Finished Training')<\/code><\/pre>\n\n\u0412 \u044d\u0442\u043e\u0442 \u0440\u0430\u0437 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0441\u0435\u0442\u0438 \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0430\u043b\u043e\u0441\u044c \u043f\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u043e\u043a\u043e\u043b\u043e 3 \u043c\u0438\u043d\u0443\u0442. \u041d\u0430\u043f\u043e\u043c\u043d\u0438\u043c \u0447\u0442\u043e \u0442\u043e\u0442 \u0436\u0435 \u044d\u0442\u0430\u043f \u043d\u0430 \u043e\u0431\u044b\u0447\u043d\u043e\u043c \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0435 \u0434\u043b\u0438\u043b\u0441\u044f 5 \u043c\u0438\u043d\u0443\u0442. \u0420\u0430\u0437\u043d\u0438\u0446\u0430 \u043d\u0435 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u0430\u044f, \u044d\u0442\u043e \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u0438\u0442 \u043f\u043e\u0442\u043e\u043c\u0443 \u0447\u0442\u043e \u043d\u0430\u0448\u0430 \u0441\u0435\u0442\u044c \u043d\u0435 \u0442\u0430\u043a\u0430\u044f \u0431\u043e\u043b\u044c\u0448\u0430\u044f. \u041f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u043c\u0430\u0441\u0441\u0438\u0432\u043e\u0432 \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0440\u0430\u0437\u043d\u0438\u0446\u0430 \u043c\u0435\u0436\u0434\u0443 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c\u044e \u0440\u0430\u0431\u043e\u0442\u044b GPU \u0438 \u0442\u0440\u0430\u0434\u0438\u0446\u0438\u043e\u043d\u043d\u043e\u0433\u043e \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0430 \u0431\u0443\u0434\u0435 \u0432\u043e\u0437\u0440\u0430\u0441\u0442\u0430\u0442\u044c.<\/p>\n
\u041d\u0430 \u044d\u0442\u043e\u043c \u043a\u0430\u0436\u0435\u0442\u0441\u044f \u0432\u0441\u0435. \u0427\u0442\u043e \u043d\u0430\u043c \u0443\u0434\u0430\u043b\u043e\u0441\u044c \u0441\u0434\u0435\u043b\u0430\u0442\u044c:<\/p>\n
\n- \u041c\u044b \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0435\u043b\u0438 \u0447\u0442\u043e \u0442\u0430\u043a\u043e\u0435 GPU \u0438 \u0432\u044b\u0431\u0440\u0430\u043b\u0438 \u0441\u0435\u0440\u0432\u0435\u0440 \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u043e\u043d \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d;<\/li>\n
- \u041c\u044b \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u043b\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u043e\u0435 \u043e\u043a\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438;<\/li>\n
- \u041c\u044b \u0441\u043e\u0437\u0434\u0430\u043b\u0438 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0443\u044e \u0441\u0435\u0442\u044c \u0434\u043b\u044f \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439 \u0438 \u043e\u0431\u0443\u0447\u0438\u043b\u0438 \u0435\u0435;<\/li>\n
- \u041c\u044b \u043f\u043e\u0432\u0442\u043e\u0440\u0438\u043b\u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0441\u0435\u0442\u0438 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c GPU \u0438 \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u0438 \u043f\u0440\u0438\u0440\u043e\u0441\u0442 \u0432 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438.<\/li>\n<\/ul>\n
\n\u0411\u0443\u0434\u0443 \u0440\u0430\u0434 \u043e\u0442\u0432\u0435\u0442\u0438\u0442\u044c \u043d\u0430 \u0432\u043e\u043f\u0440\u043e\u0441\u044b \u0432 \u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u044f\u0445.
\n
\u0418\u0441\u0442\u043e\u0447\u043d\u0438\u043a: habr.com<\/a> <\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"\u0412 \u044d\u0442\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u044f \u0440\u0430\u0441\u0441\u043a\u0430\u0436\u0443 \u043a\u0430\u043a \u0437\u0430 30 \u043c\u0438\u043d\u0443\u0442 \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0441\u0440\u0435\u0434\u0443 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0443\u044e \u0441\u0435\u0442\u044c \u0434\u043b\u044f \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439 a \u043f\u043e\u0442\u043e\u043c \u0437\u0430\u043f\u0443\u0441\u0442\u0438\u0442\u044c \u0442\u0443 \u0436\u0435 \u0441\u0435\u0442\u044c \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0435 (GPU). \u0414\u043b\u044f \u043d\u0430\u0447\u0430\u043b\u0430 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043c \u0447\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0430\u044f \u0441\u0435\u0442\u044c. \u0412 \u043d\u0430\u0448\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u044d\u0442\u043e \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0435\u0451 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u043e\u0435 \u0438\u043b\u0438 \u0430\u043f\u043f\u0430\u0440\u0430\u0442\u043d\u043e\u0435 \u0432\u043e\u043f\u043b\u043e\u0449\u0435\u043d\u0438\u0435, \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u0430\u044f \u043f\u043e \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u0443 \u043e\u0440\u0433\u0430\u043d\u0438\u0437\u0430\u0446\u0438\u0438 \u0438 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":74493,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-74492","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Rrjeti juaj i par\u00eb nervor n\u00eb kart\u00ebn grafike (GPU). 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