{"id":880,"date":"2025-07-26T07:38:17","date_gmt":"2025-07-25T23:38:17","guid":{"rendered":"https:\/\/www.hgfwq.com\/blog\/?p=880"},"modified":"2025-07-26T07:38:17","modified_gmt":"2025-07-25T23:38:17","slug":"ai-%e8%ae%ad%e7%bb%83-%e6%98%be%e5%8d%a1%e5%bf%ab%e9%80%9f%e5%85%a5%e9%97%a8%e4%b8%8e%e5%ae%9e%e7%94%a8%e6%8a%80%e5%b7%a7%e5%88%86%e4%ba%ab","status":"publish","type":"post","link":"https:\/\/www.hgfwq.com\/blog\/880.html","title":{"rendered":"ai \u8bad\u7ec3 \u663e\u5361\u5feb\u901f\u5165\u95e8\u4e0e\u5b9e\u7528\u6280\u5de7\u5206\u4eab"},"content":{"rendered":"<p><p>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u663e\u5361\u8fdb\u884cAI\u8bad\u7ec3\u3002\u6211\u4eec\u5c06\u901a\u8fc7\u4e00\u7cfb\u5217\u5b9e\u64cd\u6b65\u9aa4\uff0c\u5e2e\u52a9\u60a8\u5feb\u901f\u638c\u63e1\u4f7f\u7528\u663e\u5361\u8fdb\u884cAI\u8bad\u7ec3\u7684\u57fa\u672c\u6d41\u7a0b\u3002<\/p>\n<p style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/www.hgfwq.com\/blog\/wp-content\/uploads\/2025\/07\/P0S810y74.jpg\" alt=\"ai \u8bad\u7ec3 \u663e\u5361\u5feb\u901f\u5165\u95e8\u4e0e\u5b9e\u7528\u6280\u5de7\u5206\u4eab\" title=\"ai \u8bad\u7ec3 \u663e\u5361\u5feb\u901f\u5165\u95e8\u4e0e\u5b9e\u7528\u6280\u5de7\u5206\u4eab\"><\/p>\n<\/p>\n<p><h2>\u4efb\u52a1\u4e0e\u95ee\u9898<\/h2>\n<\/p>\n<p><p>\u4efb\u52a1\uff1a\u5229\u7528\u663e\u5361\u8fdb\u884cAI\u6a21\u578b\u8bad\u7ec3\u3002<\/p>\n<\/p>\n<p><p>\u95ee\u9898\uff1a\u5982\u4f55\u9ad8\u6548\u5730\u914d\u7f6e\u73af\u5883\uff0c\u6267\u884c\u8bad\u7ec3\u4efb\u52a1\uff0c\u5e76\u5904\u7406\u53ef\u80fd\u9047\u5230\u7684\u95ee\u9898\u3002<\/p>\n<\/p>\n<p><h2>\u64cd\u4f5c\u524d\u7684\u51c6\u5907<\/h2>\n<\/p>\n<p><p>\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u8bf7\u786e\u4fdd\u60a8\u7684\u7cfb\u7edf\u6ee1\u8db3\u4ee5\u4e0b\u8981\u6c42\uff1a<\/p>\n<\/p>\n<ul>\n<li>\u5b89\u88c5\u4e86NVIDIA\u663e\u5361\u548c\u76f8\u5e94\u7684\u9a71\u52a8\u7a0b\u5e8f\u3002<\/li>\n<li>\u5b89\u88c5\u4e86CUDA Toolkit\u3002<\/li>\n<li>\u5b89\u88c5\u4e86Python\u548c\u76f8\u5e94\u7684\u5e93\uff0c\u5982TensorFlow\u6216PyTorch\u3002<\/li>\n<\/ul>\n<p><h2>\u8be6\u7ec6\u64cd\u4f5c\u6b65\u9aa4<\/h2>\n<\/p>\n<p><h3>1. \u5b89\u88c5\u4f9d\u8d56\u5e93<\/h3>\n<\/p>\n<p><p>\u4f7f\u7528pip\u5b89\u88c5\u6240\u9700\u7684\u5e93\u3002<\/p>\n<\/p>\n<p><pre><code>pip install tensorflow-gpu<\/p>\r\n<p> \u6216\u8005<\/p>\r\n<p>pip install torch torchvision torchaudio<\/p>\r\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2. \u7f16\u5199\u8bad\u7ec3\u811a\u672c<\/h3>\n<\/p>\n<p><p>\u521b\u5efa\u4e00\u4e2aPython\u811a\u672c\uff0c\u7528\u4e8e\u5b9a\u4e49\u6a21\u578b\u3001\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668\u3002<\/p>\n<\/p>\n<p><pre><code>import torch<\/p>\r\n<p>import torch.nn as nn<\/p>\r\n<p>import torch.optim as optim<\/p>\r\n\r\n<p> \u5b9a\u4e49\u6a21\u578b<\/p>\r\n<p>class MyModel(nn.Module):<\/p>\r\n<p>    def __init__(self):<\/p>\r\n<p>        super(MyModel, self).__init__()<\/p>\r\n<p>        self.conv1 = nn.Conv2d(1, 20, 5)<\/p>\r\n<p>        self.pool = nn.MaxPool2d(2, 2)<\/p>\r\n<p>        self.conv2 = nn.Conv2d(20, 50, 5)<\/p>\r\n<p>        self.fc1 = nn.Linear(50  4  4, 500)<\/p>\r\n<p>        self.fc2 = nn.Linear(500, 10)<\/p>\r\n\r\n<p>    def forward(self, x):<\/p>\r\n<p>        x = self.pool(torch.relu(self.conv1(x)))<\/p>\r\n<p>        x = self.pool(torch.relu(self.conv2(x)))<\/p>\r\n<p>        x = x.view(-1, 50  4  4)<\/p>\r\n<p>        x = torch.relu(self.fc1(x))<\/p>\r\n<p>        x = self.fc2(x)<\/p>\r\n<p>        return x<\/p>\r\n\r\n<p> \u5b9e\u4f8b\u5316\u6a21\u578b<\/p>\r\n<p>model = MyModel()<\/p>\r\n\r\n<p> \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668<\/p>\r\n<p>criterion = nn.CrossEntropyLoss()<\/p>\r\n<p>optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)<\/p>\r\n\r\n<p> \u8bad\u7ec3\u6a21\u578b<\/p>\r\n<p>for epoch in range(2):   loop over the dataset multiple times<\/p>\r\n<p>    running_loss = 0.0<\/p>\r\n<p>    for i, data in enumerate(trainloader, 0):<\/p>\r\n<p>        inputs, labels = data<\/p>\r\n\r\n<p>         zero the parameter gradients<\/p>\r\n<p>        optimizer.zero_grad()<\/p>\r\n\r\n<p>         forward + backward + optimize<\/p>\r\n<p>        outputs = model(inputs)<\/p>\r\n<p>        loss = criterion(outputs, labels)<\/p>\r\n<p>        loss.backward()<\/p>\r\n<p>        optimizer.step()<\/p>\r\n\r\n<p>         print statistics<\/p>\r\n<p>        running_loss += loss.item()<\/p>\r\n<p>        if i % 2000 == 1999:     print every 2000 mini-batches<\/p>\r\n<p>            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss \/ 2000:.3f}')<\/p>\r\n<p>            running_loss = 0.0<\/p>\r\n\r\n<p>print('Finished Training')<\/p>\r\n<p><\/code><\/pre>\n<\/p>\n<p><h3>3. \u8fd0\u884c\u8bad\u7ec3\u811a\u672c<\/h3>\n<\/p>\n<p><p>\u5728\u7ec8\u7aef\u4e2d\u8fd0\u884c\u60a8\u7684\u8bad\u7ec3\u811a\u672c\u3002<\/p>\n<\/p>\n<p><pre><code>python train_script.py<\/p>\r\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u547d\u4ee4\u3001\u4ee3\u7801\u4e0e\u914d\u7f6e\u793a\u4f8b<\/h2>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5173\u952e\u7684\u547d\u4ee4\u548c\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>pip install tensorflow-gpu<\/strong>\uff1a\u5b89\u88c5TensorFlow GPU\u7248\u672c\u3002<\/li>\n<li><strong>pip install torch torchvision torchaudio<\/strong>\uff1a\u5b89\u88c5PyTorch\u53ca\u5176\u76f8\u5173\u5e93\u3002<\/li>\n<li><strong>model = MyModel()<\/strong>\uff1a\u521b\u5efa\u6a21\u578b\u5b9e\u4f8b\u3002<\/li>\n<li><strong>optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)<\/strong>\uff1a\u521b\u5efa\u4f18\u5316\u5668\u5b9e\u4f8b\u3002<\/li>\n<li><strong>for epoch in range(2):<\/strong>\uff1a\u8bbe\u7f6e\u8bad\u7ec3\u8f6e\u6570\u3002<\/li>\n<li><strong>outputs = model(inputs)<\/strong>\uff1a\u524d\u5411\u4f20\u64ad\u3002<\/li>\n<li><strong>loss.backward()<\/strong>\uff1a\u53cd\u5411\u4f20\u64ad\u3002<\/li>\n<\/ul>\n<p><h2>\u64cd\u4f5c\u8fc7\u7a0b\u4e2d\u53ef\u80fd\u9047\u5230\u7684\u95ee\u9898\u4e0e\u6ce8\u610f\u4e8b\u9879<\/h2>\n<\/p>\n<ul>\n<li>\u786e\u4fddCUDA Toolkit\u4e0eNVIDIA\u9a71\u52a8\u7a0b\u5e8f\u517c\u5bb9\u3002<\/li>\n<li>\u68c0\u67e5Python\u73af\u5883\u662f\u5426\u6b63\u786e\u914d\u7f6e\uff0c\u7279\u522b\u662f\u5e93\u7684\u7248\u672c\u3002<\/li>\n<li>\u5982\u679c\u9047\u5230\u5185\u5b58\u4e0d\u8db3\u7684\u95ee\u9898\uff0c\u5c1d\u8bd5\u51cf\u5c11\u6279\u91cf\u5927\u5c0f\u6216\u589e\u52a0\u663e\u5b58\u3002<\/li>\n<li>\u786e\u4fdd\u8bad\u7ec3\u6570\u636e\u96c6\u6b63\u786e\u52a0\u8f7d\uff0c\u5e76\u7b26\u5408\u6a21\u578b\u8f93\u5165\u8981\u6c42\u3002<\/li>\n<\/ul>\n<p><h2>\u5b9e\u7528\u6280\u5de7<\/h2>\n<\/p>\n<ul>\n<li>\u4f7f\u7528GPU\u76d1\u63a7\u5de5\u5177\uff0c\u5982NVIDIA-smi\uff0c\u76d1\u63a7GPU\u4f7f\u7528\u60c5\u51b5\u3002<\/li>\n<li>\u5b9a\u671f\u5907\u4efd\u8bad\u7ec3\u6570\u636e\u548c\u6a21\u578b\uff0c\u4ee5\u9632\u6570\u636e\u4e22\u5931\u3002<\/li>\n<li>\u4f7f\u7528\u5206\u5e03\u5f0f\u8bad\u7ec3\uff0c\u52a0\u901f\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u3002<\/li>\n<\/ul>\n<p>&#8220;`<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u663e\u5361\u8fdb\u884cAI\u8bad\u7ec3\u3002\u6211\u4eec\u5c06\u901a\u8fc7\u4e00\u7cfb\u5217\u5b9e\u64cd\u6b65\u9aa4\uff0c\u5e2e\u52a9\u60a8\u5feb\u901f\u638c\u63e1\u4f7f\u7528\u663e\u5361\u8fdb\u884cAI\u8bad\u7ec3\u7684\u57fa\u672c\u6d41\u7a0b\u3002 \u4efb\u52a1\u4e0e\u95ee\u9898 \u4efb\u52a1\uff1a\u5229\u7528\u663e\u5361\u8fdb\u884cAI\u6a21\u578b\u8bad\u7ec3\u3002 \u95ee\u9898\uff1a\u5982\u4f55\u9ad8\u6548\u5730\u914d\u7f6e\u73af\u5883\uff0c\u6267\u884c\u8bad\u7ec3\u4efb\u52a1\uff0c\u5e76\u5904\u7406\u53ef\u80fd\u9047\u5230\u7684\u95ee\u9898\u3002 \u64cd\u4f5c\u524d\u7684\u51c6\u5907 \u5728&#8230;<\/p>\n","protected":false},"author":1,"featured_media":881,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[386,392,391],"topic":[],"class_list":["post-880","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-haiwai","tag-ai","tag-pip","tag-391"],"_links":{"self":[{"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/posts\/880","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/comments?post=880"}],"version-history":[{"count":1,"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/posts\/880\/revisions"}],"predecessor-version":[{"id":883,"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/posts\/880\/revisions\/883"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/media\/881"}],"wp:attachment":[{"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/media?parent=880"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/categories?post=880"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/tags?post=880"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/www.hgfwq.com\/blog\/wp-json\/wp\/v2\/topic?post=880"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}