3，实现如下： In [43]: from torch import nn # 导入神经网络子库 # 创建一层 Wx+b，输出节点为 3, 输出节点数为 4 fc = nn.Linear(3, 4) fc.bias # 查看偏置向量 Out[43]: Parameter containing: tensor([-0.3838, -0.4073, -0.3051, -0 个节点，输出 3 个节点的网络层，并通过全连接层的 kernel 成员名查 看其权值矩阵?： In [45]: # 定义全连接层的输出节点为 3, 输入节点为 4 fc = nn.Linear(4, 3) fc.weight # 查看权值矩阵 W Out[45]: Parameter containing: tensor([[-0.1410, 0.1454, -0.3955 创建全连接层，指定输入节点数和输出节点数 fc = nn.Linear(28*28, 512) # 通过 fc 类实例完成一次全连接层的计算，返回输出张量 h1 = fc(x) print('h1:', h1.shape) Out[2]: h1: torch.Size([4, 512]) 预览版202112 6.2 全连接层 5 上述通过一行代码即可以创建一层全连接层实例 fc，并指定输入节点数为

MHL4tF8FQSEdRb0YvHCsYWmhA20m7dLMJuxuhFz8K148qHj1b3jz35ikOWjrg4G3782wMy9IOFPasr6NpeWV1bX1xkZ zc2t7Z9fc239QcSopODTmsewHRAFnAhzNId+IoFEAYdeMLkp/d4jSMVica+nCXgRGQkWMkp0IfnmocuJGHAkPiZyLErq2fTN1tW26qAF4ldkxaq MHL4tF8FQSEdRb0YvHCsYWmhA20m7dLMJuxuhFz8K148qHj1b3jz35ikOWjrg4G3782wMy9IOFPasr6NpeWV1bX1xkZ zc2t7Z9fc239QcSopODTmsewHRAFnAhzNId+IoFEAYdeMLkp/d4jSMVica+nCXgRGQkWMkp0IfnmocuJGHAkPiZyLErq2fTN1tW26qAF4ldkxaq MHL4tF8FQSEdRb0YvHCsYWmhA20m7dLMJuxuhFz8K148qHj1b3jz35ikOWjrg4G3782wMy9IOFPasr6NpeWV1bX1xkZ zc2t7Z9fc239QcSopODTmsewHRAFnAhzNId+IoFEAYdeMLkp/d4jSMVica+nCXgRGQkWMkp0IfnmocuJGHAkPiZyLErq2fTN1tW26qAF4ldkxaq

5) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): # x = x.view(-1, self.num_flat_features(x)) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x def num_flat_features(self, x): size 16, kernel_size=(5, 5), stride=(1, 1)) (fc1): Linear(in_features=400, out_features=120, bias=True) (fc2): Linear(in_features=120, out_features=84, bias=True) (fc3): Linear(in_features=84, out_features=10

service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Audit Config: /bin/cat /var/lib/kubelet/config.yaml Expected result: 'false' is equal to service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Audit Config: /bin/cat /var/lib/kubelet/config.yaml Expected result: 'Webhook' not have service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Audit Config: /bin/cat /var/lib/kubelet/config.yaml CIS Benchmark Rancher Self-Assessment

service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Audit Config: /bin/cat /var/lib/kubelet/config.yaml Expected result: 'false' is equal to service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Audit Config: /bin/cat /var/lib/kubelet/config.yaml Expected result: 'Webhook' not have service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Audit Config: /bin/cat /var/lib/kubelet/config.yaml CIS 1.5 Benchmark - Self-Assessment

kubelet service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Expected Result: '' is not present 4.2.2 Ensure that the --authorization-mode argument is kubelet service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Expected Result: '' is not present CIS 1.6 Benchmark - Self-Assessment Guide - Rancher v2 kubelet service. For example: systemctl daemon-reload systemctl restart kubelet.service Audit: /bin/ps -fC kubelet Expected Result: '' is not present 4.2.4 Ensure that the --read-only-port argument is set

c.go:1047 +0x5d fp=0x7fffb29cd648 sp=0x7fffb29cd618 pc=0x434a7d runtime.sysMapOS(0xc000400000, 0x6fc400000?) /usr/local/go/src/runtime/mem_linux.go:187 +0x11b fp=0x7fffb29cd690 sp=0x7fffb29cd648 pc=0x417f7b runtime.(*mcache).allocLarge(0x3f?, 0x6fc23ac00, 0x1) /usr/local/go/src/runtime/mcache.go:234 +0x85 fp=0xc00011bcf8 sp=0xc00011bcb0 pc=0x4169e5 runtime.mallocgc(0x6fc23ac00, 0x52a240, 0x1) /usr/local/g ing.go:1576 +0x10b fp=0xc000392fc0 sp=0xc000392f70 pc=0x53632b testing.(*T).Run.func1() /usr/local/go/src/testing/testing.go:1629 +0x2a fp=0xc000392fe0 sp=0xc000392fc0 pc=0x53736a runtime.goexit()

plot(kind='scatter', x='SepalRatio', y='PetalRatio')) ...: Out[5]: fc953a64c50> See the documentation for more. (GH9229) 138 Chapter 1. What’s New pandas: powerful Python figure() Out[1]: fc98025e650> In [2]: fx['FR'].plot(style='g') Out[2]: fc9716abf10> In [3]: fx['IT'].plot(style='k--', secondary_y=True) secondary_y=True) Out[3]: fc9878ba990> ../_static/whatsnew_secondary_y.png Vytautas Jancauskas, the 2012 GSOC participant, has added many new plot types. For

365c1cfd14de5b0e3b85e0fc815b0060f36ab955 3.2.0 31d 00-worker 365c1cfd14de5b0e3b85e0fc815b0060f36ab955 3.2.0 365c1cfd14de5b0e3b85e0fc815b0060f36ab955 3.2.0 31d 01-master-kubelet 365c1cfd14de5b0e3b85e0fc815b0060f36ab955 3.2.0 365c1cfd14de5b0e3b85e0fc815b0060f36ab955 3.2.0 31d 01-worker-kubelet 365c1cfd14de5b0e3b85e0fc815b0060f36ab955 3.2.0

CONV2 28x28x6 14x14x6 MAXPOOL 10x10x16 MAXPOOL 5x5x16 POOL2 F C F C FC2 FC3 S O F T M A X 120 84 10 F C FC2 LeNet-5 32x32x1 400 6 AlexNet • 2012年，AlexNet 横空出世。它首次证 明了学习到的特征可以超越手工设计 Conv3-32 Conv3-32 Conv3-32 Max-Pool Conv3-32 Conv3-128 Conv3-64 Conv3-64 Max-Pool Max-Pool FC-512 Output ConvNet Configuration Stacked layers Previous input x F(x) y=F(x) Stacked layers 替换全连接层 # 将最后的全连接层改成十分类 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") net.fc = nn.Linear(512, 10) 4.卷积神经网络使用技巧 30 参考文献 • IAN GOODFELLOW等，《深度学习》，人民邮电出版社，2017 • Andrew Ng，http://www