nn.BatchNorm2d(self.in_channels)

时间: 2024-04-05 07:13:32 浏览: 13
nn.BatchNorm2d(self.in_channels)是一个用于对输入数据进行归一化处理的函数。它的作用是在卷积层之后对数据进行归一化处理，使得数据在进行激活函数之前不会因为数值过大而导致网络性能的不稳定。这个函数的参数中，num_features表示输入数据的通道数，eps是为了计算的稳定性而添加的一个值，momentum是一个用于运行过程中均值和方差的一个估计参数，affine参数为True时，会给定可以学习的系数矩阵gamma和beta。<span class="em">1</span><span class="em">2</span><span class="em">3</span> #### 引用[.reference_title] - *1* *3* [nn.BatchNorm2d()函数详解](https://blog.csdn.net/woxiangxinwang/article/details/122788012)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"] - *2* [pytorch之卷积神经网络nn.conv2d](https://download.csdn.net/download/weixin_38580959/14033678)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]

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self.conv2_1 = nn.Conv2d(in_channels=64,out_channels=128,kernel_size=(3,3),padding=1) self.bn2_1 = nn.BatchNorm2d(128) self.relu2_1 = nn.ReLU() self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) ...

self.bn3 = nn.BatchNorm2d(num_features=out_channels*expansion)

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2. self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)：定义一个卷积层 conv1，输入通道数为 in_channels，输出通道数为 out_channels，卷积核大小为 3x3，步幅...

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nn.Conv2d(in_channels, out_channels, padding=(0,1), kernel_size=(1,3)), nn.BatchNorm2d(out_channels), nn.ReLU(), ]) in_channels = out_channels return blocks 5. 将x.size()改为x.shape()：...

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self.bn1 = nn.BatchNorm2d(10) self.bn2 = nn.BatchNorm2d(16) self.bn3 = nn.BatchNorm2d(32) 2. 在 forward 函数中，在卷积操作之后添加 BN 层的调用，例如： x = self.conv1(x) x = self.bn1(x) x =...

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- self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=1, stride=1, bias=False)：第一个卷积层，使用 1x1 的卷积核进行降维，减少通道数。 - self.bn1 = nn.BatchNorm2d(out_...

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self.conv1=nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5) self.avgpool1=nn.AvgPool2d(kernel_size=2,stride=2) self.conv2=nn.Conv2d(in_channels=6,out_channels=16,kernel_size=5) self.avgpool2=...
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