prelu激活函数pytorch代码

下面是PyTorch中PReLU的激活函数代码示例：

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.bn1 = nn.BatchNorm2d(6)
        self.prelu1 = nn.PReLU()
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.bn2 = nn.BatchNorm2d(16)
        self.prelu2 = nn.PReLU()
        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 = self.conv1(x)
        x = self.bn1(x)
        x = self.prelu1(x)
        x = F.max_pool2d(x, (2, 2))
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.prelu2(x)
        x = F.max_pool2d(x, 2)
        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 = x.size()[1:]
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

net = Net()

以上代码中包括了一个使用PReLU激活函数的神经网络模型，其中`nn.PReLU()`是PyTorch自带的PReLU激活函数，在`Net()`的构造函数中被实例化并用于各层之间的传递。在`forward()`函数中，先经过卷积层和BatchNorm层，然后再通过PReLU激活函数进行非线性变换，最后一并传入下一层。