用pytorch语言写一个VGG16神经网络的程序
时间: 2023-12-05 07:04:08 浏览: 39
好的,以下是使用 PyTorch 实现 VGG16 神经网络的示例代码:
```
import torch.nn as nn
class VGG16(nn.Module):
def __init__(self):
super(VGG16, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(512 * 7 * 7, 4096)
self.fc2 = nn.Linear(4096, 4096)
self.fc3 = nn.Linear(4096, 1000)
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout()
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.relu(x)
x = self.pool1(x)
x = self.conv3(x)
x = self.relu(x)
x = self.conv4(x)
x = self.relu(x)
x = self.pool2(x)
x = self.conv5(x)
x = self.relu(x)
x = self.conv6(x)
x = self.relu(x)
x = self.conv7(x)
x = self.relu(x)
x = self.pool3(x)
x = self.conv8(x)
x = self.relu(x)
x = self.conv9(x)
x = self.relu(x)
x = self.conv10(x)
x = self.relu(x)
x = self.pool4(x)
x = self.conv11(x)
x = self.relu(x)
x = self.conv12(x)
x = self.relu(x)
x = self.conv13(x)
x = self.relu(x)
x = self.pool5(x)
x = x.view(-1, 512 * 7 * 7)
x = self.fc1(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc3(x)
return x
```
这是一个基本的 VGG16 实现,包括 5 个卷积块和 3 个全连接层。每个卷积块包含多个卷积层和一个池化层。在实现过程中,我们使用了 PyTorch 中的 nn.Module 和 nn.Sequential 定义了卷积层、池化层和全连接层,并使用 nn.ReLU 和 nn.Dropout 定义了激活函数和 Dropout 层。在 forward 方法中,我们按照 VGG16 的结构实现了前向传播的过程。