帮我写一段卷积神经网络的代码

import torch
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 6, 3)
self.conv2 = nn.Conv2d(6, 16, 3)
self.fc1 = nn.Linear(16*6*6, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(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我们可以使用以下代码来实现卷积神经网络：import torch
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 6, 3) # 定义第一个卷积层，输入通道数为1，输出通道数为6，卷积核大小为3
self.conv2 = nn.Conv2d(6, 16, 3) # 定义第二个卷积层，输入通道数为6，输出通道数为16，卷积核大小为3
self.fc1 = nn.Linear(16*6*6, 120) # 定义第一个全连接层，输入大小为16*6*6，输出大小为120
self.fc2 = nn.Linear(120, 84) # 定义第二个全连接层，输入大小为120，输出大小为84
self.fc3 = nn.Linear(84, 10) # 定义第三个全连接层，输入大小为84，输出大小为10
def forward(self, x): # 定义前向传播函数
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # 卷积层1的前向传播
x = F.max_pool2d(F.relu(self.conv2(x)), 2) # 卷积层2的前向传播
x = x.view(-1, self.num_flat_features(x)) # 将特征展平
x = F.relu(self.fc1(x)) # 全连接层1的前向传播
x = F.relu(self.fc2(x)) # 全连接层2的前向传播
x = self.fc3(x) # 全连接层3的前向传播
return x
def num_flat_features(self, x): # 用于计算展平之后特征的大小
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features可以看到，经过上述步骤，我们可以实现卷积神经网络的代码。