def forward(self, x): x=self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(def init_(self): super(Net,self)._init_0 self.conv1 = nn.Conv2d(1,6,5) self.pool = nn.MaxPool2d(2,2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1=nn.Linear(16*4*4,120) self.fc2 =nn.Linear(120,84) self.fc3 =nn.Linear(84, 10) x))) x=x.view(-1,16*4*4) x=F.relu(self.fc1(x)) x=F.relu(self.fc2(x)) x=self.fc3(x) return x
时间: 2023-10-20 17:30:50 浏览: 44
这段代码定义了一个名为Net的卷积神经网络模型,它包括两个卷积层(conv1和conv2)和三个全连接层(fc1, fc2和fc3)。其中,输入x经过conv1和池化层(pool)后再经过conv2和池化层(pool),然后将输出展平成一维向量,经过三个全连接层后输出结果。在每个卷积层和全连接层之间都有一个激活函数ReLU,用于增强网络的非线性拟合能力。该模型的输入是一个四维张量,形状为(batch_size, 1, 28, 28),输出是一个形状为(batch_size, 10)的张量,表示对10个类别的预测概率分布。
相关问题
请将如下的代码用图片的形式表现出来 class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = torch.nn.Conv2d(3, 64, kernel_size=3, padding=1) self.conv2 = torch.nn.Conv2d(64, 64, kernel_size=3, padding=1) self.conv3 = torch.nn.Conv2d(64, 128, kernel_size=3, padding=1) self.conv4 = torch.nn.Conv2d(128, 128, kernel_size=3, padding=1) self.conv5 = torch.nn.Conv2d(128, 256, kernel_size=3, padding=1) self.conv6 = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1) self.maxpooling = torch.nn.MaxPool2d(2) self.avgpool = torch.nn.AvgPool2d(2) self.globalavgpool = torch.nn.AvgPool2d((8, 10)) self.bn1 = torch.nn.BatchNorm2d(64) self.bn2 = torch.nn.BatchNorm2d(128) self.bn3 = torch.nn.BatchNorm2d(256) self.dropout50 = torch.nn.Dropout(0.5) self.dropout10 = torch.nn.Dropout(0.1) self.fc1 = torch.nn.Linear(256, 40) def forward(self, x): batch_size = x.size(0) x = self.bn1(F.relu(self.conv1(x))) x = self.bn1(F.relu(self.conv2(x))) x = self.maxpooling(x) x = self.dropout10(x) x = self.bn2(F.relu(self.conv3(x))) x = self.bn2(F.relu(self.conv4(x))) x = self.maxpooling(x) x = self.dropout10(x) x = self.bn3(F.relu(self.conv5(x))) x = self.bn3(F.relu(self.conv6(x))) x = self.globalavgpool(x) x = self.dropout50(x) x = x.view(batch_size, -1) x = self.fc1(x) return x
很抱歉,我不能直接插入图片,但是我可以为您提供代码的格式化版本:
```
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = torch.nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.conv3 = torch.nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv4 = torch.nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.conv5 = torch.nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.conv6 = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.maxpooling = torch.nn.MaxPool2d(2)
self.avgpool = torch.nn.AvgPool2d(2)
self.globalavgpool = torch.nn.AvgPool2d((8, 10))
self.bn1 = torch.nn.BatchNorm2d(64)
self.bn2 = torch.nn.BatchNorm2d(128)
self.bn3 = torch.nn.BatchNorm2d(256)
self.dropout50 = torch.nn.Dropout(0.5)
self.dropout10 = torch.nn.Dropout(0.1)
self.fc1 = torch.nn.Linear(256, 40)
def forward(self, x):
batch_size = x.size(0)
x = self.bn1(F.relu(self.conv1(x)))
x = self.bn1(F.relu(self.conv2(x)))
x = self.maxpooling(x)
x = self.dropout10(x)
x = self.bn2(F.relu(self.conv3(x)))
x = self.bn2(F.relu(self.conv4(x)))
x = self.maxpooling(x)
x = self.dropout10(x)
x = self.bn3(F.relu(self.conv5(x)))
x = self.bn3(F.relu(self.conv6(x)))
x = self.globalavgpool(x)
x = self.dropout50(x)
x = x.view(batch_size, -1)
x = self.fc1(x)
return x
```
class LeNet(nn.Module): def __init__(self): super(LeNet, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.pool1 = nn.MaxPool2d(kernel_size=2) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.pool2 = nn.MaxPool2d(kernel_size=2) self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = F.relu(self.conv1(x)) x = self.pool1(x) x = F.relu(self.conv2(x)) x = self.pool2(x) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x
这是另一个基本的卷积神经网络模型,由两个卷积层和三个全连接层组成。这个模型的输入是一个四维张量`(batch_size, input_channels, height, width)`,其中`batch_size`表示输入的样本数量,`input_channels`表示输入的通道数,`height`和`width`分别表示输入的高度和宽度。
在`__init__`方法中,我们定义了两个卷积层和三个全连接层,具体如下:
- `self.conv1`:输入通道数为1,输出通道数为10,卷积核大小为5x5。
- `self.pool1`:最大池化层,池化核大小为2x2。
- `self.conv2`:输入通道数为10,输出通道数为20,卷积核大小为5x5。
- `self.pool2`:最大池化层,池化核大小为2x2。
- `self.fc1`:输入大小为320,输出大小为50。
- `self.fc2`:输入大小为50,输出大小为10。
- `self.fc3`:输入大小为84,输出大小为10。
在`forward`方法中,我们定义了卷积和池化操作,以及全连接层的操作,具体如下:
- `x = F.relu(self.conv1(x))`:使用`self.conv1`进行卷积操作,然后使用ReLU激活函数。
- `x = self.pool1(x)`:使用`self.pool1`进行最大池化操作。
- `x = F.relu(self.conv2(x))`:使用`self.conv2`进行卷积操作,然后使用ReLU激活函数。
- `x = self.pool2(x)`:使用`self.pool2`进行最大池化操作。
- `x = x.view(-1, 320)`:将卷积层的输出展平成一维张量,以便输入到全连接层。
- `x = F.relu(self.fc1(x))`:使用`self.fc1`进行全连接操作,然后使用ReLU激活函数。
- `x = F.relu(self.fc2(x))`:使用`self.fc2`进行全连接操作,然后使用ReLU激活函数。
- `x = self.fc3(x)`:使用`self.fc3`进行全连接操作。
- `return x`:最终的输出。
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