ln = nn.LayerNorm(2) bn = nn.BatchNorm1d(2) X = torch.tensor([[1, 2], [2, 3]], dtype=torch.float32) # 在训练模式下计算X的均值和方差 print(’layer norm:’, ln(X), ’\nbatch norm:’, bn(X)) layer norm: tensor([[-1.0000, 1.0000], [-1.0000, 1.0000]], grad_fn=<NativeLayerNormBackward0>) batch norm: tensor([[-1.0000, -1.0000], [ 1.0000, 1.0000]], grad_fn=<NativeBatchNormBackward0>) #@save class AddNorm(nn.Module): """残差连接后进行层规范化""" def __init__(self, normalized_shape, dropout, **kwargs): super(AddNorm, self).__init__(**kwargs) self.dropout = nn.Dropout(dropout) self.ln = nn.LayerNorm(normalized_shape) def forward(self, X, Y): return self.ln(self.dropout(Y) + X) 残差连接要求两个输入的形状相同，以便加法操作后输出张量的形状相同。 add_norm = AddNorm([3, 4], 0.5) add_norm.eval() add_norm(torch.ones((2, 3, 4)), torch.ones((2, 3, 4))).shape torch.Size([2, 3, 4])

这段代码展示了如何使用LayerNorm和BatchNorm对输入进行规范化。首先创建了一个LayerNorm实例ln和一个BatchNorm1d实例bn。然后，将一个大小为(2, 2)的浮点型张量X输入到ln和bn中，并打印出规范化后的结果。

ln对输入进行层规范化，计算输入的均值和方差，并对输入进行归一化。结果是一个与输入相同大小的张量，其中每个元素都经过层规范化处理。bn对输入进行批规范化，计算输入的均值和方差，并对输入进行归一化。结果也是一个与输入相同大小的张量，其中每个元素都经过批规范化处理。

接下来定义了一个AddNorm类，用于在残差连接后进行层规范化。它包含一个dropout层和一个LayerNorm层。在前向传播中，将输入X和Y进行残差连接，并通过dropout层和LayerNorm层进行规范化处理。最后，打印出残差连接后输出张量的形状。

在代码的最后，创建了一个AddNorm的实例add_norm，并对其进行了评估（eval()）。然后，将两个大小为(2, 3, 4)的张量输入到add_norm中，并打印出输出张量的形状。

结果是一个大小为(2, 3, 4)的张量，表示残差连接后的输出张量的形状与输入张量相同。

相关问题

class Wine_net(nn.Module): def __int__(self): super(Wine_net, self).__int__() self.ln1=nn.LayerNorm(11) self.fc1=nn.Linear(11,22) self.fc2=nn.Linear(22,44) self.fc3=nn.Linear(44,1) def forward(self,x): x=self.ln1(x) x=self.fc1(x) x=nn.functional.relu(x) x=self.fc2(x) x=nn.functional.relu(x) x = self.fc3(x) x = nn.functional.softmax(x) return x # 读取数据 df = pd.read_csv('winequality.csv') df1=df.drop('quality',axis=1) df2=df['quality'] train_x=torch.tensor(df1.values, dtype=torch.float32) train_y=torch.tensor(df2.values,dtype=torch.float32) # 定义模型、损失函数和优化器 model=Wine_net() loss_fn=nn.MSELoss() optimizer =torch.optim.SGD(model.parameters(), lr=0.0001) # 训练模型 for epoch in range(10): # 前向传播 y_pred = model(train_x) # 计算损失 loss = loss_fn(y_pred, train_y) # 反向传播 optimizer.zero_grad() loss.backward() optimizer.step()

这段代码中有一个错误，就是 Wine_net 类的 __init__ 方法的拼写错误。应该是双下划线 "__init__" 而不是 "__int__"。因此，你需要修改代码中的这一行：

def __int__(self):

为：

def __init__(self):

除此之外，还有一个小问题，就是在 Wine_net 类的 forward 方法中，最后一层应该使用 nn.functional.sigmoid 而不是 nn.functional.softmax。因为这是一个回归问题，你需要输出一个实数值而不是一个概率分布。

修改完成后的代码如下所示：

import torch
import torch.nn as nn
import pandas as pd

class Wine_net(nn.Module):
    def __init__(self):
        super(Wine_net, self).__init__()
        self.ln1=nn.LayerNorm(11)
        self.fc1=nn.Linear(11,22)
        self.fc2=nn.Linear(22,44)
        self.fc3=nn.Linear(44,1)
        
    def forward(self,x):
        x=self.ln1(x)
        x=self.fc1(x)
        x=nn.functional.relu(x)
        x=self.fc2(x)
        x=nn.functional.relu(x)
        x = self.fc3(x)
        x = nn.functional.sigmoid(x)
        return x

# 读取数据
df = pd.read_csv('winequality.csv')
df1=df.drop('quality',axis=1)
df2=df['quality']
train_x=torch.tensor(df1.values, dtype=torch.float32)
train_y=torch.tensor(df2.values,dtype=torch.float32)

# 定义模型、损失函数和优化器
model=Wine_net()
loss_fn=nn.MSELoss()
optimizer =torch.optim.SGD(model.parameters(), lr=0.0001)

# 训练模型
for epoch in range(10):
    # 前向传播
    y_pred = model(train_x)
    
    # 计算损失
    loss = loss_fn(y_pred, train_y)
    
    # 反向传播
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

希望能够帮到你！

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