nn.torch.cat

nn.torch.cat是PyTorch中的一个函数，用于将多个张量沿指定维度进行拼接。它的作用类似于Numpy中的concatenate函数。

具体来说，nn.torch.cat函数接受一个张量列表作为输入，并返回一个拼接后的张量。拼接的维度由用户指定，可以是任意维度。在拼接时，除了指定的维度外，其他维度的大小必须相同。

下面是nn.torch.cat函数的使用示例：

import torch

# 创建两个张量
tensor1 = torch.tensor([[1, 2, 3], [4, 5, 6]])
tensor2 = torch.tensor([[7, 8, 9], [10, 11, 12]])

# 在第0维度上拼接两个张量
result = torch.cat([tensor1, tensor2], dim=0)

print(result)

输出结果为：

tensor([[ 1,  2,  3],
        [ 4,  5,  6],
        [ 7,  8,  9],
        [10, 11, 12]])

在这个示例中，我们创建了两个2x3的张量tensor1和tensor2。然后使用nn.torch.cat函数将它们在第0维度上进行拼接，得到一个4x3的张量result。

相关问题

torch.nn.randn

引用中提到了torch.randn()函数。torch.randn()是一个PyTorch内置函数，用于生成符合标准正态分布的随机数。标准正态分布是一种均值为0，标准差为1的正态分布。这个函数可以用来生成随机的输入数据，在神经网络的前向计算过程中使用，以验证神经网络的泛化能力，并提高其对不同数据集的适应性。生成的数据分布在标准正态分布的范围内，适合在未知数据上进行预测。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [Pytorch:PyTorch中的nn.Module.forward()函数、torch.randn()函数和torch.cat()函数使用](https://blog.csdn.net/qq_30589271/article/details/129989207)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
[ .reference_list ]

import torch import os import torch.nn as nn import torch.optim as optim import numpy as np import random class Net(nn.Module): def init(self): super(Net, self).init() self.conv1 = nn.Conv2d(1, 16, kernel_size=3,stride=1) self.pool = nn.MaxPool2d(kernel_size=2,stride=2) self.conv2 = nn.Conv2d(16, 32, kernel_size=3,stride=1) self.fc1 = nn.Linear(32 * 9 * 9, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 2) def forward(self, x): x = self.pool(nn.functional.relu(self.conv1(x))) x = self.pool(nn.functional.relu(self.conv2(x))) x = x.view(-1, 32 * 9 * 9) x = nn.functional.relu(self.fc1(x)) x = nn.functional.relu(self.fc2(x)) x = self.fc3(x) return x net = Net() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) folder_path = 'random_matrices2' # 创建空的tensor x = torch.empty((40, 1, 42, 42)) # 遍历文件夹内的文件，将每个矩阵转化为tensor并存储 for j in range(40): for j in range(40): file_name = 'matrix_{}.npy'.format(j) file_path = os.path.join(folder_path, file_name) matrix = np.load(file_path) x[j] = torch.from_numpy(matrix).unsqueeze(0) #y = torch.cat((torch.zeros(20), torch.ones(20))) #y = torch.cat((torch.zeros(20, dtype=torch.long), torch.ones(20, dtype=torch.long))) y = torch.cat((torch.zeros(20, dtype=torch.long), torch.ones(20, dtype=torch.long)), dim=0) for epoch in range(10): running_loss = 0.0 for i in range(40): inputs = x[i] labels = y[i] optimizer.zero_grad() outputs = net(inputs) #loss = criterion(outputs, labels) loss = criterion(outputs.unsqueeze(0), labels.unsqueeze(0)) loss.backward() optimizer.step() running_loss += loss.item() print('[%d] loss: %.3f' % (epoch + 1, running_loss / 40)) print('Finished Training')报错RuntimeError: Expected target size [1, 2], got [1]怎么修改？

可以将 labels.unsqueeze(0) 修改为 labels.unsqueeze(0).long()，即将标签张量转化为 long 类型。修改后的代码如下：

import torch 
import os 
import torch.nn as nn 
import torch.optim as optim 
import numpy as np 
import random 

class Net(nn.Module): 
    def __init__(self): 
        super(Net, self).__init__() 
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3,stride=1) 
        self.pool = nn.MaxPool2d(kernel_size=2,stride=2) 
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3,stride=1) 
        self.fc1 = nn.Linear(32 * 9 * 9, 120) 
        self.fc2 = nn.Linear(120, 84) 
        self.fc3 = nn.Linear(84, 2) 
        
    def forward(self, x): 
        x = self.pool(nn.functional.relu(self.conv1(x))) 
        x = self.pool(nn.functional.relu(self.conv2(x))) 
        x = x.view(-1, 32 * 9 * 9) 
        x = nn.functional.relu(self.fc1(x)) 
        x = nn.functional.relu(self.fc2(x)) 
        x = self.fc3(x) 
        return x 

net = Net() 
criterion = nn.CrossEntropyLoss() 
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) 
folder_path = 'random_matrices2' 

# 创建空的tensor 
x = torch.empty((40, 1, 42, 42)) 

# 遍历文件夹内的文件，将每个矩阵转化为tensor并存储 
for j in range(40): 
    file_name = 'matrix_{}.npy'.format(j) 
    file_path = os.path.join(folder_path, file_name) 
    matrix = np.load(file_path) 
    x[j] = torch.from_numpy(matrix).unsqueeze(0) 

y = torch.cat((torch.zeros(20, dtype=torch.long), torch.ones(20, dtype=torch.long)), dim=0) 

for epoch in range(10): 
    running_loss = 0.0 
    for i in range(40): 
        inputs = x[i] 
        labels = y[i] 
        optimizer.zero_grad() 
        outputs = net(inputs) 
        loss = criterion(outputs.unsqueeze(0), labels.unsqueeze(0).long()) 
        loss.backward() 
        optimizer.step() 
        running_loss += loss.item() 
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / 40)) 

print('Finished Training')