一个名为random_label的文件夹内有40个文件，名字分别为label_1.npy到label_40.npy，转换成一个名为y的形状为(40,)的tensor，其中y中只包含0和1两个值

可以使用NumPy和PyTorch来实现这个功能。首先，使用NumPy来读取文件夹中的所有文件，并将它们转换为一个形状为(40,)的NumPy数组。然后，使用PyTorch将NumPy数组转换为一个tensor，并将所有非零值设置为1。

下面是一个示例代码：

import os
import numpy as np
import torch

# 读取文件夹中的所有文件
file_list = os.listdir('random_label')
file_list.sort()
labels = []
for file_name in file_list:
    if file_name.endswith('.npy'):
        label = np.load(os.path.join('random_label', file_name))
        labels.append(label)

# 将NumPy数组转换为tensor
y = torch.tensor(labels)

# 将所有非零值设置为1
y[y != 0] = 1

这将生成一个名为y的tensor，其中包含40个元素，每个元素都是0或1。

相关问题

一个名为random_label的文件夹内有40个文件，名字分别为label_1.npy到label_40.npy，其中1-20中存储的值是0，21-40中存储的值是1，转换成一个名为y的形状为(40,)的tensor，其中y中只包含0和1两个值

可以使用Python中的NumPy库和PyTorch库来实现。代码如下：

import numpy as np
import torch

y = np.zeros(40)
y[20:] = 1
y = torch.from_numpy(y)

首先，我们定义一个形状为(40,)的NumPy数组y，并将其全部初始化为0。然后，我们将y的第21个元素到最后一个元素（即20到39）的值设为1，以实现上述要求。最后，我们使用PyTorch的`from_numpy()`函数将NumPy数组y转换为PyTorch张量，并将其存储在名为y的变量中。

import torch import os import torch.nn as nn import torch.optim as optim import numpy as np import random import matplotlib.pyplot as plt 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_path1 = '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(i) file_path1 = os.path.join(folder_path1, file_name) matrix1 = np.load(file_path1) x[j] = torch.from_numpy(matrix1).unsqueeze(0) folder_path2 = 'random_label2' y = torch.empty((40, 1)) for k in range(40): for k in range(40): file_name = 'label_{}.npy'.format(i) file_path2 = os.path.join(folder_path2, file_name) matrix2 = np.load(file_path2) y[k] = torch.from_numpy(matrix2).unsqueeze(0) losses = [] for epoch in range(10): running_loss = 0.0 for i in range(40): inputs, labels = x[i], y[i] optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() losses.append(running_loss / 40) print('[%d] loss: %.3f' % (epoch + 1, running_loss / 40)) print('Finished Training') plt.plot(losses) plt.xlabel('Epoch') plt.ylabel('Loss') plt.show() 报错：

在第14行的for循环中，两个变量的名字都是j，应该将其中一个改为k。此外，在第19行的for循环中，变量i并未定义，应该将其改为k。最后，在第28行的loss.backward()中，需要将labels从tensor类型转换为long类型，即loss.backward()改为loss.backward(torch.tensor([1], dtype=torch.long)). 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 
import matplotlib.pyplot as plt 

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_path1 = '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_path1 = os.path.join(folder_path1, file_name) 
    matrix1 = np.load(file_path1) 
    x[j] = torch.from_numpy(matrix1).unsqueeze(0) 

folder_path2 = 'random_label2' 
y = torch.empty((40,)) 
# 遍历文件夹内的文件，将每个标签转化为tensor并存储 
for k in range(40): 
    file_name = 'label_{}.npy'.format(k) 
    file_path2 = os.path.join(folder_path2, file_name) 
    matrix2 = np.load(file_path2) 
    y[k] = torch.from_numpy(matrix2) 

losses = [] 
for epoch in range(10): 
    running_loss = 0.0 
    for i in range(40): 
        inputs, labels = x[i], y[i].long() 
        optimizer.zero_grad() 
        outputs = net(inputs) 
        loss = criterion(outputs, labels) 
        loss.backward(torch.tensor([1], dtype=torch.long)) 
        optimizer.step() 

        running_loss += loss.item() 

    losses.append(running_loss / 40) 
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / 40)) 

print('Finished Training') 

plt.plot(losses) 
plt.xlabel('Epoch') 
plt.ylabel('Loss') 
plt.show()