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
时间: 2023-12-10 17:36:58 浏览: 128
可以使用Python中的NumPy库和PyTorch库来实现。代码如下:
```python
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。
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,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:
```python
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。
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()
```
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