一个名为random_matrices的文件夹内有40个文件,名字分别为matrix_1.npy到matrix_40.npy,python依次输出40个文件的名字
时间: 2023-12-06 15:44:57 浏览: 21
可以使用循环来完成这个任务,示例代码如下:
```python
import os
folder_path = 'random_matrices'
for i in range(1, 41):
file_name = 'matrix_{}.npy'.format(i)
file_path = os.path.join(folder_path, file_name)
print(file_name)
```
这段代码中,首先定义了文件夹的路径 `folder_path`,然后使用循环遍历40个文件,每次循环生成文件名 `file_name`,然后使用 `os.path.join()` 方法将文件夹路径和文件名拼接起来得到文件路径 `file_path`,最后使用 `print()` 函数输出文件名。
相关问题
将一个名为random_matrices的文件夹内40个名为matix_{i}.npy的42×42的numpy矩阵文件,转化为一个(40,1,42,42)的tensor
可以使用以下代码实现:
```python
import numpy as np
import torch
# 创建空的tensor
tensor = 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 i in range(40):
file_path = f'random_matrices/matrix_{i}.npy'
matrix = np.load(file_path)
tensor[i] = torch.from_numpy(matrix).unsqueeze(0)
print(tensor.shape) # 输出 (40, 1, 42, 42)
```
以上代码首先创建了一个空的tensor,形状为 (40, 1, 42, 42),然后遍历文件夹内的40个文件,依次将每个矩阵转化为tensor,并存储到相应的位置。最后输出tensor的形状。注意,在将numpy数组转化为tensor时,需要使用 `torch.from_numpy()` 方法,并且需要使用 `unsqueeze()` 方法扩展维度,使得每个矩阵的形状变为 (1, 42, 42)。
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()
```