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, )) 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) 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：ValueError: Expected input batch_size (1) to match target batch_size (0). 怎么修改？

这个错误提示意味着你的标签张量 `y` 的形状不正确，需要和输出张量 `outputs` 的形状匹配。在你的代码中，`y` 的形状应该是 `(40,)`，但是在模型输出时，它的形状是 `(1, 2)`。

你可以尝试将标签张量 `y` 的形状修改为 `(40, 1)`，以便与模型输出的形状 `(40, 2)` 匹配。具体地，你可以在加载标签数据时使用以下代码：

y[k] = torch.from_numpy(matrix2).unsqueeze(0)

这样可以将形状为 `(1,)` 的标签张量变为形状为 `(1, 1)` 的张量，使其与模型输出的形状 `(1, 2)` 匹配。然后，在训练模型时，你需要将标签张量 `y` 的维度降低一维，以便与输出张量 `outputs` 的形状 `(1, 2)` 匹配：

loss = criterion(outputs, labels.squeeze(1))

这样就可以避免 `ValueError: Expected input batch_size (1) to match target batch_size (0)` 的错误了。

相关问题

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()

解释一下代码import matplotlib.pyplot as plt import numpy as np import torch import torch.utils.data.dataloader as Loader import torchvision import torchvision.transforms as transforms import torch.nn as nn import torch.nn.functional as F import torch.op

这段代码是在Python中导入一些常用的机器学习和深度学习的库，其中包括：

1. matplotlib.pyplot：用于绘制数据可视化图表的库。
2. numpy：用于数值计算的库。
3. torch：PyTorch深度学习框架的主要库。
4. torch.utils.data.dataloader：用于数据加载和预处理的库。
5. torchvision：PyTorch的图像处理库。
6. torchvision.transforms：用于对图像进行转换和预处理的库。
7. torch.nn：PyTorch的神经网络模块库。
8. torch.nn.functional：PyTorch的神经网络函数库。
9. torch.optim：PyTorch的优化器库。

通过导入这些库，我们可以在Python中方便地进行深度学习的模型构建和数据处理。