最后一批中的Pytorch损失函数错误

Question

假设我有77个样本来训练我的CNN，我的batch size是10.然后最后一批有一个7的batch size而不是10.不知怎的，当我把它传递给nn.MSELoss()这样的损失函数时，它给了我错误：

RuntimeError：张量a（10）的大小必须与非单一维度1处的张量b（7）的大小相匹配

所以pytorch不支持不同大小的批次？

我怀疑的代码：

import numpy as np
import torch
from torch import nn
import torchvision
import torch.nn.functional as F
import torch.optim as optim

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, (5,4))
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(64, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, x.shape[1] * x.shape[2] * x.shape[3])
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = Net()

batch_size = 10

# Generating Artifical data
x_train = torch.randn((77,1,20,20))
y_train = torch.randint(0,10,size=(77,),dtype=torch.float)

trainset = torch.utils.data.TensorDataset(x_train,y_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0)
# testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0)

criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

for epoch in range(20):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data
        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i%10==0:
            print('epoch{}, step{}, loss: {}'.format(epoch + 1, i + 1, running_loss))
#             print("frac post = {}".format(frac_post))
            running_loss = 0.0

Answer 1

问题不是由于批量大小，而是由于无法在CNN的10个输出和每个示例中提供的单个标签之间正确广播。如果在抛出错误的批处理期间查看模型输出和标签张量形状，

print(outputs.shape, labels.shape)
#out: torch.Size([7, 10]) torch.Size([7])

你会看到标签存储在单个张量中。根据pytorch broadcasting rules，要播放两个张量必须兼容所有尾随尺寸。在这种情况下，模型输出（10）的尾随尺寸与标签（7）的尾随尺寸不兼容。

要修复，要么为标签添加虚拟维度（假设您实际上想要广播标签以匹配您的十个网络输出），或者定义具有标量输出的网络。例如：

y_train = torch.randint(0,10,size=(77,1),dtype=torch.float)

结果是

print(outputs.shape, labels.shape)
#out: torch.Size([7, 10]) torch.Size([7,1])
# these are broadcastable