Pytorch cifar数据集：RuntimeError：大小不匹配，m1：[4 x 2048]，m2：[1568 x 10]

问题描述

我正在建立自己的具有2个基本层的网络，并在CIFAR10数据集上进行培训。我收到不匹配错误。

import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset,DataLoader
import torch.nn as nn

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

num_epochs = 5
num_classes = 10
batch_size = 4
learning_rate = 0.001

print("---------Train Dataset----------")
train_dataset = torchvision.datasets.CIFAR10(root='../data/',train=True,transform=transforms.ToTensor(),download=True)

print("---------Train Loader----------")
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)


class ConvNet(nn.Module):
    def __init__(self,num_classes=10):
        super(ConvNet,self).__init__()

        # First Layer
        self.layer1 = nn.Sequential(
            nn.Conv2d(3,16,kernel_size=3,stride=1,padding=1),nn.BatchNorm2d(16),nn.ReLU(),nn.MaxPool2d(kernel_size=2,stride=2))

        # Second Layer
        self.layer2 = nn.Sequential(
            nn.Conv2d(16,32,nn.BatchNorm2d(32),stride=2))

        # Fully connected Layer
        self.fc = nn.Linear(7 * 7 * 32,num_classes)

    def forward(self,x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0),-1)
        out = self.fc(out)
        return out


model = ConvNet(num_classes).to(device)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)

print("--------Train Model----------")

total_step = len(train_loader)
print("Total Step",total_step)

for epoch in range(num_epochs):
    for i,(images,labels) in enumerate(train_loader):

        images = images.to(device)
        labels = labels.to(device)

        print("-----Forward Pass-------")
        outputs = model(images)
        loss = criterion(outputs,labels)

        print ("----Backward Pass-------")
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print(epoch+1,num_epochs,i+1,total_step,loss.item())

解决方法

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