基于300条数据用CNN多分类预测时，训练精度特别差，代码如下class Model(Module): def __init__(self): super(Model, self).__init__() self.conv1_1 = nn.Conv2d(in_channels=3,out_channels=64,kernel_size=(3,3),padding=1) self.bn1_1 = nn.BatchNorm2d(64) self.relu1_1 = nn.ReLU() self.pool1 = nn.MaxPool2d(kernel_size=4, stride=4) self.conv2_1 = nn.Conv2d(in_channels=64,out_channels=128,kernel_size=(3,3),padding=1) self.bn2_1 = nn.BatchNorm2d(128) self.relu2_1 = nn.ReLU() self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv3_1 = nn.Conv2d(in_channels=128,out_channels=256,kernel_size=(3,3),padding=1) self.bn3_1 = nn.BatchNorm2d(256) self.relu3_1 = nn.ReLU() self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv4_1 = nn.Conv2d(in_channels=256,out_channels=512,kernel_size=(3,3)) self.bn4_1 = nn.BatchNorm2d(512) self.relu4_1 = nn.ReLU() self.conv4_2 = nn.Conv2d(in_channels=512,out_channels=512,kernel_size=(3,3)) self.bn4_2 = nn.BatchNorm2d(512) self.relu4_2 = nn.ReLU() self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv5_1 = nn.Conv2d(in_channels=512,out_channels=512,kernel_size=(3,3)) self.bn5_1 = nn.BatchNorm2d(512) self.relu5_1 = nn.ReLU() self.conv5_2 = nn.Conv2d(in_channels=512,out_channels=512,kernel_size=(3,3)) self.bn5_2 = nn.BatchNorm2d(512) self.relu5_2 = nn.ReLU() self.pool5 = nn.AdaptiveAvgPool2d(5) self.dropout1 = nn.Dropout(p=0.3) self.fc1=nn.Linear(512*5*5,512) self.relu6=nn.ReLU() self.dropout2 = nn.Dropout(p=0.2) self.fc2=nn.Linear(512,141) ，具体如何修改代码

训练精度差可能是由于数据集太小，或者模型的训练过程不够充分。可以尝试以下几个方法：

1. 增加数据集：可以通过数据增强的方法来扩充数据集，比如随机裁剪、翻转、旋转等等。

2. 调整超参数：可以尝试调整学习率、优化器、dropout等超参数来提高模型的性能。

3. 调整网络结构：可以尝试增加卷积层、全连接层，或者使用更深的网络结构来提高模型的性能。

以下是代码的修改建议：

1. 增加数据增强：可以使用 torchvision.transforms 对图像进行随机裁剪、翻转等操作，来扩充数据集。

2. 调整学习率和优化器：可以尝试使用较小的学习率，并选择合适的优化器，比如 Adam。

3. 增加全连接层：可以在模型中增加一个全连接层来提高模型性能。

4. 增加正则化：可以使用 L2 正则化来防止过拟合。

修改后的代码如下：

import torch.nn.functional as F
import torchvision.transforms as transforms

class Model(Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1_1 = nn.Conv2d(in_channels=3,out_channels=64,kernel_size=(3,3),padding=1)
        self.bn1_1 = nn.BatchNorm2d(64)
        self.relu1_1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=4, stride=4)
        self.conv2_1 = nn.Conv2d(in_channels=64,out_channels=128,kernel_size=(3,3),padding=1)
        self.bn2_1 = nn.BatchNorm2d(128)
        self.relu2_1 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3_1 = nn.Conv2d(in_channels=128,out_channels=256,kernel_size=(3,3),padding=1)
        self.bn3_1 = nn.BatchNorm2d(256)
        self.relu3_1 = nn.ReLU()
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv4_1 = nn.Conv2d(in_channels=256,out_channels=512,kernel_size=(3,3))
        self.bn4_1 = nn.BatchNorm2d(512)
        self.relu4_1 = nn.ReLU()
        self.conv4_2 = nn.Conv2d(in_channels=512,out_channels=512,kernel_size=(3,3))
        self.bn4_2 = nn.BatchNorm2d(512)
        self.relu4_2 = nn.ReLU()
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5_1 = nn.Conv2d(in_channels=512,out_channels=512,kernel_size=(3,3))
        self.bn5_1 = nn.BatchNorm2d(512)
        self.relu5_1 = nn.ReLU()
        self.conv5_2 = nn.Conv2d(in_channels=512,out_channels=512,kernel_size=(3,3))
        self.bn5_2 = nn.BatchNorm2d(512)
        self.relu5_2 = nn.ReLU()
        self.pool5 = nn.AdaptiveAvgPool2d(5)
        self.dropout1 = nn.Dropout(p=0.3)
        self.fc1=nn.Linear(512*5*5, 1024)
        self.relu6=nn.ReLU()
        self.dropout2 = nn.Dropout(p=0.2)
        self.fc2=nn.Linear(1024, 141)
        
        # 数据增强
        self.transform = transforms.Compose([
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
        ])

    def forward(self, x):
        x = self.conv1_1(x)
        x = self.bn1_1(x)
        x = self.relu1_1(x)
        x = self.pool1(x)
        x = self.conv2_1(x)
        x = self.bn2_1(x)
        x = self.relu2_1(x)
        x = self.pool2(x)
        x = self.conv3_1(x)
        x = self.bn3_1(x)
        x = self.relu3_1(x)
        x = self.pool3(x)
        x = self.conv4_1(x)
        x = self.bn4_1(x)
        x = self.relu4_1(x)
        x = self.conv4_2(x)
        x = self.bn4_2(x)
        x = self.relu4_2(x)
        x = self.pool4(x)
        x = self.conv5_1(x)
        x = self.bn5_1(x)
        x = self.relu5_1(x)
        x = self.conv5_2(x)
        x = self.bn5_2(x)
        x = self.relu5_2(x)
        x = self.pool5(x)
        x = x.view(-1, 512*5*5)
        x = self.dropout1(x)
        x = self.fc1(x)
        x = self.relu6(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        return x

在训练时，可以使用如下代码来进行数据增强：

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=model.transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                          shuffle=True, num_workers=2)