cifar |
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cifar
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须知：
同前文 代码变化部分如下图，其余部分与上文一同CIFAR-10基础优化一（加入标准化和激活函数）_百炼成丹的博客-CSDN博客 优化思路：再加入多层卷积层 卷积层层间和全连接层层间加入dropout 优化网络顺序结构 图像增广 网络结构： class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.model1 = nn.Sequential( nn.Conv2d(3, 32, 5, padding=2), nn.BatchNorm2d(32), nn.Dropout2d(0.25), nn.ReLU(), nn.Conv2d(32, 32, 5, padding=2), nn.BatchNorm2d(32), nn.MaxPool2d(2)) # nn.ReLU(), self.model2 = nn.Sequential( nn.Conv2d(32, 64, 5, padding=2), nn.BatchNorm2d(64), nn.Dropout2d(0.25), nn.ReLU(), nn.Conv2d(32, 64, 5, padding=2), nn.BatchNorm2d(64), nn.MaxPool2d(2)) # nn.ReLU(), self.model3 = nn.Sequential( nn.Conv2d(32, 128, 5, padding=2), nn.BatchNorm2d(128), nn.Dropout2d(0.3), nn.ReLU(), nn.Conv2d(32, 128, 5, padding=2), nn.BatchNorm2d(128), nn.MaxPool2d(2)) # nn.ReLU(), self.model4 = nn.Sequential( nn.Conv2d(32, 256, 5, padding=2), nn.BatchNorm2d(256), nn.Dropout2d(0.3), nn.ReLU(), nn.Conv2d(32, 256, 5, padding=2), nn.BatchNorm2d(256), nn.MaxPool2d(2)) # nn.ReLU(), self.model5 = nn.Sequential( nn.Conv2d(256, 512, 5, padding=2), nn.BatchNorm2d(512), nn.Dropout2d(0.35), nn.ReLU(), nn.Conv2d(512, 512, 5, padding=2), nn.BatchNorm2d(512), nn.MaxPool2d(2)) # nn.ReLU(), self.linear1 = nn.Sequential( nn.Flatten(), nn.Linear(512, 128), nn.BatchNorm1d(128), nn.ReLU(), nn.Dropout(0.5), nn.Linear(128, 10)) 图像增广：torchvision.transforms.ColorJitter(0.5), torchvision.transforms.RandomHorizontalFlip() train_data = torchvision.datasets.CIFAR10("../dataset", train=True, transform=torchvision.transforms.Compose( [torchvision.transforms.ColorJitter(0.5), torchvision.transforms.RandomHorizontalFlip(), torchvision.transforms.ToTensor()]))效果：
测试集准确率峰值88.2%
约64epoch后无增幅 表现效果进一步提高 但时间不理想，约40分钟上下 下一步，加入残差网络 |
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