import torch from torch import nn import d2l net = nn.Sequential( nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(96, 256, kernel_size=5, padding=2), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(256, 384, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2), nn.Flatten(), nn.Linear(6400, 4096), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096, 10)) X = torch.randn((1, 3, 224, 224),dtype=torch.float32) for layer in net: X=layer(X) print(layer.__class__.__name__,'output shape:\t',X.shape) batch_size = 32 train_iter, test_iter = d2l.load_data_cifar10(batch_size, resize=224) lr, num_epochs = 0.01, 70 d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu()),其中batch_size改为64或把resize改成96会提高准确率吗?
时间: 2023-09-17 13:13:40 浏览: 39
将batch_size从32改为64有可能提高准确率,因为使用更大的batch_size会使模型在每个epoch中看到更多的数据,进而提高模型的泛化能力。
将resize从224改为96可能会降低准确率,因为使用更小的图片尺寸会损失一定的信息量,降低模型的表达能力,进而影响模型的准确率。但是如果使用96大小的图片可以减少训练时间和内存占用,适用于一些计算资源比较有限的场景。