train_iter,test_iter=load_cifar_10("../data/",128)net=d2l.resnet18(num_classes=10,in_channels=3)# 查看网络结构x,y=next(iter(train_iter))forlayer in net:x=layer(x)print(layer.__class__.__name__,"output shape:\t",x.shape) # 简化版评估模型准确率defevalue_acc(net,data_iter,...