test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=False) class LeNet(nn.Module): def __init__(self): super(LeNet, self).__init__() self.feature = nn.Sequential( nn.Conv2d(1, 16, 5), nn.MaxPool2d(2, 2), nn.Conv2d(16, 32, 5), nn.MaxPool2d(2, 2)) ...
def test(model, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for i, data in enumerate(test_loader, 0): x, y = data x = x.cuda() y = y.cuda() optimizer.zero_grad() y_hat = model(x) test_loss += criterion(y_hat, y).item() pred = y...
test_data = torchvision.datasets.CIFAR10("./data_torchvision_dataset", train=False, transform=torchvision.transforms.ToTensor(),download=True) # 加载测试集:取四个数据,进行打包,batch_size = 64 数据一共四条,0~3 test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True, num_work...
224, mode='val')test_db= Pokemon('pokeman',224, mode='test')train_loader= DataLoader(train_db, batch_size=batchsz, shuffle=True,num_workers=4)val_loader= DataLoader(val_db, batch_size=batchsz, num_workers=2)test_loader= DataLoader(test_db, batch_size=batchsz, num_workers=2)...
validate_loader = DataLoader(validate_set, batch_size =1) test_loader = DataLoader(test_set, batch_size =1) 后续步骤 数据准备就绪后,即可训练 PyTorch 模型
train_loader = DataLoader( dataset=ImageDataset(inputs_root=in_folder + r"\train\inputs", labels_root=in_folder + r"\train\labels"), batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu ) test_loader = DataLoader(
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') 1. 2. 3. 4. 5. 6. 7.
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True) 设置随机数种子以确保实验的可复现性 torch.manual_seed(42) 训练一个神经网络模型 model = torch.nn.Sequential(torch.nn.Linear(784, 128), torch.nn.ReLU(), torch.nn.Linear(128, 10)).to(torch.device(‘cuda’ if torch.cuda...
model.eval() with torch.no_grad(): for data, target in test_loader: output = model(data) acc = accuracy(output, target) prec = precision(output, target) rec = recall(output, target) f1 = f1(output, target) print(f'Accuracy: {acc}, Precision: {prec}, Recall: {rec}, F1 Score:...
* batch_idx / len(train_loader), loss.data[0])) def test(): model.eval() # 设置为test模式 test_loss = 0 # 初始化测试损失值为0 correct = 0 # 初始化预测正确的数据个数为0 for data, target in test_loader: if args.cuda: data, target = data.cuda(), target.cuda() data, target...