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...
experiment.add_metric(LOSS_METRIC,val_loss)experiment.add_metric(ACC_METRIC,val_accuracy) 这个问题不太容易注意到,在循环中我们调用了test函数。 deftest(model,test_loader):model.eval()# ... 在test函数内部,我们将模式设置为eval!这意味着,如果我们在训练过程中调用了test函数,我们就会进eval模式,直到下...
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)...
train_loader = torch.utils.data.DataLoader( train_db, batch_size=batch_size, shuffle=True) # 验证集 val_loader = torch.utils.data.DataLoader( val_db, batch_size=batch_size, shuffle=True) # 测试集 test_loader = torch.utils.data.DataLoader( ...
30 test_loader = torch.utils.data.DataLoader(test_db, batch_size=batch_size, shuffle=True) 31 32 33 #将训练集拆分成训练集和验证集 34 print('train:', len(train_db), 'test:', len(test_db)) #train: 60000 test: 10000 35 train_db, val_db = torch.utils.data.random_split(train_db...
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...
* 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...
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:...
DataLoader(train_dataset,batch_size=4,shuffle=True,num_workers=2) test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=4,shuffle=False) # 测试不需要 shuffle 打乱顺序,保证结果的顺序 # 训练 for epoch in range(epoch_trainning): for batch_idx, (inputs, target) in enumerate(train...