from torch.autograd import Variable
import torch.nn.functional as F
def test(model,test_loader,device):
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:
data = data.to(device)
target = target.to(device)
data, target = Variable(data), Variable(target) # 计算前要把变量变成Variable形式，因为这样子才有梯度
output = model(data)
test_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss 把所有loss值进行累加
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).cpu().sum() # 对预测正确的数据个数进行累加
test_loss /= len(test_loader.dataset) | 
转播
