pytorch-------测试（18）

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

logits = torch.rand(4,10)
pred = F.softmax(logits,dim=1)
print(pred.shape)

pred_label = pred.argmax(dim=1)
print(pred_label)
#argmax选取最大值的索引，与是否经过softmax的结果一样
print(logits.argmax(dim=1))

label = torch.tensor([5,5,5,5])
correct = torch.eq(pred_label,label)
print(correct)
#计算准确率
print(correct.sum().float().item()/4)

torch.Size([4, 10])
tensor([5, 7, 9, 9])
tensor([5, 7, 9, 9])
tensor([1, 0, 0, 0], dtype=torch.uint8)
0.25

    for data, target in test_loader:
        data = data.view(-1, 28 * 28)
        data, target = data.to(device), target.cuda()
        logits = net(data)
        test_loss += criteon(logits, target).item()

        pred = logits.data.max(1)[1]
        correct += pred.eq(target.data).sum()

    test_loss /= len(test_loader.dataset)