(predicted == labels).sum().item()作用

 ⚠️(predicted == labels).sum().item()作用，举个小例子介绍：

# -*- coding: utf-8 -*-
import torch
import numpy as np 

data1 = np.array([
    [1,2,3],
    [2,3,4]
])
data1_torch = torch.from_numpy(data1)

data2 = np.array([
    [1,2,3],
    [2,3,4]
])
data2_torch = torch.from_numpy(data2)

p = (data1_torch == data2_torch) #对比后相同的值会为1，不同则会为0
print p
print type(p)

d1 = p.sum() #将所有的值相加，得到的仍是tensor类别的int值
print d1 
print type(d1)

d2 = d1.item() #转成python数字
print d2 
print type(d2)


 

返回：

(deeplearning2) userdeMBP:pytorch user$ python test.py
tensor([[1, 1, 1],
        [1, 1, 1]], dtype=torch.uint8)
<class 'torch.Tensor'>
tensor(6)
<class 'torch.Tensor'>
6
<type 'int'>

即如果有不同的话，会变成：

# -*- coding: utf-8 -*-
import torch
import numpy as np 

data1 = np.array([
    [1,2,3],
    [2,3,4]
])
data1_torch = torch.from_numpy(data1)

data2 = np.array([
    [1,2,3],
    [4,5,6]
])
data2_torch = torch.from_numpy(data2)

p = (data1_torch == data2_torch) 
print p
print type(p)

d1 = p.sum() 
print d1 
print type(d1)

d2 = d1.item() 
print d2 
print type(d2)


 

返回：

(deeplearning2) userdeMBP:pytorch user$ python test.py
tensor([[1, 1, 1],
        [0, 0, 0]], dtype=torch.uint8)
<class 'torch.Tensor'>
tensor(3)
<class 'torch.Tensor'>
3
<type 'int'>