(predicted == labels).sum().item()作用

 ⚠️(predicted == labels).sum().item()作用，舉個小例子介紹：

# -*- 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) #對比后相同的值會為1，不同則會為0 print p print type(p) d1 = p.sum() #將所有的值相加，得到的仍是tensor類別的int值 print d1 print type(d1) d2 = d1.item() #轉成python數字 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'>

 

即如果有不同的話，會變成：

# -*- 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'>