用Python和Pytorch使用softmax和cross-entropy

def softmax(x):
    return np.exp(x) / np.sum(np.exp(x), axis=0)

x=np.array([0.1, 0.9, 4.0])
 
output=softmax(x)
 
print('Softmax in Python :',output)
 
#Softmax in Python : [0.04672966 0.10399876 0.84927158]

x=torch.tensor(x)
output=torch.softmax(x,dim=0)
print(output)
 
#tensor([0.0467, 0.1040, 0.8493], dtype=torch.float64)

#Cross Entropy Loss
 
def cross_entropy(y,y_pre):
  loss=-np.sum(y*np.log(y_pre))
  return loss/float(y_pre.shape[0])

y=np.array([0,0,1]) #class #2
 
y_pre_good=np.array([0.1,0.1,0.8])
y_pre_bed=np.array([0.8,0.1,0.1])
 
l1=cross_entropy(y,y_pre_good)
l2=cross_entropy(y,y_pre_bed)
 
print('Loss 1:',l1)
print('Loss 2:',l2)
 
Loss 1: 0.07438118377140324
Loss 2: 0.7675283643313485

loss =nn.CrossEntropyLoss()

y=torch.tensor([2])
 
y_pre_good=torch.tensor([[1.0,1.1,2.5]])
y_pre_bed=torch.tensor([[3.2,0.2,0.9]])
 
 
l1=loss(y_pre_good,y)
l2=loss(y_pre_bed,y)
 
print(l1.item()) #0.3850
print(l2.item()) #2.4398