加法
a = torch.Tensor(np.arange(6).reshape((2,3)))
'''
a的值
tensor([[0., 1., 2.],
[3., 4., 5.]])
'''
b = torch.Tensor(np.arange(6).reshape((2,3)))
'''
b的值
tensor([[0., 1., 2.],
[3., 4., 5.]])
'''
res = torch.add(a,b)
print(res)
'''
结果
tensor([[ 0., 2., 4.],
[ 6., 8., 10.]])
'''
减法
a = torch.Tensor(np.arange(6).reshape((2,3)))
'''
a的值
tensor([[0., 1., 2.],
[3., 4., 5.]])
'''
b = torch.Tensor(np.random.randint(0,9,size=(2,3)))
'''
b的值
tensor([[4., 0., 5.],
[6., 8., 4.]])
'''
res = torch.sub(a,b)
print(res)
'''
结果
tensor([[-4., 1., -3.],
[-3., -4., 1.]])
'''
乘法
a = torch.Tensor(np.arange(6).reshape((2,3)))
'''
a的值
tensor([[0., 1., 2.],
[3., 4., 5.]])
'''
b = torch.Tensor(np.arange(6).reshape((2,3)))
'''
b的值
tensor([[0., 1., 2.],
[3., 4., 5.]])
'''
res = torch.mul(a, b)
print(res)
'''
tensor([[ 0., 1., 4.],
[ 9., 16., 25.]])
'''
除法
a = torch.Tensor(np.arange(6).reshape((2,3)))
'''
a的值
tensor([[0., 1., 2.],
[3., 4., 5.]])
'''
b = torch.Tensor(np.random.randint(0,9,size=(2,3)))
'''
b的值
tensor([[4., 0., 5.],
[6., 8., 4.]])
'''
res = torch.div(a,b)
print(res)
'''
结果, 注意到: 除数为0时的结果是inf, 并没有报错
tensor([[0.0000, inf, 0.4000],
[0.5000, 0.5000, 1.2500]])
'''
e为底的指数
a = torch.Tensor(np.arange(6).reshape((2,3)))
'''
a的值
tensor([[0., 1., 2.],
[3., 4., 5.]])
'''
res = torch.exp(a)
print(res)
'''
tensor([[ 1.0000, 2.7183, 7.3891],
[ 20.0855, 54.5981, 148.4132]])
'''
n次方, n次幂
a = torch.randint(0,9,(1,))
'''
a的值
tensor([2])
'''
b = torch.randint(0,9,(1,))
'''
b的值
tensor([5])
'''
res = torch.pow(input=a,exponent=b)
print(res)
'''
tensor([32])
'''
对数
a = torch.Tensor(np.arange(6).reshape((2,3)))
'''
a的值
tensor([[0., 1., 2.],
[3., 4., 5.]])
'''
res = torch.log(a)
print(res)
'''
tensor([[ -inf, 0.0000, 0.6931],
[1.0986, 1.3863, 1.6094]])
'''
res = torch.log2(a)
print(res)
'''
tensor([[ -inf, 0.0000, 1.0000],
[1.5850, 2.0000, 2.3219]])
'''
res = torch.log10(a)
print(res)
'''
tensor([[ -inf, 0.0000, 0.3010],
[0.4771, 0.6021, 0.6990]])
'''
res = torch.log1p(a)
print(res)
'''
tensor([[0.0000, 0.6931, 1.0986],
[1.3863, 1.6094, 1.7918]])
'''
绝对值
a = torch.randint(-10,-1,(1,))
'''
a的值
tensor([-10])
'''
res = torch.abs(input=aaa)
print(res)
'''
tensor([10])
'''