pytorch 数学运算

a = torch.rand(3, 4)
b = torch.rand(4)

a+b
tensor([[0.9806, 1.2292, 0.7037, 0.1900],
        [1.2180, 1.3809, 0.5762, 0.3377],
        [1.1987, 0.8854, 1.2460, 0.8422]])

>>> torch.add(a, b)
tensor([[0.9806, 1.2292, 0.7037, 0.1900],
        [1.2180, 1.3809, 0.5762, 0.3377],
        [1.1987, 0.8854, 1.2460, 0.8422]])
>>> 
>>> torch.all(torch.eq(a-b, torch.sub(a, b)))
tensor(1, dtype=torch.uint8)
>>> 
>>> torch.all(torch.eq(a*b, torch.mul(a, b)))
tensor(1, dtype=torch.uint8)
>>> 
>>> torch.all(torch.eq(a/b, torch.div(a, b)))
tensor(1, dtype=torch.uint8)

>>> a=torch.full((2, 2), 3)
>>> a
tensor([[3., 3.],
        [3., 3.]])
>>> 
>>> b = torch.ones(2, 2)
>>> 
>>> torch.mm(a, b)
tensor([[6., 6.],
        [6., 6.]])
>>> 
>>> torch.matmul(a, b)
tensor([[6., 6.],
        [6., 6.]])
>>> 
>>> [email protected]
tensor([[6., 6.],
        [6., 6.]])
>>> 
>>> 
>>> 
>>> a = torch.rand(4, 784)
>>> 
>>> x = torch.rand(4, 784)
>>> 
>>> w = torch.rand(512, 784)
>>> 
>>> ([email protected]()).shape
torch.Size([4, 512])
>>> 
>>> 
>>> 
>>> a = torch.rand(4, 3 ,28, 64)
>>> b = torch.rand(4, 3, 64, 32)
>>> 
>>> torch.mm(a, b).shape
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: matrices expected, got 4D, 4D tensors at /pytorch/aten/src/TH/generic/THTensorMath.cpp:956
>>> 
>>> torch.matmul(a, b).shape
torch.Size([4, 3, 28, 32])
>>> 
>>> b = torch.rand(4, 1, 64, 32)
>>> 
>>> torch.matmul(a, b).shape
torch.Size([4, 3, 28, 32])
>>> 
>>> b = torch.rand(4, 64, 32)
>>> 
>>> torch.matmul(a, b).shape
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 1

>>> a = torch.full([2, 2], 3)
>>> 
>>> a.pow(2)
tensor([[9., 9.],
        [9., 9.]])
>>> 
>>> a ** 2
tensor([[9., 9.],
        [9., 9.]])
>>> 
>>> aa = a ** 2
>>> 
>>> aa.sqrt()
tensor([[3., 3.],
        [3., 3.]])
>>> 
>>> aa.rsqrt()
tensor([[0.3333, 0.3333],
        [0.3333, 0.3333]])
>>> 
>>> aa**(0.5)
tensor([[3., 3.],
        [3., 3.]])

>>> a = torch.exp(torch.ones(2, 2))
>>> 
>>> a
tensor([[2.7183, 2.7183],
        [2.7183, 2.7183]])
>>> 
>>> torch.log(a)
tensor([[1., 1.],
        [1., 1.]])

>>> a = torch.tensor(3.14)
>>> 
>>> a.floor()
tensor(3.)
>>> 
>>> a.ceil()
tensor(4.)
>>> 
>>> a.trunc()
tensor(3.)
>>> 
>>> a.frac()
tensor(0.1400)
>>> 
>>> a = torch.tensor(3.499)
>>> 
>>> a.round()
tensor(3.)
>>> 
>>> a = torch.tensor(3.5)
>>> 
>>> a.round()
tensor(4.)

>>> grad = torch.rand(2, 3)*15
>>> grad
tensor([[ 8.9165,  9.5164,  0.1095],
        [12.0321,  9.3978,  1.3909]])
>>> 
>>> grad.max()
tensor(12.0321)
>>> 
>>> grad.median()
tensor(8.9165)
>>> 
>>> grad.clamp(10)
tensor([[10.0000, 10.0000, 10.0000],
        [12.0321, 10.0000, 10.0000]])
>>> 
>>> grad.clamp(0, 10)
tensor([[ 8.9165,  9.5164,  0.1095],
        [10.0000,  9.3978,  1.3909]])