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Pytoch foundation - (2) mathematical operation of tensor
2022-07-06 03:27:00 【Up and down black】
Catalog
Two 、 Maximum 、 minimum value 、 mean value 、 The absolute value 、 Sort
3、 ... and 、tensor The index of
One 、 Basic operation
import torch
x = torch.tensor([2, 2, 2], dtype=torch.float32)
y = torch.tensor([3, 4, 5], dtype=torch.float32)
- Add
out1 = torch.add(x, y)
print(out1)
- Subtraction
out2 = torch.sub(x, y)
print(out2)
- division
out3 = torch.div(x, y)
print(out3)
- Multiplication
out4 = torch.mul(x, y)
print(out4)
- Matrix multiplication
a = torch.rand((2, 5))
b = torch.rand((5, 3))
out5 = torch.mm(a, b)
print(out5)
print(out5.shape)
- Batch matrix multiplication
batch = 16
c1 = 5
c2 = 10
c3 = 20
x1 = torch.rand(batch, c1, c2) # 16*5*10
x2 = torch.rand(batch, c2, c3) # 16*10*20
out6 = torch.bmm(x1, x2) # 16*5*20
print(out6.shape)
- Index
out7 = x.pow(3)
print(out7)
- Matrix index
x = torch.tensor([[1, 1], [1, 1]]) # (2*2) The number of rows and columns must be the same
out8 = x.matrix_power(2)
print(out8) # 2*2
- broadcasting
x1 = torch.rand((1, 3))
x2 = torch.rand((3, 3))
out9 = x1 -x2
print(out9)
Two 、 Maximum 、 minimum value 、 mean value 、 The absolute value 、 Sort
import torch
x = torch.tensor([[-1, 2, 3], [4, 5, 6]], dtype=torch.float32)
y = torch.tensor([[1, 1, 2], [0, 10, 9]], dtype=torch.float32)
- Maximum 、 minimum value 、 mean value
values, indice = torch.max(x, dim=0) # dim=0 The column ,1 Said line
print(values) # Value of maximum value
print(indice) # Index of maximum value
values, indice = torch.min(x, dim=0) # dim=0 The column ,1 Said line
print(values) # The minimum value
print(indice) # Index of minimum value
values, indice = torch.mean(x, dim=0)
print(values)
print(indice)
# Find the maximum 、 Index of minimum value
out1 = torch.argmax(x, dim=0)
out2 = torch.argmin(x, dim=0)
- The absolute value
out = torch.abs(x)
print(out)
- Sort
values, indice = torch.sort(x, dim=1, descending=False) # descending=False Expressing ascending order
print(values)
print(indice)
3、 ... and 、tensor The index of
- Simple operation
x = torch.tensor([1, 4, 5, 6, 0, 8, 6, 1, 4, 5])
print(x[0]) # First element
print(x[1: 6]) # Output No 2 One to the first 6 Elements
x = torch.randn((3, 10)) # size 3x10
print(x[0]) # Output No 1 All numbers of rows , size 1x10
print(x[0, :]) # size 1x10
print(x[:, 0]) # Output No 1 All the numbers in the column , size 10x1
x = torch.randn((4, 10))
rows = [1, 3]
colums = [2, 9]
print(x[rows, colums]) # (2, 3) (4, 10)
- Conditional
x = torch.tensor([1, 4, 5, 6, 0, 8, 6, 1, 4, 5])
print(torch.where(x > 5, x, x/2)) # x>5 Time output x, Otherwise output x/2
- other
print(x.unique()) # Output non repeating elements
print(x.numel()) # Output x The number of elements in
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