In this tutorial, we will use some examples to show you how to use torch.tensor.random_() correctly. You may find there are some notices you may need concern.
torch.tensor.random_()
It is defined as:
Tensor.random_(from=0, to=None, *, generator=None)
It will fill itself with numbers sampled from the discrete uniform distribution over [from, to – 1]. However, it also return a new tensor.
For example:
import torch
import torch.nn as nn
x = torch.empty([3,5], dtype = torch.float32)
print(x)
y = x.random_(-1, 3)
print("x = ")
print(x)
print("y=")
print(y)
In this code, we first create an empty tensor x. It is:
tensor([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]])
Then we will fill it using x.random_(-1, 3). At this step ,x will be converted:
x =
tensor([[ 2., 2., 0., 2., -1.],
[ 2., 2., -1., 0., 2.],
[ 1., -1., 2., -1., -1.]])
However, y = x.random_(-1, 3), we also can find tensor.random_() will return a tensor y, it also be:
y=
tensor([[ 2., 2., 0., 2., -1.],
[ 2., 2., -1., 0., 2.],
[ 1., -1., 2., -1., -1.]])
from and to parameter
We can find Tensor.random_() contains from and to parameter, can we set value for them? For example:
print(x) y = x.random_(from = -1, to = 3)
However, run this code, we will get this error:
y = x.random_(from = -1, to = 3)
^
SyntaxError: invalid syntax
Look at this new example:
y = x.random_(-1, to = 3)
Here we only set a value to to parameter, run this code we will get:
x =
tensor([[ 0., 0., -1., 2., 0.],
[ 1., 2., 0., -1., 2.],
[-1., 0., 0., -1., 0.]])
y=
tensor([[ 0., 0., -1., 2., 0.],
[ 1., 2., 0., -1., 2.],
[-1., 0., 0., -1., 0.]])
It means we can not assign a value to from parameter except for to parameter.
How about float number in torch.tensor.random_()?
From example above, we can find from and to parameter are integers. How about they are float number?
print(x) y = x.random_(-1.0, 3.0)
Run this code, we will see:
We can find from and to parameter can not be float.
How about default parameter in torch.tensor.random_()?
If we do not set value for to parameter, x will be filled with numbers from [0, 2^mantissa]. Here mantissa is determined by x data type.
For example:
import torch
import torch.nn as nn
x = torch.empty([3,5], dtype = torch.float32)
print(x)
x.random_()
print("x = ")
print(x)
Run this code, we will get:
tensor([[-1.7629e-31, 1.0370e-42, -1.7629e-31, 1.0370e-42, -1.7629e-31],
[ 1.0370e-42, -1.7629e-31, 1.0370e-42, -1.7631e-31, 1.0370e-42],
[-1.7631e-31, 1.0370e-42, -1.7632e-31, 1.0370e-42, -1.7632e-31]])
x =
tensor([[13760033., 13636719., 16242066., 6668118., 1255615.],
[16138854., 10929515., 12875734., 15931016., 12547827.],
[11036358., 9970906., 15599017., 9812039., 10870919.]])
Here the data type of x is float32.
If the data type of x is int8, how about the result?
import torch
import torch.nn as nn
x = torch.empty([3,5], dtype = torch.int8)
print(x)
x.random_()
print("x = ")
print(x)
We will get:
tensor([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], dtype=torch.int8)
x =
tensor([[104, 101, 61, 111, 10],
[ 70, 54, 10, 102, 89],
[ 57, 21, 93, 4, 125]], dtype=torch.int8)