The question says it all. I want to get a 2-D torch.Tensor with size [a,b] filled with values from a uniform distribution (in range [r1,r2]) in PyTorch.

Please Can you try something like:

import torch as pt
pt.empty(2,3).uniform_(5,10).type(pt.FloatTensor)

This answer uses NumPy to first produce a random matrix and then converts the matrix to a PyTorch tensor. I find the NumPy API to be easier to understand.

import numpy as np

torch.from_numpy(np.random.uniform(low=r1, high=r2, size=(a, b)))

To get a uniform random distribution, you can use

torch.distributions.uniform.Uniform()

example,

import torch
from torch.distributions import uniform

distribution = uniform.Uniform(torch.Tensor([0.0]),torch.Tensor([5.0]))
distribution.sample(torch.Size([2,3])

This will give the output, tensor of size [2, 3].

If U is a random variable uniformly distributed on [0, 1], then (r1 - r2) * U + r2 is uniformly distributed on [r1, r2].

Thus, you just need:

(r1 - r2) * torch.rand(a, b) + r2​

Alternatively, you can simply use:

torch.FloatTensor(a, b).uniform_(r1, r2)​

To fully explain this formulation, let's look at some concrete numbers:

r1 = 2 # Create uniform random numbers in half-open interval [2.0, 5.0)
r2 = 5

a = 1 # Create tensor shape 1 x 7
b = 7​

We can break down the expression (r1 - r2) * torch.rand(a, b) + r2 as follows:

1.torch.rand(a, b) produces an a x b (1x7) tensor with numbers uniformly distributed in the range [0.0, 1.0).

x = torch.rand(a, b)
print(x)
# tensor([[0.5671, 0.9814, 0.8324, 0.0241, 0.2072, 0.6192, 0.4704]])​

2.(r1 - r2) * torch.rand(a, b) produces numbers distributed in the uniform range [0.0, -3.0)

print((r1 - r2) * x)
tensor([[-1.7014, -2.9441, -2.4972, -0.0722, -0.6216, -1.8577, -1.4112]])​

3.(r1 - r2) * torch.rand(a, b) + r2 produces numbers in the uniform range [5.0, 2.0)

print((r1 - r2) * x + r2)
tensor([[3.2986, 2.0559, 2.5028, 4.9278, 4.3784, 3.1423, 3.5888]])​

For those who are frustratingly bashing their keyboard yelling "why isn't this working." as I was... note the underscore behind the word uniform.

torch.FloatTensor(a, b).uniform_(r1, r2)
                               ^ here

Utilize the torch.distributions package to generate samples from different distributions.

For example to sample a 2d PyTorch tensor of size [a,b] from a uniform distribution of range(low, high) try the following sample code

import torch
a,b = 2,3   #dimension of the pytorch tensor to be generated
low,high = 0,1 #range of uniform distribution

x = torch.distributions.uniform.Uniform(low,high).sample([a,b])