Understanding PyTorch Bernoulli distribution from the documention
So I was reading the pytorch document trying to learn and understand somethings(because I'm new to the machine learning ), I found the torch.bernoulli() and I understood (I miss understood it) that it approximates the tensors that the have the values between 1 and 0 to 1 or 0 depends on the value (like classic school less than 0.5 = 0 , more the than or equals 0.5 = 1)

After some experimentations on my own that yeah it works as expected

 &gt;&gt;&gt;y = torch.Tensor([0.500])
 &gt;&gt;&gt;x
 &gt;&gt;&gt;  0.5000
     [torch.FloatTensor of size 1]    
 &gt;&gt;&gt; torch.bernoulli(x)
 &gt;&gt;&gt; 1
     [torch.FloatTensor of size 1]


But when I looked at the document something a bit weird

&gt;&gt;&gt; a = torch.Tensor(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1]
&gt;&gt;&gt; a

 0.7544  0.8140  0.9842
**0.5282** 0.0595  0.6445
 0.1925  0.9553  0.9732
[torch.FloatTensor of size 3x3]

&gt;&gt;&gt; torch.bernoulli(a)

 1  1  1
 **0**  0  1
 0  1  1
[torch.FloatTensor of size 3x3]


in the example the 0.5282 got approximated to 0 ,
how did that happen ? or it's a fault in the document because I tried it and the 0.5282 got approximated as expected to 1.

Well, Bernoulli is a probability distribution. Specifically, torch.distributions.Bernoulli() samples from the distribution and returns a binary value (i.e. either 0 or 1). Here, it returns 1 with probability p and return 0 with probability 1-p.

Below example will make the understanding clear:

In [141]: m =  torch.distributions.Bernoulli(torch.tensor([0.63]))

In [142]: m.sample() # 63% chance 1; 37% chance 0
Out[142]: tensor([ 0.])

In [143]: m.sample() # 63% chance 1; 37% chance 0
Out[143]: tensor([ 1.])

In [144]: m.sample() # 63% chance 1; 37% chance 0
Out[144]: tensor([ 0.])

In [145]: m.sample() # 63% chance 1; 37% chance 0
Out[145]: tensor([ 0.])

In [146]: m.sample() # 63% chance 1; 37% chance 0
Out[146]: tensor([ 1.])

In [147]: m.sample() # 63% chance 1; 37% chance 0
Out[147]: tensor([ 1.])

In [148]: m.sample() # 63% chance 1; 37% chance 0
Out[148]: tensor([ 1.])

In [149]: m.sample() # 63% chance 1; 37% chance 0
Out[149]: tensor([ 1.])

In [150]: m.sample() # 63% chance 1; 37% chance 0
Out[150]: tensor([ 1.])

In [151]: m.sample() # 63% chance 1; 37% chance 0
Out[151]: tensor([ 1.])


So, we sampled it 10 times, out of which we got 1s 7 times which is approximately close to 63%. We need to sample this finitely large number of times to get the exact percentage of 37 and 63 for 0s and 1s respectively; This is because of the Law of Large Numbers.
