Draws binary random numbers (0 or 1) from a Bernoulli distribution.
The input tensor should be a tensor containing probabilities to be used for drawing the binary random number. Hence, all values in input have to be in the range: 0 ≤ input i ≤ 1 0 \leq \text{input}_i \leq 1 0≤inputi≤1.
The i t h \text{i}^{th} ith element of the output tensor will draw a value 1 1 1 according to the i t h \text{i}^{th} ith probability value given in input.
out i ∼ B e r n o u l l i ( p = input i ) \text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i}) outi∼Bernoulli(p=inputi)
The returned out tensor only has values 0 or 1 and is of the same shape as input.
out can have integral dtype, but input must have floating point dtype.
input (Tensor) – the input tensor of probability values for the Bernoulli distribution
generator (torch.Generator, optional) – a pseudorandom number generator for sampling
out (Tensor, optional) – the output tensor.
Example:
>>> a = torch.empty(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1]
>>> a
tensor([[ 0.1737, 0.0950, 0.3609],
[ 0.7148, 0.0289, 0.2676],
[ 0.9456, 0.8937, 0.7202]])
>>> torch.bernoulli(a)
tensor([[ 1., 0., 0.],
[ 0., 0., 0.],
[ 1., 1., 1.]])
>>> a = torch.ones(3, 3) # probability of drawing "1" is 1
>>> torch.bernoulli(a)
tensor([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]])
>>> a = torch.zeros(3, 3) # probability of drawing "1" is 0
>>> torch.bernoulli(a)
tensor([[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]])
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