Feb 11, 2019 · Given a probability distribution, the log_prob function computes the log probability of the sampled actions. However, a catch makes it different from the torch.log function. It is …

Aug 9, 2022 · Unfortunately the matrix logarithm (unlike the matrix exponential) is not implemented yet, but matrix powers are, this means in the mean time you can approximate the …

Dec 4, 2021 · I want to get surprisal values from logit outputs from PyTorch, using log base 2. One way to do this, given a logits tensor, is: probs = nn.functional.softmax(logits, dim = 2) …

Sep 17, 2021 · I tried to find documents but cannot find anything about torch.softmax. What is the difference among torch.nn.Softmax, torch.nn.funtional.softmax, torch.softmax and …

If one takes a look in the source code of torch.distributions.Normal and finds the definition of the log_prob(value) function, one can see that the main part of the calculation is:

Dec 8, 2020 · Yes, NLLLoss takes log-probabilities (log(softmax(x))) as input.Why?. Because if you add a nn.LogSoftmax (or F.log_softmax) as the final layer of your model's output, you can …

Oct 29, 2019 · PyTorch 1.5.1 introduced a torch.autograd.functional.jacobian function. This computes the Jacobians of a function w.r.t. the input tensors.

Nov 15, 2020 · I'm looking for ways to use torch.logsumexp with two tensors with different shapes. I currently implemented logsumexp manually like this below: import torch # t1.shape = (4,1,3) …

Mar 28, 2020 · step1 = Pss-(k*Pvv) step2 = step1*s step3 = torch.exp(step2) step4 = torch.log10(1+step3) step5 = step4/s #or equivalently # train_curve = …

Feb 1, 2024 · I'm new to torch and I try to speedup some mathematical computations using torch.compile. I'm using pytorch: pytorch 2.2.0 py3.11_cpu_0 from Anaconda. The end goal is …