- Log Softmax ... Log Softmax
- 1. Introduction. Pytorch provides a few options for mutli-GPU/multi-CPU computing or in other words distributed computing. While this is unsurprising for Deep learning, what is pleasantly surprising is the support for general purpose low-level distributed or parallel computing.

- If we start from the softmax output P - this is one probability distribution . The other probability distribution is the "correct" classification output, usually denoted by Y . This is a one-hot encoded vector of size T , where all elements except one are 0.0, and one element is 1.0 - this element marks the correct class for the data being classified.
- It turns out that the Softmax is actually the generalization of the Sigmoid function, which is a Bernoulli (output 0 or 1) output unit: $ \begin{equation} [1+\text{exp}(-z)]^{-1} \end{equation} $ But where does the Sigmoid function come from, you might ask.

- Mar 19, 2019 · This is a topic for people interested in calibrating their networks to get better probability estimates in classification. TL;DR: “Neural networks tend to output overconfident probabilities. Temperature scaling is a post-processing method that fixes it. Can we do temperature scaling within the fastAI framework?” Motivation Deep network are often overconfident in their predictions ...
- Oct 23, 2019 · Many authors use the term “cross-entropy” to identify specifically the negative log-likelihood of a Bernoulli or softmax distribution, but that is a misnomer. Any loss consisting of a negative log-likelihood is a cross-entropy between the empirical distribution defined by the training set and the probability distribution defined by model.
- Finally, we will perform normalization. Note that inceptionv3 model of pytorch uses pre-trained weights from Google and they expect inputs with pixel values in between -1 to 1. PyTorch performs this ops internally and it expects inputs normalized with below given mean and standard deviation(for the sake of uniformity).