Jul 10, 2017 · Bottom line: In layman's terms, one could think of cross-entropy as the distance between two probability distributions in terms of the amount of information (bits) needed to explain that …

Jul 28, 2020 · The formula for cross entropy loss is this: $$-\sum_iy_i \ln\left (\hat {y}_i\right).$$ My question is, what is the minimum and maximum value for cross entropy loss, given that there is a …

Nov 20, 2018 · Cross-entropy with one-hot encoding implies that the target vector is all $0$, except for one $1$. So all of the zero entries are ignored and only the entry with $1$ is used for updates. You …

Nov 22, 2018 · However, this terminology is ambiguous because different probability distributions have different cross-entropy loss functions. So, in general, how does one move from an assumed …

However, the MSE loss captures this change by increasing too. So my question is why do we need cross-entropy loss? MSE loss seems to work fine. Or is it to do with the fact that the cross-entropy …

Feb 15, 2019 · loss-functions extreme-value cross-entropy Share Improve this question edited Feb 15, 2019 at 14:09

Jul 15, 2020 · I usually see a discussion of the following loss functions in the context of the following types of problems: Cross entropy loss (KL divergence) for classification problems MSE for regression …

Oct 8, 2018 · An easy way to remember this is to internalize the gradient of the cross-entropy with respect to network parameters, which is famously $t_i - o_i$. The last slide does this correctly.

Mar 6, 2018 · Cross-Entropy is one of the methods used to find how good is the predicted probability models. The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple …

Aug 18, 2018 · In the pytorch docs, it says for cross entropy loss: input has to be a Tensor of size (minibatch, C) Does this mean that for binary (0,1) prediction, the input must be converted into an …