2016年9月6日 · The sigmoid function is used for the two-class logistic regression, whereas the softmax function is used for the multiclass logistic regression (a.k.a. MaxEnt, multinomial logistic regression, softmax Regression, Maximum Entropy Classifier).

2022年6月27日 · However, "softmax" can also be applied to multi-class classification, whereas "sigmoid" is only for binary classification. "sigmoid" predicts a value between 0 and 1. Graphically it looks like this: Softmax predicts a value between 0 and 1 for each output node, all outputs normalized so that they sum to 1.

The sigmoid and the softmax function have different purposes. For a detailed explanation of when to use sigmoid vs. softmax in neural network design, you can look at this article: "Classification: Sigmoid vs. Softmax." Short summary:

2017年1月9日 · If the softmax still seems like an arbitrary choice to you, you can take a look at the justification for using the sigmoid in logistic regression: Why sigmoid function instead of anything else? The softmax is the generalization of the sigmoid for …

2020年12月11日 · Today, especially in CNNs other activation functions, also only partially linear activation functions (like relu) is being preferred over sigmoid function. There are many different functions, just to name some: sigmoid, tanh, relu, prelu, elu ,maxout, max, argmax, softmax etc. Now let's only compare sigmoid, relu/maxout and softmax:

2022年12月5日 · In summary, using softmax or sigmoid in the last layer depends on the problem you're working on, along with the associated loss function and other intricacies in your pipeline/software. In practice, if you have a multi-class problem, chances are you'll be using softmax. If you have one-class/binary problem, sigmoid or softmax are possibilities.

2019年10月24日 · The sigmoid (i.e. logistic) function is scalar, but when described as equivalent to the binary case of the softmax it is interpreted as a 2d function whose arguments have been pre-scaled by (and hence the first argument is always fixed at 0). The second binary output is calculated post-hoc by subtracting the logistic's output from 1.

2017年8月21日 · Sigmoid can be viewed as a mapping between the real numbers space and a probability space. Notice that: Sigmoid(-infinity) = 0 Sigmoid(0) = 0.5 Sigmoid(+infinity) = 1 So if the real number, output of your network, is very low, the sigmoid will decide the probability of "Class 0" is close to 0, and decide "Class 1"

2019年11月14日 · Sigmoid: Softmax: When you use a softmax, basically you get a probability of each class, (join distribution and a multinomial likelihood) whose sum is bound to be one. In the case of softmax, increasing the output value of one class makes the others go down (because sum=1 always).

2021年4月19日 · In that case, softmax would add the constraint that they need to add to one as opposed to the more relaxed constraint that they both need to be between 0 and 1 imposed by sigmoid. Softmax with 2 outputs should be equivalent to sigmoid with 1 output. Softmax with 1 output would always output 1 which could lead to a 50% accuracy bug.