The softmax function, also known as softargmax: 184 or normalized exponential function,: 198 converts a vector of K real numbers into a probability...
31 KB (4,762 words) - 00:25, 16 October 2024
classification the softmax activation is often used. The following table compares the properties of several activation functions that are functions of one fold...
24 KB (1,921 words) - 15:33, 13 October 2024
immediately generalizes to more alternatives as the softmax function, which is a vector-valued function whose i-th coordinate is e x i / ∑ i = 0 n e x i...
53 KB (7,562 words) - 07:05, 23 October 2024
activation function in data analysis Softmax function – Smooth approximation of one-hot arg max Swish function – Mathematical activation function in data...
13 KB (1,611 words) - 19:52, 22 October 2024
Multinomial logistic regression (redirect from Softmax regression)
known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier...
31 KB (5,259 words) - 01:41, 18 October 2024
Rectifier (neural networks) (redirect from Mish function)
the softmax; the softmax with the first argument set to zero is the multivariable generalization of the logistic function. Both LogSumExp and softmax are...
17 KB (2,274 words) - 15:43, 21 October 2024
gradient of LogSumExp is the softmax function. The convex conjugate of LogSumExp is the negative entropy. The LSE function is often encountered when the...
7 KB (1,152 words) - 17:21, 23 June 2024
x = 0. {\displaystyle x=0.} LogSumExp function, also called softmax function, is a convex function. The function − log det ( X ) {\displaystyle -\log...
35 KB (5,852 words) - 07:11, 5 September 2024
Capsule neural network (section Procedure softmax)
_{j}\\12:\quad \mathbf {return} ~\mathbf {v} _{j}\\\end{array}}} At line 8, the softmax function can be replaced by any type of winner-take-all network. Biologically...
28 KB (4,008 words) - 20:53, 9 September 2024
matrix. The softmax function is permutation equivariant in the sense that: softmax ( A D B ) = A softmax ( D ) B {\displaystyle {\text{softmax}}(\mathbf...
48 KB (5,266 words) - 19:04, 1 November 2024