Activation function generation method of neural network model
A technology of neural network model and activation function, applied in the direction of biological neural network model, neural architecture, etc., to achieve the effect of improving accuracy and improving the ability to learn nonlinear changes
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Example Embodiment
[0048] Example 1
[0049] This embodiment discloses a method for generating an activation function of a neural network model, such as figure 1 As shown, the steps are as follows:
[0050] S1. Select a number of different basic activation functions; in this step, generally choose 2 to 6 different basic activation functions; in this embodiment, the basic activation function can be Sigmoid function, Tanh function, ReLU function, PReLU function, PELU function or RReLU function. The multiple different basic activation functions in this embodiment are several of the above functions.
[0051] S2. Combine the multiple different basic activation functions selected in step S1 as the activation function of the neural network model. In this step, multiple different basic activation functions are combined to obtain the activation function f(x) of the neural network model in the following way:
[0052]
[0053] p n = 1-(p 1 +,p 2 +,…,+p n-1 );
[0054] Where p 1 ,p 2 ,...,P n Is the combination co...
Example Embodiment
[0064] Example 2
[0065] This embodiment discloses a method for generating an activation function of a neural network model, and the steps are as follows:
[0066] S1. Select a number of different basic activation functions; in this step, generally choose 2 to 6 different basic activation functions; in this embodiment, the basic activation function can be Sigmoid function, Tanh function, ReLU function, PReLU function, PELU function or RReLU function.
[0067] S2. Combine the multiple different basic activation functions selected in step S1 as the activation function of the neural network model. In this step, multiple different basic activation functions are combined to obtain the activation function f(x) of the neural network model in the following way:
[0068]
[0069] σ n (w n x) = 1-(σ 1 (w 1 x)+σ 2 (w 2 x)+,…+σ n-1 (w n-1 x));
[0070] Where σ i (w i x), i=1,2,...n represents the input as w i Sigmoid function of x, w 1 ,w 2 ,...,W n Is the combination coefficient of each basic a...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap