NEUNet | NEU IPGW Manager -

by xfangfang Java Version: 3.3 License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | NEUNet Summary

NEUNet is a Java library. NEUNet has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

NEU IPGW Manager (Android)

Support

Quality

Security

License

Reuse

Support

NEUNet has a low active ecosystem.

It has 7 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 12 months.

NEUNet has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of NEUNet is 3.3

Quality

NEUNet has no bugs reported.

Security

NEUNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

NEUNet does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

NEUNet releases are available to install and integrate.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed NEUNet and discovered the below as its top functions. This is intended to give you an instant insight into NEUNet implemented functionality, and help decide if they suit your requirements.

Initializes the Activity
Gets the state map
Convert hex string to byte array
Get serializable object
Set the status bar
Initialize user data
Initialize the shadow
Get raw size
Parse data
Create table data
Sends an email
Open tv
Initializes the instance
Open AliPay
Find product information
Region Drawable
convert time to unix time string
Login
Checks if the user is valid
Intercept touch event
Initializes the UI
Called when an options item is selected
Creates the web view
Initializes the table view
Add shortcut to the view
Find the online devices

Get all kandi verified functions for this library.

NEUNet Key Features

No Key Features are available at this moment for NEUNet.

NEUNet Examples and Code Snippets

No Code Snippets are available at this moment for NEUNet.

Community Discussions

Trending Discussions on NEUNet

Why does my neural network have extremely low weights after a few epochs?

QUESTION

Why does my neural network have extremely low weights after a few epochs?

Asked 2020-Aug-15 at 20:19

I just started to learn about neural network and this is my first one. The problem is that the more data I have, the lower the weight become after 2-3 epochs which is unusual and this cause my NN to learn nothing.

To repodruce In DataSet class, search for function CreateData and change nbofexample to something like 20, you'll see if you print the weights that they are in a normal range (evenly spaced between -1 and 1) but then if you set the nbofexample to something like 200, then after only 2 or 3 epochs, most of the weigths of the last layer will be extremely close from 0 and they will stay in that zone for the rest of the training. Obviously, this cause the NN to fail.

By the way, my NN is basically analyzing arrays of number between 0 and 9 divided by 10 as a normalization to check if the array is sorted. In the code below I put a lot of comments the code can be easily understand.

There's is probably an easy fix but I just don't get it :(

Here is the complete code if you want try it: (it's in python btw)

...

ANSWER

Answered 2020-Aug-15 at 20:19

Neural Networks can suffer from something known as the Vanishing Gradient Problem, caused by the more classical activations like Sigmoid or Tanh.

In laymen terms, basically activations like Sigmoid and Tanh really squeeze the inputs, right? For example, sigmoid(10) and sigmoid(100) are .9999 and 1 respectively. Even though the inputs have changed so much, the outputs have barely changed - the function is effectively constant at this point. And where a function is almost constant, its derivative tends to zero (or a very small value). These very small derivatives/gradients multiply with each other and become effectively zero, preventing your model from learning anything at all - your weights get stuck and stop updating.

I suggest you do some further reading on this topic at your own time. Among several solutions, one way to solve this is to use a different activation, like ReLU.

Source https://stackoverflow.com/questions/63430313

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install NEUNet

You can download it from GitHub.
You can use NEUNet like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the NEUNet component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: