by   Elco- Java Version: Current License: MIT

kandi X-RAY | SimpleNN Summary

kandi X-RAY | SimpleNN Summary

SimpleNN is a Java library. SimpleNN has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However SimpleNN build file is not available. You can download it from GitHub.


            kandi-support Support

              SimpleNN has a low active ecosystem.
              It has 83 star(s) with 34 fork(s). There are 13 watchers for this library.
              It had no major release in the last 6 months.
              There are 1 open issues and 5 have been closed. On average issues are closed in 0 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of SimpleNN is current.

            kandi-Quality Quality

              SimpleNN has 0 bugs and 0 code smells.

            kandi-Security Security

              SimpleNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              SimpleNN code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              SimpleNN is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              SimpleNN releases are not available. You will need to build from source code and install.
              SimpleNN has no build file. You will be need to create the build yourself to build the component from source.
              SimpleNN saves you 152 person hours of effort in developing the same functionality from scratch.
              It has 380 lines of code, 29 functions and 6 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed SimpleNN and discovered the below as its top functions. This is intended to give you an instant insight into SimpleNN implemented functionality, and help decide if they suit your requirements.
            • Renders the points
            • Apply backpropagation on the targets
            • Feeds the input network
            • Main entry point
            • Start a thread
            • Train digits matrix
            • Paint the mouse
            • Called when a mouse button is pressed
            • Called when mouse is pressed
            • Handles mouse moved
            • Called when the mouse is pressed
            • Runs the background thread
            • Called when key is pressed
            Get all kandi verified functions for this library.

            SimpleNN Key Features

            No Key Features are available at this moment for SimpleNN.

            SimpleNN Examples and Code Snippets

            No Code Snippets are available at this moment for SimpleNN.

            Community Discussions


            How can I use the LBFGS optimizer with pytorch ignite?
            Asked 2019-Sep-23 at 09:50

            I started using Ignite recently and i found it very interesting. I would like to train a model using as an optimizer the LBFGS algorithm from the torch.optim module.

            This is my code:



            Answered 2019-Sep-23 at 09:50

            The way to do it is like this:



            How to visualize our network in Matconvnet?
            Asked 2018-Mar-17 at 12:41

            Is there any function to visualize the network like this?

            I want to visualize both dagnn and simplenn architectures.



            Answered 2018-Mar-17 at 11:19

            Firstly, install Graph Visualization Software (Grphviz) by this link. Then as this describes, you should utilize dot format to take advantage of Grphviz. For instance if you wanna to plot the whole dag network follow as below :



            Can't replicate a matconvnet CNN architecture in Keras
            Asked 2017-Jul-04 at 21:08

            I have the following architecture of a Convolutional Neural Network in matconvnet which I use to train on my own data:



            Answered 2017-Jul-04 at 21:08

            In your MatConvNet version, you use SGD with momentum.

            In Keras, you use rmsprop

            With a different learning rule you should try different learning rates. Also sometimes momentum is helpful when training a CNN.

            Could you try the SGD+momentum in Keras and let me know what happens?

            Another thing that might be different is that the initialization. for example in MatConvNet you use gaussian initialization with f= 0.0125 as the standard deviation. In Keras I'm not sure about the default initialization.

            In general, if you don't use batch normalization, the network is prone to many numerical issues. If you use batch normalization in both networks, I bet the results would be similar. Is there any reason you don't want to use batch normalization?


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


            No vulnerabilities reported

            Install SimpleNN

            You can download it from GitHub.
            You can use SimpleNN 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 SimpleNN 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 For Gradle installation, please refer .


            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 .
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            gh repo clone Elco-/SimpleNN

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