convnetjs | Deep Learning in Javascript | Machine Learning library

 by   karpathy JavaScript Version: 2014.08.31 License: MIT

kandi X-RAY | convnetjs Summary

kandi X-RAY | convnetjs Summary

convnetjs is a JavaScript library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. convnetjs has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can install using 'npm i convnet' or download it from GitHub, npm.

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

            kandi-support Support

              convnetjs has a medium active ecosystem.
              It has 10587 star(s) with 2058 fork(s). There are 599 watchers for this library.
              It had no major release in the last 12 months.
              There are 51 open issues and 24 have been closed. On average issues are closed in 33 days. There are 24 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of convnetjs is 2014.08.31

            kandi-Quality Quality

              convnetjs has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              convnetjs 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

              convnetjs releases are available to install and integrate.
              Deployable package is available in npm.

            Top functions reviewed by kandi - BETA

            kandi has reviewed convnetjs and discovered the below as its top functions. This is intended to give you an instant insight into convnetjs implemented functionality, and help decide if they suit your requirements.
            • Draw the grid .
            • Creates a new filter window .
            • resize image
            • helper function to convert data to array
            • Convenience convolution logic .
            • Convenience convolution logic
            • Step 1 .
            • Shows an effect .
            • resize canvas
            • Resizes the image .
            Get all kandi verified functions for this library.

            convnetjs Key Features

            No Key Features are available at this moment for convnetjs.

            convnetjs Examples and Code Snippets

            Im not getting correct accuracy for convolutional neural network
            Lines of Code : 5dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            convnet = fully_connected(convnet, 1, activation='sigmoid')
            convnet = regression(convnet, optimizer='adam', learning_rate=learningRate, loss='binary_crossentropy', name='targets')
            convnet = dropout(convnet, 0.7)
            How to train mix of image and data in CNN using ImageAugmentation in TFlearn
            Lines of Code : 45dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import tensorflow as tf
            import tflearn
            from tflearn.layers.conv import conv_2d, max_pool_2d
            from tflearn.layers.core import input_data, dropout, fully_connected
            from tflearn.layers.estimator import regression
            from tflearn.layers.merge_ops 

            Community Discussions


            Detect if object was created with a custom prototype design pattern?
            Asked 2019-Aug-13 at 09:04

            I'm working on logic that detects any stateful variables that can be safely saved and restored via JSON as the storage vector.

            Part of this means detecting types that are "safe" to dump and restore, which is easy for:

            • numbers, strings and booleans (via typeof)
            • Array elements (via instanceof Array combined with ^ in iterated elements)
            • ES6 Class instances (via typeof object and

            There is one type I'm struggling with though. It's the one created from calling:

            var nn = new convnetjs.Net();

            Which comes from this:

            What's interesting is that Chrome dev tools detects this as a "Net" object:

            This is what you'll see if you inspect the nn var shown above.

            Here's what I've tried:

            • nn instanceof Object === true

            • Object.getPrototypeOf(nn) - interestingly, this exposes the functions assigned to Net.prototype in the link above, line 12 onwards. Seemed like a lead.

            • Object.getPrototypeOf(nn).toString() == [object Object]

            • Object.getPrototypeOf(nn) instanceof Object === true. Makes sense, since it's an object containing custom functions attached to the prototype.

            • Object.getPrototypeOf(nn) === Object

            Would anyone know or have ideas how I could detect this type of object safely? Plain objects are fine, but I don't want to overwrite objects with modified prototypes.



            Answered 2019-Aug-13 at 09:04

            If resorted to using this to verify it this is a plain object or one with custom prototypes:



            Convert Convnet.js neural network model to Keras Tensorflow
            Asked 2019-Mar-27 at 20:19

            I have a neural network model that is created in convnet.js that I have to define using Keras. Does anyone have an idea how can I do that?



            Answered 2019-Mar-27 at 20:16

            From the Convnet.js doc : "your last layer must be a loss layer ('softmax' or 'svm' for classification, or 'regression' for regression)." Also : "Create a regression layer which takes a list of targets (arbitrary numbers, not necessarily a single discrete class label as in softmax/svm) and backprops the L2 Loss."

            It's unclear. I suspect "regression" layer is just another layer of Dense (Fully connected) neurons. The 'regression' word probably refers to linear activity. So, no 'relu' this time ?

            Anyway, it would probably look something like (no sequential mode):



            Reward Function in MIT Deep Traffic Challenge?
            Asked 2018-Jul-20 at 18:12

            I have been playing around with the MIT DeepTraffic Challenge Also watching the lecture and reading the slides

            After getting a General understanding of the architecture I was wondering what exactly the reward function given by the Environment is.

            1. Is it the same as the Input of the gridcell (max. drivable Speed)?
            2. And are they using Reward Clipping, or not?

            I also found this javascript Codebase, which does not really help my understanding either.



            Answered 2018-Jul-20 at 18:12

            The reward is scaled average speed within the interval: [-3, 3].

            The implementation of the deeptraffic environment locates in this file:

            I'm trying to make it readable. Here's the WIP one:


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


            No vulnerabilities reported

            Install convnetjs

            You can install using 'npm i convnet' or download it from GitHub, npm.


            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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