deeplearning4j | training deep learning models using the JVM | Machine Learning library

 by   eclipse Java Version: Current License: Apache-2.0

kandi X-RAY | deeplearning4j Summary

kandi X-RAY | deeplearning4j Summary

deeplearning4j is a Java library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Spark applications. deeplearning4j has build file available, it has a Permissive License and it has high support. However deeplearning4j has 1424 bugs and it has 3 vulnerabilities. You can download it from GitHub, Maven.

Deeplearning4J has quite a few dependencies. For this reason we only support usage with a build tool. Add these dependencies to your pom.xml file to use Deeplearning4J with the CPU backend. A full standalone project example is available in the example repository, if you want to start a new Maven project from scratch.

            kandi-support Support

              deeplearning4j has a highly active ecosystem.
              It has 12434 star(s) with 4926 fork(s). There are 780 watchers for this library.
              It had no major release in the last 6 months.
              There are 762 open issues and 4831 have been closed. On average issues are closed in 59 days. There are 3 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of deeplearning4j is current.

            kandi-Quality Quality

              deeplearning4j has 1424 bugs (107 blocker, 60 critical, 825 major, 432 minor) and 33169 code smells.

            kandi-Security Security

              deeplearning4j has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              deeplearning4j code analysis shows 3 unresolved vulnerabilities (3 blocker, 0 critical, 0 major, 0 minor).
              There are 525 security hotspots that need review.

            kandi-License License

              deeplearning4j is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              deeplearning4j releases are not available. You will need to build from source code and install.
              Deployable package is available in Maven.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              deeplearning4j saves you 1371853 person hours of effort in developing the same functionality from scratch.
              It has 601973 lines of code, 61116 functions and 6123 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed deeplearning4j and discovered the below as its top functions. This is intended to give you an instant insight into deeplearning4j implemented functionality, and help decide if they suit your requirements.
            • Extract op descriptors .
            • Process a class and add the op names to the result set .
            • Check for workspace namespaces .
            • Generate a tensor with the shape matrices for each dimension
            • Create a grad function based on the input variables .
            • Helper function to perform the activation of the neural network
            • Get the output of all layers inside the layer .
            • Helper method to compute backprop gradients for training .
            • Helper method to get the outputs for a particular output frame
            • Gets the system info .
            Get all kandi verified functions for this library.

            deeplearning4j Key Features

            No Key Features are available at this moment for deeplearning4j.

            deeplearning4j Examples and Code Snippets

            No Code Snippets are available at this moment for deeplearning4j.

            Community Discussions


            Could not initialize class org.nd4j.linalg.factory.Nd4j in docker container
            Asked 2022-Feb-09 at 11:28

            I am trying to import a KERAS file in a docker container with a program that has the following java code sample:



            Answered 2022-Jan-11 at 11:59

            This still doesn't show a cause. NoClassDeffFoundErrors are usually related to a clashing dependencies. You could have different versions of dl4j/nd4j on your classpath but I doubt it. Most of the time this is the side effect of a native dependency crash somehow.

            Of note here:

            I wouldn't recommend running the keras converter (or any model import process) in line. I would recommend converting the models separately. This is mainly for performance reasons

            Whatever your problem is there are usually a few:

            1. glibc version with hdf5. Keras import uses hdf5 underneath the covers which means c code.

            2. Nd4j native dependency crash: this is also usually glibc related. We load nd4j in to memory to create and set native arrays (which means more java calling in to c++) that then can trigger a crash depending on what OS you're running on

            3. Another hdf5 error: this could be an invalid model or some hdf5 version error.

            In any case, we would need more information before we can help you. Whatever you're reporting here isn't enough. Could you mention your docker container OS and what version of dl4j/nd4j is bundled here?

            Edit: I see it's oracle linux 7 which is effectively RHEL/Centos. If you're using docker I would recommend a newer image maybe.

            Beyond that if it is an nd4j related crash (still not verifiable from your stack trace) if you are using the latest version you might be seeing a crash due to glibc version.

            If so there was a recent update to the nd4j classifiers you can find here:

            Older glibcs need to use linux-x86_64-compat as a migration path



            Different predictions for the same data
            Asked 2021-Dec-08 at 10:47

            I use Deeplearning4j to classify equipment names. I marked ~ 50,000 items with 495 classes, and I use this data to train the neural network.

            That is, as input, I provide a set of vectors (50,000) consisting of 0 and 1, and the expected class for each vector (0 to 494).

            I use the IrisClassifier example as a basis for the code.

            I saved the trained model to a file, and now I can use it to predict the class of equipment.

            As an example, I tried to use for prediction the same data (50,000 items) that I used for training, and compare the prediction with my markup of this data.

            The result turned out to be very good, the error of the neural network is ~ 1%.

            After that, I tried to use for prediction the first 100 vectors from these 50,000 records, and removed the rest 49900.

            And for these 100 vectors, the prediction is different when compared with the prediction for the same 100 vectors in the composition of 50,000.

            That is, the less data we provide to the trained model, the greater the prediction error.

            Even for exactly the same vectors.

            Why does this happen?

            My code.




            Answered 2021-Dec-08 at 10:12

            You need to use the same normalizer data for both training and prediction. Otherwise it will use wrong statistics when transforming your data.

            The way you are currently doing it, results in data that looks very different from the training data, that is why you get such a different result.



            DL4J-Image become too bright
            Asked 2021-Oct-14 at 08:21

            Currently, I've been asked to write CNN code using DL4J using YOLOv2 architecture. But the problem is after the model has complete, I do a simple GUI for validation testing then the image shown is too bright and sometimes the image can be displayed. Im not sure where does this problem comes from whether at earliest stage of training or else. Here, I attach the code that I have for now. For Iterator:



            Answered 2021-Oct-14 at 08:01

            CanvasFrame tries to do gamma correction by default because it's typically needed by cameras used for CV, but cheap webcams usually output gamma corrected images, so make sure to let CanvasFrame know about it this way:



            Reduce DeepLearning4j dependency size of exported jar
            Asked 2021-Sep-26 at 22:25

            In my application, I would like to use Deeplearning4j. Deeplearning4j has over 120mb of dependencies, which is a lot considering my own code is only 0.5mb.

            Is it possible to reduce the dependencies required? Would loading an already-trained network allow me to ship my application with a smaller file size?



            Answered 2021-Sep-26 at 22:25

            There are many ways to reduce the size of your jar depending on what your use case is. We cover this more recently in our docs, but I'll summarize some things to try here:

            1. DL4j is heavily based on javacpp. You can add -Djavacpp.platform=$YOUR_PLATFORM (linux-x86_64, windows-x86_64,..) to your build to reduce the number of native dependencies in there.

            2. If you are using deeplearning4j-core, that includes a lot of extra dependencies you may not need. In this case, you may only need deeplearning4j-nn for the configuration. The same goes for if you are using only samediff, you do not need the dl4j apis. I don't know enough about your use case to confirm what you do and don't need though.

            3. If you are deploying on an embedded platform, we also have the ability to reduce the number of supported operations and data types now as well. This feature is mainly for advanced users right now (involves building from source) but if you think that could also be applicable despite the first 2, please do confirm and I can try to clarify that a bit.



            What is the output of the dl4j lstm neural network?
            Asked 2021-Sep-24 at 12:58

            I am studying a text generation example The output of lstm network is a probability distribution, as I understand it, this is an double array, where each value shows the probability of the character corresponding to the index in the array. So I cannot understand the following code where we get the character index from the distribution:



            Answered 2021-Sep-24 at 12:58

            This function samples from the distribution, instead of simply returning the most probable character class.

            That also means that you aren't getting the most likely character, instead, you are getting a random character with the probability that the given probability distribution defines.

            This works by first getting a random value between 0 and 1 from a uniform distribution (rng.nextDouble()) and then finding where that value falls in the given distribution.

            You can imagine it to be something like this (if your had only a to f in your alphabet):



            Deeplearning4j - how to iterate multiple DataSets for large data?
            Asked 2021-Sep-22 at 10:48

            I'm studying Deeplearning4j (ver. 1.0.0-M1.1) for building neural networks.

            I use IrisClassifier from Deeplearning4j as an example, it works fine:



            Answered 2021-Sep-22 at 10:48

            Firstly, always use Nd4j.create(..) for ndarrays. Never use the implementation. That allows you to safely create ndarrays that will work whether you use cpus or gpus.

            2nd: Always use the RecordReaderDataSetIterator's builder rather than the constructor. It's very long and error prone.

            That is why we made the builder in the first place.

            Your NullPointer actually isn't coming from where you think it is. it's due to how you're creating the ndarray. There's no data type or anything so it can't know what to expect. Nd4j.create(..) will properly setup the ndarray for you.

            Beyond that you are doing things the right way. The record reader handles the batching for you.



            Deeplearning4j - how to use saved model?
            Asked 2021-Aug-29 at 20:34

            I'm studing Deeplearning4j (ver. 1.0.0-M1.1) for building neural networks.

            I use IrisClassifier from Deeplearning4j as an example.



            Answered 2021-Aug-29 at 20:34

            So, adding this code solved my problem:



            Maven: NDJ4 Has Backend, Error Says It Doesn't
            Asked 2021-Aug-20 at 18:11

            thanks for your time today.

            I'm getting an error for missing logger backend but I have log4j installed. I've listed the relevant errors and pom dependecies below:

            pom.xml segments:



            Answered 2021-Aug-20 at 18:11

            Well, I couldn't work out how to make log4j function. I tried what was suggested in the comments, such as including slf4j-log4j12, or nd4j-x86.

            Sometimes the best way to solve a problem is to remove it instead: I stopped trying to get log4j to function and went with nd4j-native.



            Ask About The "default" Size of Vocabulary in Word2Vec in Deeplearning4j Library
            Asked 2021-Jun-29 at 15:31

            I am currently learning about this library: Word2Vec from Deeplearning4j (Homepage, Github)

            Following is the example usage of the method:



            Answered 2021-Jun-29 at 15:31

            By default there is no limit. That means it will add all words it finds to the vocabulary.

            Also note, the examples you linked to are over 4 years old. I suggest you use the official examples:



            BUILD FAILURE while installing DL4J
            Asked 2021-Jun-08 at 21:36

            I'm trying tu buld projetct with DL4J .

            I did the following steps from ths website :

            When I do this command mvn clean install

            I get the following error :



            Answered 2021-Jun-08 at 21:36

            It looks like you modified something? Sorry if I'm not quite following what you did, but that error message indicates a missing pom.xml. The specific steps you follow are:

            1. Clone the repository


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


            No vulnerabilities reported

            Install deeplearning4j

            You can download it from GitHub, Maven.
            You can use deeplearning4j 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 deeplearning4j 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 support for the project, please go over to
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