kandi X-RAY | deeplearning4j Summary
kandi X-RAY | deeplearning4j Summary
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.
Top functions reviewed by kandi - BETA
- 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 .
deeplearning4j Key Features
deeplearning4j Examples and Code Snippets
Trending Discussions on deeplearning4j
I am trying to import a KERAS file in a docker container with a program that has the following java code sample:...
ANSWERAnswered 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:
glibc version with hdf5. Keras import uses hdf5 underneath the covers which means c code.
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
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: https://repo1.maven.org/maven2/org/nd4j/nd4j-native/1.0.0-M1.1/
Older glibcs need to use linux-x86_64-compat as a migration path
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?
ANSWERAnswered 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.
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:...
ANSWERAnswered 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:
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?...
ANSWERAnswered 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:
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.
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.
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.
I am studying a text generation example https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/advanced/modelling/charmodelling/generatetext/GenerateTxtCharCompGraphModel.java. 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:...
ANSWERAnswered 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):
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:...
ANSWERAnswered 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.
I'm studing Deeplearning4j (ver. 1.0.0-M1.1) for building neural networks.
I use IrisClassifier from Deeplearning4j as an example....
ANSWERAnswered 2021-Aug-29 at 20:34
So, adding this code solved my problem:
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:
ANSWERAnswered 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
Sometimes the best way to solve a problem is to remove it instead: I stopped trying to get log4j to function and went with
ANSWERAnswered 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: https://github.com/eclipse/deeplearning4j-examples
I'm trying tu buld projetct with DL4J .
I did the following steps from ths website : https://deeplearning4j.konduit.ai/getting-started/quickstart
When I do this command
mvn clean install
I get the following error :
ANSWERAnswered 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:
- Clone the repository
No vulnerabilities reported
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 maven.apache.org. For Gradle installation, please refer gradle.org .
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