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开源软件名称:deeplearning4j开源软件地址:https://gitee.com/mirrors/deeplearning4j开源软件介绍:
The Eclipse Deeplearning4J (DL4J) ecosystem is a set of projects intended to support all the needs of a JVM based deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks. Because Deeplearning4J runs on the JVM you can use it with a wide variety of JVM based languages other than Java, like Scala, Kotlin, Clojure and many more. The DL4J stack comprises of:
All projects in the DL4J ecosystem support Windows, Linux and macOS. Hardware support includes CUDA GPUs (10.0, 10.1, 10.2 except OSX), x86 CPU (x86_64, avx2, avx512), ARM CPU (arm, arm64, armhf) and PowerPC (ppc64le). Community SupportFor support for the project, please go over to https://community.konduit.ai/ Using Eclipse Deeplearning4J in your projectDeeplearning4J has quite a few dependencies. For this reason we only support usage with a build tool. <dependencies> <dependency> <groupId>org.deeplearning4j</groupId> <artifactId>deeplearning4j-core</artifactId> <version>1.0.0-M1.1</version> </dependency> <dependency> <groupId>org.nd4j</groupId> <artifactId>nd4j-native-platform</artifactId> <version>1.0.0-M1.1</version> </dependency></dependencies> 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. A taste of codeDeeplearning4J offers a very high level API for defining even complex neural networks. The following example code showsyou how LeNet, a convolutional neural network, is defined in DL4J. MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(seed) .l2(0.0005) .weightInit(WeightInit.XAVIER) .updater(new Adam(1e-3)) .list() .layer(new ConvolutionLayer.Builder(5, 5) .stride(1,1) .nOut(20) .activation(Activation.IDENTITY) .build()) .layer(new SubsamplingLayer.Builder(PoolingType.MAX) .kernelSize(2,2) .stride(2,2) .build()) .layer(new ConvolutionLayer.Builder(5, 5) .stride(1,1) .nOut(50) .activation(Activation.IDENTITY) .build()) .layer(new SubsamplingLayer.Builder(PoolingType.MAX) .kernelSize(2,2) .stride(2,2) .build()) .layer(new DenseLayer.Builder().activation(Activation.RELU) .nOut(500).build()) .layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .nOut(outputNum) .activation(Activation.SOFTMAX) .build()) .setInputType(InputType.convolutionalFlat(28,28,1)) .build(); Documentation, Guides and TutorialsYou can find the official documentation for Deeplearning4J and the other libraries of its ecosystem at http://deeplearning4j.konduit.ai/. Want some examples?We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples Building from sourceIt is preferred to use the official pre-compiled releases (see above). But if you want to build from source, first take a look at the prerequisites for building from source here: https://deeplearning4j.konduit.ai/multi-project/how-to-guides/build-from-source. To build everything, we can use commands like ./change-cuda-versions.sh x.x./change-scala-versions.sh 2.xx./change-spark-versions.sh xmvn clean install -Dmaven.test.skip -Dlibnd4j.cuda=x.x -Dlibnd4j.compute=xx or mvn -B -V -U clean install -pl -Dlibnd4j.platform=linux-x86_64 -Dlibnd4j.chip=cuda -Dlibnd4j.cuda=11.0 -Dlibnd4j.compute=<your GPU CC> -Djavacpp.platform=linux-x86_64 -Dmaven.test.skip=true An example of GPU "CC" or compute capability is 61 for Titan X Pascal. LicenseCommercial SupportDeeplearning4J is actively developed by the team at Konduit K.K.. [If you need any commercial support feel free to reach out to us. at [email protected] |
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