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Integration of Deeplearning4j with

Apache Apex

Priyanka Gugale

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Contributors

Ambarish Pande

Priyanka Kekane Devraj Baheti

Priya Heda

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•What is Deep Learning?•Deeplearning4j•Using deeplearning4j with Apex•Architecture•Demo screenshots•Challenges

Agenda

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•Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data.•Deep learning eliminates the need for feature engineering.•Effectively works on unsupervised data.

Deep Learning

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• Deep learning uses deep neural networks to model the high level abstractions in data.

• Deep neural networks are neural networks with more than one hidden layer.

Deep Learning

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• Colorization of black and white images•Object Classification in Photographs•Automatic Game playing•Image Caption Generation•Handwriting Recognition•Automatic Machine Translation•Adding sound to silent movies

Applications of Deep Learning

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•An Open Source Deep Learning library( released under Apache 2.0 license)

•DL4J is Distributed

•Written for Java and Scala

•Integrated with Hadoop

•Skymind is its commercial support arm

•The Neural Net platform Dl4j provides various neural networks like Long Short-Term Memory units, Convolutional Neural Networks for image processing, Deep AutoEncoder, Restricted Boltzmann Machine, Recurrent Nets, Denoising Autoencoders etc.

Deeplearning4j

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Deeplearning4j

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• Training Deep Learning models on single processor is extremely slow.

• Dl4j works with multi CPU and multi GPU systems.

• This integration will enhance the implementation of deep learning models in distributed and stream processing environments.

Using deeplearning4j with Apex

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•We achieve distributed training of neural networks using Data Parallelism.

•In data parallelism, different machines have a complete copy of the model, each machine simply gets a different portion of data.

Architecture

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●We use a method called Parameter Averaging to combine and synchronize models trained on different machines.

Architecture

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Apex Application DAG

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• The Iris flower data set or Fisher's Iris data set is a multivariate data set.

• The data set consists of 150 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres.

Demo Dataset

Iris versicolor Iris setosa Iris virginica

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DEMO

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• Had to change default packaging of Apex Application• We used Maven Shade plugin for packaging the app.

•Certain components of Nd4j are incompatible with KryoSerializer.• We are using Java Serializer for those components.

Challenges

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Thank You!