smart data conference: dl4j and datavec
TRANSCRIPT
skymind.io | deeplearning.org | gitter.im/deeplearning4j
DL4J and DataVecBuilding Production Class Deep Learning Workflows for the Enterprise
Josh Patterson / Director Field OrgSmart Data 2017 / San Francisco, CA
Josh Patterson
Director Field Engineering / SkymindCo-Author: O’Reilly’s “Deep Learning: A Practitioners Approach”
Past:
Self-Organizing Mesh Networks / Meta-Heuristics Research
Smartgrid work / TVA + NERC
Principal Field Architect / Cloudera
Topics
• Deep Learning in Production for the Enterprise
• DL4J and DataVec
• Example Workflow: Modeling Sensor Data with RNNs
Deep Learning in Production
In Practice Deep Learning Is…
• Matching Input Data Type to Specific Architecture (Image -> Convolutional Network)
• Higher Parameter Counts and more Processing Power
• Moving from “Feature Engineering” to “Automated Feature Learning”
Perceptron
Classic Multi-Layer Perceptron Architecture
RNN Architectures
Standard supervised learning
Imagecaptioning
Sentiment analysis
Video captioning,Natural language translation
Part of speechtagging
Generative models for text
Visually Understanding RNN Architecture
Evolving the Artificial Neuron for RNNs
Convolutional Network Architecture
Automated Feature Learning
• Hand-coding features has long been standard operation in machine learning
• Deep Learning got smart about matching architectures to data types
• Going forward, hand-coded features will be considered the “technical debt of machine learning”
Quick Usage Guide
• If I have Timeseries or Audio Input: Use a Recurrent Neural Network
• If I have Image input: Use a Convolutional Neural Network
• If I have Video input: Use a hybrid Convolutional + Recurrent Architecture!
• Applications in NLP: Word2Vec + variants
The Challenge of the Fortune 500
Take business problem and translate it into a product-izable solution
• Get data together
• Understand modeling, pull together expertise
Get the right data workflow / infra architecture to production-ize application
• Security
• Integration
“Google is living a few years in the future and sending the rest of us messages”
-- Doug Cutting in 2013
HoweverMost organizations are not built like Google
(and Jeff Dean does not work at your company…)
Anyone building Next-Gen infrastructure has to consider these things
Production Considerations
• Security – even though I can build a model, will IT let me run it?
• Data Warehouse Integration – can I easily run this In the existing IT footprint?
• Speedup – once I need to go faster, how hard is it to speed up modeling?
DL4J and DataVec
DL4J and DataVec
• DL4J – ASF 2.0 Licensed JVM Platform for Enterprise Deep Learning
• DataVec - a tool for machine learning ETL (Extract, Transform, Load) operations.
• Both run natively on Spark on CPU or GPU as Backends
• DL4J Suite certified on CDH5, HDP2.4, and upcoming IBM IOP platform.
ND4J: The Need for SpeedJavaCPP• Auto generate JNI Bindings for C++• Allows for easy maintenance and deployment of C++ binaries in Java
CPU Backends• OpenMP (multithreading within native operations)• OpenBLAS or MKL (BLAS operations)• SIMD-extensions
GPU Backends• DL4J supports Cuda 7.5 (+cuBLAS) at the moment, and will support 8.0 support as soon as it comes
out.• Leverages cuDNN as well
https://github.com/deeplearning4j/dl4j-benchmark
Prepping Data is Time Consuming
http://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#633ea7f67f75
Preparing Data for Modeling is Hard
DL4J Workflow Toolchain
ETL(DataVec)
Vectorization
(DataVec)
Modeling(DL4J)
Evaluation(Arbiter)
Execution Platforms: Spark/Hadoop, Single Machine
ND4J - Linear Algebra Runtime: CPU, GPU
Model Import
• Import models from: Keras
• Keras imports data from: TensorFlow, Caffe, etc
• Example: Import VGGNet16
• Allows integration engineers to work with pre-built models
Coming Soon: DL4J as Keras Backend
• Allows Data Scientist to run python Keras commands and then execute on DL4J
• Sets up ability to run Keras jobs on Spark + Hadoop, securely
• Gives Python Data Scientists a better path to production class environment in the Enterprise
Modeling Sensor Data with RNNs and DL4J
NERC Sensor Data CollectionopenPDC PMU Data Collection circa 2009
• 120 Sensors• 30 samples/second• 4.3B Samples/day• Housed in Hadoop
Classifying UCI Sensor Data: Trends
A – Downward TrendB – CyclicC – NormalD – Upward ShiftE – Upward TrendF – Downward Shift
Loading and Transforming Timeseries Data with DataVec
SequenceRecordReader trainFeatures = new CSVSequenceRecordReader();trainFeatures.initialize(new NumberedFileInputSplit(featuresDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));SequenceRecordReader trainLabels = new CSVSequenceRecordReader();trainLabels.initialize(new NumberedFileInputSplit(labelsDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));
int minibatch = 10;int numLabelClasses = 6;DataSetIterator trainData = new SequenceRecordReaderDataSetIterator(trainFeatures, trainLabels, minibatch, numLabelClasses, false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);
//Normalize the training dataDataNormalization normalizer = new NormalizerStandardize();normalizer.fit(trainData); //Collect training data statistics
trainData.reset();trainData.setPreProcessor(normalizer); //Use previously collected statistics to normalize on-the-fly
Configuring a Recurrent Neural Network with DL4JMultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1) .updater(Updater.NESTEROVS).momentum(0.9).learningRate(0.005) .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue) .gradientNormalizationThreshold(0.5) .list() .layer(0, new GravesLSTM.Builder().activation("tanh").nIn(1).nOut(10).build()) .layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .activation("softmax").nIn(10).nOut(numLabelClasses).build()) .pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);net.init();
Train the Network on Local Machineint nEpochs = 40;String str = "Test set evaluation at epoch %d: Accuracy = %.2f, F1 = %.2f";
for (int i = 0; i < nEpochs; i++) { net.fit(trainData);
//Evaluate on the test set: Evaluation evaluation = net.evaluate(testData); System.out.println(String.format(str, i, evaluation.accuracy(), evaluation.f1()));
testData.reset(); trainData.reset();}
Train the Network on SparkTrainingMaster tm = new ParameterAveragingTrainingMaster(true,executors_count,1,batchSizePerWorker,1,0); //Create Spark multi layer network from configurationSparkDl4jMultiLayer sparkNetwork = new SparkDl4jMultiLayer(sc, net, tm);
int nEpochs = 40;String str = "Test set evaluation at epoch %d: Accuracy = %.2f, F1 = %.2f";
for (int i = 0; i < nEpochs; i++) { sparkNetwork.fit(trainDataRDD);
//Evaluate on the test set: Evaluation evaluation = net.evaluate(testData); System.out.println(String.format(str, i, evaluation.accuracy(), evaluation.f1()));
testData.reset(); trainData.reset();}
Modeling Character Data with RNNs (LSTMs) and DL4JGenerating Beer Reviews
Loading and Vectorizing Data with DataVec
Text: Pours a nice golden…
Category: LagerAppearance: 4.0Taste: 4.5Palate: 3.0Aroma: 3.5
• Characters: one-hot vector over vocabulary
• Categories: one-hot vector over beers• Ratings: score (we actually rescale)
Replicate static inputs at every step
t 1 2 3 4 5 6 7 8 9 10 11 12 …
a 0 0 0 0 0 0 1 0 0 0 0 0 …
c 0 0 0 0 0 0 0 0 0 0 1 0 …
o 0 1 0 0 0 0 0 0 0 0 0 0 …
r 0 0 0 1 0 0 0 0 0 0 0 0 …
0 0 0 0 0 1 0 1 0 0 0 0 …
… … … … … … … … … … … … … …
Lager 1 1 1 1 1 1 1 1 1 1 1 1 …
Porter 0 0 0 0 0 0 0 0 0 0 0 0 …
… … … … … … … … … … … … … …
Appear
4 4 4 4 4 4 4 4 4 4 4 4 …
Palate 3 3 3 3 3 3 3 3 3 3 3 3 …
… … … … … … … … … … … … … …
INDArray input = Nd4j.zeros(new int[]{ reviews.size(), inputColumnCount, maxLength });INDArray targets = Nd4j.zeros(new int[]{ reviews.size(), outputColumnCount, maxLength });INDArray mask = Nd4j.zeros(new int[]{ reviews.size(), maxLength });
/* iterate over samples in miniBatch, look up style index, etc. */
char currChar = STOPWORD; int currCharIdx = convertCharacterToIndex(currChar) for (int j =0; j < reviewChars.length; j++){ char nextChar = reviewChars[j]; int nextCharIdx = convertCharacterToIndex(nextChar); input.putScalar(new int[]{ mbIdx, currCharIdx, j }, 1); input.putScalar(new int[]{ mbIdx, ratingOffset, j }, review.overall); input.putScalar(new int[]{ mbIdx, ratingOffset + 1, j }, review.appearance); input.putScalar(new int[]{ mbIdx, ratingOffset + 2, j }, review.aroma); input.putScalar(new int[]{ mbIdx, ratingOffset + 3, j }, review.palate); input.putScalar(new int[]{ mbIdx, ratingOffset + 4, j }, taste); input.putScalar(new int[]{ mbIdx, styleIndexColumn, j }, 1); mask.putScalar(new int[]{ mbIdx, j }, 1); targets.putScalar(new int[]{ mbIdx, nextCharIdx, j }, 1); currChar = nextChar; currCharIdx = nextCharIdx; }
/* ... */return new DataSet(input,labels, mask, mask2);
Vectorizing JSON Beer Reviews
Setting Up LSTM ArchitectureMultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .seed(rngSeed) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(0.1) .iterations(1) .updater(Updater.RMSPROP).rmsDecay(0.95) .regularization(true).l2(0.001) .weightInit(WeightInit.XAVIER) .list() .layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(lstmLayerSize).activation("tanh").build()) .layer(1, new GravesLSTM.Builder().nOut(lstmLayerSize).activation("tanh").build()) .layer(2, new RnnOutputLayer.Builder(LossFunction.MCXENT) .activation("softmax").nOut(nOut).build()) .backpropType(BackpropType.TruncatedBPTT) .tBPTTForwardLength(tbpttLength) .tBPTTBackwardLength(tbpttLength) .pretrain(false) .backprop(true) .build();MultiLayerNetwork net = new MultiLayerNetwork(conf);net.init();
Optimization: SGD with RMSProp(NOTE: can be set on per layer basis)
Weight initialization and regularization: L2 weight decay(again, can be set per layer)
Hidden layers: 2 x Graves-style LSTM layers
Output layer: plain dense layer with softmax activation
Loss function: cross entropy (KL divergence between character distributions: neural net vs. empirical)
RNN-specific config for truncatedbackprop-through-time
Training Our LSTMfor(int epoch = 0; i < numEpochs; i++) { net.fit(trainData); /* Save model, print logging messages, etc. */
/* Compute held-out data performance. */ double cost = 0; double count = 0; while(heldoutData.hasNext()) { DataSet minibatch = heldoutData.next(); cost += net.scoreExamples(heldoutData, false).sumNumber().doubleValue(); count += minibatch.getLabelsMaskArray().sumNumber().doubleValue(); } log.info(String.format("Epoch %4d test set average cost: %.4f", i, cost / count));
/* Rest dataset iterators. */ trainData.reset() heldoutData.reset()}
Compute performance on held-out data.
Training. fit can be applied to DataSetIterator, DataSet, INDArray, etc.
Generating Beer Reviews from the LSTM ModelINDArray input = Nd4j.zeros(new int[]{iter.inputColumns()});
/* Load static data into vector. */
StringBuilder sb = new StringBuilder();int prevCharIdx = 0;int currCharIdx = 0;while (true) { input.putScalar(prevCharIdx, 0); input.putScalar(currCharIdx, 1); INDArray output = net.rnnTimeStep(input); double[] outputProbDistribution = new double[numCharacters]; for (int j = 0; j < outputProbDistribution.length; j++) outputProbDistribution[j] = output.getDouble(s, j); prevCharIdx = currCharIdx; currCharIdx = sampleFromDistribution(outputProbDistribution, rng); sb.append(convertIndexToCharacter(currCharIdx)); if (currCharIdx == STOPWORD) break;}String reviewSample = sb.toString();
Load input vector for single step.
Get probability distribution over next character by running RNN for one step.
Sample character from probability distribution.
Stop if we generate STOPWORD.
A Generated Beer Review…
More Resources
• DL4J Github:
• https://github.com/deeplearning4j/deeplearning4j
• DataVec Github
• https://github.com/deeplearning4j/DataVec
• Examples from this talk:
• https://github.com/deeplearning4j/dl4j-examples
Thank you!
Please visit skymind.io/learn for more information
OR
Visit us at booth P33