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Spark Summit East Talk

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- 1. Deep recurrent neural network for sequence learning in Spark Yves MABIALA THALES
- 2. Outline • Thales & Big Data • On the difficulty of Sequence Learning • Deep Learning for Sequence Learning • Spark implementation of Deep Learning • Use cases – Predictive maintenance – NLP
- 3. Thales & Big Data Thales systems produce a huge quantity of data Transportation systems (ticketing, supervision, …) Security (radar traces, network logs, …) Satellite (photos, videos, …) which is often Massive Heterogeneous Extremely dynamic and where understanding the dynamics of the monitored phenomena is mandatory Sequence Learning
- 4. What is sequence learning ? Sequence learning refers to a set of ML tasks where a model has to either deal with sequences as input, produce sequences as output or both Goal : Understand the dynamic of a sequence to – Classify – Predict – Model Typical applications – Text • Classify texts (sentiment analysis) • Generate textual description of images (image captioning) – Video • Video classification – Speech • Speech to text
- 5. How is it typically handled ? Taking into account the dynamic is difficult – Often people do not bother • E.g. text analysis using bag of word (one hot encoding) – Problem for certain tasks such as sentiment classification (order of the words is important) – Or use popular statistical approaches • (Hidden) Markov model for prediction (and classification) – Shortterm dependency (order 1) : 𝑃(𝑋$ = 𝑥 (𝑋$'( = 𝑥$'(,… , 𝑋$', = 𝑥$',)⁄ ) = 𝑃(𝑋$ = 𝑥$ 𝑋$'( = 𝑥$'()⁄ • Autoregressive approaches for time series forecasting The chair is red 1 0 1 1 0 0 0 0 The cat is on a chair The cat is young 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 The is chair red young cat on a
- 6. Link with artificial neural network ? Artificial neural network is a set of statistical models inspired from the brain – Transforms the input by applying at each layer (non linear) functions – More layers equals more capabilities (≥ 2 hidden layers : Deep Learning) • From manual features building to feature learning Set of transformation and activation operations – Affine : 𝒀 = 𝑾 𝒕 𝑿 + 𝒃, sigmoid activation : 𝟏 𝟏8𝐞𝐱𝐩 ('𝑿) , tanh activation : 𝒀 = 𝐭𝐚𝐧𝐡 ( 𝑿) • Only affine + activation layers = multi layer perceptron (available in Spark ML since 1.5.0) – Convolutional : Apply a spatial convolution on the 1D/2D input (signal, image, …) : 𝐘 = 𝒄𝒐𝒏𝒗 𝑿, 𝑾 + 𝒃 • Learns spatial features used for classification (images) , prediction – Recurrent : Introduces a recurrent part to learn dependencies between observations (features related to the dynamic) Objective – Find the best weights W to minimize the difference between the predicted output and the desired one (using back-propagation algorithm) input hidden layers output
- 7. Able to cope with varying size sequences either at the input or at the output Recurrent Neural Network basics One to many (fixedsize input, sequence output) e.g. Image captioning Many to many (sequence input to sequence output) e.g. Speech to text Many to one (sequence input to fixedsize output) e.g. Text classification Artificial neural networks with one or more recurrent layers Classical neural network Recurrent neural network 𝒀 𝒌'𝟑 𝒀 𝒌'𝟐 𝒀 𝒌'𝟏 𝒀 𝒌 𝒀 𝒌 𝑿 𝒌'𝟑 𝑿 𝒌'𝟐 𝑿 𝒌'𝟏 𝑿 𝒌 𝒀 𝒌 = 𝒇(𝑾 𝒕 𝑿 𝒌 + 𝑯𝒀 𝒌'𝟏) 𝑿 𝒌𝑿 𝒀 𝒌 = 𝒇(𝑾 𝒕 𝑿 𝒌) 𝒀 Unrolled through time 𝒀 𝒌'𝟑 𝒀 𝒌'𝟐 𝒀 𝒌'𝟏 𝒀 𝒌 𝑿 𝒀 𝒌'𝟑 𝒀 𝒌'𝟐 𝒀 𝒌'𝟏 𝒀 𝒌 𝑿 𝒌'𝟑 𝑿 𝒌'𝟐 𝑿 𝒌'𝟏 𝑿 𝒌 𝑿 𝒌'𝟑 𝑿 𝒌'𝟐 𝑿 𝒌'𝟏 𝑿 𝒌 𝒀
- 8. On the difficulty of training recurrent networks RNNs are (were) known to be difficult to learn – More weights and more computational steps • More computationally expensive (accelerator needed for matrix ops : Blas or GPU) • More data needed to converge (scalability over Big Data architectures : Spark) – Theano, Tensor Flow, Caffe do not have distributed versions – Unable to learn long range dependencies (Graves & Al 2014) • At a given time t, RNN does not remember the observations before 𝑋J', ⇒ New RNN architectures with memory preservation (more context) 𝑍$ = 𝑓 𝑊N O 𝑋$ + 𝐻N 𝑌$'( 𝑅$ = 𝑓(𝑊S O 𝑋$ + 𝐻S 𝑌$'() 𝐻T$ = tanh(𝑊YJZ[ O 𝑋$ + 𝑈 𝑌$'( o 𝑅$ ) 𝑌$ = 1 − 𝑍$ 𝑌$'( + 𝑍$ 𝐻T$ LSTM GRU
- 9. Recurrent neural networks in Spark Spark implementation of DL algorithms (data parallel) – All the needed blocks • Affine, convolutional, recurrent layers (Simple and GRU) • Sigmoid, tanh, reLU activations • SGD, rmsprop, adadelta optimizers – CPU (and GPU backend) – Fully compatible with existing DL library in Spark ML Performance – On 6 nodes cluster (CPU) • 5.46 average speedup (some communication overhead) – About the same speedup as MLP in Spark ML Driver Worker 1 Worker 2 Worker 3 Resulting gradients (2) Model broadcast (1)
- 10. Use case 1 : predictive maintenance (1) Context – Thales and its clients build systems in different domains • Transportation (ticketing, controlling) • Defense (radar) • Satellites – Need better and more accurate maintenance services • From planned maintenance (every x days) to an alert maintenance • From expert detection to automatic failure prediction • From whole subsystem changes to more localized reparations Goal – Detect early signs of a (sub)system failure using data coming from sensors monitoring the health of a system (HUMS)
- 11. Use case 1 : predictive maintenance (2) Example on a real system – 20 sensors (20 values every 5 minutes), label (failure or not) – Take 3 hours of data and predict the probability of failure in the next hour (fully customizable) Learning using MLLIB
- 12. Use case 1 : predictive maintenance (3) Recurrent net learning Impact of recurrent nets – Logistic regression • 70% detection with 70% accuracy – Recurrent Neural Network • 85% detection with 75% accuracy
- 13. Use case 2 : Sentiment analysis (1) Context – Social network analysis application developed at Thales (Twitter, Facebook, blogs, forums) • Analyze both the content of the texts and the relations (texts, actors) – Multiple (big data) analysis • Actor community detection • Text clustering (themes) • … Focus on – Sentiment analysis on the collected texts • Classify texts based on their sentiment
- 14. Use case 2 : Sentiment analysis (2) Learning dataset – Sentiment140 + Kaggle challenge (1.5M labeled tweets) – 50% positives, 50% negatives Compare Bag of words + classifier approaches (Naïve Bayes, SVM, logistic regression) versus RNN
- 15. Use case 2 : Sentiment analysis (3) NB SVM Log Reg NeuralNet (perceptron) RNN (GRU) 100 61.4 58.4 58.4 55.6 NA 1 000 70.6 70.6 70.6 70.8 68.1 10 000 75.4 75.1 75.4 76.1 72.3 100 000 78.1 76.6 76.9 78.5 79.2 700 000 80 78.3 78.3 80 84.1 Results 40 45 50 55 60 65 70 75 80 85 90 NB SVM LogReg NeuralNet RNN (GRU)
- 16. The end… THANK YOU !

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