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Outline
▌Thales & Big Data
▌On the difficulty of Sequence Learning
▌Deep Learning for Sequence Learning
▌Spark implementation of Deep Learning
▌Use casesPredictive maintenanceNLP
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Thales & Big DataThales systems produce a huge quantity of data
Transportation systems (ticketing, supervision, …)Security (radar traces, network logs, …)Satellite (photos, videos, …)
which is oftenMassiveHeterogeneousExtremely dynamic
and where understanding the dynamic of the monitored phenomena is mandatory Sequence Learning
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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 toClassifyPredictModel
Typical applicationsText
- Classify texts (sentiment analysis)- Generate textual description of images (image captioning)
Video- Video classification
Speech- Speech to text
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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)
– Short term dependency (order 1) : = - 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
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Link with artificial neural network ?Artificial neural networks are statistical models inspired from the brain
Transforms the input by applying at each layer (non linear) functionsMore layers equals more capabilities (hidden layers : Deep Learning)
Set of transformation and activation operationsAffine : sigmoid activation : , tanh activation :
Convolutional : Apply a spatial convolution on the 1D/2D input (signal, image, …): - Learns spatial features used for classification or prediction (mostly on images/videos)
Recurrent : Learn dependencies between successive observations (features related to the dynamic)
ObjectiveFind the best weights W to minimize the difference between the predicted output and the desired one (using back-propagation algorithm)
inputhidden layers
output
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Able to cope with varying size sequences either at the input or at the output
Recurrent Neural Network basics
One to many (fixed size 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 fixed size
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 𝒌𝒀 𝒌
𝑿𝒌−𝟑 𝑿𝒌−𝟐 𝑿𝒌−𝟏 𝑿𝒌𝒀 𝒌= 𝒇 (𝑾 𝒕𝑿𝒌+𝑯𝒀 𝒌−𝟏)𝑿𝒌𝑿
𝒀 𝒌= 𝒇 (𝑾 𝒕𝑿𝒌)
𝒀Unrolled 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
𝑿𝒌−𝟑 𝑿𝒌−𝟐 𝑿𝒌−𝟏 𝑿𝒌𝑿𝒌−𝟑 𝑿𝒌−𝟐 𝑿𝒌−𝟏 𝑿𝒌
𝒀
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On the difficulty of training recurrent networksRNNs 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 versionsUnable to learn long range dependencies (Graves & Al 2014)
- At a given time t, RNN does not remember the observations before Þ New RNN architectures with memory preservation (more
context)
LSTM GRU
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Recurrent neural networks in Spark
Spark implementation of DL algorithms (data parallel)All the needed blocks- Affine, convolutional, recurrent layers (Simple and GRU)- SGD, rmsprop, adadelta optimizers- Sigmoid, tanh, reLu activationsCPU (and GPU backend)Fully compatible with existing DL library in Spark ML
PerformanceOn 6 nodes cluster (CPU)- 5.46 average speedup (some communication overhead)
– About the same speedup as MLP in Spark ML
Driver
Worker 1Worker 2Worker 3
Resulting gradients (2)
Model broadcast (1)
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Use case 1 : predictive maintenance (1)
ContextThales 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
GoalDetect early signs of a (sub)system failure using data coming from sensors monitoring the health of a system (HUMS)
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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
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Use case 1 : predictive maintenance (3)Recurrent net learning
Impact of recurrent netsLogistic regression- 70% detection with 70% accuracyRecurrent Neural Network
• 85% detection with 75% accuracy
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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 onSentiment analysis on the collected texts
- Classify texts based on their sentiment
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Use case 2 : Sentiment analysis (2)Learning dataset
Sentiment140 + Kaggle challenge (1.5M labeled tweets)50% positives, 50% negatives
Compare Bag of words + traditional classifiers (Naïve Bayes, SVM, logistic regression) versus RNN
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Use case 2 : Sentiment analysis (3)
NB SVM Log Reg
Neural Net (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
100
1000
1000
0
1000
00
7000
004045505560657075808590 NB
SVMLo-gRegNeu-ralNetRNN (GRU)