Deep Learning with H 2 O 0xdata, H2O.ai Scalable In-Memory Machine Learning Hadoop User Group, Chicago, 7/16/14 Arno Candel
H2O Distributed Deep Learning by Arno Candel 071614
Aug 27, 2014
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice in traditional business analytics.
This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of enterprise-scale problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization and optimization for class imbalance. World record performance on the classic MNIST dataset, best-in-class accuracy for eBay text classification and others showcase the power of this game changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
About the Speaker: Arno Candel
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes.
He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich.
This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of enterprise-scale problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization and optimization for class imbalance. World record performance on the classic MNIST dataset, best-in-class accuracy for eBay text classification and others showcase the power of this game changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
About the Speaker: Arno Candel
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes.
He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich.
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Deep Learning with H2O
!
0xdata, H2O.aiScalable In-Memory Machine Learning
!
Hadoop User Group, Chicago, 7/16/14
Arno Candel
Who am I?
PhD in Computational Physics, 2005from ETH Zurich Switzerland
!
6 years at SLAC - Accelerator Physics Modeling 2 years at Skytree, Inc - Machine Learning 7 months at 0xdata/H2O - Machine Learning
!
15 years in HPC, C++, MPI, Supercomputing
@ArnoCandel
H2O Deep Learning, @ArnoCandel
OutlineIntro & Live Demo (5 mins)
Methods & Implementation (20 mins)
Results & Live Demos (25 mins)
MNIST handwritten digits
text classification
Weather prediction
Q & A (10 mins)
3
H2O Deep Learning, @ArnoCandel
Distributed in-memory math platform ➔ GLM, GBM, RF, K-Means, PCA, Deep Learning
Easy to use SDK / API➔ Java, R, Scala, Python, JSON, Browser-based GUI
!Businesses can use ALL of their data (w or w/o Hadoop)
➔ Modeling without Sampling
Big Data + Better Algorithms ➔ Better Predictions
H2O Open Source in-memoryPrediction Engine for Big Data
4
H2O Deep Learning, @ArnoCandel
About H20 (aka 0xdata)Pure Java, Apache v2 Open Source Join the www.h2o.ai/community!
5
+1 Cyprien Noel for prior work
H2O Deep Learning, @ArnoCandel
Customer Demands for Practical Machine Learning
6
Requirements Value
In-Memory Fast (Interactive)
Distributed Big Data (No Sampling)
Open Source Ownership of Methods
API / SDK Extensibility
H2O was developed by 0xdata to meet these requirements
H2O Deep Learning, @ArnoCandel
H2O Integration
H2O
HDFS HDFS HDFS
YARN Hadoop MR
R ScalaJSON Python
Standalone Over YARN On MRv1
7
H2O H2O
Java
H2O Deep Learning, @ArnoCandel
H2O Architecture
Distributed In-Memory K-V storeCol. compression
Machine Learning
Algorithms
R EngineNano fast
Scoring Engine
Prediction Engine
Memory manager
e.g. Deep Learning
8
MapReduce
H2O Deep Learning, @ArnoCandel
H2O - The Killer App on Spark9
http://databricks.com/blog/2014/06/30/sparkling-water-h20-spark.html
H2O Deep Learning, @ArnoCandel 10
John Chambers (creator of the S language, R-core member) names H2O R API in top three promising R projects
H2O R CRAN package
H2O Deep Learning, @ArnoCandel
H2O + R = Happy Data Scientist
11
Machine Learning on Big Data with R:Data resides on the H2O cluster!
H2O Deep Learning, @ArnoCandel
H2O Deep Learning in Action
Train: 60,000 rows 784 integer columns 10 classes Test: 10,000 rows 784 integer columns 10 classes
12
MNIST = Digitized handwritten digits database (Yann LeCun)
Live Demo Build a H2O Deep Learning model on MNIST train/test data
Data: 28x28=784 pixels with (gray-scale) values in 0…255
Yann LeCun: “Yet another advice: don't get fooled by people who claim to have a solution to Artificial General Intelligence. Ask them what error rate they get on MNIST or ImageNet.”
H2O Deep Learning, @ArnoCandel
Wikipedia:Deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using
architectures composed of multiple non-linear transformations.
What is Deep Learning?
Example: Input data(image)
Prediction (who is it?)
13
Facebook's DeepFace (Yann LeCun) recognises faces as well as humans
H2O Deep Learning, @ArnoCandel
Deep Learning is Trending
20132012
Google trends
2011
14
Businesses are usingDeep Learning techniques!
Google Brain (Andrew Ng, Jeff Dean & Geoffrey Hinton) !FBI FACE: $1 billion face recognition project !Chinese Search Giant Baidu Hires Man Behind the “Google Brain” (Andrew Ng)
H2O Deep Learning, @ArnoCandel
Deep Learning Historyslides by Yan LeCun (now Facebook)
15
Deep Learning wins competitions AND
makes humans, businesses and machines (cyborgs!?) smarter
H2O Deep Learning, @ArnoCandel
What is NOT DeepLinear models are not deep (by definition)
!
Neural nets with 1 hidden layer are not deep (no feature hierarchy)
!
SVMs and Kernel methods are not deep (2 layers: kernel + linear)
!
Classification trees are not deep (operate on original input space)
16
H2O Deep Learning, @ArnoCandel
1970s multi-layer feed-forward Neural Network (supervised learning with stochastic gradient descent using back-propagation) !+ distributed processing for big data (H2O in-memory MapReduce paradigm on distributed data) !+ multi-threaded speedup (H2O Fork/Join worker threads update the model asynchronously) !+ smart algorithms for accuracy (weight initialization, adaptive learning, momentum, dropout, regularization)
!
= Top-notch prediction engine!
Deep Learning in H2O17
H2O Deep Learning, @ArnoCandel
“fully connected” directed graph of neurons
age
income
employment
married
single
Input layerHidden layer 1
Hidden layer 2
Output layer
3x4 4x3 3x2#connections
information flow
input/output neuronhidden neuron
4 3 2#neurons 3
Example Neural Network18
H2O Deep Learning, @ArnoCandel
age
income
employmentyj = tanh(sumi(xi*uij)+bj)
uij
xi
yj
per-class probabilities sum(pl) = 1
zk = tanh(sumj(yj*vjk)+ck)
vjk
zk pl
pl = softmax(sumk(zk*wkl)+dl)
wkl
softmax(xk) = exp(xk) / sumk(exp(xk))
“neurons activate each other via weighted sums”
Prediction: Forward Propagation
activation function: tanh alternative:
x -> max(0,x) “rectifier”
pl is a non-linear function of xi: can approximate ANY function
with enough layers!
bj, ck, dl: bias values(indep. of inputs)
19
married
single
H2O Deep Learning, @ArnoCandel
age
income
employment
xi
Automatic standardization of data xi: mean = 0, stddev = 1
!horizontalize categorical variables, e.g.
{full-time, part-time, none, self-employed} ->
{0,1,0} = part-time, {0,0,0} = self-employed
Automatic initialization of weights !
Poor man’s initialization: random weights wkl !
Default (better): Uniform distribution in+/- sqrt(6/(#units + #units_previous_layer))
Data preparation & InitializationNeural Networks are sensitive to numerical noise, operate best in the linear regime (not saturated)
20
married
single
wkl
H2O Deep Learning, @ArnoCandel
Mean Square Error = (0.22 + 0.22)/2 “penalize differences per-class” ! Cross-entropy = -log(0.8) “strongly penalize non-1-ness”
Training: Update Weights & Biases
Stochastic Gradient Descent: Update weights and biases via gradient of the error (via back-propagation):
For each training row, we make a prediction and compare with the actual label (supervised learning):
married10.8predicted actual
Objective: minimize prediction error (MSE or cross-entropy)
w <— w - rate * ∂E/∂w
1
21
single00.2
E
wrate
H2O Deep Learning, @ArnoCandel
Backward Propagation
!∂E/∂wi = ∂E/∂y * ∂y/∂net * ∂net/∂wi
= ∂(error(y))/∂y * ∂(activation(net))/∂net * xi
Backprop: Compute ∂E/∂wi via chain rule going backwards
wi
net = sumi(wi*xi) + b
xiE = error(y)
y = activation(net)
How to compute ∂E/∂wi for wi <— wi - rate * ∂E/∂wi ?
Naive: For every i, evaluate E twice at (w1,…,wi±∆,…,wN)… Slow!
22
H2O Deep Learning, @ArnoCandel
H2O Deep Learning Architecture
K-V
K-V
HTTPD
HTTPD
nodes/JVMs: sync
threads: async
communication
w
w w
w w w w
w1 w3 w2w4
w2+w4w1+w3
w* = (w1+w2+w3+w4)/4
map: each node trains a copy of the weights
and biases with (some* or all of) its
local data with asynchronous F/J
threads
initial model: weights and biases w
updated model: w*
H2O atomic in-memoryK-V store
reduce: model averaging:
average weights and biases from all nodes,
speedup is at least #nodes/log(#rows) arxiv:1209.4129v3
Keep iterating over the data (“epochs”), score from time to time
Query & display the model via
JSON, WWW
2
2 431
1
1
1
43 2
1 2
1
i
*user can specify the number of total rows per MapReduce iteration
23
H2O Deep Learning, @ArnoCandel
Adaptive learning rate - ADADELTA (Google)Automatically set learning rate for each neuron based on its training history
Grid Search and Checkpointing Run a grid search to scan many hyper-parameters, then continue training the most promising model(s)
RegularizationL1: penalizes non-zero weights L2: penalizes large weightsDropout: randomly ignore certain inputs
24
“Secret” Sauce to Higher Accuracy
H2O Deep Learning, @ArnoCandel
Detail: Adaptive Learning Rate!
Compute moving average of ∆wi2 at time t for window length rho: !
E[∆wi2]t = rho * E[∆wi2]t-1 + (1-rho) * ∆wi2
!Compute RMS of ∆wi at time t with smoothing epsilon:
!RMS[∆wi]t = sqrt( E[∆wi2]t + epsilon )
Adaptive annealing / progress: Gradient-dependent learning rate, moving window prevents “freezing” (unlike ADAGRAD: no window)
Adaptive acceleration / momentum: accumulate previous weight updates, but over a window of time
RMS[∆wi]t-1
RMS[∂E/∂wi]t
rate(wi, t) =
Do the same for ∂E/∂wi, then obtain per-weight learning rate:
cf. ADADELTA paper
25
H2O Deep Learning, @ArnoCandel
Detail: Dropout Regularization26
Training: For each hidden neuron, for each training sample, for each iteration, ignore (zero out) a different random fraction p of input activations.
!
age
income
employment
married
singleX
X
X
Testing: Use all activations, but reduce them by a factor p
(to “simulate” the missing activations during training).
cf. Geoff Hinton's paper
H2O Deep Learning, @ArnoCandel
MNIST: digits classification
Standing world record: Without distortions or convolutions, the best-ever published error rate on test set: 0.83% (Microsoft)
27
Time to check in on the demo!
Let’s see how H2O did in the past 20 minutes!
H2O Deep Learning, @ArnoCandel
Frequent errors: confuse 2/7 and 4/9
H2O Deep Learning on MNIST: 0.87% test set error (so far)
28
test set error: 1.5% after 10 mins 1.0% after 1.5 hours 0.87% after 4 hours
World-class results!
No pre-training No distortions
No convolutions No unsupervised
training
Running on 4 nodes with 16 cores each
H2O Deep Learning, A. Candel
Weather Dataset29
Predict “RainTomorrow” from Temperature, Humidity, Wind, Pressure, etc.
H2O Deep Learning, A. Candel
Live Demo: Weather Prediction
Interactive ROC curve with real-time updates
30
3 hidden Rectifier layers, Dropout,
L1-penalty
12.7% 5-fold cross-validation error is at least as good as GBM/RF/GLM models
5-fold cross validation
H2O Deep Learning, @ArnoCandel
Live Demo: Grid Search
How did I find those parameters? Grid Search!(works for multiple hyper parameters at once)
31
Then continue training the best model
H2O Deep Learning, @ArnoCandel
Use Case: Text Classification
Goal: Predict the item from seller’s text description
32
Train: 578,361 rows 8,647 cols 467 classes Test: 64,263 rows 8,647 cols 143 classes
“Vintage 18KT gold Rolex 2 Tone in great condition”
Data: Binary word vector 0,0,1,0,0,0,0,0,1,0,0,0,1,…,0
vintagegold condition
Let’s see how H2O does on the ebay dataset!
H2O Deep Learning, @ArnoCandel
Out-Of-The-Box: 11.6% test set error after 10 epochs! Predicts the correct class (out of 143) 88.4% of the time!
33
Note 2: No tuning was done(results are for illustration only)
Train: 578,361 rows 8,647 cols 467 classes Test: 64,263 rows 8,647 cols 143 classes
Note 1: H2O columnar-compressed in-memory store only needs 60 MB to store 5 billion values (dense CSV needs 18 GB)
Use Case: Text Classification
H2O Deep Learning, @ArnoCandel
Parallel Scalability (for 64 epochs on MNIST, with “0.87%” parameters)
34
Speedup
0.00
10.00
20.00
30.00
40.00
1 2 4 8 16 32 63
H2O Nodes
(4 cores per node, 1 epoch per node per MapReduce)
2.7 mins
Training Time
0
25
50
75
100
1 2 4 8 16 32 63
H2O Nodes
in minutes
H2O Deep Learning, @ArnoCandel
Tips for H2O Deep Learning!General: More layers for more complex functions (exp. more non-linearity) More neurons per layer to detect finer structure in data (“memorizing”) Add some regularization for less overfitting (smaller validation error) Do a grid search to get a feel for convergence, then continue training. Try Tanh first, then Rectifier, try max_w2 = 50 and/or L1=1e-5. Try Dropout (input: 20%, hidden: 50%) with test/validation set after finding good parameters for convergence on training set. Distributed: More training samples per iteration: faster, but less accuracy? With ADADELTA: Try epsilon = 1e-4,1e-6,1e-8,1e-10, rho = 0.9,0.95,0.99 Without ADADELTA: Try rate = 1e-4…1e-2, rate_annealing = 1e-5…1e-8, momentum_start = 0.5, momentum_stable = 0.99, momentum_ramp = 1/rate_annealing. Try balance_classes = true for imbalanced classes. Use force_load_balance and replicate_training_data for small datasets.
35
H2O Deep Learning, @ArnoCandel 36
… and more docs coming soon!
Draft
All parameters are available from R…
H2O brings Deep Learning to R
H2O Deep Learning, @ArnoCandel
POJO Model Export for Production Scoring
37
Plain old Java code is auto-generated to take your H2O Deep Learning models into production!
H2O Deep Learning, @ArnoCandel
Deep Learning Auto-Encoders for Anomaly Detection
38
Toy example: Find anomaly in ECG heart beat data. First, train a model on what’s “normal”: 20 time-series samples of 210 data points each
Deep Auto-Encoder: Learn low-dimensional non-linear “structure” of the data that allows to reconstruct the orig. data
Also for categorical data!
H2O Deep Learning, @ArnoCandel
Deep Learning Auto-Encoders for Anomaly Detection
39
Test set with anomaly
Test set prediction is reconstruction, looks “normal”
Found anomaly! large reconstruction error
Model of what’s “normal”
+
=>
H2O Deep Learning, @ArnoCandel
H2O Steam: Scoring Platform
40
H2O Deep Learning, @ArnoCandel
H2O Steam: More Coming Soon!
41
H2O Deep Learning, @ArnoCandel
Key Take-AwaysH2O is a distributed in-memory data science platform. It was designed for high-performance machine learning applications on big data. !
H2O Deep Learning is ready to take your advanced analytics to the next level - Try it on your data! !
Join our Community and Meetups! git clone https://github.com/0xdata/h2o http://docs.0xdata.com www.h2o.ai/community @hexadata
42
Related Documents
