Python for Image Understanding: Deep Learning with Convolutional Neural Nets

Roelof Pieters
Roelof PietersCofounder creative.ai
for Image
Understanding:
Deep Learning with
Convolutional Neural Nets
Roelof Pieters
PhD candidate at KTH & Data Science
consultant at Graph Technologies 
@graphific
London 2015roelof@graph-technologies.com
What is Deep
Learning?
“Deep learning is a set of algorithms in
machine learning that attempt to learn in
multiple levels, corresponding to different
levels of abstraction.” (much debated definition)
A Definition
• A host of statistical machine learning
techniques
• Enables the automatic learning of feature
hierarchies
• Generally based on artificial neural
networks
A typology
Deep learning is:
Manually designed features are often over-specified,
incomplete and take a long time to design and validate
Learned features are easy to adapt, fast to learn
Deep learning provides a very flexible, (possibly?)
universal, learnable framework for representing world,
visual and linguistic information.
Deep learning can learn unsupervised (from raw text/
audio/images/whatever content) and supervised (with
specific labels like positive/negative)

Summary by Richard Socher.
Old vs new school?
No More Handcrafted Features !
6
“Brain”-like: Feature Hierarchies
input layer
output layer
hidden layers
Feature Hierarchies: Vision
input layer
output layer
hidden layers
Feature Hierarchies: Audio
cars elephants chairs
Feature Hierarchies: And so on…
History: audio recognition
chart by Clarifai
History: image recognition
chart by Clarifai
History: image recognition
Krizhevsky et al. ImageNet Classification with Deep Convolutional
Neural Networks, ILSVRC2010
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
16
Karpathy, A., Fei Fei, L. (2015) 

Deep Visual-Semantic Alignments for Generating Image Descriptions
Image-Text: Joint Visual Semantic embeddings
Video Scene Detection
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html
http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
How does Deep
Learning work?
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
Activation Functions
Sigmoid
Activation Functions
Tanh
Activation Functions
ReLU
often approximated by just
optimization strategies
So what’s there for
Python?
python has a wide range of deep learning-related
libraries available
Deep Learning with Python
Low level
High level
(efficient gpu-powered math)
(computer-vision oriented DL framework,

model-zoo, prototxt model definitions) 

pythonification ongoing!
(wrapper for theano, yaml, experiment-oriented)
(theano-wrapper, models in python code, 

abstracts theano away)
(theano-extension, models in python code, 

theano not hidden)
and of course:
python has a wide range of deep learning-related
libraries available
Deep Learning with Python
Low level
High level
deeplearning.net/software/theano
caffe.berkeleyvision.org
deeplearning.net/software/pylearn2
keras.io
lasagne.readthedocs.org/en/latest
and of course:
python has a wide range of deep learning-related
libraries available
Deep Learning with Python
Low level
High level
deeplearning.net/software/theano
caffe.berkeleyvision.org
deeplearning.net/software/pylearn2
keras.io
lasagne.readthedocs.org/en/latest
and of course:
we will use lasagne in our examples
Doing Deep
Learning?
1. Preprocess the data
2. Choose architecture
3. Train
4. Optimize/Regularize
5. Tips/Tricks
Training a (deep) Neural Network
1. Preprocess the data
2. Choose architecture
3. Train
4. Optimize/Regularize
5. Tips/Tricks
Training a (deep) Neural Network
• Mean subtraction
• Normalization
• PCA and Whitening
1. Preprocess the data
• Mean subtraction
• Normalization
• PCA and Whitening
1. Preprocess the data: Normalization
(mean image visualised of cifar-10)
• Mean subtraction
• Normalization
• PCA and Whitening
1. Preprocess the data: PCA & Whitening
1. Preprocess the data: PCA & Whitening
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
1. Preprocess the data, the right way
Warning:
• compute preprocessing statistics on training data
• apply on all (training / validation / test) data
1. Preprocess the data
2. Choose architecture
3. Train
4. Optimize/Regularize
5. Tips/Tricks
Training a (deep) Neural Network
• Deep Belief Network (DBN)
• Convolutional Net (CNN)
• Recurrent Net (RNN)
2. Choosing the right architecture
• Deep Belief Network (DBN)
• Convolutional Net (CNN)
• Recurrent Net (RNN)
2. Choosing the right architecture
• Deep Belief Network (DBN)
• Convolutional Net (CNN)
• Recurrent Net (RNN)
2. Choosing the right architecture
• Deep Belief Network (DBN)
• Convolutional Net (CNN)
• Recurrent Net (RNN)
2. Choosing the right architecture
Convolutional Neural Net
Pbird
Psunset
Pdog
Pcat
Convolutional Neural Net
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
DrawCNN: visualizing the units' connections
Agrawal, et al. Analyzing the performance of multilayer neural networks for object recognition. ECCV, 2014
Szegedy, et al. Intriguing properties of neural networks.arXiv preprint arXiv:1312.6199, 2013
Zeiler, M. et al. Visualizing and Understanding Convolutional Networks, ECCV 2014
1. Preprocess the data
2. Choose architecture
3. Train
4. Optimize/Regularize
5. Tips/Tricks
Training a (deep) Neural Network
1. Preprocess the data
2. Choose architecture
3. Train (Code Finally!)
4. Optimize/Regularize
5. Tips/Tricks
Training a (deep) Neural Network
Training a (deep) Neural Network
(…)
layer definitions
layer

parameters
Training a (deep) Neural Network
hyper

parameters
Training a (deep) Neural Network
Lift off!
Training a (deep) Neural Network
1. Preprocess the data
2. Choose architecture
3. Train
4. Optimize/Regularize
5. Tips/Tricks
Debug
Debug Training: Visualize Loss Curve
Debug Training: Visualize Loss Curve
Debug Training: Visualize Loss Curve
This looks (too) linear: lower your learning rate!
This looks (too) wide: increase your batch size!
Debug Training: Visualize Accuracy
big gap: overfitting: regularize!
no gap: underfitting
(increase model size)
Debug Training: Visualize Weights
(usually: first layer)
complete mess, doesn't get past the random initialisation
Debug Training: Visualize Weights
(usually: first layer)
better, but still “noisy” weights
mostly solvable
by stronger
regularisation
Debug Training: Visualize Weights
(usually: first layer)
good: now operates as “edge detector”
Training a (deep) Neural Network
1. Preprocess the data
2. Choose architecture
3. Train
4. Optimize/Regularize
5. Tips/Tricks
Debug
• Tweak Hyperparameters / Architecture
• Data Augmentation
• Dropout
• Batch Normalization
Optimize / Regularize
• Tweak Hyperparameters / Architecture
• Data Augmentation
• Dropout
• Batch Normalization
Optimize / Regularize
• Grid search won't work on your millions + parameters
• Random Search? Mwah…
• Bayesian Optimization: Yeh baby!
• Spearmint: https://github.com/HIPS/Spearmint
• Hypergrad: https://github.com/HIPS/hypergrad
Choosing Hyperparameters
• Tweak Hyperparameters / Architecture
• Data Augmentation
• Dropout
• Batch Normalization
Overfitting
Data Augmentation
Data Augmentation
http://benanne.github.io/2015/03/17/plankton.html
(realtime data augmentation at Kaggle’s #1 National Data Science Bowl

≋ Deep Sea ≋ team)
rotation: random with angle between 0° and 360° (uniform)
translation: random with shift between -10 and 10 pixels (uniform)
rescaling: random with scale factor between 1/1.6 and 1.6 (log-
uniform)
flipping: yes or no (bernoulli)
shearing: random with angle between -20° and 20° (uniform)
stretching: random with stretch factor between 1/1.3 and 1.3 (log-
uniform)
• Tweak Hyperparameters / Architecture
• Data Augmentation
• Dropout
• Batch Normalization
Optimize / Regularize
Dropout as Regularization
(naively trained net)
Overfits !
Dropout as Regularization
(naively trained net)
Overfits !
Dropout!
Dropout as Regularization
(naively trained net)
Overfits !
Dropout!
Dropout as Regularization
(naively trained net) (net with dropout)
less strongly overfitted & 

can run for more epochs higher accuracy
Overfitting
• Tweak Hyperparameters / Architecture
• Data Augmentation
• Dropout
• Batch Normalization
•Normalize the activations in
each layer within a minibatch
•Learn the mean and variance
of each layer as parameters
Batch Normalization as regularization
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 

S Ioffe and C Szegedy (2015)
85%
50%
15%
Training a (deep) Neural Network
1. Preprocess the data
2. Choose architecture
3. Train
4. Optimize/Regularize
5. Further Tips & Tricks to improve Model Accuracy
Debug
• Ensembles
• Finetuning pre-trained/earlier-trained net
• Sticking extracted layer features in another classifier (ie
SVM)
Other “Tricks”
• Ensembles
• Finetuning pre-trained/earlier-trained net
• Sticking extracted layer features in another classifier (ie
SVM)
Other “Tricks”
Ensembles
• majority vote when hard predictions (ie classes)
• average vote for soft predictions (continious scale)
• make sure classifiers are uncorrelated
• cross validate ensemble weights (by grid search, or
rank average)
• stacked
• blending
Ensembles
(10 similar nets with varying hyperparameters on same tiny-imagenet dataset)
avg: 0.3647
Ensembles
(10 similar nets with varying hyperparameters on same tiny-imagenet dataset)
predict by mean of all: 0.4244
avg: 0.3647
Ensembles
(10 similar nets with varying hyperparameters on same tiny-imagenet dataset)
(10 similar nets with varying hyperparameters on same tiny-imagenet dataset)
predict by mean of all: 0.4244
leave out model9: 0.4259
avg: 0.3647
Ensembles
(10 similar nets with varying hyperparameters on same tiny-imagenet dataset)
0.4259 0.4244
0.3647
Ensembles
danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/
(ensembling specialist nets by Daniel Nouri, Kaggle facial keypoint tutorial)
Ensembles
“Specialists” Ensemble
https://www.kaggle.com/c/facial-keypoints-detection/
try it yourself :)
3 similar nets trained on the same data but with different
hyper parameters.

RMSE’s:
•2,08449
•2,04575
•2.01565
together: 

1.93397
disclaimer: Kaggle is not real life, people…
Machine learning
systems can easily be
fooled
but beware… / no
free lunch:
Saliency Maps
K. Simonyan, A. Vedaldi, A. Zisserman , "Deep Inside Convolutional Networks: Visualising Image
Classification Models and Saliency Maps", ICLR Workshop 2014
first we predict on a pixel level
Fooling ConvNets
Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint, 2013.
Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Deep Neural Networks are Easily Fooled: High
Confidence Predictions for Unrecognizable Images." arXiv preprint
then we do our “magic”
Fooling ConvNets
Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint, 2013.
Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Deep Neural Networks are Easily Fooled: High
Confidence Predictions for Unrecognizable Images." arXiv preprint
then we do our “magic”
Failing ConvNets
Failing ConvNets
“Suddenly, a leopard print sofa appears”, rocknrollnerd.github.io
Failing ConvNets
“Suddenly, a leopard print sofa appears”, rocknrollnerd.github.io
Failing ConvNets
“Suddenly, a leopard print sofa appears”, rocknrollnerd.github.io
Failing ConvNets
thanks for listening ;)
questions?
or find me @graphific
• Computer Vision: 

Fei-Fei Li & Andrej Karpathy, Stanford course “Convolutional
Neural Networks for Visual Recognition”

http://vision.stanford.edu/teaching/cs231n
• Natural Language Processing:

Richard Socher, Stanford course “Deep Learning for Natural
Language Processing”,

http://cs224d.stanford.edu/
• Neural Nets:

Geoffrey Hinton, Coursera/Toronto, “Neural Networks for
Machine Learning"

https://www.coursera.org/course/neuralnets
Wanna Play?
• Bunch of tutorials: 

http://deeplearning.net/tutorial/
• Book:

Yoshua Bengio, et al, “Deep Learning”

http://www.iro.umontreal.ca/~bengioy/dlbook/
• UFLDL Tutorial

http://deeplearning.stanford.edu/tutorial/
• Reading Lists:

http://deeplearning.net/reading-list/ 

http://memkite.com/deep-learning-bibliography/
• Podcast

Talking Machines, http://www.thetalkingmachines.com/
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