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Deep Learning as a Cat/Dog Detector

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Roelof Pieters
Roelof Pieters

Talk given at PYCON Stockholm 2015 Intro to Deep Learning + taking pretrained imagenet network, extracting features, and RBM on top = 97 Accuracy after 1 hour (!) of training (in top 10% of kaggle cat vs dog competition)

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Deep Learning:
 and Deep Data-Science 12  May  2015 @graphific Roelof Pieters www.csc.kth.se/~roelof/ roelof@kth.se Graph Technologies R&D roelof@graph-technologies.com slides online at:
 https://www.slideshare.net/roelofp/deep-learning-as-a-catdog-detector
BUT FIRST… CAT PERSON? DOG PERSON? are you a…
in the next few minutes DETECTOR we ll be making a
main Libraries •sckikit-learn (machine learning) 
 http://scikit-learn.org •caffe (deep learning) - for training deep neural nets (for today: loading a pre-trained one) 
 http://caffe.berkeleyvision.org •theano (efficient gpu-powered math)
 http://www.deeplearning.net/software/theano/ •ipython notebook
 http://ipython.org/notebook.html
Code is ahead, soon… 
 I promise :)
“Data science is clearly a blend of the hackers' art, statistics and machine learning…” —Hilary Mason & Chris Wiggins, 2010 Data Science ?
(Drew Connoway 2010)
1 feature > Features = Awesomeness
1 feature 2 features > Features = Awesomeness
1 feature 2 features too few features/dimensions = overfitting
1 feature 2 features too few features/dimensions = overfitting 3 features > Features = Awesomeness
More Features = Awesomeness! 1 feature 2 features 3 features
++ Data Needs also grow!
(picture by Dato)
(picture by Dato)
Deep Learning? •A host of statistical machine learning techniques •Enables the automatic learning of feature hierarchies •Generally based on artificial neural networks
(picture by Dato) Deep Learning
Deep Learning? •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, (almost?) 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) (as summarised by Richard Socher 2014)
23 2006+ : The Deep Learning Conspirators
(chart by Clarifai) Audio Recognition
Image Recognition (chart by Clarifai)
Natural Langauge Processing …
Natural Langauge Processing … …
DL? How ? almost at the code…
(picture by Dato) Coding time! our ingredients…
(picture by Dato) Kaggle’s Cat vs Dog dataset (25k dog/cat pictures)
https://www.kaggle.com/c/dogs-vs-cats/data
Pretrained Convolutional 
 Neural Net (CNN)
(picture by Dato) 97% accuracy in < 1h MultiLayer Perceptron
Cooking Instructions1 Load Pretrained Net features image 2 Extract features for all training images 3. train MLP on those features
Cooking Instructions1 Load Pretrained Net
https://github.com/BVLC/caffe/wiki/Model-Zoo No Free Lunch… But Free Models!
# imports demo
# load pretrained deep neural net (convnet from Krizhevsky et al.'s NIPS 2012 ImageNet classification paper) demo
Cooking Instructions features image 2 Extract features for all training images 1 Load Pretrained Net
# feed image into the network and return internal feature representation of layer fc6 demo
#extract features from images demo
(…) #dump features as pickle file demo
Cooking Instructions1 Load Pretrained Net features image 2 Extract features for all training images 3. train MLP on those features
Pylearn2: Multilayer Perceptron (MLP) on top of extracted features #imports demo
#load earlier extracted features and labels #convert to input that pylearn understands demo
# create
 layers of
 MLP # with 
 softmax
 as final layer # trainer initialized
 with SGD, 
 momentum,
 dropout demo
# train/test splits demo
#start our MLP (pylearn experiment method) (…) demo already after 5 min:
90% 1 hour 50% accuracy
97% 1 hour 94% start at iteration #2 accuracy (…)
So are YOU more like a Dog or Cat? DETECTOR
(I might put it up as a Flask site online, if people are interested?) What 
 about
 me?
EASY PIEZY… THATS ALL!
as PhD candidate KTH/CSC: “Always interested in discussing Machine Learning, Deep Architectures, Graphs, and Language Technology” roelof@kth.se www.csc.kth.se/~roelof/ Data Science ConsultancyAcademic/Research roelof@graph-systems.com www.graph-technologies.com Gve Systems Graph Technologies 64 In Touch!
Wanna Know More? bit.ly/SthlmDL
• Theano - CPU/GPU symbolic expression compiler in python (from LISA lab at University of Montreal). http://deeplearning.net/software/theano/ • Pylearn2 - library designed to make machine learning research easy. http://deeplearning.net/software/ pylearn2/ • Torch - Matlab-like environment for state-of-the-art machine learning algorithms in lua (from Ronan Collobert, Clement Farabet and Koray Kavukcuoglu) http://torch.ch/ • more info: http://deeplearning.net/software links/ Wanna Play ? 66 Wanna Play ? General Deep Learning
• RNNLM (Mikolov)
 http://rnnlm.org • NB-SVM
 https://github.com/mesnilgr/nbsvm • Word2Vec (skipgrams/cbow)
 https://code.google.com/p/word2vec/ (original)
 http://radimrehurek.com/gensim/models/word2vec.html (python) • GloVe
 http://nlp.stanford.edu/projects/glove/ (original)
 https://github.com/maciejkula/glove-python (python) • Socher et al / Stanford RNN Sentiment code:
 http://nlp.stanford.edu/sentiment/code.html • Deep Learning without Magic Tutorial:
 http://nlp.stanford.edu/courses/NAACL2013/ 67 Wanna Play ? NLP
• cuda-convnet2 (Alex Krizhevsky, Toronto) (c++/ CUDA, optimized for GTX 580) 
 https://code.google.com/p/cuda-convnet2/ • Caffe (Berkeley) (Cuda/OpenCL, Theano, Python)
 http://caffe.berkeleyvision.org/ • OverFeat (NYU) 
 http://cilvr.nyu.edu/doku.php?id=code:start 68 Wanna Play ? Computer Vision

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