7 steps for highly effective deep neural networks

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Natalino will provide a fast-paced lecture on recognizing handwritten numbers with ANNs. In the past years much progress has been booked on the recognition of images: multi-layer perceptrons, inception networks, skip and residual nets, lstm, attention networks. In this talk, Natalino will go through 7 different architectures on how to classify handwritten numbers using keras and tensorflow. For each neural network architecture, he will spend some time on the theory, the coding, and the quality of the results. Code and Fun included!. original file: https://drive.google.com/file/d/0BwNrPuGaMi8PbVhUYUVKWUhGRjQ

Données & analyses

7 steps for highly effective
deep neural networks
Natalino Busa - Head of Data Science

2 Natalino Busa - @natbusa
O’Reilly Author and Speaker
Teradata EMEA Practice Lead on Open Source Technologies
Teradata Principal Data Scientist
ING Group Enterprise Architect: Cybersecurity, Marketing, Fintech
Cognitive Finance Group Advisory Board Member
Philips Senior Researcher, Data Architect
Linkedin and Twitter:
@natbusa

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Natalino Busa - @natbusa
X: independent variable
Y: dependent variable
Linear Regression:
How to best fit a line to some data

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input : 784 numbers
output: 10 classes
28x28 pixels
Classification: Handwritten digits

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Sharing the (Not so) Secret Lore:
Keras & Tensorflow
Some smaller projects:
Tflearn, Tensorlayers
http://keras.io/

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1: Single Layer Perceptron
“dendrites”
Axon’s
response
Activation function

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More activation functions:
https://en.wikipedia.org/wiki/Activation_function

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Single Layer Neural Network
Takes: n-input features: Map them to a soft “binary” space
∑
x1
x2
xn
f
1: Single Layer Perceptron (binary classifier)

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1: Single Layer Perceptron (multi-class classifier)
Values between 0 and 1
Sum of all outcomes = 1
It produces an estimate of a probability!
From soft binary space to predicting probabilities:
Take n inputs, Divide by the sum of the predicted values
∑
x1
x2
xn
f
∑ f
softmax ‘1’: 95%
‘8’: 5%

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Natalino Busa - @natbusa
1: Single Layer Perceptron
Minimize costs:
The cost function depends on:
- Parameters of the model
- How the model “composes”
Goal :
Reduce the mean probability error
modify the parameters to reduce the error! Vintage math from last century

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Supervised Learning
Stack layers of perceptrons
- Feed Forward Network
- Scoring goes
from input to output
- Back propagate the error
from output to input
SOFTMAX
Input parameters
classes (estimated probabilities)
Feed-forwardfunctions
Cost function
supervised : actual output
BackPropagateErrors

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1. Single Layer Perceptron

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1. Single Layer Perceptron

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1. Single Layer Perceptron

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1. Single Layer Perceptron

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2. Multi Layer Perceptron

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2. Multi Layer Perceptron

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2. Multi Layer Perceptron

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2. Multi Layer Perceptron

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3. Convolution
From Krizehvsky et al. (2012)

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3. Convolution
diagrams:
By Aphex34 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45659236
By Aphex34 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45673581
CC0, https://en.wikipedia.org/w/index.php?curid=48817276
convolution Max pooling RELU / ELU

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3. Convolution

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3. Convolution

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3. Batch Normalization

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3. Batch Normalization ( example for MLP)

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3. Batch Normalization ( example for MLP)

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3. Batch Normalization ( example for MLP)
Activation function:
SIGMOID

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3. Batch Normalization ( example for MLP)
Activation function:
RELU

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Natalino Busa - @natbusa
4. Regularization: Prevent overfitting in ANNs
- Batch Normalization
- RELU/ELU
- RESIDUAL / SKIP Networks
- DROP LAYER
- REDUCE PRECISION (HUFFMAN ENCODING)
In general ANN are parameters rich, constraining the
parameter space usually produces better results and speed
up the learning

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5. Inception architectures
Cannot be stacked!

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5. Inception architectures

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5. Inception architectures
compression
Avoid
dimensions
explosion

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5. Inception architectures

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5. Inception architectures (top architecture)

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6. Residual Networks
https://culurciello.github.io/tech/2016/06/04/nets.html

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6. Residual Networks

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6. Residual + Inception Networks

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7. LSTM on Images

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7. LSTM on Images

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7. LSTM on Images
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

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7. LSTM on Images

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7. LSTM on Images

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7. LSTM on ConvNets (bonus slide)

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All the codez :)
https://github.com/natbusa/deepnumbers

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Natalino Busa - @natbusa
Meta- References
… just a few articles, but extremely dense in content.
A must read!
https://keras.io/
http://karpathy.github.io/neuralnets/
https://culurciello.github.io/tech/2016/06/04/nets.html
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
https://gab41.lab41.org/batch-normalization-what-the-hey-d480039a9e3b

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1. 7 steps for highly effective deep neural networks Natalino Busa - Head of Data Science

2. 2 Natalino Busa - @natbusa O’Reilly Author and Speaker Teradata EMEA Practice Lead on Open Source Technologies Teradata Principal Data Scientist ING Group Enterprise Architect: Cybersecurity, Marketing, Fintech Cognitive Finance Group Advisory Board Member Philips Senior Researcher, Data Architect Linkedin and Twitter: @natbusa

3. 3 Natalino Busa - @natbusa X: independent variable Y: dependent variable Linear Regression: How to best fit a line to some data

4. 4 Natalino Busa - @natbusa input : 784 numbers output: 10 classes 28x28 pixels Classification: Handwritten digits

5. 5 Natalino Busa - @natbusa Sharing the (Not so) Secret Lore: Keras & Tensorflow Some smaller projects: Tflearn, Tensorlayers http://keras.io/

6. 6 Natalino Busa - @natbusa 1: Single Layer Perceptron “dendrites” Axon’s response Activation function

7. 7 Natalino Busa - @natbusa More activation functions: https://en.wikipedia.org/wiki/Activation_function

8. 8 Natalino Busa - @natbusa Single Layer Neural Network Takes: n-input features: Map them to a soft “binary” space ∑ x1 x2 xn f 1: Single Layer Perceptron (binary classifier)

9. 9 Natalino Busa - @natbusa 1: Single Layer Perceptron (multi-class classifier) Values between 0 and 1 Sum of all outcomes = 1 It produces an estimate of a probability! From soft binary space to predicting probabilities: Take n inputs, Divide by the sum of the predicted values ∑ x1 x2 xn f ∑ f softmax ‘1’: 95% ‘8’: 5%

10. 10 Natalino Busa - @natbusa 1: Single Layer Perceptron Minimize costs: The cost function depends on: - Parameters of the model - How the model “composes” Goal : Reduce the mean probability error modify the parameters to reduce the error! Vintage math from last century

11. 11 Natalino Busa - @natbusa Supervised Learning Stack layers of perceptrons - Feed Forward Network - Scoring goes from input to output - Back propagate the error from output to input SOFTMAX Input parameters classes (estimated probabilities) Feed-forwardfunctions Cost function supervised : actual output BackPropagateErrors

12. 12 Natalino Busa - @natbusa Let’s go!

13. 13 Natalino Busa - @natbusa 1. Single Layer Perceptron

14. 14 Natalino Busa - @natbusa 1. Single Layer Perceptron

15. 15 Natalino Busa - @natbusa 1. Single Layer Perceptron

16. 16 Natalino Busa - @natbusa 1. Single Layer Perceptron

17. 17 Natalino Busa - @natbusa Tensorboard

18. 18 Natalino Busa - @natbusa Tensorboard

19. 19 Natalino Busa - @natbusa Tensorboard

20. 20 Natalino Busa - @natbusa 2. Multi Layer Perceptron

21. 21 Natalino Busa - @natbusa 2. Multi Layer Perceptron

22. 22 Natalino Busa - @natbusa 2. Multi Layer Perceptron

23. 23 Natalino Busa - @natbusa 2. Multi Layer Perceptron

24. 24 Natalino Busa - @natbusa 3. Convolution From Krizehvsky et al. (2012)

25. 25 Natalino Busa - @natbusa 3. Convolution diagrams: By Aphex34 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45659236 By Aphex34 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45673581 CC0, https://en.wikipedia.org/w/index.php?curid=48817276 convolution Max pooling RELU / ELU

26. 26 Natalino Busa - @natbusa 3. Convolution

27. 27 Natalino Busa - @natbusa 3. Convolution

28. 28 Natalino Busa - @natbusa 3. Batch Normalization

29. 29 Natalino Busa - @natbusa 3. Batch Normalization ( example for MLP)

30. 30 Natalino Busa - @natbusa 3. Batch Normalization ( example for MLP)

31. 31 Natalino Busa - @natbusa 3. Batch Normalization ( example for MLP) Activation function: SIGMOID

32. 32 Natalino Busa - @natbusa 3. Batch Normalization ( example for MLP) Activation function: RELU

33. 33 Natalino Busa - @natbusa 4. Regularization: Prevent overfitting in ANNs - Batch Normalization - RELU/ELU - RESIDUAL / SKIP Networks - DROP LAYER - REDUCE PRECISION (HUFFMAN ENCODING) In general ANN are parameters rich, constraining the parameter space usually produces better results and speed up the learning

34. 34 Natalino Busa - @natbusa 5. Inception architectures Cannot be stacked!

35. 35 Natalino Busa - @natbusa 5. Inception architectures

36. 36 Natalino Busa - @natbusa 5. Inception architectures compression Avoid dimensions explosion

37. 37 Natalino Busa - @natbusa 5. Inception architectures

38. 38 Natalino Busa - @natbusa 5. Inception architectures (top architecture)

39. 39 Natalino Busa - @natbusa 6. Residual Networks https://culurciello.github.io/tech/2016/06/04/nets.html

40. 40 Natalino Busa - @natbusa 6. Residual Networks

41. 41 Natalino Busa - @natbusa 6. Residual + Inception Networks

42. 42 Natalino Busa - @natbusa 7. LSTM on Images

43. 43 Natalino Busa - @natbusa 7. LSTM on Images

44. 44 Natalino Busa - @natbusa 7. LSTM on Images http://colah.github.io/posts/2015-08-Understanding-LSTMs/

45. 45 Natalino Busa - @natbusa 7. LSTM on Images

46. 46 Natalino Busa - @natbusa 7. LSTM on Images

47. 47 Natalino Busa - @natbusa 7. LSTM on ConvNets (bonus slide)

48. 48 Natalino Busa - @natbusa All the codez :) https://github.com/natbusa/deepnumbers

49. 49 Natalino Busa - @natbusa Meta- References … just a few articles, but extremely dense in content. A must read! https://keras.io/ http://karpathy.github.io/neuralnets/ https://culurciello.github.io/tech/2016/06/04/nets.html http://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://gab41.lab41.org/batch-normalization-what-the-hey-d480039a9e3b

7 steps for highly effective deep neural networks

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