James L. Weaver
Developer Advocate
 


jweaver@pivotal.io
JavaFXpert.com

@JavaFXpert

Katharine Beaumont
Developer / Mathematician

 


kbe@voxxed.com
voxxed.com

@KatharineCodes

Machine Learning

Deep Learning and Reinforcement Learning

About Presenter Katharine Beaumont

Writer and editor for Voxxed, interviewer for Devoxx and Voxxed Days, developer for fun :-)

@KatharineCodes

Perpetual student, wandering into software development from maths, science, publishing, politics, law...

Developer / Mathematician / Writer / Speaker -  Voxxed

About Presenter James Weaver

Java Champion, JavaOne Rockstar, plays well with others, etc :-)

@JavaFXpert

Author of several Java/JavaFX/RaspPi books

Developer Advocate & International Speaker for Pivotal

From introductory video in Machine Learning course (Stanford University & Coursera) taught by Andrew Ng.

@KatharineCodes  @JavaFXpert

Self-driving cars

@KatharineCodes  @JavaFXpert

Generating image descriptions

@KatharineCodes  @JavaFXpert

Supervised Learning

@KatharineCodes  @JavaFXpert

S​upervised learning regression problem

@KatharineCodes  @JavaFXpert

Unsupervised Learning

@KatharineCodes  @JavaFXpert

Unsupervised learning finds structure in unlabeled data

(e.g. market segment discovery, and social network analysis)

@KatharineCodes  @JavaFXpert

Reinforcement Learning

@KatharineCodes  @JavaFXpert

AlphaGo is a recent reinforcement learning success story

@KatharineCodes  @JavaFXpert

Supervised Learning

(Let's dive in now)

@KatharineCodes  @JavaFXpert

Supervised learning classification problem

(using the Iris flower data set)

@KatharineCodes  @JavaFXpert

@KatharineCodes  @JavaFXpert

Modeling the brain works well with machine learning
(ya think?)

(inputs)

(output)

@KatharineCodes  @JavaFXpert

Neural net visualization app (uses Spring and DL4J)

@KatharineCodes  @JavaFXpert

Entering feature values for prediction (classification)

@KatharineCodes  @JavaFXpert

Anatomy of an Artificial Neural Network

(aka Deep Belief Network when multiple hidden layers)

 

@KatharineCodes  @JavaFXpert

Simple neural network trained for XOR logic

forward propagation

@KatharineCodes  @JavaFXpert

Feedforward calculations with XOR example

For each layer:

Multiply inputs by weights:

(1 x 8.54) + (0 x 8.55) = 8.54

Add bias:

8.54 + (-3.99) = 4.55

Use sigmoid activation function:

1 / (1 + e

-4.55

) = 0.99

@KatharineCodes  @JavaFXpert

Simple neural network trained for XOR logic

back propagation (minimize cost function)

@KatharineCodes  @JavaFXpert

Back propagation

(Uses gradient descent to iteratively minimize the cost function)

@KatharineCodes  @JavaFXpert

Great website for data science / machine learning enthusiasts

@KatharineCodes  @JavaFXpert

Let’s use a dataset from kaggle.com to train a neural net on speed dating

@KatharineCodes  @JavaFXpert

Identify features and label we’ll use in the model

Let’s use 65% of the 8378 rows for training and 35% for testing

@KatharineCodes  @JavaFXpert

Code that configures our speed dating neural net

@KatharineCodes  @JavaFXpert

Trying our new speed dating neural net example

In this example, all features are continuous, and output is a one-hot vector

@KatharineCodes  @JavaFXpert

Making predictions with our speed dating neural net

Note that input layer neuron values are normalized

@KatharineCodes  @JavaFXpert

Output from training Speed Dating dataset

In iterationDone(), iteration: 0, score: 0.8100
In iterationDone(), iteration: 20, score: 0.5991
In iterationDone(), iteration: 40, score: 0.5414
In iterationDone(), iteration: 60, score: 0.5223
​In iterationDone(), iteration: 80, score: 0.5154

Examples labeled as 0 classified by model as 0: 1356 times
Examples labeled as 0 classified by model as 1: 354 times
Examples labeled as 1 classified by model as 0: 413 times
Examples labeled as 1 classified by model as 1: 800 times

==========================Scores========================
 Accuracy:  0.7351
 Precision: 0.7269
 Recall:    0.7239
 F1 Score:  0.7254

@KatharineCodes  @JavaFXpert

}

Is Optimizing your Neural Network a Dark Art ?

Excellent article by Preetham V V on neural networks and choosing hyperparameters

@KatharineCodes  @JavaFXpert

Regression Sum example

Features are continuous values, output is continuous value

@KatharineCodes  @JavaFXpert

Training a neural network to play Tic-Tac-Toe

@KatharineCodes  @JavaFXpert

Tic-Tac-Toe neural network architecture

Input layer: 9 one-hot vectors (27 nodes)

  • 1,0,0 (empty cell)
  • 0,1,0 (X in cell)
  • 0,0,1 (O in cell)

Hidden layer: 54 sigmoid neurons

Output layer: One-hot vector (9 nodes)

Client developed in JavaFX with Gluon mobile

0

0

0

0

0

1

1

1

1

0

0

0

0

0

0

0

0

1

0

/player?gameBoard=XXOOXIIOI

&strategy=neuralNetwork

"gameBoard": "XXOOXXIOI",

{

}

  ...

Java/Spring REST microservice

@KatharineCodes  @JavaFXpert

Tic-Tac-Toe training dataset

0,    1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0
3,    0,1,0, 0,0,1, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0
3,    0,1,0, 1,0,0, 0,0,1, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0
1,    0,1,0, 1,0,0, 1,0,0, 0,0,1, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0
1,    0,1,0, 1,0,0, 1,0,0, 1,0,0, 0,0,1, 1,0,0, 1,0,0, 1,0,0, 1,0,0
2,    0,1,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 0,0,1, 1,0,0, 1,0,0, 1,0,0
1,    0,1,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 0,0,1, 1,0,0, 1,0,0
2,    0,1,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 0,0,1, 1,0,0
2,    0,1,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 1,0,0, 0,0,1
4,    0,1,0, 0,0,1, 1,0,0, 0,1,0, 1,0,0, 1,0,0, 0,0,1, 1,0,0, 1,0,0
...

Play cell

Game board cell states before play

Leveraging the neural network as a function approximator

@KatharineCodes  @JavaFXpert

Tic-Tac-Toe training dataset

Generated using game theory minimax algorithm

@KatharineCodes  @JavaFXpert

Taking Tic-Tac-Toe for a spin

@KatharineCodes  @JavaFXpert

Reinforcement Learning

(Let's dive in now)

@KatharineCodes  @JavaFXpert

Using BURLAP for Reinforcement Learning

@KatharineCodes  @JavaFXpert

Learning to Navigate a Grid World with Q-Learning

@KatharineCodes  @JavaFXpert

Rules of this Grid World

  • Agent may move left, right, up, or down (actions)
  • Reward is 0 for each move
  • Reward is 5 for reaching top right corner (terminal state)
  • Agent can't move into a wall or off-grid
  • Agent doesn't have a model of the grid world.  It must discover as it interacts.

Challenge: Given that there is only one state that gives a reward, how can the agent work out what actions will get it to the reward?

(AKA the credit assignment problem)

Goal of an episode is to maximize total reward

@KatharineCodes  @JavaFXpert

Visualizing training episodes

From BasicBehavior example in  https://github.com/jmacglashan/burlap_examples

@KatharineCodes  @JavaFXpert

This Grid World's MDP (Markov Decision Process)

In this example, all actions are deterministic

@KatharineCodes  @JavaFXpert

Agent learns optimal policy from interactions with the environment (s, a, r, s')

@KatharineCodes  @JavaFXpert

Q-Learning approach to reinforcement learning

Left Right Up Down
...
2, 7 2.65 4.05 0.00 3.20
2, 8 3.65 4.50 4.50 3.65
2, 9 4.05 5.00 5.00 4.05
2, 10 4.50 4.50 5.00 3.65
...

Q-Learning table of expected values (cumulative discounted rewards) as a result of taking an action from a state and following an optimal policy.  Here's an explanation of how calculations in a Q-Learning table are performed.

Actions

States

@KatharineCodes  @JavaFXpert

Intuition and mathematics

[TODO: replace this slide with a set of slides that provide intuition and maths for filling in this Q-Learning table] 

Q-Learning approach to reinforcement learning

@KatharineCodes  @JavaFXpert

Expected future discounted rewards, and polices

@KatharineCodes  @JavaFXpert

This example used discount factor 0.9

Low discount factors cause agent to prefer immediate rewards

@KatharineCodes  @JavaFXpert

How often should the agent try new paths vs. greedily taking known paths?

@KatharineCodes  @JavaFXpert

Tic-Tac-Toe with Reinforcement Learning

Learning to win from experience rather than by being trained

@KatharineCodes  @JavaFXpert

Inspired by the Tic-Tac-Toe Example section...

@KatharineCodes  @JavaFXpert

Tic-Tac-Toe Learning Agent and Environment

X

O

Our learning agent is the "X" player, receiving +5 for winning, -5 for losing, and -1 for each turn

The "O" player is part of the Environment.  State and reward updates that it gives the Agent consider the "O" play.

@KatharineCodes  @JavaFXpert

Tic-Tac-Toe state is the game board and status

States 0 1 2 3 4 5 6 7 8
O I X I O X X I O, O won N/A N/A N/A N/A N/A N/A N/A N/A N/A
I  I  I  I  I  I O I X, in prog 1.24 1.54 2.13 3.14 2.23 3.32 N/A 1.45 N/A
I  I O I  I X O I X, in prog 2.34 1.23 N/A 0.12 2.45 N/A N/A 2.64 N/A
I  I O O X X O I X, in prog +4.0 -6.0 N/A N/A N/A N/A N/A -6.0 N/A
X I O I  I X O I X, X won N/A N/A N/A N/A N/A N/A N/A N/A N/A
...

Q-Learning table of expected values (cumulative discounted rewards) as a result of taking an action from a state and following an optimal policy

Actions (Possible cells to play)

Unoccupied cell represented with an I in the States column

@KatharineCodes  @JavaFXpert

Tic-Tac-Toe with Reinforcement Learning

@KatharineCodes  @JavaFXpert

Summary of neural network links (1/2)

@KatharineCodes  @JavaFXpert

Summary of neural network links (2/2)

@KatharineCodes  @JavaFXpert

Summary of reinforcement learning links

BURLAP library: http://burlap.cs.brown.edu

BURLAP examples including BasicBehavior:
https://github.com/jmacglashan/burlap_examples

Markov Decision Process:
https://en.wikipedia.org/wiki/Markov_decision_process

Q-Learning table calculations: http://artint.info/html/ArtInt_265.html

Exploitation vs. exploration:
https://en.wikipedia.org/wiki/Multi-armed_bandit

Reinforcement Learning: An Introduction:
https://webdocs.cs.ualberta.ca/~sutton/book/bookdraft2016sep.pdf

Tic-tac-toe reinforcement learning app:
https://github.com/JavaFXpert/tic-tac-toe-rl

@KatharineCodes  @JavaFXpert

James L. Weaver
Developer Advocate
 


jweaver@pivotal.io
JavaFXpert.com

@JavaFXpert

Katharine Beaumont
Developer / Speaker

 


kbe@voxxed.com
voxxed.com

@KatharineCodes

Machine Learning

Deep Learning and Reinforcement Learning

Machine Learning Exposed: Deep Learning and Reinforcement Learning

By javafxpert

Machine Learning Exposed: Deep Learning and Reinforcement Learning

Shedding light on machine learning, being gentle with the math.

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