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Machine Learning

James L. Weaver
Developer Advocate

Email: jweaver@pivotal.io
http://JavaFXpert.com

@JavaFXpert

About the Presenter

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

Developer Advocate & International Speaker for Pivotal

@JavaFXpert

Author of several Java/JavaFX/RaspPi books

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

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Self-driving cars

Generating image descriptions

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Supervised Learning

Supervised learning regression problem

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(from Andrew Ng’s Machine Learning course)

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Unsupervised Learning

Unsupervised learning finds structure in unlabeled data

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

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Reinforcement Learning

**AlphaGo is a recent reinforcement learning success story**

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Source: https://gogameguru.com/i/2016/03/AlphaGo-Lee-Sedol-game-3-game-over.jpg

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Supervised Learning

(Let's dive in now)

Supervised learning classification problem

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(using the Iris flower data set)

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

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(inputs)

(output)

Anatomy of an Artificial Neural Network

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(aka Deep Belief Network when multiple hidden layers)

Neural net visualization app (uses Spring and DL4J)

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github.com/JavaFXpert/visual-neural-net-server

github.com/JavaFXpert/ng2-spring-websocket-client

Entering feature values for prediction (classification)

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Visual Neural Network application architecture

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Spring makes REST services and WebSockets easy as π

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The app leverages machine learning libraries found at deeplearning4j.org

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To quickly create a Spring project, visit start.spring.io

Simple neural network trained for XOR logic

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forward propagation

Feedforward calculations with XOR example

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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

Simple neural network trained for XOR logic

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back propagation (minimize cost function)

Back propagation

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(Uses gradient descent to iteratively minimize the cost function)

Output from training Iris dataset

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In iterationDone(), iteration: 0, score: 1.0726
In iterationDone(), iteration: 300, score: 0.2017
In iterationDone(), iteration: 600, score: 0.0482
In iterationDone(), iteration: 900, score: 0.0266

Examples labeled as 0 classified by model as 0: 9 times
Examples labeled as 1 classified by model as 1: 14 times
Examples labeled as 1 classified by model as 2: 3 times
Examples labeled as 2 classified by model as 2: 27 times

==========================Scores========================
 Accuracy:  0.9434
 Precision: 0.9667
 Recall:    0.9412
 F1 Score:  0.9538

See Precision and recall (Wikipedia)

Great website for data science / machine learning enthusiasts

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kaggle.com

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

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Identify features and label we’ll use in the model

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

Code that configures our speed dating neural net

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Trying our new speed dating neural net example

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

Making predictions with our speed dating neural net

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Note that input layer neuron values are normalized

Regression Sum example

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Features are continuous values, output is continuous value

Training a neural network to play Tic-Tac-Toe

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Tic-Tac-Toe neural network architecture

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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

/player?gameBoard=XXOOXIIOI

&strategy=neuralNetwork

"gameBoard": "XXOOXXIOI",

{

}

...

Java/Spring REST microservice

https://github.com/JavaFXpert/tictactoe-player

Tic-Tac-Toe training dataset

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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

Tic-Tac-Toe training dataset

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**Generated using game theory minimax algorithm**

https://github.com/JavaFXpert/tic-tac-toe-minimax written in Java by @RoyVanRijn using guidance from the excellent Tic Tac Toe: Understanding The Minimax Algorithm article by @jasonrobertfox

Taking Tic-Tac-Toe for a spin

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Is Optimizing your Neural Network a Dark Art ?

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

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Reinforcement Learning

(Let's dive in now)

Using BURLAP for Reinforcement Learning

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burlap.cs.brown.edu

Learning to Navigate a Grid World with Q-Learning

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Rules of this Grid World

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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

Visualizing training episodes

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

This Grid World's MDP (Markov Decision Process)

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In this example, all actions are deterministic

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

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Source: http://www.mdpi.com/sensors/sensors-15-06668/article_deploy/html/images/sensors-15-06668-g002-1024.png

Q-Learning approach to reinforcement learning

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	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

Expected future discounted rewards, and polices

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This example used discount factor 0.9

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Low discount factors cause agent to prefer immediate rewards

Exploration vs. Exploitation

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

Tic-Tac-Toe with Reinforcement Learning

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Learning to win from experience rather than by being trained

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

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...of Reinforcement Learning: An Introduction

Tic-Tac-Toe Learning Agent and Environment

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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.

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

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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
...