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

kaggle.com

@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

See Precision and recall (Wikipedia)

@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

1

0

1

0

/player?gameBoard=XXOOXIIOI

&strategy=neuralNetwork

"gameBoard": "XXOOXXIOI",

{

}

...

Java/Spring REST microservice

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

@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