marcoscravero2175 / Machine-Learning-Doodle-Recognition

A project on making a game like Quick Draw using Machine Learning with Convolutional Neural Network.

Machine Learning: Doodle Recognition with Convolutional Neural Network

Step by step tutorial series on making a game like Quick, Draw!. It is an online game that challenges players to draw a doodle and then artificial intelligence guesses what the drawings represent.

This tutorial series shows the whole process of developing the same game called Doodle Predictor that runs directly in the browser.

To get a quick insight into the project, watch the video trailer:
https://www.youtube.com/watch?v=kLF5vx5Ya1A

Tutorial Info

The game is made in HTML5 using Phaser 2 framework and TensorFlow.js library for machine learning.

To classify drawings, we will implement an Artificial Intelligence (AI) based on Machine Learning (ML) and Convolutional Neural Network (CNN).

To avoid loading big data files, the model will be trained on a small subset of the Quick Draw Dataset.

Here is a screenshot of the fully completed Doodle Predictor game:

Play Doodle Predictor here:
https://www.askforgametask.com/tutorial/doodle-recognition-convolutional-neural-network-intro

Start reading articles about the full series here:
https://www.askforgametask.com/tutorial/doodle-recognition-convolutional-neural-network-part1

Visit us:
https://www.askforgametask.com

Tutorial Topics

This project is divided into the following 8 parts:

• Part 1: Project Setup
• Part 2: Getting Data
• Part 3: Building the Model
• Part 4: Training the Model
• Part 5: Predicting Samples
• Part 6: Drawing Doodles
• Part 7: Recognizing Doodles
• Part 8: Adding More Doodle Categories

Each part contains a video and a fully commented source code so you can easily follow the game development step by step.

Running the Game

To play this game locally, you need to run it in a browser using a local web server as follows:

1. install, configure and run a web server: for instance, let's suppose your server is XAMPP installed in C:\Xampp

2. navigate to the server document root: C:\Xampp\htdocs

3. create a new folder called 'doodle_predictor': C:\Xampp\htdocs\doodle_predictor

4. download the project

5. copy all project files directly in C:\Xampp\htdocs\doodle_predictor

6. now you should have the following folder structure under C:\Xampp\htdocs\doodle_predictor:
   \assets (with game resources)
   \data (with datasets)
   \libs (with libraries)
   \part1 (with source files of the part 1)
   ...

7. open a web browser and navigate to the http://localhost:<port>/doodle_predictor/part1
   to run the game of the part 1

Part 1: Project Setup

In this part, we just create a new Phaser Game object and build the basic game structure.

Updates:

main.js

• created the Main Program with the main state
• defined all substates of the main state
• added the main loop where the entire game logic will be implemented

ui.js

• created a new User Interface class to allow users an interaction with the game
• created 'Play More Games' button with the corresponding trigger function
• created text object for showing messages in the status-bar

Links:

• Article (Part 1)
• Video (Part 1)

Part 2: Getting Data

In this part, we load three datasets (car, fish and snowman) and split them into training and test data.

Updates:

main.js

• added routines for opening and loading datasets

ui.js

• no changes

cnn.js

• created a new CNN class for implementing a Convolutional Neural Network model
• added function for splitting dataset into training and test data

Part 3: Building the Model

In this part, we build a Convolutional Neural Network (CNN) model for image classification.

Updates:

main.js

• minor changes

ui.js

• no changes

cnn.js

• added code for creating a Convolutional Neural Network (CNN) model

Part 4: Training the Model

In this part, we train, test and evaluate the Convolutional Neural Network (CNN) model. Additionally, we plot graphs of the model's Loss and Accuracy during training.

Updates:

main.js

• added logic for starting and executing the training in the main loop
• added actions that must be performed on clicking "Train More" button

ui.js

• created bitmaps for displaying loss and accuracy graphs
• created "Train More" button with the corresponding trigger function
• added function for plotting graphs

cnn.js

• defined CNN parameters
• added function for executing the training loop
• added function for fetching Training Batch
• added function for fetching Test Batch

Part 5: Predicting Samples

Coming soon...

Part 6: Drawing Doodles

Coming soon...

Part 7: Recognizing Doodles

Coming soon...

Part 8: Adding More Doodle Categories

Coming soon...