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Machine Learning.pptx
- 1. 9/15/2020 Using TensorFlow and Keras to emulate expert behavior
- 2. • Senior Software Engineer working on every stack since 2009 • Spent my career working as a full stack developer and have been involved in all aspects of delivering high quality software • Started with Daugherty in February of 2015 – Since then I have been working for many different brand name clients in the Saint Louis area developing and delivering pragmatic solutions for their business needs • Clients – AEP River Operations – Anheuser Busch – New Balance – Bryan Cave – Express Scripts – Bayer 2 Instructor Biography
- 3. Confidential and Proprietary to Daugherty Business Solutions 3 • Terminology – Machine Learning – Convolutional Neural Networks • Frameworks – TensorFlow – Keras • Problem Statement • Solution – Architecture – Data/Encoding – Training Technique • Demo – Model Configuration – Game Replay • Questions? Presentation Outline
- 4. • Machine Learning – The study of computer algorithms that improve automatically through experience • Convolutional Neural Networks – A class of deep neural networks most commonly applied to analyzing visual imagery 4 Terminology
- 5. • TensorFlow – Symbolic math library frequently used for machine learning applications such as neural networks – Developed by Google and released as open source in 2015 • Keras – Open source neural-network library designed to enable fast experimentation with deep neural networks 5 Frameworks
- 7. 7 Solution – Data Generation Java Virtual Machine (JVM) Bots Python Interpreter Engine Game state Orders Inter-Process Communication done via TCP using Py4J Play Results File System
- 8. 8 Solution – Training Python Interpreter Training File System Keras Tensorflow Encode Play Results Train Model Load Play Results Create Model Save Model
- 9. 9 Solution – Play Java Virtual Machine (JVM) Bots Python Interpreter Engine Game state Orders Inter-Process Communication done via TCP using Py4J Neural Network Orders Game state
- 10. • Game State – The replay data from the data generation phase are loaded from the file system – These are converted into turn slices that have a representation of every ant at every turn and the movement decision that was made for that ant – All ant move decisions up to the point where the bot got the most points are included in the training set – The examples for training then become a classification problem where the view of the ant at a specific turn should be labeled with the direction that the ant was moved to • Encoding – Dense, 1d convolutional neural networks, and 2d convolutional neural networks were tested for this problem • 2d Convolutional networks seemed to have the best performance – input shape of n (examples) x 12 (rows) x 12 (columns) x 7 channels (game_state) – Output shape of n (examples) x 5 (direction) – ~68% test accuracy with ~700k examples – The view of the ant and the movement decision are all categorical data so one hot encoding is used to convert enumeration values into binary arrays 10 Game State Representation and Data Encoding
- 11. • Categorical cross entropy used for loss function and categorical accuracy used for the testing metric. • Tested variations of models with different values – Learning_rate – Strides – Channel count • Tensorboard – Pretty useful tool that can provide visualizations of the model training run loss and accuracy – The visualations of model weights is very cool looking but I’m still not sure how to use that • Custom graphs that would plot the learning rate as a function of batch size to evaluate whether the model had a high bias or a high variance problem 11 Training Technique
- 12. Demo 12
- 13. Questions? 13
Editor's Notes
- Link to github page for model https://github.com/Jason-Turan0/ants/blob/43e4eb32eef4113917930270653143a2cfa67b73/ants_ai/training/neural_network/model_factory.py#L76 Demo game played by neural network