This project is being moved to a new github repository. Also, the main project is being used in the url:
http://aiconscience.ddns.net/ai-models/
TensorFlow Boost's Repository
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Through project, we are creating a framework thanks to which you can read any kind of tag labeled data (like Kaggle problems, CSV and images); create train, validation and test set from them; choose the best machine learning algorithm for your data, and, besides, change the algorithm features.
Updating to Version 1.1:
- New advanced and simplified structure
- Using Tensorflow 2.0.0
Update Version 0.1:
- Now it is possible to save models configurations by problem and load previous models configurations.
- See json information by time in two different ways: "Information" and "Configuration".
- See graphs progress during training. After each epoch it will be saved a graph.
- You can decide if you want to save graphs after validation/test accuracies are surpass your limit.
- You can reset the configuration making backups.
- You can change dropout during training easily.
- You can restore previous tensorflow models easily.
- You have a method by problem: for each problem you can solve, you could created a method to process each input in a different way.
- Yoy have a "Setting.json" file for each problem where you only have to put the paths where you want to process your problem.
- You can see loss and accuracies in graphs and printed in the console.
- You can easily change the epochs and batch sizes.
- You have a CNN example treating a signal problem.
- You have a Framework web to test models trained with TFBoost.
Next Version:
- You will be able to do all this with a simple and beautiful user interface (in curse).
- You will have an example of a LSTM project (in curse)
Future Versions:
- In future, this project contains a graph visualization BEFORE TensorFlow generates his graphs.
All project use "Google Python Style Guide": https://google.github.io/styleguide/pyguide.html
TensorFlow Boost Web example (NEW):
http://aiconscience.ddns.net/ai-models/
TensorFlow Boost works as follows:
An example of 'information.json':
An example of a Accuracy Graph:
An example of a Loss Graph:
An example of code: Step by step structure (Python-Tensorflow Code)
"TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc."