https://github.com/phtrempe/l2a
This is a small project which aims to show an example of applied machine learning in Python 3 with the Keras library and its TensorFlow backend to train a neural network model for it to learn to add two integers.
https://github.com/phtrempe/l2a
applied data data-science deep-learning keras machine-learning neural-network tensorboard tensorflow
Last synced: 3 months ago
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This is a small project which aims to show an example of applied machine learning in Python 3 with the Keras library and its TensorFlow backend to train a neural network model for it to learn to add two integers.
- Host: GitHub
- URL: https://github.com/phtrempe/l2a
- Owner: PhTrempe
- License: mit
- Created: 2017-09-15T15:35:05.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-09-24T20:02:41.000Z (over 7 years ago)
- Last Synced: 2025-01-12T23:30:22.168Z (4 months ago)
- Topics: applied, data, data-science, deep-learning, keras, machine-learning, neural-network, tensorboard, tensorflow
- Language: Python
- Homepage:
- Size: 9.77 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# l2a (Learn to Add)
## Description
This is a small project which aims to show an example of applied machine
learning in Python 3 with the Keras library and its TensorFlow backend to
train a neural network model for it to learn to add two integers.
The project also aims to follow the 7 Steps of Machine Learning presented by
Google in [this](https://www.youtube.com/watch?v=nKW8Ndu7Mjw) YouTube video.
1. Gathering Data (`dataset_generator.py`)
1. Preparing Data (`dataset_preparer.py`)
1. Choosing a Model (`model_builder.py`)
1. Training (`trainer.py`)
1. Evaluation (`trainer.py`)
1. Hyperparameter Tuning (`hyperparameters.py`)
1. Prediction (`predictor.py`)
# Usage
## Installing Dependencies
conda install numpy scipy
pip install tensorflow tensorflow-gpu keras h5py
## Running the Training Process
python trainer.py
This will first generate a dataset if none exists yet.
It will then prepare the dataset if no prepared dataset exists yet.
After that, it will build the model using the model builder
(cf. `model_builder.py`) if no model exists yet.
If an existing model is found, this model will be loaded to continue its
training.
Once the prepared dataset and model are loaded, the training process is started.
N.B. Feel free to cancel the training process at any point, since it will be
possible to resume it later on by running the trainer again.
## Visualizing Training with TensorBoard
tensorboard --logdir=./logs
## Running Predictions
This will build the model using the model builder
(cf. `model_builder.py`) if no model exists yet.
If an existing model is found, this model will be loaded.
Once the model is loaded, it is used to make predictions on given inputs.
python predictor.py