Utilizing OpenGuided Waves dataset, this project involves pitch-catch values corresponding to Lamb waves on a carbon fiber plate at various temperatures. Implementing a Variational Autoencoder (VAE), the aim is to generate missing signals in the dataset based on user input for the desired temperature."
This model is a Variational Autoencoder with a architecture made by dense layers. It will utilize a free dataset uploaded in openguidedwaves (more info on GET_START) to build a model capable of generate lamb waves signal at a desired TEMPERATURE
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── models
│ ├── model_data <- Models data required for visualized
│ └── weight <- Trained and serialized model weight
│ ├── band <- model trained with all the temperature in the dataset
│ ├── sparse <- model trained with clusters of temperature in the dataset
│ └── standard <- model trained with all the temperature in the dataset
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│ └── print_h5_tree.py <- Generate the h5 tree to understand the structure of the dataset
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to turn raw data into python list
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn python list into features for modeling
│ │ └── build_features.py
│ │
│ └── models <- Scripts to train models and then use trained models to make
│ │ predictions
│ ├── predict_model.py
│ └── train_model.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience