Image reconstruction with 4 dimentional detection
CAE, VAE with additional layer for deprediction, connecting to the latent (middle) layer.
0. Put dataset in data directry with separate files.
1. Data processing
- run data_processing.py
- change plenty of png data to npy file, which helps calculation cost smaller
- normalized [0, 255] -> [0.0, 1.0]
2. train CAE, VAE
- run CAE(or VAE)_learning.py
- save best model, whose error is the smallest
3. test CAE, VAE
- run CAE(or VAE)_test.py
- output reconstruction image data
- extract latent space value
- output prediction result
4. PCA on latent space value
- use PCA.py
- output analysis with some graphs