lightcone
A framework to explore the latent space of convolutional autoencoders
implemented in pytorch.
Example
Compose your decoder and your encoder into the lightcone
autoencoder:
from lightcone.models import AutoEncoder
model = AutoEncoder(encoder=your_encoder, decoder=your_decoder)
After model has been training, the latent space can be explored in a
Jupyter-Notebook as follows
model.explore(data_loader=your_data_loader)
Jupyter Dash
Make sure to install and activate the Jupyter notebook extenstion
jupyter nbextension install --py jupyter_dash
jupyter nbextension enable --py jupyter_dash
Metadata
Release files for lightcone 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lightcone-0.1.0.tar.gz | 7.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lightcone-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 14.6 kB
Release files / lightcone-0.1.0.tar.gz
| Download URL | lightcone-0.1.0.tar.gz |
|---|---|
| Size | 7.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.1 CPython/3.11.1
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Release files / lightcone-0.1.0-py3-none-any.whl
| Download URL | lightcone-0.1.0-py3-none-any.whl |
|---|---|
| Size | 7.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.11.1
|