🎨 Mixture-Density-Nets
A small PyTorch library for Mixture Density Networks.
Install
simply run
pip install mixture-density-nets
Example
from mixture_density_nets import MDN, MDDistribution
# ....
mdn = MDN(in_dim, out_dim, n_components)
# ....
mu, sigma, lambda_ = mdn(net(input_data))
dist = MDDistribution(mu, sigma, lambda_)
loss = dist.nll(targets).mean()
# ...
samples, clusters = dist.sample(n=20) # draw 20 samples
For a more thorough example see example.ipynb.
1 maintainer
Metadata
1 maintainer
Release files for Mixture-Density-Nets 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| Mixture-Density-Nets-0.1.1.tar.gz | 3.1 kB | Without provenance | Details for Mixture-Density-Nets-0.1.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| Mixture_Density_Nets-0.1.1-py3-none-any.whl | Python 3 | none | any | Details for Mixture_Density_Nets-0.1.1-py3-none-any.whl |
Total release size: 6.7 kB
Release files / Mixture-Density-Nets-0.1.1.tar.gz
| Download URL | Mixture-Density-Nets-0.1.1.tar.gz |
|---|---|
| Size | 3.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a14ef9b5cbaeb46f579334cd3b15417d5ebb3482e4142d9ce70a135fb9d593b9
|
|
BLAKE2b-256 checksum How to use checksums |
a06e673a54c7766af0d19f21bd42cbc8af98d0118789400f192dfed652b27acb
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.5
|
Release files / Mixture_Density_Nets-0.1.1-py3-none-any.whl
| Download URL | Mixture_Density_Nets-0.1.1-py3-none-any.whl |
|---|---|
| Size | 3.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
dac5a95f589be5d88318a744e0765fbbd0af19dd82de0a93b7cd1165ba7a261c
|
|
BLAKE2b-256 checksum How to use checksums |
3f701abcae4f795b718dff08d80aa99cbeca0d989b7985ccdcfef06c84f110cc
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.5
|
Security for Mixture-Density-Nets 0.1.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
