API to compose PyTorch neural networks on the fly.
Project description
Compose PyTorch neural networks with ease.
Installation
From PYPI (current version: v0.0.4
)
pip install torch-nets
Alternatively, install the development version from GitHub:
git clone https://github.com/mvinyard/torch-nets.git;
cd torch-nets; pip install -e .
Example API use-cases
from torch_nets import TorchNet
Create a feed-forward neural network
The only required arguments are in_features
and out_features
. The network can be made as simple or complex as you want through optional parameters.
net = TorchNet(
in_features=50,
out_features=50,
hidden=[400, 400],
activation="LeakyReLU",
dropout=0.2,
bias=True,
output_bias=True,
)
See output
TorchNet(
(hidden_1): Sequential(
(linear): Linear(in_features=50, out_features=400, bias=True)
(dropout): Dropout(p=0.2, inplace=False)
(activation): LeakyReLU(negative_slope=0.01)
)
(hidden_2): Sequential(
(linear): Linear(in_features=400, out_features=400, bias=True)
(dropout): Dropout(p=0.2, inplace=False)
(activation): LeakyReLU(negative_slope=0.01)
)
(output): Sequential(
(linear): Linear(in_features=400, out_features=50, bias=True)
)
)
Documentation
For more information, including examples of additional use-cases please visit the documentation (coming soon)! Additional use-cases include: Encoder
, Decoder
, AugmentedTorchNet
.
Potential future plans
- Flexible composition of
torch.optim
funcs. - Potential
pytorch_lightning
use-cases.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
torch-nets-0.0.4.tar.gz
(22.8 kB
view hashes)
Built Distribution
torch_nets-0.0.4-py3-none-any.whl
(27.9 kB
view hashes)
Close
Hashes for torch_nets-0.0.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 496b40823b408542c9af676757cfae755e8ed9936757127ac648eed11ea2628a |
|
MD5 | ff6aed5a61c188272c1628b57bec04ba |
|
BLAKE2b-256 | c55c91b75255e3edb1b0cce71b9a68dc0c69c7a81b9f315e13f472b14d50b422 |