Compose PyTorch neural networks with ease.
Installation
From PYPI (current version: v0.0.5)
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.optimfuncs. - Potential
pytorch_lightninguse-cases.
1 maintainer
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