Compose PyTorch neural networks with ease.
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 .
from torch_nets import TorchNet
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)
)
)
For more information, including examples of additional use-cases please visit the documentation (coming soon)! Additional use-cases include: Encoder
, Decoder
, AugmentedTorchNet
.
- Flexible composition of
torch.optim
funcs. - Potential
pytorch_lightning
use-cases.