A flexible API for instantiating pytorch neural networks composed of sequential linear layers (torch.nn.Linear). Additionally, makes use of other elements within the torch.nn module.
Test implementation 1: Sequential linear neural network
import flexinet
nn = flexinet.models.NN()# example
nn = flexinet.models.compose_nn_sequential(in_dim=50,
out_dim=50,
activation_function=Tanh(),
hidden_layer_nodes={1: [500, 500], 2: [500, 500]},
dropout=True,
dropout_probability=0.1,
)Test implementation 2: vanilla linear VAE
Installation
To install the latest distribution from PYPI:
pip install flexinetAlternatively, one can install the development version:
git clone https://github.com/mvinyard/flexinet.git; cd flexinet;
pip install -e .Example
import flexinet as fn
import torch
X = torch.load("X_data.pt")
X_data = fn.pp.random_split(X)
X_data.keys()
dict_keys(['test', 'valid', 'train'])
model = fn.models.LinearVAE(X_data,
latent_dim=20,
hidden_layers=5,
power=2,
dropout=0.1,
activation_function_dict={'LeakyReLU': LeakyReLU(negative_slope=0.01)},
optimizer=torch.optim.Adam
reconstruction_loss_function=torch.nn.BCELoss(),
reparameterization_loss_function=torch.nn.KLDivLoss(),
device="cuda:0",
)
model.train(epochs=10_000, print_frequency=50, lr=1e-4)
model.plot_loss()
Contact
If you have suggestions, questions, or comments, please reach out to Michael Vinyard via email
