lightning-factory 0.1.1

Quickly, with very little code, create PyTorch Lightning models

Lightning Factory

Lightning Factory is a Python library designed to simplify the creation of PyTorch Lightning models for various types of neural networks. It follows the parameterized factory pattern and allows users to specify custom configurations or use common defaults for quick prototyping.

Usage

pip install lightning_factory

To create a feed-forward neural network model:

import lightning_factory as lf

model = lf.ffnn(layers=[5, 3, 3, 1])

Easily define the layer structure:

Set default parameters when constructing the factory:

from lightning_factory import LightningFactory
from lightning_factory import LossFunction
from lightning_factory import ActivationFunction

lf = LightningFactory(
    loss_function=LossFunction.MSE,
    batch_size=32,
    activation_function=ActivationFunction.Softplus
)
model1 = lf.ffnn(layers=[5,3,3,1])
model2 = lf.ffnn(layers=[5,8,4,2,1], activation_function=ActivationFunction.Tanh)

The LightningFactory class uses the following defaults when making a class:

default = {
    'layers': None,
    'learning_rate': 0.001,
    'max_epochs': 8,
    'batch_size': 64,
    'loss_function': 'BCE',
    'activation_function': 'ReLU',
    'optimizer': 'Adam',
    'dropout': 0,
    'l1_regularization': 0,
    'l2_regularization': 0,
    'weight_initialization': 'xavier_uniform'
}

Testing Coverage

Name	Stmts	Miss	Branch	BrPart	Cover	Missing
lightning_factory/LightningFactory.py	18	0	8	0	100%
lightning_factory/__init__.py	3	0	0	0	100%
lightning_factory/create_ffnn.py	41	29	14	0	22%	19-30, 34-36, 40-46, 50-59, 63-71
lightning_factory/enums.py	121	0	18	0	100%
lightning_factory/functions.py	4	2	0	0	50%	6-7
TOTAL	187	31	40	0	80%

To run a coverage report

coverage report --format=markdown