Home¶
lucidmode A Lucid Framework for Interpretable Machine Learning Models |
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Author: |
IFFranciscoME - if.francisco.me@gmail.com |
Version: |
v0.4.1-beta1.0 |
License: |
GPL-3.0 License. |
Repository: |
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Datasets¶
Public Datasets: MNIST, Fashion MNIST
Special Datasets: OHLCV + Symbolic Features of Cryptocurrencies (ETH, BTC)
Artificial Neural Network¶
Feedforward Multilayer perceptron with backpropagation.
Methods¶
fit: Fit model to data
predict: Prediction according to model
Functionality¶
Weights Initialization: With 4 types of criterias (zeros, xavier, common, he)
Activation Functions: sigmoid, tanh, softmax
Cost Functions: Sum of Squared Error, Binary Cross-Entropy, Multi-Class Cross-Entropy
Regularization: L1, L2, ElasticNet for weights in cost function and in gradient updating
Optimization: Weights optimization with Stochastic, Batch and Gradient Descent
Metrics: Accuracy, Confusion Matrix (Binary and Multiclass), Confusion Tensor (Multiclass OvR)
Interpretability¶
Visualizations: Cost evolution, Weights on layers, Convolution operation, Image catalog
Author/Principal Maintainer¶
IFFranciscoME Associate Professor of Financial Engineering and Financial Machine Learning @ITESO (Western Institute of Technology and Higher Education)
License¶
GNU General Public License v3.0
Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights..
Contact¶
For more information in reggards of this repo, please contact if.francisco.me@gmail.com