shallowgibbs-doublebackpropagation 1.0.2

Shallow Gibbs Double Backpropagation

shallowgibbs-doublebackpropagation

Double Backpropagation Algorithm -- Implementation with the Shallow Gibbs Model

Double Backpropagation with the Shallow Gibbs Model

Installation

Install using pip pip install shallowgibbs-doublebackpropagation

Requirements

• Python 3.6 or greater
• scipy
• tensorflow
• pandas
• numpy
• joblib
• scikit-learn

Usage

Import the Shallow Gibbs Double Backpropagation module import shallowgibbs.doublebackpropagation as SGDBS You need to load some initial predictions from the Shallow Gibbs Model, or any alike-Structured Model. The model requires as parameters: the weigths matrix (W), the biases (b), and the response covariance matrix (Sigma). The model framework backpropagation is updated per observation using:

MSE = \left(y_{i}-\hat{y}_{i}\right)=\|y_{i}-\hat{y}_{e s t, i}\|^{2})

starting from \hat{\psi}_{0} with the equations:

\begin{gathered}
\hat{\psi}_{1, i} \longleftarrow \hat{\psi}_{0}-\epsilon_{\psi, 0} \frac{\partial M S E\left(y_{i}-\hat{y}_{e s t, i}\right)}{\partial \psi} \\
\hat{\psi}_{t, i} \longleftarrow \hat{\psi}_{t-1, i}-\epsilon_{\psi, t-1} \frac{\partial M S E\left(y_{i}-\hat{y}_{e s t}, i\right)}{\partial \psi}
\end{gathered}

where \psi is the set of parameters (w,b,Sigma) in our case. They are two additional equations that complete those above explained in reference [2] and well introduced in [1]. There are about the Training data, and test data predictions update. Please read reference [2] and the Jupyter Notebook for a guide note of usage and application.

Pypi Project Page

https://pypi.org/project/shallowgibbs-doublebackpropagation/1.0.2/

Notebook Page

https://github.com/kgalahassa/shallowgibbs-doublebackpropagation-notebook

References

[1] Muua, Alejandro, Alahassa, Nonvikan Karl-Augustt. The Shallow Gibbs Network, Double Backpropagation and Differential Machine learning, ScienceOpen Preprints (2021). https://www.scienceopen.com/document?vid=9aab283e-130f-4922-accb-20bef8faff9f

[2] Alejandro Murua, Nonvikan Karl-Augustt Alahassa. Double Back-Propagation and Differential Machine Learning. The Ninth Annual Canadian Statistics Student Conference (CSSC), Jun 2021, Ottawa, Canada. (hal-03265399) https://hal.archives-ouvertes.fr/hal-03265399