Version 0.5.1

13/10/2021

====== Version 0.5.1 =======
EJML 0.41

Version 0.5

08/08/2019

====== Version 0.5 =======

Class
Layer -> Dense

Package
TrainingAlgorithm -> optimizer

Optimizer:
Backpropagation, CG, SCG, SGD
improvement print screen and log

Performance
abstract Z in activation functions

0.4

01/10/2018

====== Version 0.4 =======

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Performance Improvements
time execution reduced by 50% in SCG (Scaled Conjugate Gradient)

EJML Updated 0.34 -> 0.36

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Accelerate Methods

• Momentum
• Momentum Rumelhart
• Adam (Adaptive moment estimation)

Version 0.3

24/04/2018

====== Version 0.3 =======

---

Performance Improvements
time execution reduced by 75%

Internal Architecture Vectorized

EJML Updated 0.30 -> 0.34

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Neural Network

• removed outputAll

Feedforward

• outputLayers -> activations
• activationsDropout
• fixed getParameters

Layers

• outputDropout

Initializations

• HeInit

LossFunctions

• Improvement CrossEntropy
• Added Logistic Loss Function
• MSE vectorized
• RMSE vectorized

Backpropagation

• removed gradient
• removed minibatch
• fixed L2 regularization
• Performance incresed

Training Algorithms

• Conjugate Gradient Added
• Scaled Conjugate Gradient Added
• Scaled Conjugate Gradient with minibatch added
• Clean SGD
• SGD Numeric Added
• TrainingALgorithm Reorganized

Beta Rules Added for Conjugate Gradient

• Fletcher Reeves
• PolakRebiere

Gradients

• DropoutGradient
• NoiseGradient
• NumericGradient
• StandardGradient
• Gradient
• GradientFactory

Update Rules

• Momentum
• SGC
• Update
• UpdateRule

TransferFunctions

• update EJML 0.30 -> 0.34

Test Added

Initial Realease

• Feedforward
• Layer
• Logsig
• Tansig
• Softplus
• Open
• Save
• SGD
• Confusion Matrix
• RMSE
• MSE
• CrossEntropy
• Initialization