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ANN Toolbox

Artificial Neural Network toolbox
(28450 downloads for this version - 53120 downloads for all versions)
Details
Version
0.5
Authors
Ryurick M. Hristev
Allan Cornet
Owner Organization
Private Individual
Maintainers
S G
Administrator ATOMS
License
Creation Date
December 1, 2018
Source created on
Scilab 6.0.x
Binaries available on
Scilab 5.5.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Scilab 6.0.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Scilab 6.1.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Install command
--> atomsInstall("ANN_Toolbox")
Description
            This toolbox deals with Artificial Neural Networks. It is based on the
"Matrix ANN" book.

* User manual: https://atoms.scilab.org/toolboxes/ANN_Toolbox/0.5/files/ANN_Toolbox_0.5.pdf
* Changelog: https://atoms.scilab.org/toolboxes/ANN_Toolbox/0.5/files/changelog.txt
* Report bugs @ https://gitlab.com/scilab/forge/ann-toolbox/-/issues

CURRENT FEATURES
----------------
 - Only layered feedforward networks are supported *directly* at the moment
   (for others use the "hooks" provided)
 - Unlimited number of layers
 - Unlimited number of neurons per each layer separately
 - User defined activation function (defaults to logistic)
 - User defined error function (defaults to SSE)
 - Algorithms implemented so far:
    * standard (vanilla) with or without bias, on-line or batch
    * momentum with or without bias, on-line or batch
    * SuperSAB with or without bias, on-line or batch
    * Conjugate gradients
    * Jacobian computation
    * Computation of result of multiplication between 'vector' and
Hessian
 - Some helper functions provided

For full descriptions start with the toplevel "ANN" man page.

CONTENTS
--------
ann_FF — Algorithms for feedforward nets.
ann_FF_ConjugGrad — Conjugate Gradient algorithm.
ann_FF_Hess — computes Hessian by finite differences.
ann_FF_INT — internal implementation of feedforward nets.
ann_FF_Jacobian — computes Jacobian by finite differences.
ann_FF_Jacobian_BP — computes Jacobian trough backpropagation.
ann_FF_Mom_batch — batch backpropagation with momentum.
ann_FF_Mom_batch_nb — batch backpropagation with momentum (without bias).
ann_FF_Mom_online — online backpropagation with momentum.
ann_FF_Mom_online_nb — online backpropagation with momentum.
ann_FF_SSAB_batch — batch SuperSAB algorithm.
ann_FF_SSAB_batch_nb — batch SuperSAB algorithm (without bias).
ann_FF_SSAB_online — online SuperSAB training algorithm.
ann_FF_SSAB_online_nb — online backpropagation with SuperSAB
ann_FF_Std_batch — standard batch backpropagation.
ann_FF_Std_batch_nb — standard batch backpropagation (without bias).
ann_FF_Std_online — online standard backpropagation.
ann_FF_Std_online_nb — online standard backpropagation
ann_FF_VHess — multiplication between a "vector" V and Hessian
ann_FF_grad — error gradient trough finite differences.
ann_FF_grad_BP — error gradient trough backpropagation
ann_FF_grad_BP_nb — error gradient trough backpropagation (without bias)
ann_FF_grad_nb — error gradient trough finite differences
ann_FF_init — initialize the weight hypermatrix.
ann_FF_init_nb — initialize the weight hypermatrix (without bias).
ann_FF_run — run patterns trough a feedforward net.
ann_FF_run_nb — run patterns trough a feedforward net (without bias).
ann_d_log_activ — derivative of logistic activation function
ann_d_sum_of_sqr — derivative of sum-of-squares error
ann_log_activ — logistic activation function
ann_pat_shuffle — shuffles randomly patterns for an ANN
ann_sum_of_sqr — calculates sum-of-squares error
            
Files (6)
[98.34 kB]
Source code archive

[277.06 kB]
Miscellaneous file
User manual
[413.25 kB]
OS-independent binary for Scilab 5.5.x

[445.05 kB]
OS-independent binary for Scilab 6.0.x

[445.05 kB]
OS-independent binary for Scilab 6.1.x

[3.69 kB]
Miscellaneous file

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