carta
phd1994(1)

phd19941

Neural network implementations in fortran

Part of profphd_net(1).

Overview

profnet_* binaries are neural network implementations in Fortran. Due to the original design of the code, a specific binary is compiled for each particular network architecture, changing certain constants in the source code. Therefore, there is a binary for every network architecture used. Note: certain array structures are intentionally indexed out of bounds in some of the binaries.

Note:

This binary should only be used to run with pre-made training data, do not try to use it to train your network as it will produce undesired results. It was made to be used only as part of wrapping (dependent) packages and not as a standalone neural network program.

Options

This list is not exhaustive.

filePar

file with input parameters (also gives fileIn, fileOut)

1

"switch"

2

number of input units

3

number of hidden units

4

number of output units

5

number of samples

6

bitacc (typically 100)

7

file with input vectors

8

file with junctions

9

file with output of NN ("none" -> no file written)

10

optional=dbg

[inter]

will bring up dialog

Notes

1st MUST be "switch"!

tested only with 2 layers!

History

  • Debian 13 (Trixie) to Debian 12 (Bookworm): the text shown here
  • Debian 11 (Bullseye)
  • Debian 10 (Buster)
  • Debian 9 (Stretch)
  • Debian 8 (Jessie)
  • Debian 7 (Wheezy)