[Marvin-devel] [LibNN] Xml export

marvin-devel

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

[Marvin-devel] [LibNN] Xml export

From:	Olivier Ricordeau
Subject:	[Marvin-devel] [LibNN] Xml export
Date:	Mon, 01 Sep 2003 15:26:00 +0200
User-agent:	Mozilla/5.0 (X11; U; Linux i686; fr-FR; rv:1.4) Gecko/20030630

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

Hi all,

        I've just implemented the XML export in LibNN.
The following code instanciates an empty Multi Layer Perceptron and an
empty Time Delay Neural Network, and exports them as XML:

"open MlpNN
open TdNN
open XmlMlpnnVisitor
open XmlTdnnVisitor

let _ =
  let net = new mlpNN and
    vis = new xmlMlpnnVisitor in
    vis#setFileName "mlpnn.xml";
    vis#visit net;
    let net2 = new tdNN and
      vis2 = new xmlTdnnVisitor in
      vis2#setFileName "tdnn.xml";
      vis2#visit net2"

Groovy, isn't it? :)

- --
- -= *Olivier RICORDEAU* =-        http://freefolks.org
          < LibNN addict http://libnn.org >
lynx -source http://freefolks.org/key.asc | gpg --import
address@hidden _May the source be with you_
-----BEGIN PGP SIGNATURE-----
Version: GnuPG v1.2.3 (GNU/Linux)

iD8DBQE/U0jnDFRYKP6DJEwRAl0fAJ0T+PSVsqIJ1DKx+ZSIecbW2fenywCeIjwC
orqBHdvCb9qN6J4kwqMwTOw=
=qwj/
-----END PGP SIGNATURE-----

<!--  This file was generated by LibNN  -->
<!--          http://libnn.org          -->


<?xml version="1.0"?>


<!-- The DTD -->
<!DOCTYPE nn [

<!-- Neural Network (the XML file's root. `nn' class) -->
<!ELEMENT nn (step,corpus,env,(mlpnn|tdnn))>

<!-- Neural Networks kinds (concrete classes which inherit from the -->
<!-- `nn' class)                                                    -->
<!-- Multi Layer Perceptron (`mlpNN' class) -->
<!ELEMENT mlpnn (mlpnn_output_activation,mlpnn_input_sum,mlpnn_error,
mlpnn_weights,mlpnn_gradients,layer_nb,mlpnn_neurons_per_layer)>
<!ELEMENT mlpnn_output_activation (3d_array)>
<!ELEMENT mlpnn_input_sum (3d_array)>
<!ELEMENT mlpnn_error (3d_array)>
<!ELEMENT mlpnn_weights (4d_array)>
<!ELEMENT mlpnn_gradients (4d_array)>
<!ELEMENT mlpnn_neurons_per_layer (2d_array)>
<!-- Time Delay Neural Network (`tdNNN' class) -->
<!ELEMENT tdnn (tdnn_output_activation,tdnn_input_sum,tdnn_error,
tdnn_weights,tdnn_gradients,layer_nb,tdnn_delay,tdnn_features_nb,
tdnn_time_nb,tdnn_field_size)>
<!ELEMENT tdnn_output_activation (4d_array)>
<!ELEMENT tdnn_input_sum (4d_array)>
<!ELEMENT tdnn_error (4d_array)>
<!ELEMENT tdnn_weights (5d_array)>
<!ELEMENT tdnn_gradients (5d_array)>
<!ELEMENT tdnn_delay (2d_array)>
<!ELEMENT tdnn_features_nb (2d_array)>
<!ELEMENT tdnn_time_nb (2d_array)>
<!ELEMENT tdnn_field_size (2d_array)>

<!-- Common stuffs -->
<!-- Corpus (`corpus' class) -->
<!ELEMENT corpus (pattern*)>
<!-- Pattern (`pattern' class) -->
<!ELEMENT pattern (input,output)>
<!ELEMENT input (2d_array)>
<!ELEMENT output (2d_array)>
<!-- Environment (`env' class) -->
<!ELEMENT env (verbosity,randlimit,channel)>
<!ELEMENT verbosity (#CDATA)>
<!ELEMENT randlimit (#CDATA)>
<!ELEMENT channel (#CDATA)>
<!-- Arrays -->
<!ELEMENT 2d_array ((x,y,value)*)>
<!ELEMENT 3d_array ((x,y,z,value)*)>
<!ELEMENT 4d_array ((x,y,z,t,value)*)>
<!ELEMENT 5d_array ((x,y,z,t,u,value)*)>
<!ELEMENT x (#CDATA)>
<!ELEMENT y (#CDATA)>
<!ELEMENT z (#CDATA)>
<!ELEMENT t (#CDATA)>
<!ELEMENT u (#CDATA)>
<!ELEMENT value (#CDATA)>
<!-- Others -->
<!ELEMENT step (#CDATA)>
<!ELEMENT layer_nb (#CDATA)>

]>


<!-- Dump of the neural network -->
<nn>

<step>0.01</step>

<corpus>
</corpus>

<env>
<verbosity>0</verbosity>
<randlimit>2.</randlimit>
<channel>stderr</channel>
</env>

<mlpnn>
<mlpnn_output_activation>
<3d_array>
<x>0</x><y>0</y><value>0.</value>
</3d_array>
</mlpnn_output_activation>
<mlpnn_input_sum>
<3d_array>
<x>0</x><y>0</y><value>0.</value>
</3d_array>
</mlpnn_input_sum>
<mlpnn_error>
<3d_array>
<x>0</x><y>0</y><value>0.</value>
</3d_array>
</mlpnn_error>
<mlpnn_weights>
<4d_array>
<x>0</x><y>0</y><z>0</z><value>0.</value>
</4d_array>
</mlpnn_weights>
<mlpnn_gradients>
<4d_array>
<x>0</x><y>0</y><z>0</z><value>0.</value>
</4d_array>
</mlpnn_gradients>
<layer_nb>0</layer_nb>
<mlpnn_neurons_per_layer>
<2d_array>
<x>0</x><value>0</value>
</2d_array>
</mlpnn_neurons_per_layer>
</mlpnn>

</nn>

<!-- End of the dump -->

<!--  This file was generated by LibNN  -->
<!--          http://libnn.org          -->


<?xml version="1.0"?>


<!-- The DTD -->
<!DOCTYPE nn [

<!-- Neural Network (the XML file's root. `nn' class) -->
<!ELEMENT nn (step,corpus,env,(mlpnn|tdnn))>

<!-- Neural Networks kinds (concrete classes which inherit from the -->
<!-- `nn' class)                                                    -->
<!-- Multi Layer Perceptron (`mlpNN' class) -->
<!ELEMENT mlpnn (mlpnn_output_activation,mlpnn_input_sum,mlpnn_error,
mlpnn_weights,mlpnn_gradients,layer_nb,mlpnn_neurons_per_layer)>
<!ELEMENT mlpnn_output_activation (3d_array)>
<!ELEMENT mlpnn_input_sum (3d_array)>
<!ELEMENT mlpnn_error (3d_array)>
<!ELEMENT mlpnn_weights (4d_array)>
<!ELEMENT mlpnn_gradients (4d_array)>
<!ELEMENT mlpnn_neurons_per_layer (2d_array)>
<!-- Time Delay Neural Network (`tdNNN' class) -->
<!ELEMENT tdnn (tdnn_output_activation,tdnn_input_sum,tdnn_error,
tdnn_weights,tdnn_gradients,layer_nb,tdnn_delay,tdnn_features_nb,
tdnn_time_nb,tdnn_field_size)>
<!ELEMENT tdnn_output_activation (4d_array)>
<!ELEMENT tdnn_input_sum (4d_array)>
<!ELEMENT tdnn_error (4d_array)>
<!ELEMENT tdnn_weights (5d_array)>
<!ELEMENT tdnn_gradients (5d_array)>
<!ELEMENT tdnn_delay (2d_array)>
<!ELEMENT tdnn_features_nb (2d_array)>
<!ELEMENT tdnn_time_nb (2d_array)>
<!ELEMENT tdnn_field_size (2d_array)>

<!-- Common stuffs -->
<!-- Corpus (`corpus' class) -->
<!ELEMENT corpus (pattern*)>
<!-- Pattern (`pattern' class) -->
<!ELEMENT pattern (input,output)>
<!ELEMENT input (2d_array)>
<!ELEMENT output (2d_array)>
<!-- Environment (`env' class) -->
<!ELEMENT env (verbosity,randlimit,channel)>
<!ELEMENT verbosity (#CDATA)>
<!ELEMENT randlimit (#CDATA)>
<!ELEMENT channel (#CDATA)>
<!-- Arrays -->
<!ELEMENT 2d_array ((x,y,value)*)>
<!ELEMENT 3d_array ((x,y,z,value)*)>
<!ELEMENT 4d_array ((x,y,z,t,value)*)>
<!ELEMENT 5d_array ((x,y,z,t,u,value)*)>
<!ELEMENT x (#CDATA)>
<!ELEMENT y (#CDATA)>
<!ELEMENT z (#CDATA)>
<!ELEMENT t (#CDATA)>
<!ELEMENT u (#CDATA)>
<!ELEMENT value (#CDATA)>
<!-- Others -->
<!ELEMENT step (#CDATA)>
<!ELEMENT layer_nb (#CDATA)>

]>


<!-- Dump of the neural network -->
<nn>

<step>0.01</step>

<corpus>
</corpus>

<env>
<verbosity>0</verbosity>
<randlimit>2.</randlimit>
<channel>stderr</channel>
</env>

<tdnn>
<tdpnn_output_activation>
<4d_array>
<x>0</x><y>0</y><z>0</z><value>0.</value>
</4d_array>
</tdnn_output_activation>
<tdnn_input_sum>
<4d_array>
<x>0</x><y>0</y><z>0</z><value>0.</value>
</4d_array>
</tdnn_input_sum>
<tdnn_error>
<4d_array>
<x>0</x><y>0</y><z>0</z><value>0.</value>
</4d_array>
</tdnn_error>
<tdnn_weights>
<5d_array>
<x>0</x><y>0</y><z>0</z><t>0</t><value>0.</value>
</5d_array>
</tdnn_weights>
<tdnn_gradients>
<5d_array>
<x>0</x><y>0</y><z>0</z><t>0</t><value>0.</value>
</5d_array>
</tdnn_gradients>
<layer_nb>0</layer_nb>
<tdnn_delay>
<2d_array>
<x>0</x><value>0</value>
</2d_array>
</tdnn_delay>
<tdnn_features_nb>
<2d_array>
<x>0</x><value>0</value>
</2d_array>
</tdnn_features_nb>
<tdnn_time_nb>
<2d_array>
<x>0</x><value>0</value>
</2d_array>
</tdnn_time_nb>
<tdnn_field_size>
<2d_array>
<x>0</x><value>0</value>
</2d_array>
</tdnn_field_size>
</tdnn>

</nn>

<!-- End of the dump -->

[Prev in Thread]

Current Thread

[Next in Thread]

[Marvin-devel] [LibNN] Xml export, Olivier Ricordeau <=

Next by Date: [Marvin-devel] [LibNN] integration in madkit
Next by thread: [Marvin-devel] [LibNN] integration in madkit
Index(es):
- Date
- Thread