Documentation / User's manual in C++ :

The artificial neural networks done within Uranie need input from OPT++ and can also benefit from the computation power of graphical process unit (GPU) if available. Their implementation is done through the TANNModeler Uranie-class, and their conception and working flow is detailed in three steps in the following part, summarised in Figure V.3. For a thoroughly description of the artificial neural network, see [metho]

Figure V.3. Schematic description of the working flow of an artificial neural network as used in Uranie

The first step is the creation of the artificial neural network in Uranie; there are several compulsory information that should be given at this stage:

The three last information are gathered in a single string, using commas to separate clearly the different parts. This is further discussed in Section V.5.2.

The second step is the training of the ANN. Every formal neuron is a model that does not talk to any other neuron on the considered hidden layer, and that is characterised by (taking, for illustration purpose, an index ):

Combining these with the inputs give the internal state of the considered neuron,

Finally, the constructed neural network can be (and should be) exported. Different format are available to allow the user to plug the resulting function in its code whether it is using Uranie or not.

The TANNModeler constructor is specified with a TDataServer which contains the input attributes and the output attribute and with an integer to define the number of hidden neurons in the hidden layer. All these information are stored in a string. These string is the second argument of the constructor.

TDataServer * tds = new TDataServer("tds","my TDS");
tds->fileDataRead("flowrate_sampler_launcher_500.dat");

TANNModeler* tann = new TANNModeler(tds, "rw:r:tu:tl:hu:hl:l:kw,4,yhat");

In case one wants to use several hidden layer, the number of hidden neurons in each layer has to be specified in the architecture string, separated by commas. For example with three layers of 2, 3 and 4 neurons (dummy example for illustration purpose only), one would write something like this:

TANNModeler* tannML = new TANNModeler(tds, "rw:r:tu:tl:hu:hl:l:kw,2,3,4,yhat");

To split the data of the TDataServer in two databases, learning and test, we specify either a proportion (real value between 0.0 and 1.0) of patterns or the number of patterns (integer greater than 2) to build the learning database. The other patterns are stored in the test database. No validation base is explicitly created by Uranie.

The training and testing database split is done based on the ratio introduced in the TANNModeler constructor, introduced previously. This is further discussed in [metho].

tann->setNormalization(TANNModeler::kMinusOneOne);
tann->setFcnTol(1e-8);
tann->train(3, 2, "test");

tann->exportFunction("c++", "uranie_ann_flowrate","ANNflowrate");
tann->exportFunction("fortran", "uranie_ann_flowrate","ANNflowrate");
tann->exportFunction("pmml", "uranie_ann_flowrate.pmml", "ANNflowrate4","new");