IsValid() public method

Determine if the specified neural network can be flat. If it can a null is returned, otherwise, an error is returned to show why the network cannot be flattened.
public IsValid ( IEngineMachineLearning eml ) : String
eml IEngineMachineLearning The network to check.
return String
Exemplo n.º 1
0
        /// <summary>
        /// Create the flat neural network.
        /// </summary>
        public void Flatten()
        {
            bool isRBF = false;
            IDictionary <ILayer, FlatLayer>      regular2flat = new Dictionary <ILayer, FlatLayer>();
            IDictionary <FlatLayer, ILayer>      flat2regular = new Dictionary <FlatLayer, ILayer>();
            IList <ObjectPair <ILayer, ILayer> > contexts     = new List <ObjectPair <ILayer, ILayer> >();

            this.flat = null;

            ValidateForFlat val = new ValidateForFlat();

            if (val.IsValid(this.network) == null)
            {
                if (this.layers.Count == 3 &&
                    this.layers[1] is RadialBasisFunctionLayer)
                {
                    RadialBasisFunctionLayer rbf = (RadialBasisFunctionLayer)this.layers[1];
                    this.flat = new FlatNetworkRBF(this.network.InputCount,
                                                   rbf.NeuronCount, this.network.OutputCount,
                                                   rbf.RadialBasisFunction);
                    FlattenWeights();
                    this.flatUpdate = FlatUpdateNeeded.None;
                    return;
                }

                int         flatLayerCount = CountNonContext();
                FlatLayer[] flatLayers     = new FlatLayer[flatLayerCount];

                int index = flatLayers.Length - 1;
                foreach (ILayer layer in this.layers)
                {
                    if (layer is ContextLayer)
                    {
                        ISynapse inboundSynapse = network.Structure
                                                  .FindPreviousSynapseByLayerType(layer,
                                                                                  typeof(BasicLayer));
                        ISynapse outboundSynapse = network
                                                   .Structure
                                                   .FindNextSynapseByLayerType(layer, typeof(BasicLayer));

                        if (inboundSynapse == null)
                        {
                            throw new NeuralNetworkError(
                                      "Context layer must be connected to by one BasicLayer.");
                        }

                        if (outboundSynapse == null)
                        {
                            throw new NeuralNetworkError(
                                      "Context layer must connect to by one BasicLayer.");
                        }

                        ILayer inbound  = inboundSynapse.FromLayer;
                        ILayer outbound = outboundSynapse.ToLayer;

                        contexts
                        .Add(new ObjectPair <ILayer, ILayer>(inbound, outbound));
                    }
                    else
                    {
                        double bias = this.FindNextBias(layer);

                        IActivationFunction activationType;
                        double[]            param = new double[1];

                        if (layer.ActivationFunction == null)
                        {
                            activationType = new ActivationLinear();
                            param          = new double[1];
                            param[0]       = 1;
                        }
                        else
                        {
                            activationType = layer.ActivationFunction;
                            param          = layer.ActivationFunction.Params;
                        }

                        FlatLayer flatLayer = new FlatLayer(activationType, layer
                                                            .NeuronCount, bias, param);

                        regular2flat[layer]     = flatLayer;
                        flat2regular[flatLayer] = layer;
                        flatLayers[index--]     = flatLayer;
                    }
                }

                // now link up the context layers
                foreach (ObjectPair <ILayer, ILayer> context in contexts)
                {
                    // link the context layer on the FlatLayer
                    ILayer   layer   = context.B;
                    ISynapse synapse = this.network
                                       .Structure
                                       .FindPreviousSynapseByLayerType(layer, typeof(BasicLayer));
                    FlatLayer from = regular2flat[context.A];
                    FlatLayer to   = regular2flat[synapse.FromLayer];
                    to.ContextFedBy = from;
                }

                this.flat = new FlatNetwork(flatLayers);

                // update the context indexes on the non-flat network
                for (int i = 0; i < flatLayerCount; i++)
                {
                    FlatLayer fedBy = flatLayers[i].ContextFedBy;
                    if (fedBy != null)
                    {
                        ILayer   fedBy2  = flat2regular[flatLayers[i + 1]];
                        ISynapse synapse = FindPreviousSynapseByLayerType(fedBy2, typeof(ContextLayer));
                        if (synapse == null)
                        {
                            throw new NeuralNetworkError("Can't find parent synapse to context layer.");
                        }
                        ContextLayer context = (ContextLayer)synapse.FromLayer;

                        // find fedby index
                        int fedByIndex = -1;
                        for (int j = 0; j < flatLayerCount; j++)
                        {
                            if (flatLayers[j] == fedBy)
                            {
                                fedByIndex = j;
                                break;
                            }
                        }

                        if (fedByIndex == -1)
                        {
                            throw new NeuralNetworkError("Can't find layer feeding context.");
                        }

                        context.FlatContextIndex = this.flat.ContextTargetOffset[fedByIndex];
                    }
                }

                // RBF networks will not train every layer
                if (isRBF)
                {
                    this.flat.EndTraining = flatLayers.Length - 1;
                }

                FlattenWeights();

                if (this.IsConnectionLimited)
                {
                }

                this.flatUpdate = FlatUpdateNeeded.None;
            }
            else
            {
                this.flatUpdate = FlatUpdateNeeded.Never;
            }
        }
Exemplo n.º 2
0
        /// <summary>
        /// Create the flat neural network.
        /// </summary>
        public void Flatten()
        {
            bool isRBF = false;
            IDictionary<ILayer, FlatLayer> regular2flat = new Dictionary<ILayer, FlatLayer>();
            IDictionary<FlatLayer, ILayer> flat2regular = new Dictionary<FlatLayer, ILayer>();            
            IList<ObjectPair<ILayer, ILayer>> contexts = new List<ObjectPair<ILayer, ILayer>>();            
            this.flat = null;

            ValidateForFlat val = new ValidateForFlat();

            if (val.IsValid(this.network) == null)
            {
                if (this.layers.Count == 3
                        && this.layers[1] is RadialBasisFunctionLayer)
                {
                    RadialBasisFunctionLayer rbf = (RadialBasisFunctionLayer)this.layers[1];
                    this.flat = new FlatNetworkRBF(this.network.InputCount,
                            rbf.NeuronCount, this.network.OutputCount,
                            rbf.RadialBasisFunction);
                    FlattenWeights();
                    this.flatUpdate = FlatUpdateNeeded.None;
                    return;
                }

                int flatLayerCount = CountNonContext();
                FlatLayer[] flatLayers = new FlatLayer[flatLayerCount];                

                int index = flatLayers.Length - 1;
                foreach (ILayer layer in this.layers)
                {

                    if (layer is ContextLayer)
                    {
                        ISynapse inboundSynapse = network.Structure
                                .FindPreviousSynapseByLayerType(layer,
                                        typeof(BasicLayer));
                        ISynapse outboundSynapse = network
                                .Structure
                                .FindNextSynapseByLayerType(layer, typeof(BasicLayer));

                        if (inboundSynapse == null)
                            throw new NeuralNetworkError(
                                    "Context layer must be connected to by one BasicLayer.");

                        if (outboundSynapse == null)
                            throw new NeuralNetworkError(
                                    "Context layer must connect to by one BasicLayer.");

                        ILayer inbound = inboundSynapse.FromLayer;
                        ILayer outbound = outboundSynapse.ToLayer;

                        contexts
                                .Add(new ObjectPair<ILayer, ILayer>(inbound, outbound));
                    }
                    else
                    {
                        double bias = this.FindNextBias(layer);

                        IActivationFunction activationType;
                        double[] param = new double[1];

                        if (layer.ActivationFunction == null)
                        {
                            activationType = new ActivationLinear();
                            param = new double[1];
                            param[0] = 1;
                        }
                        else
                        {
                            activationType = layer.ActivationFunction;
                            param = layer.ActivationFunction.Params;
                        }

                        FlatLayer flatLayer = new FlatLayer(activationType, layer
                                .NeuronCount, bias, param);

                        regular2flat[layer] = flatLayer;
                        flat2regular[flatLayer] = layer;
                        flatLayers[index--] = flatLayer;
                    }
                }

                // now link up the context layers
                foreach (ObjectPair<ILayer, ILayer> context in contexts)
                {
                    // link the context layer on the FlatLayer
                    ILayer layer = context.B;
                    ISynapse synapse = this.network
                            .Structure
                            .FindPreviousSynapseByLayerType(layer, typeof(BasicLayer));
                    FlatLayer from = regular2flat[context.A];
                    FlatLayer to = regular2flat[synapse.FromLayer];
                    to.ContextFedBy = from;
                }

                this.flat = new FlatNetwork(flatLayers);

                // update the context indexes on the non-flat network
                for (int i = 0; i < flatLayerCount; i++)
                {
                    FlatLayer fedBy = flatLayers[i].ContextFedBy;
                    if (fedBy != null)
                    {
                        ILayer fedBy2 = flat2regular[flatLayers[i + 1]];
                        ISynapse synapse = FindPreviousSynapseByLayerType(fedBy2, typeof(ContextLayer));
                        if (synapse == null)
                            throw new NeuralNetworkError("Can't find parent synapse to context layer.");
                        ContextLayer context = (ContextLayer)synapse.FromLayer;

                        // find fedby index
                        int fedByIndex = -1;
                        for(int j=0;j<flatLayerCount;j++)
                        {
                            if( flatLayers[j]==fedBy )
                            {
                                fedByIndex = j;
                                break;
                            }
                        }

                        if (fedByIndex == -1)
                            throw new NeuralNetworkError("Can't find layer feeding context.");

                        context.FlatContextIndex = this.flat.ContextTargetOffset[fedByIndex];
                    }
                }

                // RBF networks will not train every layer
                if (isRBF)
                {
                    this.flat.EndTraining = flatLayers.Length - 1;
                }

                FlattenWeights();

                if (this.IsConnectionLimited)
                {

                }

                this.flatUpdate = FlatUpdateNeeded.None;
            }
            else
                this.flatUpdate = FlatUpdateNeeded.Never;
        }