public void HiddenLayer_WithSortOrder_InitializesProperty()
{
// Arrange
_layer = new HiddenLayer(5);
// Assert
Assert.IsTrue(_layer.SortOrder == 5);
}
public void Initialize()
{
_network = new DFFNeuralNetwork(_inputLayerNeuronCount, _hiddenLayersCount, _hiddenLayerNeuronCount, _outputLayerNeuronCount);
_inputLayer = _network.Layers.OfType <IInputLayer>().First();
_outputLayer = _network.Layers.OfType <IOutputLayer>().First();
_hiddenLayer = _network.Layers.OfType <IHiddenLayer>().First();
_trainingIterations = new List <INetworkTrainingIteration>();
}
public void HiddenLayer_WithSortOrderAndNeurons_InitializesProperty()
{
// Arrange
var hiddenNeuron = new HiddenNeuron();
_layer = new HiddenLayer(5, new List <IHiddenNeuron>()
{
hiddenNeuron
});
// Assert
Assert.IsTrue(_layer.Neurons.Count() == 1);
Assert.IsTrue(_layer.Neurons.First() == hiddenNeuron);
}
public void Initialize()
{
_layer = new HiddenLayer(1);
}
/// <summary>
/// Creates a NetworkTopology from a text file.
/// </summary>
/// <param name="file">The location of the file to read from.</param>
/// <param name="objectFactories">The object factories that will
/// be used to tranform the text types into objects.</param>
/// <param name="settings">The currently loaded NetworkSettings.</param>
/// <returns>Returns the NetworkTopology located inside of the file.
/// If the file can not be parsed null is returned.</returns>
/// <exception cref="IOException">System.IO.IOException</exception>
private static NetworkTopology LoadText(
string file,
ref Dictionary<string, IUserObjectFactory> objectFactories,
ref NetworkSettings settings)
{
//Check to varify that the file can be loaded.
if (GetVersion(file) != "1.0") return null;
NetworkTopology topology = new NetworkTopology();
//Retrieves the RawTypes from the file
List<RawType> rawTypes = GetRawTypes(file);
Dictionary<string, IHiddenLayer> hiddenLayers = new Dictionary<string, IHiddenLayer>();
//Factory to create the object with
string factoryName;
//Transforms all the rawTypes into objects
for (int i = 0; i < rawTypes.Count; i++)
{
factoryName = settings.DefaultFactory;
if (rawTypes[i].Fields.ContainsKey("factory")) factoryName = rawTypes[i].Fields["factory"];
if (rawTypes[i].Type == "layer")
{
if (rawTypes[i].Fields["type"] == "hidden")
{
hiddenLayers.Add(rawTypes[i].Fields["layerName"],
objectFactories[factoryName].CreateUserObject<IHiddenLayer>(
rawTypes[i].Fields["dataType"],
rawTypes[i].Fields));
}
else if (rawTypes[i].Fields["type"] == "output")
{
topology.OutputLayer =
objectFactories[factoryName].CreateUserObject<IOutputLayer>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
}
else if (rawTypes[i].Fields["type"] == "input")
{
topology.InputLayer =
objectFactories[factoryName].CreateUserObject<IInputLayer>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
}
}
else if (rawTypes[i].Type == "node")
{
INode node = objectFactories[factoryName].CreateUserObject<INode>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
if (hiddenLayers.ContainsKey(rawTypes[i].Fields["layer"]))
{
hiddenLayers[rawTypes[i].Fields["layer"]].SetNode(node, -1);
}
}
else if (rawTypes[i].Type == "preprocessor")
{
topology.PreProcessor =
objectFactories[factoryName].CreateUserObject<IPreProcessor>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
if(settings.Mode == NetworkMode.Training)
topology.TrainingPreProcessor =
objectFactories[factoryName].CreateUserObject<ITrainingPreProcessor>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
}
else if (rawTypes[i].Type == "postprocessor")
{
topology.PostProcessor = objectFactories[factoryName].
CreateUserObject<IPostProcessor>(rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
}
else if (rawTypes[i].Type == "topology")
{
topology.MetaData = rawTypes[i].Fields;
if(!rawTypes[i].Fields.ContainsKey("algorithmFactory"))
{
rawTypes[i].Fields.Add("algorithmFactory", factoryName);
}
topology.TrainingAlgorithm = objectFactories[rawTypes[i].Fields["algorithmFactory"]].
CreateUserObject<ITrainingAlgorithm>(
rawTypes[i].Fields["trainingAlgorithm"],
rawTypes[i].Fields);
}
}
//This tranformation is required because nodes are linked to hidden layers
//by names. The name of the layer is the key in the dictionary.
IHiddenLayer[] layer = new IHiddenLayer[hiddenLayers.Count];
int j = 0;
foreach (KeyValuePair<string, IHiddenLayer> temp in hiddenLayers)
{
layer[j] = temp.Value;
j++;
}
topology.HiddenLayers = layer;
return topology;
}
/// <summary>
/// Creates a NetworkTopology from a text file.
/// </summary>
/// <param name="file">The location of the file to read from.</param>
/// <param name="objectFactories">The object factories that will
/// be used to tranform the text types into objects.</param>
/// <param name="settings">The currently loaded NetworkSettings.</param>
/// <returns>Returns the NetworkTopology located inside of the file.
/// If the file can not be parsed null is returned.</returns>
/// <exception cref="IOException">System.IO.IOException</exception>
private static NetworkTopology LoadText(
string file,
ref Dictionary <string, IUserObjectFactory> objectFactories,
ref NetworkSettings settings)
{
//Check to varify that the file can be loaded.
if (GetVersion(file) != "1.0")
{
return(null);
}
NetworkTopology topology = new NetworkTopology();
//Retrieves the RawTypes from the file
List <RawType> rawTypes = GetRawTypes(file);
Dictionary <string, IHiddenLayer> hiddenLayers = new Dictionary <string, IHiddenLayer>();
//Factory to create the object with
string factoryName;
//Transforms all the rawTypes into objects
for (int i = 0; i < rawTypes.Count; i++)
{
factoryName = settings.DefaultFactory;
if (rawTypes[i].Fields.ContainsKey("factory"))
{
factoryName = rawTypes[i].Fields["factory"];
}
if (rawTypes[i].Type == "layer")
{
if (rawTypes[i].Fields["type"] == "hidden")
{
hiddenLayers.Add(rawTypes[i].Fields["layerName"],
objectFactories[factoryName].CreateUserObject <IHiddenLayer>(
rawTypes[i].Fields["dataType"],
rawTypes[i].Fields));
}
else if (rawTypes[i].Fields["type"] == "output")
{
topology.OutputLayer =
objectFactories[factoryName].CreateUserObject <IOutputLayer>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
}
else if (rawTypes[i].Fields["type"] == "input")
{
topology.InputLayer =
objectFactories[factoryName].CreateUserObject <IInputLayer>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
}
}
else if (rawTypes[i].Type == "node")
{
INode node = objectFactories[factoryName].CreateUserObject <INode>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
if (hiddenLayers.ContainsKey(rawTypes[i].Fields["layer"]))
{
hiddenLayers[rawTypes[i].Fields["layer"]].SetNode(node, -1);
}
}
else if (rawTypes[i].Type == "preprocessor")
{
topology.PreProcessor =
objectFactories[factoryName].CreateUserObject <IPreProcessor>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
if (settings.Mode == NetworkMode.Training)
{
topology.TrainingPreProcessor =
objectFactories[factoryName].CreateUserObject <ITrainingPreProcessor>(
rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
}
}
else if (rawTypes[i].Type == "postprocessor")
{
topology.PostProcessor = objectFactories[factoryName].
CreateUserObject <IPostProcessor>(rawTypes[i].Fields["dataType"], rawTypes[i].Fields);
}
else if (rawTypes[i].Type == "topology")
{
topology.MetaData = rawTypes[i].Fields;
if (!rawTypes[i].Fields.ContainsKey("algorithmFactory"))
{
rawTypes[i].Fields.Add("algorithmFactory", factoryName);
}
topology.TrainingAlgorithm = objectFactories[rawTypes[i].Fields["algorithmFactory"]].
CreateUserObject <ITrainingAlgorithm>(
rawTypes[i].Fields["trainingAlgorithm"],
rawTypes[i].Fields);
}
}
//This tranformation is required because nodes are linked to hidden layers
//by names. The name of the layer is the key in the dictionary.
IHiddenLayer[] layer = new IHiddenLayer[hiddenLayers.Count];
int j = 0;
foreach (KeyValuePair <string, IHiddenLayer> temp in hiddenLayers)
{
layer[j] = temp.Value;
j++;
}
topology.HiddenLayers = layer;
return(topology);
}
