/// <summary>Creates an instance of the <code>ConvolutionalNN</code> class.</summary>
/// <param name="firstPart">The first part of the convolutional neural network.</param>
/// <param name="fnn">The second part of the convolutional neural network.</param>
/// <param name="createIO">Whether the input image and the output array of the network are to be created.</param>
public ConvolutionalNN(PurelyConvolutionalNN firstPart, FeedForwardNN fnn, bool createIO = true)
{
this.firstPart = firstPart;
this.i2a = new ImageToArray(firstPart.OutputDepth, firstPart.OutputWidth, firstPart.OutputHeight, false);
this.fnn = fnn;
this.errorArray = Backbone.CreateArray <float>(firstPart.OutputDepth * firstPart.OutputWidth * firstPart.OutputHeight);
this.errorImage = new Image(this.firstPart.OutputDepth, this.firstPart.OutputWidth, this.firstPart.OutputHeight);
if (createIO)
{
this.SetInputGetOutput(new Image(firstPart.InputDepth, firstPart.InputWidth, firstPart.InputHeight));
}
this.siameseID = new object();
}
/// <summary>Creates an instance of the <code>DeConvolutionalNN</code> class.</summary>
/// <param name="firstPart">First part of the network.</param>
/// <param name="cnn">Second part of the network.</param>
/// <param name="createIO">Whether the input array and the output image of the netwok are to be created.</param>
public DeConvolutionalNN(FeedForwardNN firstPart, PurelyConvolutionalNN cnn, bool createIO = true)
{
this.firstPart = firstPart;
this.a2i = new ArrayToImage(cnn.InputDepth, cnn.InputWidth, cnn.InputHeight, false);
this.cnn = cnn;
this.errorArray = Backbone.CreateArray <float>(cnn.InputDepth * cnn.InputWidth * cnn.InputHeight);
this.errorImage = new Image(cnn.InputDepth, cnn.InputWidth, cnn.InputHeight);
this.layersConnected = false;
if (createIO)
{
this.SetInputGetOutput(Backbone.CreateArray <float>(firstPart.InputSize));
}
this.siameseID = new object();
}
/// <summary>Either creates a siamese of the given <code>PurelyConvolutionalNN</code> class or clones it.</summary>
/// <param name="original">The original instance to be created a siamese of or cloned.</param>
/// <param name="siamese"><code>true</code> if a siamese is to be created, <code>false</code> if a clone is.</param>
protected PurelyConvolutionalNN(PurelyConvolutionalNN original, bool siamese) : base(original, siamese)
{
int maxDepth = original.Layers.First().InputDepth;
int maxWidth = original.Layers.First().InputWidth;
int maxHeight = original.Layers.First().InputHeight;
foreach (IImagesLayer layer in original.Layers)
{
maxDepth = Math.Max(maxDepth, layer.OutputDepth);
maxWidth = Math.Max(maxWidth, layer.OutputWidth);
maxHeight = Math.Max(maxHeight, layer.OutputHeight);
}
this.error1 = new Image(maxDepth, maxWidth, maxHeight);
this.error2 = new Image(maxDepth, maxWidth, maxHeight);
this.layersConnected = false;
}
/// <summary>Either creates a siamese of the given <code>ConvolutionalNN</code> instance or clones it.</summary>
/// <param name="original">The original instance to be created a siamese of or cloned.</param>
/// <param name="siamese"><code>true</code> if a siamese is to be created, <code>false</code> otherwise.</param>
protected ConvolutionalNN(ConvolutionalNN original, bool siamese)
{
this.errorArray = Backbone.CreateArray <float>(firstPart.OutputDepth * firstPart.OutputWidth * firstPart.OutputHeight);
this.errorImage = new Image(this.firstPart.OutputDepth, this.firstPart.OutputWidth, this.firstPart.OutputHeight);
this.layersConnected = false;
if (siamese)
{
this.firstPart = (PurelyConvolutionalNN)original.CreateSiamese();
this.i2a = (ImageToArray)original.i2a.CreateSiamese();
this.fnn = (FeedForwardNN)original.fnn.CreateSiamese();
this.siameseID = original.SiameseID;
}
else
{
this.firstPart = (PurelyConvolutionalNN)original.firstPart.Clone();
this.i2a = (ImageToArray)original.i2a.Clone();
this.fnn = (FeedForwardNN)original.fnn.Clone();
this.siameseID = new object();
}
}
/// <summary>Either creates a siamese or clones the given <code>DeConvolutionalNN</code> instance.</summary>
/// <param name="original">The original instance to be created a siamese of or cloned.</param>
/// <param name="siamese"><code>true</code> if a siamese is to be created, <code>false</code> if a clone is.</param>
protected DeConvolutionalNN(DeConvolutionalNN original, bool siamese)
{
this.errorArray = Backbone.CreateArray <float>(original.cnn.InputDepth * original.cnn.InputWidth * original.cnn.InputHeight);
this.errorImage = new Image(original.cnn.InputDepth, original.cnn.InputWidth, original.cnn.InputHeight);
this.layersConnected = false;
if (siamese)
{
this.firstPart = (FeedForwardNN)original.firstPart.CreateSiamese();
this.a2i = (ArrayToImage)original.a2i.CreateSiamese();
this.cnn = (PurelyConvolutionalNN)original.cnn.CreateSiamese();
this.siameseID = original.SiameseID;
}
else
{
this.firstPart = (FeedForwardNN)original.firstPart.Clone();
this.a2i = (ArrayToImage)original.a2i.Clone();
this.cnn = (PurelyConvolutionalNN)original.cnn.Clone();
this.siameseID = new object();
}
}