/// <summary>Creates an instance of the <code>ImageAutoencoder</code> class.</summary>
/// <param name="encoder">The first part of the autoencoder.</param>
/// <param name="decoder">The second part of the autoencoder.</param>
/// <param name="codeSize">The size of the output of the encoder and of the input of the decoder.</param>
public ImageAutoencoder(ConvolutionalNN encoder, DeConvolutionalNN decoder, int codeSize, bool createIO = true) : base(encoder, decoder, codeSize)
{
this.depth = encoder.InputDepth;
this.width = encoder.InputWidth;
this.height = encoder.InputHeight;
if (createIO)
{
this.SetInputGetOutput(new Image(encoder.InputDepth, encoder.InputWidth, encoder.InputHeight));
}
}
/// <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();
}
}