/// <summary>
/// Creates convolutional layer with specified kernel, and appropriate map
/// dimensions in regard to previous layer - fromLayer param
/// </summary>
/// <param name="fromLayer"> previous layer, which will be connected to this layer </param>
/// <param name="kernel"> kernel for all feature maps in this layer </param>
// public ConvolutionalLayer(FeatureMapsLayer fromLayer, Kernel kernel) {
// Dimension2D fromDimension = fromLayer.getMapDimensions();
// int mapWidth = fromDimension.getWidth() - (kernel.getWidth() - 1);
// int mapHeight = fromDimension.getHeight() - (kernel.getHeight() - 1);
// this.mapDimensions = new Dimension2D(mapWidth, mapHeight);
//
// createFeatureMaps(1, this.mapDimensions, ConvolutionalLayer.DEFAULT_NEURON_PROP);
// }
/// <summary>
/// Creates convolutional layer with specified kernel, appropriate map
/// dimensions in regard to previous layer (fromLayer param) and specified
/// number of feature maps with default neuron settings for convolutional
/// layer.
/// </summary>
/// <param name="fromLayer"> previous layer, which will be connected to this layer </param>
/// <param name="kernel"> kernel for all feature maps </param>
/// <param name="numberOfMaps"> number of feature maps to create in this layer </param>
public ConvolutionalLayer(FeatureMapsLayer fromLayer, Dimension2D kernelDimension, int numberOfMaps)
{
Dimension2D fromDimension = fromLayer.MapDimensions;
int mapWidth = fromDimension.Width - kernelDimension.Width + 1;
int mapHeight = fromDimension.Height - kernelDimension.Height + 1;
this.mapDimensions = new Dimension2D(mapWidth, mapHeight);
createFeatureMaps(numberOfMaps, this.mapDimensions, kernelDimension, ConvolutionalLayer.DEFAULT_NEURON_PROP);
}
/// <summary>
/// Creates convolutional layer with specified kernel, appropriate map
/// dimensions in regard to previous layer (fromLayer param) and specified
/// number of feature maps with given neuron properties.
/// </summary>
/// <param name="fromLayer"> previous layer, which will be connected to this layer </param>
/// <param name="kernel"> kernel for all feature maps </param>
/// <param name="numberOfMaps"> number of feature maps to create in this layer </param>
/// <param name="neuronProp"> settings for neurons in feature maps </param>
public ConvolutionalLayer(FeatureMapsLayer fromLayer, Dimension2D kernelDimension, int numberOfMaps, NeuronProperties neuronProp)
{
Dimension2D fromDimension = fromLayer.MapDimensions;
int mapWidth = fromDimension.Width - kernelDimension.Width + 1;
int mapHeight = fromDimension.Height - kernelDimension.Height + 1;
this.mapDimensions = new Dimension2D(mapWidth, mapHeight);
createFeatureMaps(numberOfMaps, this.mapDimensions, kernelDimension, neuronProp);
}
/// <summary>
/// Creates pooling layer with specified kernel, appropriate map
/// dimensions in regard to previous layer (fromLayer param) and specified
/// number of feature maps with given neuron properties.
/// </summary>
/// <param name="fromLayer"> previous layer, which will be connected to this layer </param>
/// <param name="kernel"> kernel for all feature maps </param>
/// <param name="numberOfMaps"> number of feature maps to create in this layer </param>
/// <param name="neuronProp"> settings for neurons in feature maps </param>
public PoolingLayer(FeatureMapsLayer fromLayer, Dimension2D kernelDim, int numberOfMaps, NeuronProperties neuronProp)
{
this.kernel = kernel;
Dimension2D fromDimension = fromLayer.MapDimensions;
int mapWidth = fromDimension.Width / kernel.Width;
int mapHeight = fromDimension.Height / kernel.Height;
this.mapDimensions = new Dimension2D(mapWidth, mapHeight);
createFeatureMaps(numberOfMaps, mapDimensions, kernelDim, neuronProp);
}
/// <summary>
/// Creates pooling layer with specified kernel, appropriate map
/// dimensions in regard to previous layer (fromLayer param) and specified
/// number of feature maps with default neuron settings for pooling layer.
/// Number of maps in pooling layer must be the same as number of maps in previous
/// layer.
/// </summary>
/// <param name="fromLayer"> previous layer, which will be connected to this layer </param>
/// <param name="kernel"> kernel for all feature maps </param>
public PoolingLayer(FeatureMapsLayer fromLayer, Dimension2D kernelDim)
{
this.kernel = new Kernel(kernelDim);
int numberOfMaps = fromLayer.NumberOfMaps;
Dimension2D fromDimension = fromLayer.MapDimensions;
int mapWidth = fromDimension.Width / kernel.Width;
int mapHeight = fromDimension.Height / kernel.Height;
this.mapDimensions = new Dimension2D(mapWidth, mapHeight);
createFeatureMaps(numberOfMaps, mapDimensions, kernelDim, DEFAULT_NEURON_PROP);
}
