/// <summary>
/// Runs through the network and makes a prediction.
/// </summary>
/// <param name="input">The input image to run the prediction on.</param>
/// <returns>The values from the final layer. The largest value is the networks prediction.</returns>
public double[] FeedForward(double[] input)
{
Matrix <double> _currentImages = CreateMatrix.DenseOfRowVectors(CreateVector.DenseOfArray <double>(input));
foreach (ILayerInformation _layerInformation in this.LayerInformation)
{
switch (_layerInformation.LayerType)
{
case (LayerType.Convolutional):
ConvolutionalLayerInformation _convInfo = _layerInformation as ConvolutionalLayerInformation;
_currentImages = this.Convolve(_convInfo, _currentImages);
break;
case (LayerType.Pooling):
PoolingLayerInformation _poolInfo = _layerInformation as PoolingLayerInformation;
_currentImages = this.Pool(_poolInfo, _currentImages);
break;
case (LayerType.NonLinear):
NonLinearLayerInformation _nonLinearInfo = _layerInformation as NonLinearLayerInformation;
_currentImages = this.NonLinear(_nonLinearInfo, _currentImages);
break;
}
}
double[] _fullyConnectedInput = CreateVector.DenseOfEnumerable <double>(_currentImages.EnumerateRows().SelectMany(a => a)).ToArray();
return(this.FullyConnectedNetwork.FeedForward(_currentImages.Enumerate().ToArray()));
}
/// <summary>
/// Runs through a non-linear layer of the network.
/// </summary>
/// <param name="layerInfo">The layer info used for this layer.</param>
/// <param name="inputImages">
/// A matrix of all the images that will be ran through the non-linear function. Each row is
/// an image.
/// </param>
/// <returns>A matrix of all the resulting images. Each row is an image.</returns>
internal Matrix <double> NonLinear(NonLinearLayerInformation layerInfo, Matrix <double> inputImages)
{
switch (layerInfo.NonLinearFunction)
{
case NonLinearFunction.Sigmoid:
return(NonLinearTransformations.Sigmoid(inputImages));
case NonLinearFunction.Tanh:
return(NonLinearTransformations.Tanh(inputImages));
case NonLinearFunction.ReLU:
return(NonLinearTransformations.ReLU(inputImages));
case NonLinearFunction.LReLU:
return(NonLinearTransformations.LReLU(inputImages));
default:
return(null);
}
}
