/// <summary>
/// Setup the layer.
/// </summary>
/// <param name="colBottom">Specifies the collection of bottom (input) Blobs.</param>
/// <param name="colTop">Specifies the collection of top (output) Blobs.</param>
public override void LayerSetUp(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
base.LayerSetUp(colBottom, colTop);
// Internal softmax layer.
LayerParameter softmax_param = new LayerParameter(LayerParameter.LayerType.SOFTMAX);
softmax_param.softmax_param.axis = m_param.infogain_loss_param.axis;
softmax_param.loss_weight.Clear();
softmax_param.loss_weight.Add(1);
m_softmaxLayer = new SoftmaxLayer <T>(m_cuda, m_log, softmax_param);
m_colSoftmaxBottomVec.Clear();
m_colSoftmaxBottomVec.Add(colBottom[0]);
m_colSoftmaxTopVec.Clear();
m_colSoftmaxTopVec.Add(m_blobProb);
m_softmaxLayer.Setup(m_colSoftmaxBottomVec, m_colSoftmaxTopVec);
// ignore label.
m_nIgnoreLabel = m_param.loss_param.ignore_label;
// normalization
m_log.CHECK(!m_param.loss_param.normalize, "normalize is drepreciated, use 'normalization'.");
m_normalization = m_param.loss_param.normalization;
// matrix H
if (colBottom.Count < 3)
{
m_log.CHECK(m_param.infogain_loss_param.source != null, "Infogain matrix source must be specified.");
PersistCaffe <T> persist = new PersistCaffe <T>(m_log, true);
BlobProto blobProto = persist.LoadBlobProto(m_param.infogain_loss_param.source, 1);
m_blobInfoGain.FromProto(blobProto);
}
}
/// <summary>
/// The SoftmaxCrossEntropyLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies the LayerParameter of type SOFTMAXCROSSENTROPY_LOSS.
/// </param>
public SoftmaxCrossEntropyLossLayer(CudaDnn <T> cuda, Log log, LayerParameter p)
: base(cuda, log, p)
{
m_type = LayerParameter.LayerType.SOFTMAXCROSSENTROPY_LOSS;
m_blobSoftmaxOutput = new Blob <T>(cuda, log);
m_blobSoftmaxOutput.Name = m_param.name + " softmax out";
m_blobLoss = new Blob <T>(cuda, log);
m_blobLoss.Name = m_param.name + " loss";
LayerParameter param_softmax = p.Clone(false);
param_softmax.loss_weight.Clear();
m_softmaxLayer = new SoftmaxLayer <T>(cuda, log, param_softmax);
}