.. _program_listing_file_src_layers_loss.cpp:

Program Listing for File loss.cpp
=================================

|exhale_lsh| :ref:`Return to documentation for file <file_src_layers_loss.cpp>` (``src/layers/loss.cpp``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   #include "layers/loss.h"
   
   namespace marian {
   
   // @TODO, simplify this. Currently here for back-compat
   Ptr<LabelwiseLoss> newLoss(Ptr<Options> options, bool inference) {
     float smoothing = inference ? 0.f : options->get<float>("label-smoothing");
     float factorWeight = options->get<float>("factor-weight", 1.0f);
     std::string costType = options->get<std::string>("cost-type", "ce-mean");
     bool unlikelihood = options->get<bool>("unlikelihood-loss", false);
   
     if(costType == "ce-rescore") {  // per-batch-item scores (while ce-mean reduces over batch)
       bool wordScores = options->get<bool>("word-scores", false);
       return New<RescorerLoss>(wordScores);
     } else if(unlikelihood) {
       ABORT_IF(
           !options->hasAndNotEmpty("data-weighting")
               && options->get<std::string>("data-weighting-type") != "word",
           "Unlikelihood loss training requires error annotation in form of per-target-label scores");
       return New<SequenceUnlikelihoodLoss>(
           smoothing, factorWeight);  // this is a mix of CE-loss and unlikelihood less depending on
                                      // values given for data-weighting
     } else {  // same as ce-mean  --@TODO: better check all allowed values, and fail for invalid ones.
               // E.g. what about ce-sum?
       return New<CrossEntropyLoss>(smoothing, factorWeight);
     }
   }
   
   // see loss.h for detailed explanations of each class
   Ptr<MultiRationalLoss> newMultiLoss(Ptr<Options> options) {
     std::string multiLossType = options->get<std::string>("multi-loss-type", "sum");
     if(multiLossType == "sum")  // sum of sums
       return New<SumMultiRationalLoss>();
     else if(multiLossType == "scaled")  // sum of scaled sums, first element is reference scale
       return New<ScaledMultiRationalLoss>();
     else if(multiLossType == "mean")  // sum of means
       return New<MeanMultiRationalLoss>();
     else
       ABORT("Unknown multi-loss-type {}", multiLossType);
   }
   
   }  // namespace marian