Deep learning framework by BAIR
Created by Yangqing Jia Lead Developer Evan Shelhamer
MultinomialLogisticLoss
./include/caffe/layers/multinomial_logistic_loss_layer.hpp
./src/caffe/layers/multinomial_logistic_loss_layer.cpp
LossParameter loss_param
./src/caffe/proto/caffe.proto
// Message that stores parameters shared by loss layers message LossParameter { // If specified, ignore instances with the given label. optional int32 ignore_label = 1; // How to normalize the loss for loss layers that aggregate across batches, // spatial dimensions, or other dimensions. Currently only implemented in // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers. enum NormalizationMode { // Divide by the number of examples in the batch times spatial dimensions. // Outputs that receive the ignore label will NOT be ignored in computing // the normalization factor. FULL = 0; // Divide by the total number of output locations that do not take the // ignore_label. If ignore_label is not set, this behaves like FULL. VALID = 1; // Divide by the batch size. BATCH_SIZE = 2; // Do not normalize the loss. NONE = 3; } // For historical reasons, the default normalization for // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID. optional NormalizationMode normalization = 3 [default = VALID]; // Deprecated. Ignored if normalization is specified. If normalization // is not specified, then setting this to false will be equivalent to // normalization = BATCH_SIZE to be consistent with previous behavior. optional bool normalize = 2; }