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;
}