Deep learning framework by BAIR
Created by
Yangqing Jia
Lead Developer
Evan Shelhamer
Accuracy
scores the output as the accuracy of output with respect to target – it is not actually a loss and has no backward step.
Accuracy
./include/caffe/layers/accuracy_layer.hpp
./src/caffe/layers/accuracy_layer.cpp
AccuracyParameter accuracy_param
)./src/caffe/proto/caffe.proto
):message AccuracyParameter {
// When computing accuracy, count as correct by comparing the true label to
// the top k scoring classes. By default, only compare to the top scoring
// class (i.e. argmax).
optional uint32 top_k = 1 [default = 1];
// The "label" axis of the prediction blob, whose argmax corresponds to the
// predicted label -- may be negative to index from the end (e.g., -1 for the
// last axis). For example, if axis == 1 and the predictions are
// (N x C x H x W), the label blob is expected to contain N*H*W ground truth
// labels with integer values in {0, 1, ..., C-1}.
optional int32 axis = 2 [default = 1];
// If specified, ignore instances with the given label.
optional int32 ignore_label = 3;
}