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Commit 7ef50c6c authored by Davis King's avatar Davis King
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merged

parents d635c1d7 a9c7de47
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Showing with 123 additions and 11 deletions
dlib/svm/structural_assignment_trainer.h
View file @ 7ef50c6c
......@@ -143,6 +143,48 @@ namespace dlib
force_assignment = new_value;
}
void set_loss_per_false_association (
double loss
)
{
// make sure requires clause is not broken
DLIB_ASSERT(loss > 0,
"\t void structural_assignment_trainer::set_loss_per_false_association(loss)"
<< "\n\t Invalid inputs were given to this function "
<< "\n\t loss: " << loss
<< "\n\t this: " << this
);
loss_per_false_association = loss;
}
double get_loss_per_false_association (
) const
{
return loss_per_false_association;
}
void set_loss_per_missed_association (
double loss
)
{
// make sure requires clause is not broken
DLIB_ASSERT(loss > 0,
"\t void structural_assignment_trainer::set_loss_per_missed_association(loss)"
<< "\n\t Invalid inputs were given to this function "
<< "\n\t loss: " << loss
<< "\n\t this: " << this
);
loss_per_missed_association = loss;
}
double get_loss_per_missed_association (
) const
{
return loss_per_missed_association;
}
const assignment_function<feature_extractor> train (
const std::vector<sample_type>& samples,
const std::vector<label_type>& labels
......@@ -173,7 +215,8 @@ namespace dlib
structural_svm_assignment_problem<feature_extractor> prob(samples,labels, fe, force_assignment, num_threads);
structural_svm_assignment_problem<feature_extractor> prob(samples,labels, fe, force_assignment, num_threads,
loss_per_false_association, loss_per_missed_association);
if (verbose)
prob.be_verbose();
......@@ -204,6 +247,8 @@ namespace dlib
bool verbose;
unsigned long num_threads;
unsigned long max_cache_size;
double loss_per_false_association;
double loss_per_missed_association;
void set_defaults ()
{
......@@ -213,6 +258,8 @@ namespace dlib
eps = 0.01;
num_threads = 2;
max_cache_size = 5;
loss_per_false_association = 1;
loss_per_missed_association = 1;
}
feature_extractor fe;
......
dlib/svm/structural_assignment_trainer_abstract.h
View file @ 7ef50c6c
......@@ -52,6 +52,8 @@ namespace dlib
- #get_max_cache_size() == 5
- #get_feature_extractor() == a default initialized feature_extractor
- #forces_assignment() == false
- #get_loss_per_false_association() == 1
- #get_loss_per_missed_association() == 1
!*/
explicit structural_assignment_trainer (
......@@ -66,6 +68,8 @@ namespace dlib
- #get_max_cache_size() == 40
- #get_feature_extractor() == fe
- #forces_assignment() == false
- #get_loss_per_false_association() == 1
- #get_loss_per_missed_association() == 1
!*/
const feature_extractor& get_feature_extractor (
......@@ -132,6 +136,46 @@ namespace dlib
of 0 means caching is not used at all.
!*/
void set_loss_per_false_association (
double loss
);
/*!
requires
- loss > 0
ensures
- #get_loss_per_false_association() == loss
!*/
double get_loss_per_false_association (
) const;
/*!
ensures
- returns the amount of loss experienced for associating two objects
together that shouldn't be associated. If you care more about avoiding
accidental associations than ensuring all possible associations are
identified then then you can increase this value.
!*/
void set_loss_per_missed_association (
double loss
);
/*!
requires
- loss > 0
ensures
- #get_loss_per_missed_association() == loss
!*/
double get_loss_per_missed_association (
) const;
/*!
ensures
- returns the amount of loss experienced for failing to associate two
objects that are supposed to be associated. If you care more about
getting all the associations than avoiding accidentally associating
objects that shouldn't be associated then you can increase this value.
!*/
void be_verbose (
);
/*!
......
dlib/svm/structural_svm_assignment_problem.h
View file @ 7ef50c6c
......@@ -63,16 +63,27 @@ namespace dlib
const std::vector<label_type>& labels_,
const feature_extractor& fe_,
bool force_assignment_,
unsigned long num_threads = 2
unsigned long num_threads,
const double loss_per_false_association_,
const double loss_per_missed_association_
) :
structural_svm_problem_threaded<matrix_type,feature_vector_type>(num_threads),
samples(samples_),
labels(labels_),
fe(fe_),
force_assignment(force_assignment_)
force_assignment(force_assignment_),
loss_per_false_association(loss_per_false_association_),
loss_per_missed_association(loss_per_missed_association_)
{
// make sure requires clause is not broken
#ifdef ENABLE_ASSERTS
DLIB_ASSERT(loss_per_false_association > 0 && loss_per_missed_association > 0,
"\t structural_svm_assignment_problem::structural_svm_assignment_problem()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t loss_per_false_association: " << loss_per_false_association
<< "\n\t loss_per_missed_association: " << loss_per_missed_association
<< "\n\t this: " << this
);
if (force_assignment)
{
DLIB_ASSERT(is_forced_assignment_problem(samples, labels),
......@@ -193,8 +204,6 @@ namespace dlib
}
cost.set_size(size, size);
const double loss_for_false_association = 1;
const double loss_for_missed_association = 1;
typename feature_extractor::feature_vector_type feats;
// now fill out the cost assignment matrix
......@@ -213,7 +222,7 @@ namespace dlib
// add in the loss since this corresponds to an incorrect prediction.
if (c != labels[idx][r])
{
cost(r,c) += loss_for_false_association;
cost(r,c) += loss_per_false_association;
}
}
else
......@@ -221,7 +230,7 @@ namespace dlib
if (labels[idx][r] == -1)
cost(r,c) = 0;
else
cost(r,c) = loss_for_missed_association;
cost(r,c) = loss_per_missed_association;
}
}
......@@ -254,9 +263,9 @@ namespace dlib
if (assignment[i] != labels[idx][i])
{
if (assignment[i] == -1)
loss += loss_for_missed_association;
loss += loss_per_missed_association;
else
loss += loss_for_false_association;
loss += loss_per_false_association;
}
}
......@@ -267,6 +276,8 @@ namespace dlib
const std::vector<label_type>& labels;
const feature_extractor& fe;
bool force_assignment;
const double loss_per_false_association;
const double loss_per_missed_association;
};
// ----------------------------------------------------------------------------------------
......
dlib/svm/structural_svm_assignment_problem_abstract.h
View file @ 7ef50c6c
......@@ -45,10 +45,14 @@ namespace dlib
const std::vector<label_type>& labels,
const feature_extractor& fe,
bool force_assignment,
unsigned long num_threads = 2
unsigned long num_threads,
const double loss_per_false_association,
const double loss_per_missed_association
);
/*!
requires
- loss_per_false_association > 0
- loss_per_missed_association > 0
- is_assignment_problem(samples,labels) == true
- if (force_assignment) then
- is_forced_assignment_problem(samples,labels) == true
......@@ -63,6 +67,12 @@ namespace dlib
- This object will use num_threads threads during the optimization
procedure. You should set this parameter equal to the number of
available processing cores on your machine.
- When solving the structural SVM problem, we will use
loss_per_false_association as the loss for incorrectly associating
objects that shouldn't be associated.
- When solving the structural SVM problem, we will use
loss_per_missed_association as the loss for failing to associate to
objects that are supposed to be associated with each other.
!*/
};
......
dlib/svm/svm_rank_trainer_abstract.h
View file @ 7ef50c6c
......@@ -97,7 +97,7 @@ namespace dlib
train. You can think of this epsilon value as saying "solve the
optimization problem until the average ranking accuracy is within epsilon
of its optimal value". Here we mean "ranking accuracy" in the same sense
used by test_ranking_function() and cross_validate_multiclass_trainer().
used by test_ranking_function() and cross_validate_ranking_trainer().
!*/
unsigned long get_max_iterations (
......
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