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[language_classification] Automatic hyperparameter optimization using either the cross-validation set or two distinct subsets of the training set

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Al
2016-01-17 21:11:37 -05:00
parent af5689ee52
commit f808f74271
6 changed files with 299 additions and 112 deletions
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11
src/logistic_regression_trainer.h
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@@ -17,14 +17,6 @@
#include "tokens.h"
#include "trie.h"
#define DEFAULT_GAMMA_SCHEDULE {0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0}
#define DEFAUlT_LAMBDA_SCHEDULE {0.0, 1e-5, 1e-4, 0.001, 0.01, 0.1, \
0.2, 0.5, 1.0, 2.0, 5.0, 10.0}
#define DEFAULT_GAMMA_0 1.0
#define DEFAULT_LAMBDA 0.0
#define DEFAULT_GAMMA 0.1
/**
* Helper struct for training logistic regression model
*/
@@ -43,11 +35,10 @@ typedef struct logistic_regression_trainer {
uint32_t iters; // Number of iterations, used to decay learning rate
uint32_t epochs; // Number of epochs
double gamma_0; // Initial learning rate
double gamma; // Simple scalar learning rate
} logistic_regression_trainer_t;
logistic_regression_trainer_t *logistic_regression_trainer_init(trie_t *feature_ids, khash_t(str_uint32) *label_ids);
logistic_regression_trainer_t *logistic_regression_trainer_init(trie_t *feature_ids, khash_t(str_uint32) *label_ids, double gamma_0, double lambda);
bool logistic_regression_trainer_train_batch(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
double logistic_regression_trainer_batch_cost(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);