tommy/libpostal
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

[classification] correcting cost functions in SGD and FTRL for use in parameter sweeps

Browse Source
This commit is contained in:
Al
2017-04-05 14:08:51 -04:00
parent 6219cc6378
commit caebf4e2c9
7 changed files with 169 additions and 87 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
src/language_classifier_train.c
View File

@@ -191,12 +191,12 @@ double compute_cv_accuracy(logistic_regression_trainer_t *trainer, char *filenam
}
double_matrix_zero(p_y);
if (!sparse_matrix_add_unique_columns(x, trainer->unique_columns, trainer->batch_columns)) {
if (!sparse_matrix_add_unique_columns_alias(x, trainer->unique_columns, trainer->batch_columns)) {
log_error("Error adding unique columns\n");
exit(EXIT_FAILURE);
}
double_matrix_t *theta = logistic_regression_trainer_get_regularized_weights(trainer);
double_matrix_t *theta = logistic_regression_trainer_get_weights(trainer);
if (!logistic_regression_model_expectation(theta, x, p_y)) {
log_error("Predict cv batch failed\n");
@@ -242,6 +242,7 @@ double compute_total_cost(logistic_regression_trainer_t *trainer, char *filename
double total_cost = 0.0;
size_t num_batches = 0;
size_t num_examples = 0;
// Need to regularize the weights
double_matrix_t *theta = logistic_regression_trainer_get_regularized_weights(trainer);
@@ -251,6 +252,8 @@ double compute_total_cost(logistic_regression_trainer_t *trainer, char *filename
double batch_cost = logistic_regression_trainer_minibatch_cost(trainer, minibatch->features, minibatch->labels);
total_cost += batch_cost;
num_examples += minibatch->features->n;
language_classifier_minibatch_destroy(minibatch);
num_batches++;
@@ -260,6 +263,10 @@ double compute_total_cost(logistic_regression_trainer_t *trainer, char *filename
}
}
double reg_cost = logistic_regression_trainer_regularization_cost(trainer, num_examples);
log_info("cost = %f, reg_cost = %f, m = %zu\n", total_cost, reg_cost, num_examples);
total_cost += reg_cost;
language_classifier_data_set_destroy(data_set);
return total_cost;
@@ -305,7 +312,7 @@ bool language_classifier_train_epoch(logistic_regression_trainer_t *trainer, cha
}
if (compute_cost) {
train_cost = logistic_regression_trainer_minibatch_cost(trainer, minibatch->features, minibatch->labels);
train_cost = logistic_regression_trainer_minibatch_cost_regularized(trainer, minibatch->features, minibatch->labels);
log_info("cost = %f\n", train_cost);
}
@@ -323,7 +330,7 @@ bool language_classifier_train_epoch(logistic_regression_trainer_t *trainer, cha
if (train_batches > 0 && num_batches == (size_t)train_batches) {
log_info("Epoch %u, trained %zu examples\n", trainer->epochs, num_batches * minibatch_size);
train_cost = logistic_regression_trainer_minibatch_cost(trainer, minibatch->features, minibatch->labels);
train_cost = logistic_regression_trainer_minibatch_cost_regularized(trainer, minibatch->features, minibatch->labels);
log_info("cost = %f\n", train_cost);
break;
}