|
| | msecnn_raulkviana.train_model_utils.model_statistics (J_history, predicted, ground_truth, pred_vector, gt_vector, f1_list, recall_list, precision_list, accuracy_list, train_or_val="train") |
| | Evaluates model with metrics, such as accuracy and f1_score.
|
| |
| | msecnn_raulkviana.train_model_utils.right_size (CUs) |
| |
| | msecnn_raulkviana.train_model_utils.compute_conf_matrix (predicted, ground_truth) |
| | Computes the confusion matrix.
|
| |
| | msecnn_raulkviana.train_model_utils.compute_top_k_accuracy (pred_vector, gt_vector, topk) |
| | Computes the top k accuracy score.
|
| |
| | msecnn_raulkviana.train_model_utils.compute_num_splits_sent (pred_lst) |
| | Computes the num of splits that would be analyzed by the encoder.
|
| |
| | msecnn_raulkviana.train_model_utils.compute_multi_thres_performance (pred_lst, gt_lst) |
| | Computes multi-threshold performance.
|
| |
| | msecnn_raulkviana.train_model_utils.compute_ROC_curve (pred_vector, gt_vector, pred_num) |
| | Computes ROC curve.
|
| |
| | msecnn_raulkviana.train_model_utils.model_simple_metrics (predicted, ground_truth) |
| | Evaluates model with metrics 4 metrics, such as accuracy, f1_score, recall and precision.
|
| |
| | msecnn_raulkviana.train_model_utils.obtain_best_modes (rs, pred) |
| | Converts a prediction into a specific number that corresponds to the best way to split (non-split, quad tree, binary vert tree...)
|
| |
| | msecnn_raulkviana.train_model_utils.obtain_mode (pred) |
| | Converts a prediction into a specific number that corresponds to the best way to split (non-split, quad tree, binary vert tree...)
|
| |
| | msecnn_raulkviana.train_model_utils.one_hot_enc (tensor, num_classes=6) |
| | Implements one-hot encoding to a specific tensor with the set of split modes.
|
| |
| | msecnn_raulkviana.train_model_utils.print_parameters (model, optimizer) |
| | Prints the parameters from the state dictionaries of the model and optimizer.
|
| |
| | msecnn_raulkviana.train_model_utils.save_model_parameters (dir_name, f_name, model) |
| | Saves only the model parameters to a specific folder.
|
| |
| | msecnn_raulkviana.train_model_utils.save_model (dir_name, f_name, model, optimizer, loss, acc) |
| | Saves the parameters of the model and of the optimizer, and also the loss and the accuracy.
|
| |
| | msecnn_raulkviana.train_model_utils.load_model_parameters_stg (model, path, stg, dev) |
| | Loads all stages but make sure that the stage number 'stg' has the same parameters has the previous.
|
| |
| | msecnn_raulkviana.train_model_utils.load_model_parameters_eval (model, path, dev) |
| | Loads all stages, meant to be used with the eval_model script.
|
| |
| | msecnn_raulkviana.train_model_utils.load_model_stg_12_stg_3 (model, path, dev) |
| | THis function makes it possible to load parameters from the first and second stage to the third.
|
| |
| | msecnn_raulkviana.train_model_utils.load_model_stg_3_stg_4 (model, path, dev) |
| | This function makes it possible to load parameters from the third stage to the fourth.
|
| |
| | msecnn_raulkviana.train_model_utils.load_model_stg_4_stg_5 (model, path, dev) |
| | This function makes it possible to load parameters from the fourth stage to the fith.
|
| |
| | msecnn_raulkviana.train_model_utils.load_model_stg_5_stg_6 (model, path, dev) |
| | This function makes it possible to load parameters from the fourth stage to the fith.
|
| |
| | msecnn_raulkviana.train_model_utils.print_current_time () |
| | Prints current time.
|
| |
