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@@ -255,15 +255,15 @@ def main():
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# prepare train and validation dataset
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X_train, X_val, y_train, y_val = train_test_split(x_data_train, y_dataset_train, test_size=p_val_size, shuffle=False)
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- y_train = to_categorical(y_train)
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- y_val = to_categorical(y_val)
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- y_test = to_categorical(y_dataset_test)
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+ y_train_cat = to_categorical(y_train)
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+ y_val_cat = to_categorical(y_val)
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+ y_test_cat = to_categorical(y_dataset_test)
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print('-----------------------------')
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print("-- Fitting model with custom class_weight", class_weight)
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print('-----------------------------')
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- model.fit(X_train, y_train,
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- validation_data=(X_val, y_val),
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+ model.fit(X_train, y_train_cat,
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+ validation_data=(X_val, y_val_cat),
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initial_epoch=initial_epoch,
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epochs=p_epochs,
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batch_size=p_batch_size,
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@@ -293,11 +293,11 @@ def main():
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acc_train_score = accuracy_score(y_train, y_train_prediction)
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acc_val_score = accuracy_score(y_val, y_val_prediction)
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- acc_test_score = accuracy_score(y_test, y_test_prediction)
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+ acc_test_score = accuracy_score(y_dataset_test, y_test_prediction)
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roc_train_score = roc_auc_score(y_train, y_train_prediction)
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roc_val_score = roc_auc_score(y_val, y_val_prediction)
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- roc_test_score = roc_auc_score(y_test, y_val_prediction)
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+ roc_test_score = roc_auc_score(y_dataset_test, y_val_prediction)
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# save model performance
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if not os.path.exists(cfg.output_results_folder):
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