web_tool/ModelSessionKerasExample.py [71:132]:
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        return output

    def retrain(self, **kwargs):
        x_train = np.array(self.augment_x_train)
        y_train = np.array(self.augment_y_train)
        
        if x_train.shape[0] == 0:
            return {
                "message": "Need to add training samples in order to train",
                "success": False
            }

        try:
            self.augment_model.fit(x_train, y_train)
            score = self.augment_model.score(x_train, y_train)
            LOGGER.debug("Fine-tuning accuracy: %0.4f" % (score))

            self.augment_model_trained = True
            
            return {
                "message": "Fine-tuning accuracy on data: %0.2f" % (score),
                "success": True
            }
        except Exception as e:
            return {
                "message": "Error in 'retrain()': %s" % (e),
                "success": False
            }

    def add_sample_point(self, row, col, class_idx):
        
        if self._last_tile is not None:
            self.augment_x_train.append(self._last_tile[row, col, :].copy())
            self.augment_y_train.append(class_idx)
            return {
                "message": "Training sample for class %d added" % (class_idx),
                "success": True
            }
        else:
            return {
                "message": "Must run model before adding a training sample",
                "success": False
            }

    def undo(self):
        if len(self.augment_y_train) > 0:
            self.augment_x_train.pop()
            self.augment_y_train.pop()
            return {
                "message": "Undid training sample",
                "success": True
            }
        else:
            return {
                "message": "Nothing to undo",
                "success": False
            }

    def reset(self):
        self._last_tile = None
        self.augment_x_train = []
        self.augment_y_train = []
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web_tool/ModelSessionRandomForest.py [50:111]:
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        return output

    def retrain(self, **kwargs):
        x_train = np.array(self.augment_x_train)
        y_train = np.array(self.augment_y_train)
        
        if x_train.shape[0] == 0:
            return {
                "message": "Need to add training samples in order to train",
                "success": False
            }

        try:
            self.augment_model.fit(x_train, y_train)
            score = self.augment_model.score(x_train, y_train)
            LOGGER.debug("Fine-tuning accuracy: %0.4f" % (score))

            self.augment_model_trained = True
            
            return {
                "message": "Fine-tuning accuracy on data: %0.2f" % (score),
                "success": True
            }
        except Exception as e:
            return {
                "message": "Error in 'retrain()': %s" % (e),
                "success": False
            }

    def add_sample_point(self, row, col, class_idx):
        
        if self._last_tile is not None:
            self.augment_x_train.append(self._last_tile[row, col, :].copy())
            self.augment_y_train.append(class_idx)
            return {
                "message": "Training sample for class %d added" % (class_idx),
                "success": True
            }
        else:
            return {
                "message": "Must run model before adding a training sample",
                "success": False
            }

    def undo(self):
        if len(self.augment_y_train) > 0:
            self.augment_x_train.pop()
            self.augment_y_train.pop()
            return {
                "message": "Undid training sample",
                "success": True
            }
        else:
            return {
                "message": "Nothing to undo",
                "success": False
            }

    def reset(self):
        self._last_tile = None
        self.augment_x_train = []
        self.augment_y_train = []
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