def _create_figure(predictions_dict): """Creates and returns a new figure that visualizes attention scores for for a single model predictions. """ # Find out how long the predicted sequence is target_words = list(predictions_dict["predicted_tokens"]) prediction_len = _get_prediction_length(predictions_dict) # Get source words source_len = predictions_dict["features.source_len"] source_words = predictions_dict["features.source_tokens"][:source_len] # Plot fig = plt.figure(figsize=(8, 8)) plt.imshow( X=predictions_dict["attention_scores"][:prediction_len, :source_len], interpolation="nearest", cmap=plt.cm.Blues) plt.xticks(np.arange(source_len), source_words, rotation=45) plt.yticks(np.arange(prediction_len), target_words, rotation=-45) fig.tight_layout() return fig
def _create_figure(predictions_dict): """Creates and returns a new figure that visualizes attention scores for for a single model predictions. """ # Find out how long the predicted sequence is target_words = list(predictions_dict["predicted_tokens"]) prediction_len = _get_prediction_length(predictions_dict) # Get source words source_len = predictions_dict["features.source_len"] source_words = predictions_dict["features.source_tokens"][:source_len] # Plot fig = plt.figure(figsize=(8, 8)) plt.imshow( X=predictions_dict["attention_scores"][:prediction_len, :source_len], interpolation="nearest", cmap=plt.cm.Blues) plt.xticks(np.arange(source_len), source_words, rotation=45) plt.yticks(np.arange(prediction_len), target_words, rotation=-45) fig.tight_layout() return fig
def _get_scores(predictions_dict): """Returns the attention scores, sliced by source and target length. """ prediction_len = _get_prediction_length(predictions_dict) source_len = predictions_dict["features.source_len"] return predictions_dict["attention_scores"][:prediction_len, :source_len]
def _get_scores(predictions_dict): """Returns the logits, sliced by source and target length. """ prediction_len = _get_prediction_length(predictions_dict) source_len = predictions_dict["features.source_len"] return predictions_dict["logits"]
def _get_scores(predictions_dict): """Returns the attention scores, sliced by source and target length. """ prediction_len = _get_prediction_length(predictions_dict) source_len = predictions_dict["features.source_len"] return predictions_dict["attention_scores"][:prediction_len, :source_len]