How to add Precision and Recall Metrics

[SasipreetamMorsa]

I would like to get Precision and Recall metrics during the training of the Varmisuse task. I have tried modifying varmisuse_task.py with the following code:

Inside make_task_output_model, at approximately line 438
`predicted = tf.argmax(tf.nn.softmax(logits), 1, output_type=tf.int32)
prediction_is_correct = tf.equal(predicted, correct_choices)
accuracy = tf.reduce_mean(tf.cast(prediction_is_correct, tf.float32))

    TP = tf.count_nonzero(predicted * correct_choices)
    TN = tf.count_nonzero((1-predicted) * (1-correct_choices))
    FP = tf.count_nonzero(predicted * (1-correct_choices))
    FN = tf.count_nonzero((1-predicted) * correct_choices)

    precision = tf.divide(TP, TP+FP)
    recall = tf.divide(TP, TP+FN)

    tf.summary.scalar('accuracy', accuracy)
    model_ops['task_metrics'] = {
        'loss': tf.reduce_mean(per_graph_loss),
        'total_loss': tf.reduce_sum(per_graph_loss),
        'accuracy': accuracy,
        'precision': precision,
        'recall': recall,
        'num_correct_predictions': tf.reduce_sum(tf.cast(prediction_is_correct, tf.int32)),
    }`

Inside pretty_print_epoch_task_metrics:

acc = sum([m['num_correct_predictions'] for m in task_metric_results]) / float(num_graphs) precision = sum([m['precision'] for m in task_metric_results]) / float(num_graphs) recall = sum([m['recall'] for m in task_metric_results]) / float(num_graphs) return "Accuracy: %.3f | Precision: %.3f | Recall: %.3f" % (acc, precision, recall)

However, this code outputs nan for both Precision and Recall. If anyone knows why this happens and could point me in the right direction, I would greatly appreciate it.