Confusion Metrics written in tensorflow format

tensorflow_confusion_metrics.py

# from https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
def tf_confusion_metrics(model, actual_classes, session, feed_dict):
  predictions = tf.argmax(model, 1)
  actuals = tf.argmax(actual_classes, 1)

  ones_like_actuals = tf.ones_like(actuals)
  zeros_like_actuals = tf.zeros_like(actuals)
  ones_like_predictions = tf.ones_like(predictions)
  zeros_like_predictions = tf.zeros_like(predictions)

  tp_op = tf.reduce_sum(
    tf.cast(
      tf.logical_and(
        tf.equal(actuals, ones_like_actuals), 
        tf.equal(predictions, ones_like_predictions)
      ), 
      "float"
    )
  )

  tn_op = tf.reduce_sum(
    tf.cast(
      tf.logical_and(
        tf.equal(actuals, zeros_like_actuals), 
        tf.equal(predictions, zeros_like_predictions)
      ), 
      "float"
    )
  )

  fp_op = tf.reduce_sum(
    tf.cast(
      tf.logical_and(
        tf.equal(actuals, zeros_like_actuals), 
        tf.equal(predictions, ones_like_predictions)
      ), 
      "float"
    )
  )

  fn_op = tf.reduce_sum(
    tf.cast(
      tf.logical_and(
        tf.equal(actuals, ones_like_actuals), 
        tf.equal(predictions, zeros_like_predictions)
      ), 
      "float"
    )
  )

  tp, tn, fp, fn = \
    session.run(
      [tp_op, tn_op, fp_op, fn_op], 
      feed_dict
    )

  tpr = float(tp)/(float(tp) + float(fn))
  fpr = float(fp)/(float(tp) + float(fn))

  accuracy = (float(tp) + float(tn))/(float(tp) + float(fp) + float(fn) + float(tn))

  recall = tpr
  precision = float(tp)/(float(tp) + float(fp))
  
  f1_score = (2 * (precision * recall)) / (precision + recall)
  
  print 'Precision = ', precision
  print 'Recall = ', recall
  print 'F1 Score = ', f1_score
  print 'Accuracy = ', accuracy

I think for multi-labels, there is no real true negative, so in the replay from @carlthome, the calculation of tn is no need and actually wrong

You're not supposed to use the sigmoid for a one-hot encoding because you get numerical instability. You need to use softmax EVERY TIME for one-hot else you will get erroneous results form the floating-point rounding errors.

The fpr is wrong. It should be fpr = float(fp)/(float(fp) + float(tn))
https://en.wikipedia.org/wiki/False_positive_rate