The Hamming Loss counts the number of labels for which our prediction is wrong normalizing it.
The standard implementation of the HammingLoss as a metric relies on counting the wrong predictions, with something along these lines: (on TF)
count_non_zero = tf.math.count_nonzero(actuals - predictions)
return tf.reduce_mean(count_non_zero / actuals.get_shape()[-1])
Implementing the Hamming Loss as an actual loss requires it to be differentiable, which is not this case due to the tf.math.count_nonzero.
An alternative (and approximated) method would be counting the non-zero labels in this way, but unluckily the NN doesn't seem to improve.
def hamming_loss(y_true, y_pred):
y_true = tf.convert_to_tensor(y_true, name="y_true")
y_pred = tf.convert_to_tensor(y_pred, name="y_pred")
diff = tf.cast(tf.math.abs(y_true - y_pred), dtype=tf.float32)
#Counting non-zeros in a differentiable way
epsilon = K.epsilon()
nonzero = tf.reduce_mean(tf.math.abs( diff / (tf.math.abs(diff) + epsilon)))
return tf.reduce_mean(nonzero / K.int_shape(y_pred)[-1])
Concluding, what's the correct implementation of the Hamming Loss for TensorFlow?