Computes hamming loss.
loss_hamming( mode, name = "hamming_loss", threshold = NULL, dtype = tf$float32, ... )
| mode | multi-class or multi-label |
|---|---|
| name | (optional) String name of the metric instance. |
| threshold | Elements of `y_pred` greater than threshold are converted to be 1, and the rest 0. If threshold is None, the argmax is converted to 1, and the rest 0. |
| dtype | (optional) Data type of the metric result. Defaults to `tf$float32`. |
| ... | additional arguments that are passed on to function `fn`. |
hamming loss: float
Hamming loss is the fraction of wrong labels to the total number of labels. In multi-class classification, hamming loss is calculated as the hamming distance between `actual` and `predictions`. In multi-label classification, hamming loss penalizes only the individual labels.
if (FALSE) { # multi-class hamming loss hl = loss_hamming(mode='multiclass', threshold=0.6) actuals = tf$constant(list(as.integer(c(1, 0, 0, 0)),as.integer(c(0, 0, 1, 0)), as.integer(c(0, 0, 0, 1)),as.integer(c(0, 1, 0, 0))), dtype=tf$float32) predictions = tf$constant(list(c(0.8, 0.1, 0.1, 0), c(0.2, 0, 0.8, 0), c(0.05, 0.05, 0.1, 0.8), c(1, 0, 0, 0)), dtype=tf$float32) hl$update_state(actuals, predictions) paste('Hamming loss: ', hl$result()$numpy()) # 0.25 # multi-label hamming loss hl = loss_hamming(mode='multilabel', threshold=0.8) actuals = tf$constant(list(as.integer(c(1, 0, 1, 0)),as.integer(c(0, 1, 0, 1)), as.integer(c(0, 0, 0,1))), dtype=tf$int32) predictions = tf$constant(list(c(0.82, 0.5, 0.90, 0), c(0, 1, 0.4, 0.98), c(0.89, 0.79, 0, 0.3)), dtype=tf$float32) hl$update_state(actuals, predictions) paste('Hamming loss: ', hl$result()$numpy()) # 0.16666667 }