loss_hamming {tfaddons}

Hamming loss

Description

Computes hamming loss.

Usage

loss_hamming(
  mode,
  name = "hamming_loss",
  threshold = NULL,
  dtype = tf$float32,
  ...
)

Arguments

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'.

Details

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.

Value

hamming loss: float

Examples

## Not run: 

# 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


## End(Not run)