Metric
Metric
Description
A Metric
object encapsulates metric logic and state that can be used to track model performance during training. It is what is returned by the family of metric functions that start with prefix metric_*
.
Arguments
Arguments | Description |
---|---|
name | (Optional) string name of the metric instance. |
dtype | (Optional) data type of the metric result. |
Section
Usage with compile
model %>% compile(
optimizer = 'sgd',
loss = 'mse',
metrics = list(metric_SOME_METRIC(), metric_SOME_OTHER_METRIC())
)
Standalone usage
m <- metric_SOME_METRIC()
for (e in seq(epochs)) {
for (i in seq(train_steps)) {
c(y_true, y_pred, sample_weight = NULL) %<-% ...
m$update_state(y_true, y_pred, sample_weight)
}
cat('Final epoch result: ', as.numeric(m$result()), "\n")
m$reset_state()
}
Custom Metric (subclass)
To be implemented by subclasses:
initialize()
: All state variables should be created in this method by callingself$add_weight()
like: ```
self\(var <- self\)add_weight(…)
- `update_state()`: Has all updates to the state variables like: ```
self$var$assign_add(...)
result()
: Computes and returns a value for the metric from the state variables.Example custom metric subclass: ```
metric_binary_true_positives <- new_metric_class(
classname = “BinaryTruePositives”,
initialize = function(name = ‘binary_true_positives’, …) {
super$initialize(name = name, ...)
self$true_positives <-
self$add_weight(name = 'tp', initializer = 'zeros')
},
update_state = function(y_true, y_pred, sample_weight = NULL) {
y_true <- k_cast(y_true, "bool")
y_pred <- k_cast(y_pred, "bool")
values <- y_true & y_pred
values <- k_cast(values, self$dtype)
if (!is.null(sample_weight)) {
sample_weight <- k_cast(sample_weight, self$dtype)
sample_weight <- tf$broadcast_to(sample_weight, values$shape)
values <- values * sample_weight
}
self$true_positives$assign_add(tf$reduce_sum(values))
},
result = function()
self$true_positives
)
model %>% compile(…, metrics = list(metric_binary_true_positives()))
The same `metric_binary_true_positives` could be built with `%py_class%` like this:
metric_binary_true_positives(keras\(metrics\)Metric) %py_class% {
initialize <-
update_state <-
result <-
}
```
Value
A (subclassed) Metric
instance that can be passed directly to compile(metrics = )
, or used as a standalone object. See ?Metric
for example usage.