| Metric {keras3} | R Documentation |
Subclass the base Metric class
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_*, as well as what is
returned by custom metrics defined with Metric().
Usage
Metric(
classname,
initialize = NULL,
update_state = NULL,
result = NULL,
...,
public = list(),
private = list(),
inherit = NULL,
parent_env = parent.frame()
)
Arguments
classname |
String, the name of the custom class. (Conventionally, CamelCase). |
initialize, update_state, result |
Recommended methods to implement. See description section. |
..., public |
Additional methods or public members of the custom class. |
private |
Named list of R objects (typically, functions) to include in
instance private environments. |
inherit |
What the custom class will subclass. By default, the base keras class. |
parent_env |
The R environment that all class methods will have as a grandparent. |
Value
A function that returns Metric instances, similar to the
builtin metric functions.
Examples
Usage with compile():
model |> compile( optimizer = 'sgd', loss = 'mse', metrics = c(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$reset_state()
}
Full Examples
Usage with compile():
model <- keras_model_sequential()
model |>
layer_dense(64, activation = "relu") |>
layer_dense(64, activation = "relu") |>
layer_dense(10, activation = "softmax")
model |>
compile(optimizer = optimizer_rmsprop(0.01),
loss = loss_categorical_crossentropy(),
metrics = metric_categorical_accuracy())
data <- random_uniform(c(1000, 32))
labels <- random_uniform(c(1000, 10))
model |> fit(data, labels, verbose = 0)
To be implemented by subclasses (custom metrics):
-
initialize(): All state variables should be created in this method by callingself$add_variable()like:self$var <- self$add_variable(...). -
update_state(): Updates all the state variables like:self$var$assign(...). -
result(): Computes and returns a scalar value or a named list of scalar values for the metric from the state variables.
Example subclass implementation:
metric_binary_true_positives <- Metric(
classname = "BinaryTruePositives",
initialize = function(name = 'binary_true_positives', ...) {
super$initialize(name = name, ...)
self$true_positives <-
self$add_weight(shape = shape(),
initializer = 'zeros',
name = 'true_positives')
},
update_state = function(y_true, y_pred, sample_weight = NULL) {
y_true <- op_cast(y_true, "bool")
y_pred <- op_cast(y_pred, "bool")
values <- y_true & y_pred # `&` calls op_logical_and()
values <- op_cast(values, self$dtype)
if (!is.null(sample_weight)) {
sample_weight <- op_cast(sample_weight, self$dtype)
sample_weight <- op_broadcast_to(sample_weight, shape(values))
values <- values * sample_weight # `*` calls op_multiply()
}
self$true_positives$assign(self$true_positives + op_sum(values))
},
result = function() {
self$true_positives
}
)
model <- keras_model_sequential(input_shape = 32) |> layer_dense(10)
model |> compile(loss = loss_binary_crossentropy(),
metrics = list(metric_binary_true_positives()))
model |> fit(data, labels, verbose = 0)
Methods defined by the base Metric class:
-
__call__(...)
Calling a metric instance self like
m(...)is equivalent to calling:function(...) { m$update_state(...) m$result() } -
initialize(dtype=NULL, name=NULL)
Initialize self.
Args:
-
name: (Optional) string name of the metric instance. -
dtype: (Optional) data type of the metric result.
-
-
add_variable(shape, initializer, dtype=NULL, aggregation = 'sum', name=NULL)
-
add_weight(shape=shape(), initializer=NULL, dtype=NULL, name=NULL)
-
get_config()
Return the serializable config of the metric.
-
reset_state()
Reset all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
-
result()
Compute the current metric value.
Returns: A scalar tensor, or a named list of scalar tensors.
-
stateless_result(metric_variables)
-
stateless_reset_state()
-
stateless_update_state(metric_variables, ...)
-
update_state(...)
Accumulate statistics for the metric.
Readonly properties
-
dtype -
variables
Symbols in scope
All R function custom methods (public and private) will have the following symbols in scope:
-
self: The custom class instance. -
super: The custom class superclass. -
private: An R environment specific to the class instance. Any objects assigned here are invisible to the Keras framework. -
__class__andas.symbol(classname): the custom class type object.
See Also
Other metrics:
custom_metric()
metric_auc()
metric_binary_accuracy()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_iou()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_mean_wrapper()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_precision_at_recall()
metric_r2_score()
metric_recall()
metric_recall_at_precision()
metric_root_mean_squared_error()
metric_sensitivity_at_specificity()
metric_sparse_categorical_accuracy()
metric_sparse_categorical_crossentropy()
metric_sparse_top_k_categorical_accuracy()
metric_specificity_at_sensitivity()
metric_squared_hinge()
metric_sum()
metric_top_k_categorical_accuracy()
metric_true_negatives()
metric_true_positives()