Custom metric function
Arguments
- name
name used to show training progress output
- metric_fn
An R function with signature
function(y_true, y_pred)that accepts tensors.
Details
You can provide an arbitrary R function as a custom metric. Note that
the y_true and y_pred parameters are tensors, so computations on
them should use op_* tensor functions.
Use the custom_metric() function to define a custom metric.
Note that a name ('mean_pred') is provided for the custom metric
function: this name is used within training progress output.
If you want to save and load a model with custom metrics, you should
also call register_keras_serializable(), or
specify the metric in the call the load_model(). For example:
load_model("my_model.keras", c('mean_pred' = metric_mean_pred)).
Alternatively, you can wrap all of your code in a call to
with_custom_object_scope() which will allow you to refer to the
metric by name just like you do with built in keras metrics.
Alternative ways of supplying custom metrics:
custom_metric():Arbitrary R function.metric_mean_wrapper(): Wrap an arbitrary R function in aMetricinstance.Create a custom
Metric()subclass.
See also
Other metrics: 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_concordance_correlation() 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_pearson_correlation() 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()