Module metric

The metric module.

Modules

• state
  State module.
• store
  Module responsible to save and exposes data collected during training.

Structs

• AccuracyInput
  The accuracy metric input type.
• AccuracyMetric
  The accuracy metric.
• AurocInput
  The AUROC metric input type.
• AurocMetric
  The Area Under the Receiver Operating Characteristic Curve (AUROC, also referred to as ROC AUC) for binary classification.
• ConfusionStatsInput
  Input for [ConfusionStats]
• CpuMemory
  Memory information
• CpuTemperature
  CPU Temperature in celsius degrees
• CpuUse
  General CPU Usage metric
• CudaMetric
  Track basic cuda infos.
• FBetaScoreMetric
  The F-beta score metric.
• HammingScore
  The hamming score, sometimes referred to as multi-label or label-based accuracy.
• HammingScoreInput
  The hamming score input type.
• IterationSpeedMetric
  The loss metric.
• LearningRateMetric
  Track the learning rate across iterations.
• LossInput
  The loss metric input type.
• LossMetric
  The loss metric.
• MetricEntry
  Data type that contains the current state of a metric at a given time.
• MetricMetadata
  Metric metadata that can be used when computing metrics.
• PrecisionMetric
  The Precision Metric
• RecallMetric
  The Recall Metric
• TopKAccuracyInput
  The top-k accuracy metric input type.
• TopKAccuracyMetric
  The Top-K accuracy metric.

Enums

• ClassReduction
  The reduction strategy for classification metrics.
• NumericEntry
  Numeric metric entry.

Traits

• Adaptor
  Adaptor are used to transform types so that they can be used by metrics.
• ItemLazy
  Items that are lazy are not ready to be processed by metrics.
• Metric
  Metric trait.
• Numeric
  Declare a metric to be numeric.

Functions

• format_float
  Format a float with the given precision. Will use scientific notation if necessary.