Module metric
Expand description
The metric module.
Modules§
Structs§
- Accuracy
Input - The accuracy metric input type.
- Accuracy
Metric - The accuracy metric.
- Auroc
Input - The AUROC metric input type.
- Auroc
Metric - The Area Under the Receiver Operating Characteristic Curve (AUROC, also referred to as ROC AUC) for binary classification.
- Confusion
Stats Input - Input for confusion statistics error types.
- CpuMemory
- Memory information
- CpuTemperature
- CPU Temperature in celsius degrees
- CpuUse
- General CPU Usage metric
- Cuda
Metric - Track basic cuda infos.
- FBeta
Score Metric - The F-beta score metric.
- Hamming
Score - The hamming score, sometimes referred to as multi-label or label-based accuracy.
- Hamming
Score Input - The hamming score input type.
- Iteration
Speed Metric - The loss metric.
- Learning
Rate Metric - Track the learning rate across iterations.
- Loss
Input - The loss metric input type.
- Loss
Metric - The loss metric.
- Metric
Entry - Data type that contains the current state of a metric at a given time.
- Metric
Metadata - Metric metadata that can be used when computing metrics.
- Precision
Metric - The Precision Metric
- Recall
Metric - The Recall Metric
- TopK
Accuracy Input - The top-k accuracy metric input type.
- TopK
Accuracy Metric - The Top-K accuracy metric.
Enums§
- Class
Reduction - The reduction strategy for classification metrics.
- Numeric
Entry - Numeric metric entry.
Traits§
- Adaptor
- Adaptor are used to transform types so that they can be used by metrics.
- Item
Lazy - 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.