LogoLogo
  • Tensorleap
  • Examples
    • Semantic Segmentation
    • Image Analysis
    • Sentiment Analysis
    • MNIST Project Walkthrough
    • IMDB Project Walkthrough
  • Quickstart using CLI
  • Guides
    • Full Guides
      • MNIST Guide
        • Dataset Integration
        • Model Integration
        • Model Perception Analysis
        • Advanced Metrics
      • IMDB Guide
        • Dataset Integration
        • Model Integration
        • Model Perception Analysis
        • Advanced Metrics
    • Integration Script
      • Preprocess Function
      • Input Encoder
      • Ground Truth Encoder
      • Metadata Function
      • Visualizer Function
      • Prediction
      • Custom Metrics
      • Custom Loss Function
      • Custom Layers
      • Unlabeled Data
      • Examples
        • CelebA Object Detection (YoloV7)
        • Wikipedia Toxicity (using Tensorflow Datasets)
        • Confusion Matrix
        • CelebA Classification (using GCS)
  • Platform
    • Resources Management
    • Project
    • Dataset
    • Secret Manager
    • Network
      • Dataset Node
      • Layers
      • Loss and Optimizer
      • Visualizers
      • Import Model
      • Metrics
    • Evaluate / Train Model
    • Metrics Dashboard
    • Versions
    • Issues
    • Tests
    • Analysis
      • helpers
        • detection
          • YOLO
    • Team management
    • Insights
  • API
    • code_loader
      • leap_binder
        • add_custom_metric
        • set_preprocess
        • set_unlabeled_data_preprocess
        • set_input
        • set_ground_truth
        • set_metadata
        • add_prediction
        • add_custom_loss
        • set_visualizer
      • enums
        • DatasetMetadataType
        • LeapDataType
      • datasetclasses
        • PreprocessResponse
      • visualizer_classes
        • LeapImage
        • LeapImageWithBBox
        • LeapGraph
        • LeapText
        • LeapHorizontalBar
        • LeapImageMask
        • LeapTextMask
  • Tips & Tricks
    • Import External Code
  • Legal
    • Terms of Use
    • Privacy Policy
Powered by GitBook
On this page

Was this helpful?

  1. Guides
  2. Integration Script

Prediction

PreviousVisualizer FunctionNextCustom Metrics

Last updated 2 years ago

Was this helpful?

Each model graph has at least one prediction output. The prediction type is set to each prediction, and defines the associated labels.

Use the function to add a prediction type to the list or prediction types. From this list, a type can be assigned to each prediction output.

Example of usage:

leap_binder.add_prediction(
    name='toxicity',
    labels=['non-toxic','toxic']
)

For more info, see .

In the view, each prediction (output) node can be pointed to its corresponding Prediction Type in the Layer Details panel.

Guides

Full examples can be found at the Dataset Integration section of the following guides:

leap_binder.add_prediction
leap_binder.add_prediction
Network
MNIST Guide
IMDB Guide