torchbearer
0.1.5

Notes

  • Using the Metric API
    • Default Keys
    • Metric Decorators
      • Lambda Metrics
      • Metric Output - to_dict
    • Data Flow - The Metric Tree

Examples

  • Quickstart Guide
    • Defining the Model
    • Training on Cifar10
    • Source Code
  • Training a Variational Auto-Encoder
    • Defining the Model
    • Defining the Data
    • Defining the Loss
      • PyTorch method
      • Using Torchbearer State
    • Visualising Results
    • Training the Model
    • Source Code
  • Training a GAN
    • Data and Constants
    • Model
    • Loss
    • Metrics
    • Training
    • Visualising
    • Source Code
  • Optimising functions
    • The Model
    • The Loss
    • Optimising
    • Viewing Progress

Package Reference

  • torchbearer
  • torchbearer.callbacks
    • Model Checkpointers
    • Logging
    • Tensorboard
    • Early Stopping
    • Gradient Clipping
    • Learning Rate Schedulers
    • Weight Decay
    • Decorators
  • torchbearer.metrics
    • Base Classes
    • Decorators - The Decorator API
    • Metric Wrappers
    • Metric Aggregators
    • Base Metrics
torchbearer
  • Docs »
  • Welcome to torchbearer’s documentation!
  • Edit on GitHub

Welcome to torchbearer’s documentation!¶

Notes

  • Using the Metric API

Examples

  • Quickstart Guide
  • Training a Variational Auto-Encoder
  • Training a GAN
  • Optimising functions

Package Reference

  • torchbearer
  • torchbearer.callbacks
  • torchbearer.metrics

Indices and tables¶

  • Index
  • Module Index
  • Search Page
Next

© Copyright 2018, Ethan Harris and Matthew Painter. Revision d971ac7f.

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