torchbearer
0.3.2

Notes

  • Using the Metric API
    • Default Keys
    • Metric Decorators
      • Lambda Metrics
      • Metric Output - to_dict
    • Data Flow - The Metric Tree
  • Serializing a Trial
    • Setting up a Mock Example
    • Reloading the Trial for More Epochs
    • Trying to Reload to a PyTorch Module
    • Robust Signature for Module
    • Source Code
  • Using the Tensorboard Callback
    • Setup
    • Logging the Model Graph
    • Logging Batch Metrics
    • Logging Epoch Metrics
    • Source Code
  • Logging to Visdom
    • Model Setup
    • Logging Epoch and Batch Metrics
    • Visdom Client Parameters
    • Source Code

Deep Learning

  • 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
    • Closures
    • Training
    • Visualising
    • Source Code
  • Visualising CNNs: The Class Appearance Model
    • Background
    • Loading the Model
    • Running with the Callback
    • Results
    • Source Code

Differentiable Programming

  • Optimising functions
    • The Model
    • The Loss
    • Optimising
    • Viewing Progress
    • Source Code
  • Linear Support Vector Machine (SVM)
    • SVM Recap
    • Defining the Model
    • Creating Synthetic Data
    • Subgradient Descent
    • Visualizing the Training
    • Final Comments
    • Source Code
  • Breaking ADAM
    • Online Optimization
    • Stochastic Optimization
    • Conclusions
    • Source Code

Package Reference

  • torchbearer
    • Trial
    • State
    • Utilities
  • torchbearer.callbacks
    • Base Classes
    • Imaging
      • Main Classes
      • Deep Inside Convolutional Networks
    • Model Checkpointers
    • Logging
    • Tensorboard, Visdom and Others
    • Early Stopping
    • Gradient Clipping
    • Learning Rate Schedulers
    • Learning Rate Finders
    • Weight Decay
    • Weight / Bias Initialisation
    • Decorators
  • torchbearer.metrics
    • Base Classes
    • Decorators - The Decorator API
    • Metric Wrappers
    • Metric Aggregators
    • Base Metrics
    • Timer
  • torchbearer.variational
    • Distributions
    • Divergences
    • Auto-Encoding
    • Datasets
    • Visualisation
torchbearer
  • Docs »
  • Python Module Index

Python Module Index

t
 
t
- torchbearer
    torchbearer.callbacks
    torchbearer.callbacks.callbacks
    torchbearer.callbacks.checkpointers
    torchbearer.callbacks.csv_logger
    torchbearer.callbacks.decorators
    torchbearer.callbacks.early_stopping
    torchbearer.callbacks.gradient_clipping
    torchbearer.callbacks.imaging
    torchbearer.callbacks.imaging.imaging
    torchbearer.callbacks.imaging.inside_cnns
    torchbearer.callbacks.init
    torchbearer.callbacks.lr_finder
    torchbearer.callbacks.printer
    torchbearer.callbacks.tensor_board
    torchbearer.callbacks.terminate_on_nan
    torchbearer.callbacks.torch_scheduler
    torchbearer.callbacks.weight_decay
    torchbearer.cv_utils
    torchbearer.metrics
    torchbearer.metrics.aggregators
    torchbearer.metrics.decorators
    torchbearer.metrics.default
    torchbearer.metrics.metrics
    torchbearer.metrics.primitives
    torchbearer.metrics.roc_auc_score
    torchbearer.metrics.timer
    torchbearer.metrics.wrappers
    torchbearer.state
    torchbearer.trial
    torchbearer.variational
    torchbearer.variational.auto_encoder
    torchbearer.variational.datasets
    torchbearer.variational.distributions
    torchbearer.variational.divergence
    torchbearer.variational.visualisation

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