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v0.3.2

Getting Started

  • Getting started
    • Backends
    • Installation

Tutorials:

  • Delira Introduction
    • Loading Data
      • The Dataset
      • The Dataloader
      • The Datamanager
      • Sampler
    • Models
      • __init__
      • closure
      • prepare_batch
    • Abstract Networks for specific Backends
      • PyTorch
        • forward
        • prepare_batch
        • closure example
      • Other examples
    • Training
      • Parameters
      • Trainer
      • Experiment
    • Logging
      • MultiStreamHandler
      • Logging with Visdom - The trixi Loggers
        • Types of VisdomHandlers
    • More Examples
  • Classification with Delira - A very short introduction
    • Logging and Visualization
    • Data Preparation
      • Loading
      • Augmentation
    • Training
    • See Also
  • Generative Adversarial Nets with Delira - A very short introduction
    • HyperParameters
    • Logging and Visualization
    • Data Preparation
      • Loading
      • Augmentation
    • Training
    • See Also
  • Segmentation in 2D using U-Nets with Delira - A very short introduction
    • Logging and Visualization
    • Data Praparation
      • Loading
      • Augmentation
    • Training
    • See Also
  • Segmentation in 3D using U-Nets with Delira - A very short introduction
    • Logging and Visualization
    • Data Praparation
      • Loading
      • Augmentation
    • Training
    • See Also

API Documentation:

  • API Documentation
    • Delira
      • Data Loading
        • Arbitrary Data
        • Nii
        • Sampler
      • IO
      • Logging
        • MultiStreamHandler
        • TrixiHandler
      • Models
        • Classification
        • Generative Adversarial Networks
        • Segmentation
      • Training
        • Parameters
        • Network Trainer
        • Experiment
        • Callbacks
        • Losses
        • AurocMetricPyTorch
        • AccurarcyMetricPyTorch
        • pytorch_batch_to_numpy
        • pytorch_tensor_to_numpy
        • float_to_pytorch_tensor
        • create_optims_default_pytorch
        • create_optims_gan_default_pytorch
        • create_optims_default_tf
      • Utilities
      • Class Hierarchy Diagrams
  • GitHub
delira
  • Docs »
  • API Documentation »
  • Delira »
  • Training
  • Edit on GitHub

TrainingΒΆ

The training subpackage implements Callbacks, a class for Hyperparameters, training routines and wrapping experiments.

  • Parameters
    • Parameters
  • Network Trainer
    • AbstractNetworkTrainer
    • PyTorchNetworkTrainer
    • TfNetworkTrainer
  • Experiment
    • AbstractExperiment
    • PyTorchExperiment
    • TfExperiment
  • Callbacks
    • AbstractCallback
    • EarlyStopping
    • DefaultPyTorchSchedulerCallback
    • CosineAnnealingLRCallback
    • ExponentialLRCallback
    • LambdaLRCallback
    • MultiStepLRCallback
    • ReduceLROnPlateauCallback
    • StepLRCallback
    • CosineAnnealingLRCallbackPyTorch
    • ExponentialLRCallbackPyTorch
    • LambdaLRCallbackPyTorch
    • MultiStepLRCallbackPyTorch
    • ReduceLROnPlateauCallbackPyTorch
    • StepLRCallbackPyTorch
  • Losses
    • BCEFocalLossPyTorch
    • BCEFocalLossLogitPyTorch
  • AurocMetricPyTorch
  • AccurarcyMetricPyTorch
  • pytorch_batch_to_numpy
  • pytorch_tensor_to_numpy
  • float_to_pytorch_tensor
  • create_optims_default_pytorch
  • create_optims_gan_default_pytorch
  • create_optims_default_tf
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© Copyright 2019, Justus Schock, Oliver Rippel, Christoph Haarburger Revision 5fa9689a.

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