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v0.3.0

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 »
  • Overview: module code

All modules for which code is available

  • delira.data_loading.data_loader
  • delira.data_loading.data_manager
  • delira.data_loading.dataset
  • delira.data_loading.load_utils
  • delira.data_loading.nii
  • delira.data_loading.sampler.abstract_sampler
  • delira.data_loading.sampler.lambda_sampler
  • delira.data_loading.sampler.random_sampler
  • delira.data_loading.sampler.sequential_sampler
  • delira.data_loading.sampler.weighted_sampler
  • delira.io.tf
  • delira.io.torch
  • delira.logging.multistream_handler
  • delira.logging.trixi_handler
  • delira.models.abstract_network
  • delira.models.classification.classification_network
  • delira.models.classification.classification_network_3D
  • delira.models.classification.classification_network_tf
  • delira.models.gan.generative_adversarial_network
  • delira.models.segmentation.unet
  • delira.training.abstract_trainer
  • delira.training.callbacks.abstract_callback
  • delira.training.callbacks.early_stopping
  • delira.training.callbacks.pytorch_schedulers
  • delira.training.experiment
  • delira.training.losses
  • delira.training.metrics
  • delira.training.parameters
  • delira.training.pytorch_trainer
  • delira.training.tf_trainer
  • delira.training.train_utils
  • delira.utils.config
  • delira.utils.decorators
  • delira.utils.imageops
  • delira.utils.path
  • delira.utils.time
  • logging

© Copyright 2019, Justus Schock, Oliver Rippel, Christoph Haarburger Revision bae606ae.

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