DeepOBS
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User Guide

  • Quick Start
    • Installation
    • Set-Up Data Sets
    • Contributing to DeepOBS
  • Simple Example
    • Create new Run Script
    • Run new Optimizer
    • Analyzing the Runs
  • Overview
    • Data Downloading
    • Data Loading
    • Model Loading
    • Runners
    • Baseline Results
    • Runtime Estimation
    • Visualization
  • Suggested Protocol
    • Decide for a Framework
    • Create new Run Script
    • (Possibly) Write Your Own Runner
    • Identify Tunable Hyperparameters
    • Decide for a Tuning Method
    • Specify the Tuning Domain
    • Bound the Tuning Resources
    • Report Stochasticity
    • Run on a Variety of Test Problems
    • Plot Results
    • Report Measures for Speed
  • How to Write Customized Runner
    • Decide for a Framework
    • Implement the Training Loop
    • Read in Hyperparameters and Training Parameters from the Command Line
    • Specify How the Hyperparameters and Training Parameters Should Be Added to the Run Name
  • Tuning Automation
    • Grid Search
    • Random Search
    • Bayesian Optimization (GP)

API Reference

  • Analyzer
    • Validate Output
    • Plot Optimizer Performances
    • Get the Best Runs
    • Plot Hyperparameter Sensitivity
    • Estimate Runtime
  • TensorFlow
    • Data Sets
      • 2D Data Set
      • Quadratic Data Set
      • MNIST Data Set
      • FMNIST Data Set
      • CIFAR-10 Data Set
      • CIFAR-100 Data Set
      • SVHN Data Set
      • ImageNet Data Set
      • Tolstoi Data Set
    • Test Problems
      • 2D Test Problems
        • 2D Beale
        • 2D Branin
        • 2D Rosenbrock
      • Quadratic Test Problems
        • Quadratic Deep
      • MNIST Test Problems
        • MNIST LogReg
        • MNIST MLP
        • MNIST 2c2d
        • MNIST VAE
      • Fashion-MNIST Test Problems
        • Fashion-MNIST LogReg
        • Fashion-MNIST MLP
        • Fashion-MNIST 2c2d
        • Fashion-MNIST VAE
      • CIFAR-10 Test Problems
        • CIFAR-10 3c3d
        • CIFAR-10 VGG16
        • CIFAR-10 VGG19
      • CIFAR-100 Test Problems
        • CIFAR-100 3c3d
        • CIFAR-100 VGG16
        • CIFAR-100 VGG19
        • CIFAR-100 All-CNN-C
        • CIFAR-100 WideResNet 40-4
      • SVHN Test Problems
        • SVHN 3c3d
        • SVHN WideResNet 16-4
      • ImageNet Test Problems
        • ImageNet VGG16
        • ImageNet VGG19
        • ImageNet Inception v3
      • Tolstoi Test Problems
        • Tolstoi Char RNN
    • Runner
      • TF Runner
      • Standard Runner
      • Learning Rate Schedule Runner
    • Config
  • PyTorch
    • Data Sets
      • Quadratic Data Set
      • MNIST Data Set
      • FMNIST Data Set
      • CIFAR-10 Data Set
      • CIFAR-100 Data Set
      • SVHN Data Set
      • Tolstoi Data Set
    • Test Problems
      • Quadratic Test Problems
        • Quadratic Deep
      • MNIST Test Problems
        • MNIST MLP
        • MNIST 2c2d
        • MNIST VAE
      • Fashion-MNIST Test Problems
        • Fashion-MNIST MLP
        • Fashion-MNIST 2c2d
        • Fashion-MNIST VAE
      • CIFAR-10 Test Problems
        • CIFAR-10 3c3d
      • CIFAR-100 Test Problems
        • CIFAR-100 3c3d
        • CIFAR-100 All-CNN-C
      • SVHN Test Problems
        • SVHN Wide Resnet
    • Runner
      • PT Runner
      • Standard Runner
      • Learning Rate Schedule Runner
    • Config
  • Tuner
    • Grid Search
    • Random Search
    • Gaussian Process
    • Tuner
    • Parallelized Tuner
    • Tuning Utilities
      • General Utilities
      • Bayesian Specific Utilities
  • Scripts
    • Prepare Data
      • Named Arguments
    • Download Baselines
      • Named Arguments
    • Plot Results
      • Positional Arguments
      • Named Arguments
  • Config
DeepOBS
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© Copyright 2019, Frank Schneider Revision 95e226a1.

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