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Monitorch documentation

  • Demo notebooks
  • API
  • Demo notebooks
  • API

Section Navigation

  • Introduction: PyTorchInspector, Lenses and Visualizers
  • Loss and Metrics: Ecommerece Text Classification
  • Activations: MNIST
  • Gradient Geometry: Sentiment Analysis with RNNs
  • Ouput and Parameter Norm: Deep Q-Network for CartPole
  • Demo notebooks

Demo notebooks#

  • Introduction: PyTorchInspector, Lenses and Visualizers
    • Abstract
    • Imports and Dataset
    • Neural Net Definition
    • Inspector Definition
    • Base Usage
    • Detach-Attach
    • Hot-Swapping Visualizer
    • Next Steps
  • Loss and Metrics: Ecommerece Text Classification
    • Abstract
    • Imports and Dataset
    • Neural Network Definition
    • Explicit Loss saving
    • Automatic Loss Saving
    • What to Look for
    • Next steps
  • Activations: MNIST
    • Abstract
    • Imports
    • Activation Lenses
    • 2D Examples
    • MNIST
    • What to Look for
    • Next Steps
  • Gradient Geometry: Sentiment Analysis with RNNs
    • Abstract
    • Imports and Datasets
    • Gradient Geometry Lens
    • RNN
    • LSTM
    • What to Look for
    • Next Steps
  • Ouput and Parameter Norm: Deep Q-Network for CartPole
    • Abstract
    • Imports and Environment
    • Output Norm Lens
    • Parameter Norm Lens
    • CartPole
    • What to Look for
    • Next Steps

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Introduction: PyTorchInspector, Lenses and Visualizers

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