torchbearer
0.3.0
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
Training
Visualising
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
torchbearer.callbacks
torchbearer.metrics
torchbearer.variational
torchbearer
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torchbearer.variational
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v: 0.3.0
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