torchbearer
0.2.0
Notes
Using the Metric API
Default Keys
Metric Decorators
Lambda Metrics
Metric Output - to_dict
Data Flow - The Metric Tree
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
Trial
Model (Deprecated)
Utilities
torchbearer.callbacks
Model Checkpointers
Logging
Tensorboard
Early Stopping
Gradient Clipping
Learning Rate Schedulers
Weight Decay
Decorators
torchbearer.metrics
Base Classes
Decorators - The Decorator API
Metric Wrappers
Metric Aggregators
Base Metrics
Timer
torchbearer
Docs
»
Welcome to torchbearer’s documentation!
Edit on GitHub
Welcome to torchbearer’s documentation!
¶
Notes
Using the Metric API
Using the Tensorboard Callback
Logging to Visdom
Deep Learning
Quickstart Guide
Training a Variational Auto-Encoder
Training a GAN
Differentiable Programming
Optimising functions
Linear Support Vector Machine (SVM)
Breaking ADAM
Package Reference
torchbearer
torchbearer.callbacks
torchbearer.metrics
Indices and tables
¶
Index
Module Index
Search Page