GPflow Documentation¶
Intro:
- Introduction
- GPflow manual
- GPflow with TensorFlow 2
- GPflow 2 Upgrade Guide
- Kernel Input Dims
- Parameter and tf.Variable
- Parameter Assignment
- Parameter trainable status
- SciPy Optimizer
- Model Initialisers
- SVGP Initialiser
- Autoflow
- Use of tf.function
- Model Compilation
- Sessions and Graphs
- Defer Build
- Return Types from Auto-flowed Methods
- Parameter Values
- Model Class
- Periodic Base Kernel
- Predict Full Covariance
- Predictive (log)density
- Settings / Configuration
- Data Types
- Transforms
- Stationary kernel subclasses
- Likelihoods
- Priors
- Name Scoping
- Model Persistence
Examples:
API:
- gpflow
- gpflow.Module
- gpflow.Parameter
- gpflow.default_float
- gpflow.default_int
- gpflow.default_jitter
- gpflow.set_trainable
- gpflow.base
- gpflow.conditionals
- gpflow.config
- gpflow.covariances
- gpflow.expectations
- gpflow.inducing_variables
- gpflow.kernels
- gpflow.kullback_leiblers
- gpflow.likelihoods
- gpflow.logdensities
- gpflow.mean_functions
- gpflow.models
- gpflow.monitor
- gpflow.optimizers
- gpflow.posteriors
- gpflow.probability_distributions
- gpflow.quadrature
- gpflow.utilities