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21 September 2021, 22:50:56 UTC
Revision 6e43fecb3942b98dfc6da24237662da40deb54f1 authored by st-- on 22 October 2019, 16:54:27 UTC, committed by GitHub on 22 October 2019, 16:54:27 UTC 
Change step_callback behaviour of Scipy optimizer in gpflow2 to behave as in gpflow1 (#1111)
 
Changes behaviour of step_callback so that the callback only gets called once per optimisation step, not once per objective function evaluation. Makes gpflow2's Scipy() behave more like gpflow1's ScipyOptimizer()/monitoring.

* Call step_callback through scipy.minimize's callback procedure (at the end of each step); previous behaviour was to call step_callback at each evaluation of objective function

* split off from unpack_tensors (now stateless) the new assign_tensors (assigns to variables)

* change callback_func to pass variables and their current values to step_callback, not assigning/computing loss/gradients

* fix type signature

* Improve docstring
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Tip revision: 6e43fecb3942b98dfc6da24237662da40deb54f1 authored by st-- on 22 October 2019, 16:54:27 UTC
Change step_callback behaviour of Scipy optimizer in gpflow2 to behave as in gpflow1 (#1111)
Tip revision: 6e43fec
GLOSSARY.md 
## Glossary

GPflow does not always follow standard Python naming conventions,
and instead tries to apply the notation in the relevant GP papers.\
The following is the convention we aim to use in the code.

---

<dl>
  <dt>GPR</dt>
  <dd>Gaussian process regression</dd>

  <dt>SVGP</dt>
  <dd>stochastic variational inference for Gaussian process models</dd>

  <dt>Shape constructions [..., A, B]</dt>
  <dd>the way of describing tensor shapes in docstrings and comments. Example: <i>[..., N, D, D]</i>, this is a tensor with an arbitrary number of leading dimensions indicated using the ellipsis sign, and the last two dimensions are equal</dd>

  <dt>X</dt>
  <dd>(and variations like Xnew) refers to input points; always of rank 2, e.g. shape <i>[N, D]</i>, even when <i>D=1</i></dd>

  <dt>Y</dt>
  <dd>(and variations like Ynew) refers to observed output values, potentially with multiple output dimensions; always of rank 2, e.g. shape <i>[N, P]</i>, even when <i>P=1</i></dd>

  <dt>Z</dt>
  <dd>refers to inducing points</dd>

  <dt>M</dt>
  <dd>stands for the number of inducing features (e.g. length of Z)</dd>

  <dt>N</dt>
  <dd>stands for the number of data or minibatch size in docstrings and shape constructions</dd>

  <dt>P</dt>
  <dd>stands for the number of output dimensions in docstrings and shape constructions</dd>

  <dt>D</dt>
  <dd>stands for the number of input dimensions in docstrings and shape constructions</dd>
</dl>
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