
.. _NEWS:

=============
Release Notes
=============

Theano 0.3 (2010-11-23)
=======================

This is the first major release of Theano since 0.1. Version 0.2 development started internally but it was never advertised as a release.

There have been so many changes since 0.1 that we have lost track of many of them. Below is a *partial* list of changes since 0.1.

 * GPU code using NVIDIA's CUDA framework is now generated for many Ops.
 * Some interface changes since 0.1:
     * A new "shared variable" system to allow reusing memory space between Theano functions.
         * A new memory contract has been formally written for Theano, for people who want to minimize memory copies.
     * The old module system has been deprecated.
     * By default, inputs to a Theano function will not be silently downcasted (e.g. from float64 to float32).
     * An error is now raised when using the result of logical operation on Theano variable in an 'if' (i.e. an implicit call to __nonzeros__).
     * An error is now raised when we receive a non-aligned ndarray as input to a function (this is not supported).
     * An error is raised when the list of dimensions passed to dimshuffle() contains duplicates or is otherwise not sensible.
     * Call NumPy BLAS bindings for gemv operations in addition to the already supported gemm.
     * If gcc is unavailable at import time, Theano now falls back to a Python-based emulation mode after raising a warning.
     * An error is now raised when tensor.grad is called on a non-scalar Theano variable (in the past we would implicitly do a sum on the tensor to make it a scalar).
     * Added support for "erf" and "erfc" functions.
 * The current default value of the parameter axis of theano.{max,min,argmax,argmin,max_and_argmax} is deprecated. We now use the default NumPy behavior of operating on the entire tensor.
 * Theano is now available from PyPI and installable through "easy_install" or "pip".


Theano 0.1
==========

*Release date: 2009-04-02*

What works
----------

- building symbolic expression.
- arranging symbolic expressions into Modules so that multiple functions
  can work on the same data.
- symbolic gradient descent.
- graph optimization.
- compilation to C for many kinds of expression.
- a debugging mode that checks that your expression results are correct,
  using a variety of sanity checks.

What's missing?
---------------

- An algorithm library. We're missing a library of examples and standard
  component implementations.  Some examples will find their way into
  the Theano repo, but standard algorithms will go into the 'pylearn'
  project (toolbox style). Now that we have a stable foundation, we
  can reach a consensus on style for algorithms.
