Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.
Project description
Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Theano features:
tight integration with NumPy: a similar interface to NumPy’s. numpy.ndarrays are also used internally in Theano-compiled functions.
transparent use of a GPU: perform data-intensive computations up to 140x faster than on a CPU (support for float32 only).
efficient symbolic differentiation: Theano can compute derivatives for functions of one or many inputs.
speed and stability optimizations: avoid nasty bugs when computing expressions such as log(1+ exp(x) ) for large values of x.
dynamic C code generation: evaluate expressions faster.
extensive unit-testing and self-verification: includes tools for detecting and diagnosing bugs and/or potential problems.
Theano has been powering large-scale computationally intensive scientific research since 2007, but it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal).
Release Notes for Theano 0.3.0 (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”.
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