Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

Last update: Dec 31, 2022

Related tags

Deep Learning Theano

Overview

============================================================================================================
`MILA will stop developing Theano <https://groups.google.com/d/msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ>`_.

The PyMC developers are continuing Theano development in a `fork <https://github.com/pymc-devs/theano-pymc>`_.
============================================================================================================


To install the package, see this page:
   http://deeplearning.net/software/theano/install.html

For the documentation, see the project website:
   http://deeplearning.net/software/theano/

Related Projects:
   https://github.com/Theano/Theano/wiki/Related-projects

It is recommended that you look at the documentation on the website, as it will be more current than the documentation included with the package.

In order to build the documentation yourself, you will need sphinx. Issue the following command:

::

   python ./doc/scripts/docgen.py

Documentation is built into ``html/``

The PDF of the documentation can be found at ``html/theano.pdf``

================
DIRECTORY LAYOUT
================

``Theano`` (current directory) is the distribution directory.

* ``Theano/theano`` contains the package
* ``Theano/theano`` has several submodules:
 
  * ``gof`` + ``compile`` are the core
  * ``scalar`` depends upon core
  * ``tensor`` depends upon ``scalar``
  * ``sparse`` depends upon ``tensor``
  * ``sandbox`` can depend on everything else

* ``Theano/examples`` are copies of the example found on the wiki
* ``Theano/benchmark`` and ``Theano/examples`` are in the distribution, but not in
  the Python package
* ``Theano/bin`` contains executable scripts that are copied to the bin folder
  when the Python package is installed
* Tests are distributed and are part of the package, i.e. fall in
  the appropriate submodules
* ``Theano/doc`` contains files and scripts used to generate the documentation
* ``Theano/html`` is where the documentation will be generated

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

Related tags

Overview

Owner

Does MAML Only Work via Feature Re-use? A Data Set Centric Perspective

FcaNet: Frequency Channel Attention Networks

Unofficial PyTorch Implementation for HifiFace (https://arxiv.org/abs/2106.09965)

A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

Text to Image Generation with Semantic-Spatial Aware GAN

Streamlit tool to explore coco datasets

Repository for the paper "Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language Data"

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

Tools for robust generative diffeomorphic slice to volume reconstruction

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

Cognition-aware Cognate Detection

A Number Recognition algorithm

Based on the given clinical dataset, Predict whether the patient having Heart Disease or Not having Heart Disease

Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics".

You Only Look Once for Panopitic Driving Perception

This repo contains the official implementations of EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

[SDM 2022] Towards Similarity-Aware Time-Series Classification

for taichi voxel-challange event

Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

A Python implementation of active inference for Markov Decision Processes