Theano Library
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A Theano Library is a Python-based library focused on defining, optimizing, and evaluating mathematical expressions.
- Context:
- It can be used as a Deep Learning Library.
- It can (typically) be slow because it is Python-based.
- It can integrate with Numpy.
- It can make use of a CUDA GPU Platform or (soon) an OpenCL Platform[1].
- Example(s):
- Theano v1.0 (final release).
- …
- Counter-Example(s):
- TensorFlow.
- Torch Library.
- Deeplearning4j.
- Keras.
- MXNet.
- sklearn.neural_networks.
- pylearn, and numpy.
- cuda-convnet a C++-based convolutional/feed-forward neural networks.
- deepnet.
- CUV NN library.
- nnForge Library, a C++ library for training convolutional fully-connected neural networks.
- Caffe Framework.
- See: numpy, Automatic Differentiation, CUDA GPU Platform, Keras Library.
References
2017a
- https://groups.google.com/forum/#!topic/theano-users/7Poq8BZutbY
- QUOTE: After almost ten years of development, we have the regret to announce that we will put an end to our Theano development after the 1.0 release, which is due in the next few weeks.
2017b
Software | Creator | Software licenseTemplate:Efn | Open source | Platform | Written in | Interface | OpenMP support | OpenCL support | CUDA support | Automatic differentiation[1] | Has pretrained models | Recurrent nets | Convolutional nets | RBM/DBNs | Parallel execution (multi node) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Theano | Université de Montréal | BSD license | Yes | Cross-platform | Python | Python | Yes | Template:Depends | Yes | Yes[2][3] | Template:Depends | Yes | Yes | Yes | Yes[4] |
2016
- http://github.com/ASIDataScience/training-neural-networks-notebook/blob/master/Training-Neural-Networks-Theano.ipynb
- QUOTE: Theano allows us to write relatively concise code that follows the structure of the underlying maths. ...
import theano ;
import theano.tensor ;
- QUOTE: Theano allows us to write relatively concise code that follows the structure of the underlying maths. ...
2015
- http://en.wikipedia.org/wiki/Automatic_differentiation#Software
- Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays both on CPU and GPU efficiently.
2015
- http://deeplearning.net/software/theano/
- Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:
- tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
- transparent use of a GPU – Perform data-intensive calculations up to 140x faster than with CPU.(float32 only)
- efficient symbolic differentiation – Theano does your derivatives for function with one or many inputs.
- Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:
2014
- http://deeplearning.net/software/theano/introduction.html#introduction
- Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It can also surpass C on a CPU by many orders of magnitude by taking advantage of recent GPUs.
Theano combines aspects of a computer algebra system (CAS) with aspects of an optimizing compiler. It can also generate customized C code for many mathematical operations. This combination of CAS with optimizing compilation is particularly useful for tasks in which complicated mathematical expressions are evaluated repeatedly and evaluation speed is critical. For situations where many different expressions are each evaluated once Theano can minimize the amount of compilation/analysis overhead, but still provide symbolic features such as automatic differentiation.
- Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It can also surpass C on a CPU by many orders of magnitude by taking advantage of recent GPUs.
2014
- http://markus.com/install-theano-on-aws/
- QUOTE: Theano is an amazing Python package for deep learning that can utilize NVIDIA's CUDA toolkit to run on the gpu. The gpu is orders of magnitude faster than the cpu for math operations (such as matrix multiplication), which is essential for many machine learning algorithms.
2010
- (Bergstra et al., 2010) ⇒ James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. “Theano: a CPU and GPU math expression compiler.” In: Proceedings of the Python for scientific computing conference (SciPy).