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Pylearn specifications


Unique identifier OMICS_28757
Name Pylearn
Alternative name pylearn2
Software type Framework/Library
Interface Command line interface, Application programming interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux
Programming languages Python
Parallelization CUDA
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 0.6rc3
Stability Stable
Maintained No




No version available



This tool is not maintained anymore.

Additional information

Publication for Pylearn

Pylearn citations


Ten quick tips for machine learning in computational biology

BioData Min
PMCID: 5721660
PMID: 29234465
DOI: 10.1186/s13040-017-0155-3

[…] ch as the extremely popular Bioconductor package []). On the other hand, Python is a high-level interpreted programming language, which provides multiple fast machine learning libraries (for example, Pylearn2 [], Scikit-learn []), mathematical libraries (such as Theano []), and data mining toolboxes (such as Orange []). Torch, instead, is a programming language based upon lua [], a platform, and a […]


Toolkits and Libraries for Deep Learning

J Digit Imaging
PMCID: 5537091
PMID: 28315069
DOI: 10.1007/s10278-017-9965-6

[…] Pylearn2 is a machine learning research library developed by Laboratoire d’Informatique des Systèmes Adaptatifs (LISA) at University of Montreal []. Pylearn2 offers a collection of classical machine l […]


Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

PMCID: 5629961
PMID: 29018612
DOI: 10.7717/peerj.3874

[…] does not overlap with training and validation set. On this test set, the best Object-Net achieved an error rate of 4.71%, whereas the Separator-Net achieved 5.58%. The CNN models were implemented in Pylearn2 (), a machine learning library built on top of Theano (; ). […]


Stochastic Synapses Enable Efficient Brain Inspired Learning Machines

Front Neurosci
PMCID: 4925698
PMID: 27445650
DOI: 10.3389/fnins.2016.00241

[…] fire neurons (Cao et al., ; Diehl et al., ). Such mapping techniques have the advantage that they can leverage the capabilities of existing machine learning frameworks such as Caffe (Jia et al., ) or pylearn2 (Goodfellow et al., ) for brain-inspired computers. Although mapping techniques do not offer a solution for on-line, real-time learning, they resulted in the best performing spike-based imple […]


Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Sci Rep
PMCID: 4876324
PMID: 27212078
DOI: 10.1038/srep26286

[…] To train the convolutional neural network we made use of the open-source ‘deep learning’ libraries Theano 0.7 and pylearn2 0.1.As it is impossible to feed entire whole-slide images to the network at once, we randomly extracted small patches from the whole-slide image for training. Whole-slide results can then be […]


An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

Int J Mol Sci
PMCID: 4519904
PMID: 26198229
DOI: 10.3390/ijms160715384

[…] he input layer consists of 1911 features, and the second and third layers each consist of 240 maxout nodes []. The final output is a two-class multinomial node. The deep network was trained using the PyLearn2 library [] with stochastic gradient descent on batches of 1000 training examples. In addition to the maxout nodes, a dropout procedure [] was used, which randomly dropped out the output of so […]


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Pylearn institution(s)
Departement d’Informatique et de Recherche Operationelle, Universite de Montreal, Montreal, QC, Canada; Center for Theoretical Neuroscience, University of Waterloo, Waterloo, Belgium

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