Multivariate Pattern Analysis in Python

# LeCun et al. (1999): The MNIST Dataset Of Handwritten Digits (Images)

The MNIST dataset of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

See http://yann.lecun.com/exdb/mnist for more information.

Note

The version that is offered here is identical to the four files distributed there, but has been converted into a single HDF5 file than can easily be read by PyMVPA.

## Terms Of Use

Yann LeCun (Courant Institute, NYU) and Corinna Cortes (Google Labs, New York) hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.

## Download

A single hdf5 file containing entire MNIST dataset is available from

> http://data.pymvpa.org/datasets/mnist/

## Requirements

- HDF5 access facility.
- PyMVPA 0.5 (or later) provides the `h5load()` function (utilizes H5PY package).

## Instructions

    >>> from mvpa2.suite import *
    >>> filepath = os.path.join(pymvpa_datadbroot, 'mnist', "mnist.hdf5")
    >>> datasets = h5load(filepath)
    >>> train = datasets['train']
    >>> test = datasets['test']
    >>> print train
    <Dataset: 60000x784@uint8, <sa: labels>>
    >>> print test
    <Dataset: 10000x784@uint8, <sa: labels>>
    >>> # assign a mapper able to recreate 28x28 pixel image arrays
    >>> test.a.mapper = FlattenMapper(shape=(28, 28))
    >>> test.mapper.reverse(test).shape
    (10000, 28, 28)

## References

LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.