datasets.txt

Datasets
========

MNIST Dataset
+++++++++++++


 The `MNIST <http://yann.lecun.com/exdb/mnist>`_ dataset consists of handwritten 
 digit images and it is divided in 60 000 examples for the training set and 
 10 000 examples for testing. All examples have been size-normalized and 
 centered in a fixed size image of 28 x 28 pixels. In the original dataset 
 each pixel of the image is represented by a value between 0 and 255, where 
 0 is black, 255 is  white and anything in between is a different shade of grey. 


 Here are some examples of MNIST digits: 

    |0| |1| |2| |3| |4| |5|

 .. |0| image:: images/mnist_0.png
 .. |1| image:: images/mnist_1.png
 .. |2| image:: images/mnist_2.png
 .. |3| image:: images/mnist_3.png
 .. |4| image:: images/mnist_4.png
 .. |5| image:: images/mnist_5.png
 
 For convenience we pickled the dataset to make it easier to use in python.
 It is available for download `here <http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz>`_.
 The pickled file represents a tuple of 3 lists : the training set, the 
 validation set and the testing set. Each element of any of the three lists 
 represents a minibatch of 20 examples. Such an element is a tuple composed 
 of the list of 20 images and the list of class labels for each of the
 images. An image is represented as numpy 1-dimensional array of 784 (28 x 28) float
 values between 0 and 1 ( 0 stands for black, 1 for white). The labels
 are numbers between 0 and 9 indicating which digit the image
 represents. Loading and accessing the dataset in the python can be done as 
 follows: 

 .. code-block:: python

   import cPickle, gzip, numpy
 

   f = gzip.open('mnist.pkl.gz','rb')
   (training_set, validation_set, testing_set) = cPickle.load(f)
   f.close()
   
   # accessing training example i of minibatch j 
   image = training_set[j][0][i]
   label = training_set[j][1][i]