Mercurial > pylearn
comparison datasets/MNIST.py @ 470:bd937e845bbb
new stuff: algorithms/logistic_regression, datasets/MNIST
| author | James Bergstra <bergstrj@iro.umontreal.ca> |
|---|---|
| date | Wed, 22 Oct 2008 15:56:53 -0400 |
| parents | |
| children | 45b3eb429c15 |
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| 469:4335309f4924 | 470:bd937e845bbb |
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| 1 """ | |
| 2 Various routines to load/access MNIST data. | |
| 3 """ | |
| 4 from __future__ import absolute_import | |
| 5 | |
| 6 import numpy | |
| 7 | |
| 8 from ..amat import AMat | |
| 9 | |
| 10 default_path = '/u/bergstrj/pub/data/mnist.amat' | |
| 11 """the location of a file containing mnist data in .amat format""" | |
| 12 | |
| 13 | |
| 14 def head(n=10, path=None): | |
| 15 """Load the first MNIST examples. | |
| 16 | |
| 17 Returns two matrices: x, y. x has N rows of 784 columns. Each row of x represents the | |
| 18 28x28 grey-scale pixels in raster order. y is a vector of N integers. Each element y[i] | |
| 19 is the label of the i'th row of x. | |
| 20 | |
| 21 """ | |
| 22 path = path if path is not None else default_path | |
| 23 | |
| 24 dat = AMat(path=path, head=n) | |
| 25 | |
| 26 return dat.input, numpy.asarray(dat.target, dtype='int64').reshape(dat.target.shape[0]) | |
| 27 | |
| 28 def all(path=None): | |
| 29 return head(n=None, path=path) | |
| 30 | |
| 31 |