Mercurial > pylearn
annotate datasets/MNIST.py @ 479:fbfd3932fd00
typo
author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Mon, 27 Oct 2008 17:28:48 -0400 |
parents | 11e0357f06f4 |
children | 19ab9ce916e3 |
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new stuff: algorithms/logistic_regression, datasets/MNIST
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1 """ |
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2 Various routines to load/access MNIST data. |
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3 """ |
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4 from __future__ import absolute_import |
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5 |
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6 import numpy |
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7 |
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8 from ..amat import AMat |
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9 |
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10 default_path = '/u/bergstrj/pub/data/mnist.amat' |
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11 """the location of a file containing mnist data in .amat format""" |
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12 |
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13 |
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14 def head(n=10, path=None): |
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15 """Load the first MNIST examples. |
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16 |
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17 Returns two matrices: x, y. x has N rows of 784 columns. Each row of x represents the |
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18 28x28 grey-scale pixels in raster order. y is a vector of N integers. Each element y[i] |
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19 is the label of the i'th row of x. |
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20 |
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21 """ |
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22 path = path if path is not None else default_path |
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23 |
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24 dat = AMat(path=path, head=n) |
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25 |
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26 return dat.input, numpy.asarray(dat.target, dtype='int64').reshape(dat.target.shape[0]) |
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typo in MNIST.train_valid_test
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28 def train_valid_test(ntrain=50000, nvalid=10000, ntest=10000, path=None): |
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29 all_x, all_targ = head(ntrain+nvalid+ntest, path=path) |
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30 |
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31 train = all_x[0:ntrain], all_targ[0:ntrain] |
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32 valid = all_x[ntrain:ntrain+nvalid], all_targ[ntrain:ntrain+nvalid] |
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33 test = all_x[ntrain+nvalid:ntrain+nvalid+ntest], all_targ[ntrain+nvalid:ntrain+nvalid+ntest] |
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34 |
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35 return train, valid, test |
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36 |
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James Bergstra <bergstrj@iro.umontreal.ca>
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37 def all(path=None): |
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James Bergstra <bergstrj@iro.umontreal.ca>
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38 return head(n=None, path=path) |
11e0357f06f4
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
471
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39 |
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James Bergstra <bergstrj@iro.umontreal.ca>
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40 |