Mercurial > pylearn

annotate datasets/MNIST.py @ 505:74b3e65f5f24

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added smallNorb dataset, switched to PYLEARN_DATA_ROOT
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 29 Oct 2008 17:09:04 -0400
parents 19ab9ce916e3
children 58810b63292b
rev   line source
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bd937e845bbb 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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6 import os
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7 import numpy
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9 from ..amat import AMat
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10 from .config import data_root
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12 def head(n=10, path=None):
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13 """Load the first MNIST examples.
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15 Returns two matrices: x, y. x has N rows of 784 columns. Each row of x represents the
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16 28x28 grey-scale pixels in raster order. y is a vector of N integers. Each element y[i]
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17 is the label of the i'th row of x.
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19 """
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20 path = os.path.join(data_root(), 'mnist','mnist.amat') if path is None else path
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22 dat = AMat(path=path, head=n)
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19ab9ce916e3 slightly more sophisticated system for finding the mnist data
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24 try:
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25 assert dat.input.shape[0] == n
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26 assert dat.target.shape[0] == n
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27 except Exception , e:
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28 raise Exception("failed to read MNIST data", (dat, e))
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30 return dat.input, numpy.asarray(dat.target, dtype='int64').reshape(dat.target.shape[0])
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32 def train_valid_test(ntrain=50000, nvalid=10000, ntest=10000, path=None):
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33 all_x, all_targ = head(ntrain+nvalid+ntest, path=path)
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35 train = all_x[0:ntrain], all_targ[0:ntrain]
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36 valid = all_x[ntrain:ntrain+nvalid], all_targ[ntrain:ntrain+nvalid]
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37 test = all_x[ntrain+nvalid:ntrain+nvalid+ntest], all_targ[ntrain+nvalid:ntrain+nvalid+ntest]
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39 return train, valid, test
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40
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41 def all(path=None):
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42 return head(n=None, path=path)
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