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annotate datasets/MNIST.py @ 503:c7ce66b4e8f4

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Extensions to algorithms, and some cleanup (by defining linear_output result).
author Joseph Turian <turian@gmail.com>
date Wed, 29 Oct 2008 03:29:18 -0400
parents 11e0357f06f4
children 19ab9ce916e3
rev   line source
470
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 numpy
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8 from ..amat import AMat
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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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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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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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26 return dat.input, numpy.asarray(dat.target, dtype='int64').reshape(dat.target.shape[0])
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27
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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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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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37 def all(path=None):
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38 return head(n=None, path=path)
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