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annotate pylearn/datasets/MNIST.py @ 1403:6ade5b39b773

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int8 should be enough to represent digits from 0 to 9
author Pascal Lamblin <lamblinp@iro.umontreal.ca>
date Fri, 21 Jan 2011 20:40:57 -0500
parents a13142cbeabd
children 83d3c9ee6d65
rev   line source
470
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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1 """
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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2 Various routines to load/access MNIST data.
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James Bergstra <bergstrj@iro.umontreal.ca>
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3 """
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James Bergstra <bergstrj@iro.umontreal.ca>
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4
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5 import os
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6 import numpy
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7
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dumitru@deepnets.mtv.corp.google.com
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8 from pylearn.io.pmat import PMat
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dumitru@deepnets.mtv.corp.google.com
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9 from pylearn.datasets.config import data_root # config
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dumitru@deepnets.mtv.corp.google.com
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10 from pylearn.datasets.dataset import Dataset
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James Bergstra <bergstrj@iro.umontreal.ca>
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12 def head(n=10, path=None):
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James Bergstra <bergstrj@iro.umontreal.ca>
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13 """Load the first MNIST examples.
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14
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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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18
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19 """
818
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dumitru@deepnets.mtv.corp.google.com
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20 if path is None:
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dumitru@deepnets.mtv.corp.google.com
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21 path = os.path.join(data_root(), 'mnist','mnist_all.pmat')
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22
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951272679910 get the mnist data from the pmat file and not the amat file
Frederic Bastien <bastienf@iro.umontreal.ca>
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23 dat = PMat(fname=path)
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24
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25 rows=dat.getRows(0,n)
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19ab9ce916e3 slightly more sophisticated system for finding the mnist data
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26
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27 return rows[:,0:-1], numpy.asarray(rows[:,-1], dtype='int8')
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f30bb746f279 put in comment a fct call all that seam to don't be used and conflict with numpy and python name.
Frederic Bastien <bastienf@iro.umontreal.ca>
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f30bb746f279 put in comment a fct call all that seam to don't be used and conflict with numpy and python name.
Frederic Bastien <bastienf@iro.umontreal.ca>
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30 #What is the purpose of this fct?
f30bb746f279 put in comment a fct call all that seam to don't be used and conflict with numpy and python name.
Frederic Bastien <bastienf@iro.umontreal.ca>
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31 #If still usefull, rename it as it conflict with the python an numpy nake all.
f30bb746f279 put in comment a fct call all that seam to don't be used and conflict with numpy and python name.
Frederic Bastien <bastienf@iro.umontreal.ca>
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32 #def all(path=None):
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Frederic Bastien <bastienf@iro.umontreal.ca>
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33 # return head(n=None, path=path)
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34
475
11e0357f06f4 typo in MNIST.train_valid_test
James Bergstra <bergstrj@iro.umontreal.ca>
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35 def train_valid_test(ntrain=50000, nvalid=10000, ntest=10000, path=None):
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36 all_x, all_targ = head(ntrain+nvalid+ntest, path=path)
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37
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38 rval = Dataset()
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39
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40 rval.train = Dataset.Obj(x=all_x[0:ntrain],
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41 y=all_targ[0:ntrain])
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42 rval.valid = Dataset.Obj(x=all_x[ntrain:ntrain+nvalid],
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parents: 511
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43 y=all_targ[ntrain:ntrain+nvalid])
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 511
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44 rval.test = Dataset.Obj(x=all_x[ntrain+nvalid:ntrain+nvalid+ntest],
b054271b2504 new file structure layout, factories, etc.
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parents: 511
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45 y=all_targ[ntrain+nvalid:ntrain+nvalid+ntest])
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46
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47 rval.n_classes = 10
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desjagui@atchoum.iro.umontreal.ca
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48 rval.img_shape = (28,28)
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49 return rval
475
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James Bergstra <bergstrj@iro.umontreal.ca>
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50
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51
627
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James Bergstra <bergstrj@iro.umontreal.ca>
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52 def full():
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53 return train_valid_test()
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54
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Pascal Lamblin <lamblinp@iro.umontreal.ca>
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55 #useful for test, keep it
658
6d927441a38f added pylearn.datasets.MNIST.first_10 and pylearn.datasets.MNIST.first_100. They are usefull to test with small dataset.
Frederic Bastien <bastienf@iro.umontreal.ca>
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56 def first_10():
6d927441a38f added pylearn.datasets.MNIST.first_10 and pylearn.datasets.MNIST.first_100. They are usefull to test with small dataset.
Frederic Bastien <bastienf@iro.umontreal.ca>
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57 return train_valid_test(ntrain=10, nvalid=10, ntest=10)
6d927441a38f added pylearn.datasets.MNIST.first_10 and pylearn.datasets.MNIST.first_100. They are usefull to test with small dataset.
Frederic Bastien <bastienf@iro.umontreal.ca>
parents: 653
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58
829
3f44379177b2 More descriptive error message when fpconst is missing.
Pascal Lamblin <lamblinp@iro.umontreal.ca>
parents: 824
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59 #useful for test, keep it
658
6d927441a38f added pylearn.datasets.MNIST.first_10 and pylearn.datasets.MNIST.first_100. They are usefull to test with small dataset.
Frederic Bastien <bastienf@iro.umontreal.ca>
parents: 653
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60 def first_100():
6d927441a38f added pylearn.datasets.MNIST.first_10 and pylearn.datasets.MNIST.first_100. They are usefull to test with small dataset.
Frederic Bastien <bastienf@iro.umontreal.ca>
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61 return train_valid_test(ntrain=100, nvalid=100, ntest=100)
6d927441a38f added pylearn.datasets.MNIST.first_10 and pylearn.datasets.MNIST.first_100. They are usefull to test with small dataset.
Frederic Bastien <bastienf@iro.umontreal.ca>
parents: 653
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62
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James Bergstra <bergstrj@iro.umontreal.ca>
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63 def first_1k():
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64 return train_valid_test(ntrain=1000, nvalid=200, ntest=200)
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65
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66 def first_10k():
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67 return train_valid_test(ntrain=10000, nvalid=2000, ntest=2000)
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68