Mercurial > pylearn
annotate pylearn/datasets/icml07.py @ 1531:88f361283a19 tip
Fix url/name to pylearn2.
author | Frederic Bastien <nouiz@nouiz.org> |
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date | Mon, 09 Sep 2013 10:08:05 -0400 |
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rev | line source |
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1 """ Functions related to the datasets used in Larochelle et al. 2007 (incl. modified MNIST). |
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2 """ |
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3 import os, sys |
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4 import numpy |
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5 |
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6 from config import get_filepath_in_roots |
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7 from pylearn.io.amat import AMat |
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8 from pylearn.datasets.config import data_root # config |
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9 from pylearn.datasets.dataset import Dataset |
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10 |
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11 class MNIST_rotated_background(object): |
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12 |
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13 def __init__(self, n_train=10000, n_valid=2000, n_test=50000): |
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14 |
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15 basedir = os.path.join(data_root(), 'icml07data', 'npy') |
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16 |
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17 x_all = numpy.load(os.path.join(basedir, 'mnist_rotated_background_images_inputs.npy')) |
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18 y_all = numpy.load(os.path.join(basedir, 'mnist_rotated_background_images_labels.npy')) |
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19 |
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20 vstart = n_train |
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21 tstart = n_train + n_valid |
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22 |
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23 self.train = Dataset.Obj(x=x_all[:n_train], y=y_all[:n_train]) |
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24 self.valid = Dataset.Obj(x=x_all[vstart:vstart+n_valid], |
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25 y=y_all[vstart:vstart+n_valid]) |
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26 self.test = Dataset.Obj(x=x_all[tstart:tstart+n_test], |
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27 y=y_all[tstart:tstart+n_test]) |
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28 |
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29 self.n_classes = 10 |
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30 self.img_shape = (28,28) |
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31 |
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32 |
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33 class DatasetLoader(object): |
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34 """ |
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35 A class for loading an ICML07 dataset into memory. |
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36 |
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37 The class has functionality to |
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38 - download the dataset from the internet (in amat format) |
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39 - convert the dataset from amat format to npy format |
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40 - load the dataset from either amat or npy source files |
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41 """ |
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42 def __init__(self, http_source, |
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43 n_inputs, n_classes, |
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44 n_train, n_valid, n_test, |
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45 npy_filename_root, |
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46 amat_filename_root=None, |
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47 amat_filename_train=None, |
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48 amat_filename_test=None, |
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49 amat_filename_all=None, |
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50 ): |
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51 self.__dict__.update(locals()) |
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52 del self.__dict__['self'] |
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53 |
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54 def download(self, todir): |
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55 #TODO: write a system call to wget to dl the file from self.http_source |
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56 raise NotImplementedError() |
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57 |
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58 def load_from_amat(self): |
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59 if self.amat_filename_all is not None: |
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60 amat_all = AMat(self.amat_filename_all) |
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61 allmat = amat_all.all |
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62 assert allmat.shape[0] == self.n_train + self.n_valid + self.n_test, allmat.shape |
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63 else: |
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64 if self.amat_filename_root is not None: |
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65 amat_train = AMat(self.amat_filename_root+'_train.amat') |
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66 amat_test = AMat(self.amat_filename_root+'_test.amat') |
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67 else: |
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68 amat_train = AMat(self.amat_filename_train) |
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69 amat_test = AMat(self.amat_filename_test) |
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70 assert amat_train.all.shape[0] == self.n_train + self.n_valid |
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71 assert amat_test.all.shape[0] == self.n_test |
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72 allmat = numpy.vstack((amat_train.all, amat_test.all)) |
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73 # CHECKPOINT: allmat has been computed by this point. |
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74 assert allmat.shape[1] == self.n_inputs+1 |
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75 inputs = allmat[:, :self.n_inputs].astype('float32') |
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76 labels = allmat[:, self.n_inputs].astype('int8') |
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77 assert numpy.allclose(labels, allmat[:, self.n_inputs]) |
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78 assert numpy.all(labels < self.n_classes) |
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79 return inputs, labels |
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80 |
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81 def load_from_amat_save_to_numpy(self): |
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82 inputs, labels = self.load_from_amat() |
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83 numpy.save(self.npy_filename_root+'_inputs.npy', inputs) |
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84 numpy.save(self.npy_filename_root+'_labels.npy', labels) |
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85 return inputs, labels |
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86 |
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87 def load_from_numpy(self, mmap_mode='r'): |
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88 """Much faster than load_from_amat""" |
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89 inputs = numpy.load(self.npy_filename_root+'_inputs.npy', mmap_mode=mmap_mode) |
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90 labels = numpy.load(self.npy_filename_root+'_labels.npy', mmap_mode=mmap_mode) |
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91 assert inputs.shape == (self.n_train + self.n_valid + self.n_test, self.n_inputs) |
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92 assert labels.shape[0] == inputs.shape[0] |
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93 assert numpy.all(labels < self.n_classes) |
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94 return inputs, labels |
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95 |
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96 def icml07_loaders(new_version=True, rootdir=None): |
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97 if rootdir is None: |
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98 rootdir = get_filepath_in_roots('icml07data_twiki') |
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99 if rootdir is None: |
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101 rval = dict( |
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102 mnist_basic=DatasetLoader( |
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103 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist.zip', |
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104 amat_filename_root=os.path.join(rootdir, 'mnist'), |
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105 npy_filename_root=os.path.join(rootdir, 'mnist_basic'), |
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106 n_inputs=784, |
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107 n_classes=10, |
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108 n_train=10000, |
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109 n_valid=2000, |
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110 n_test=50000 |
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111 ), |
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112 mnist_background_images=DatasetLoader( |
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113 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_background_images.zip', |
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114 amat_filename_root=os.path.join(rootdir, 'mnist_background_images'), |
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115 npy_filename_root=os.path.join(rootdir, 'mnist_background_images'), |
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116 n_inputs=784, |
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117 n_classes=10, |
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118 n_train=10000, |
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119 n_valid=2000, |
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120 n_test=50000 |
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121 ), |
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122 mnist_background_random=DatasetLoader( |
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123 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_background_random.zip', |
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124 amat_filename_root=os.path.join(rootdir, 'mnist_background_random'), |
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125 npy_filename_root=os.path.join(rootdir, 'mnist_background_random'), |
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126 n_inputs=784, |
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127 n_classes=10, |
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128 n_train=10000, |
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129 n_valid=2000, |
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130 n_test=50000 |
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131 ), |
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132 rectangles=DatasetLoader( |
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133 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/rectangles.zip', |
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134 amat_filename_root=os.path.join(rootdir, 'rectangles'), |
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135 npy_filename_root=os.path.join(rootdir, 'rectangles'), |
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136 n_inputs=784, |
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137 n_classes=10, |
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138 n_train=1000, |
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139 n_valid=200, |
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140 n_test=50000 |
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141 ), |
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142 rectangles_images=DatasetLoader( |
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143 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/rectangles_images.zip', |
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144 amat_filename_root=os.path.join(rootdir, 'rectangles_im'), |
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145 npy_filename_root=os.path.join(rootdir, 'rectangles_images'), |
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146 n_inputs=784, |
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147 n_classes=10, |
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148 n_train=10000, |
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149 n_valid=2000, |
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150 n_test=50000 |
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151 ), |
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152 convex=DatasetLoader( |
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153 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/convex.zip', |
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154 amat_filename_root=os.path.join(rootdir, 'convex'), |
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155 npy_filename_root=os.path.join(rootdir, 'convex'), |
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156 n_inputs=784, |
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157 n_classes=10, |
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158 n_train=6500, #not sure about this train/valid split |
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159 n_valid=1500, |
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160 n_test=50000 |
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161 ), |
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162 ) |
1354
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163 for level in range(1,7): |
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164 rval['mnist_noise_%i'%level] = DatasetLoader( |
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165 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_noise_variation.tar.gz', |
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166 amat_filename_all=os.path.join(rootdir, |
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167 'mnist_noise_variations_all_%i.amat'%level), |
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168 npy_filename_root=os.path.join(rootdir, 'mnist_noise_%i'%level), |
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169 n_inputs=784, |
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170 n_classes=10, |
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171 n_train=10000, |
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172 n_valid=2000, |
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173 n_test=2000 |
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174 ) |
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175 |
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176 if new_version: |
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177 rval['mnist_rotated'] = DatasetLoader( |
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178 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_rotation_new.zip', |
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179 amat_filename_test=os.path.join(rootdir, |
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180 'mnist_all_rotation_normalized_float_test.amat'), |
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181 amat_filename_train=os.path.join(rootdir, |
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182 'mnist_all_rotation_normalized_float_train_valid.amat'), |
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183 npy_filename_root=os.path.join(rootdir, 'mnist_rotated'), |
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184 n_inputs=784, |
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185 n_classes=10, |
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186 n_train=10000, |
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187 n_valid=2000, |
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188 n_test=50000 |
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189 ) |
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190 rval['mnist_rotated_background_images'] = DatasetLoader( |
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191 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_rotation_back_image_new.zip', |
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192 amat_filename_test=os.path.join(rootdir, |
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193 'mnist_all_background_images_rotation_normalized_test.amat'), |
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194 amat_filename_train=os.path.join(rootdir, |
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195 'mnist_all_background_images_rotation_normalized_train_valid.amat'), |
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196 npy_filename_root=os.path.join(rootdir, 'mnist_rotated_background_images'), |
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197 n_inputs=784, |
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198 n_classes=10, |
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199 n_train=10000, |
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200 n_valid=2000, |
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201 n_test=50000 |
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202 ) |
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203 else: |
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204 raise NotImplementedError('TODO: what are the amat_filenames here') |
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205 rval['mnist_rotated'] = DatasetLoader( |
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206 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_rotation.zip') |
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207 rval['mnist_rotated_background_images'] = DatasetLoader( |
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208 http_source='http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_rotation_back_image.zip') |
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209 return rval |
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210 |
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211 |