Mercurial > ift6266

annotate deep/amt/amt.py @ 445:868f82777839

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added jobman all test + val error and sigmoid output
author xaviermuller
date Tue, 04 May 2010 11:17:27 -0400
parents 5777b5041ac9
children 7bdd412754ea
rev   line source
412
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
humel
parents: 403
diff changeset
1 # Script usage : python amt.py filname.cvs type
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
humel
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diff changeset
2 """
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diff changeset
3 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv all
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diff changeset
4 Testing on : all
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5 Total entries : 300.0
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6 Turks per batch : 3
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7 Average test error : 45.3333333333%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
humel
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8 Error variance : 7.77777777778%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
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diff changeset
9 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv 36
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diff changeset
10 Testing on : 36
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11 Total entries : 300.0
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12 Turks per batch : 3
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13 Average test error : 51.6666666667%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
humel
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14 Error variance : 3.33333333333%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
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diff changeset
15 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv upper
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16 Testing on : upper
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17 Total entries : 63.0
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18 Turks per batch : 3
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19 Average test error : 53.9682539683%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
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20 Error variance : 1.77777777778%
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21 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv lower
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22 Testing on : lower
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23 Total entries : 135.0
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24 Turks per batch : 3
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25 Average test error : 37.037037037%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
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26 Error variance : 3.77777777778%
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27 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv digits
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28 Testing on : digits
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29 Total entries : 102.0
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30 Turks per batch : 3
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31 Average test error : 50.9803921569%
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32 Error variance : 1.33333333333%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
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33 """
399
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34
402
83413ac10913 Added more stats printing. Now you dont need to parameters which dataset you are testing, it will detect it automatically
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35 import csv,numpy,re,decimal
399
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36
402
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37 DATASET_PATH = { 'nist' : '/data/lisa/data/ift6266h10/amt_data/nist/',
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38 'p07' : '/data/lisa/data/ift6266h10/amt_data/p07/',
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39 'pnist' : '/data/lisa/data/ift6266h10/amt_data/pnist/' }
399
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40
402
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41 CVSFILE = None
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42 #PATH = None
403
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43 answer_labels = [ 'Answer.c'+str(i+1) for i in range(10) ]
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44 img_url = 'Input.image_url'
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45 turks_per_batch = 3
402
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46 image_per_batch = 10
412
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47 TYPE = None
399
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48
412
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49 def all_classes_assoc():
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50 answer_assoc = {}
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51 for i in range(0,10):
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52 answer_assoc[str(i)]=i
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53 for i in range(10,36):
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54 answer_assoc[chr(i+55)]=i
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55 for i in range(36,62):
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56 answer_assoc[chr(i+61)]=i
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57 return answer_assoc
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58
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59 def upper_classes_assoc():
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60 answer_assoc = {}
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61 for i in range(10,36):
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62 answer_assoc[chr(i+55)]=i
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63 return answer_assoc
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64
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65 def lower_classes_assoc():
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66 answer_assoc = {}
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67 for i in range(36,62):
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68 answer_assoc[chr(i+61)]=i
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69 return answer_assoc
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70
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71 def digit_classes_assoc():
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72 answer_assoc = {}
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73 for i in range(0,10):
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74 answer_assoc[str(i)]=i
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75 return answer_assoc
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76
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77 def tsix_classes_assoc():
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78 answer_assoc = {}
430
5777b5041ac9 fixed error computation for 36 classes
Dumitru Erhan <dumitru.erhan@gmail.com>
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79 for i in range(0,10):
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Dumitru Erhan <dumitru.erhan@gmail.com>
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80 answer_assoc[str(i)]=i
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81 for i in range(10,36):
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82 answer_assoc[chr(i+55)]=i
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83 answer_assoc[chr(i+87)]=i
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84 return answer_assoc
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85
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86 def upper_label_assoc(ulabel):
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87 for i in range(len(ulabel)):
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88 if ulabel[i] < 10 or ulabel[i] > 35 :
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89 ulabel[i] = -1
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90 return ulabel
401
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91
412
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92 def lower_label_assoc(ulabel):
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93 for i in range(len(ulabel)):
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94 if ulabel[i] < 36 or ulabel[i] > 61 :
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95 ulabel[i] = -1
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96 return ulabel
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97
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98 def tsix_label_assoc(ulabel):
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99 for i in range(len(ulabel)):
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100 if ulabel[i] > 35 and ulabel[i] < 62 :
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101 ulabel[i] = ulabel[i] - 26
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102 return ulabel
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103
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104 def digit_label_assoc(ulabel):
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105 for i in range(len(ulabel)):
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106 if ulabel[i] < 0 or ulabel[i] > 9 :
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107 ulabel[i] = -1
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108
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109 return ulabel
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110
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111 def classes_answer(type):
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112 if type == 'all':
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113 return all_classes_assoc()
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114 elif type == '36':
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115 return tsix_classes_assoc()
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116 elif type == 'lower':
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117 return lower_classes_assoc()
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118 elif type == 'upper':
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119 return upper_classes_assoc()
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120 elif type == 'digits':
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121 return digit_classes_assoc()
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122 else:
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123 raise ('Inapropriate option for the type of classification :' + type)
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124
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125
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126 def test_error(assoc_type=TYPE):
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127 answer_assoc = classes_answer(assoc_type)
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128
399
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129 turks = []
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130 reader = csv.DictReader(open(CVSFILE), delimiter=',')
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131 entries = [ turk for turk in reader ]
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132
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133 errors = numpy.zeros((len(entries),))
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134 if len(entries) % turks_per_batch != 0 :
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135 raise Exception('Wrong number of entries or turks_per_batch')
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136
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137 total_uniq_entries = len(entries) / turks_per_batch
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138
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139 errors = numpy.zeros((len(entries),))
412
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140 num_examples = numpy.zeros((len(entries),))
399
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141 error_means = numpy.zeros((total_uniq_entries,))
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142 error_variances = numpy.zeros((total_uniq_entries,))
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143
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144 for i in range(total_uniq_entries):
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145 for t in range(turks_per_batch):
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146 errors[i*turks_per_batch+t],num_examples[i*turks_per_batch+t] = get_error(entries[i*turks_per_batch+t],assoc_type)
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147 #error_means[i] = errors[i*turks_per_batch:(i+1)*turks_per_batch].mean()
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148 error_variances[i] = errors[i*turks_per_batch:(i+1)*turks_per_batch].var()
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149
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150 percentage_error = 100. * errors.sum() / num_examples.sum()
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151 print 'Testing on : ' + str(assoc_type)
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152 print 'Total entries : ' + str(num_examples.sum())
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153 print 'Turks per batch : ' + str(turks_per_batch)
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154 print 'Average test error : ' + str(percentage_error) +'%'
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155 print 'Error variance : ' + str(error_variances.mean()*image_per_batch) +'%'
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156
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157
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158 def find_dataset(entry):
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159 file = parse_filename(entry[img_url])
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160 return file.split('_')[0]
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161
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162 def get_error(entry, type):
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163 answer_assoc = classes_answer(type)
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164 labels = get_labels(entry,type)
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165 test_error = 0
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166 cnt = 0
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167 for i in range(len(answer_labels)):
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168 if labels[i] == -1:
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169 cnt+=1
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170 else:
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171 answer = entry[answer_labels[i]]
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172 try:
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173 if answer_assoc[answer] != labels[i]:
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174 test_error+=1
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175 except:
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176 test_error+=1
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177 return test_error,image_per_batch-cnt
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178
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179
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180 def get_labels(entry,type):
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181 file = parse_filename(entry[img_url])
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182 path = DATASET_PATH[find_dataset(entry)]
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183 f = open(path+file,'r')
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184 str_labels = re.sub("\s+", "",f.readline()).strip()[1:-2].split('.')
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185 unrestricted_labels = [ int(element) for element in str_labels ]
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186 if type == 'all':
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187 return unrestricted_labels
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188 elif type == '36':
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189 return tsix_label_assoc(unrestricted_labels)
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190 elif type == 'lower':
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191 return lower_label_assoc(unrestricted_labels)
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192 elif type == 'upper':
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193 return upper_label_assoc(unrestricted_labels)
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194 elif type == 'digits':
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195 return digit_label_assoc(unrestricted_labels)
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196 else:
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197 raise ('Inapropriate option for the type of classification :' + str(type))
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198
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199
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200 def parse_filename(string):
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201 filename = string.split('/')[-1]
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202 return filename.split('.')[0]+'.txt'
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203
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204 if __name__ =='__main__':
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205 import sys
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206 CVSFILE = sys.argv[1]
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207 test_error(sys.argv[2])