Mercurial > ift6266

annotate baseline/mlp/mlp_get_error_from_model.py @ 613:5e481b224117

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fix the reading of PNIST dataset following Dumi compression of the data.
author Frederic Bastien <nouiz@nouiz.org>
date Thu, 06 Jan 2011 13:57:05 -0500
parents 9b6e0af062af
children
rev   line source
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1 __docformat__ = 'restructedtext en'
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2
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3 import pdb
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4 import numpy as np
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5 import pylab
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6 import time
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7 import pylearn
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8 from pylearn.io import filetensor as ft
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9
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10 data_path = '/data/lisa/data/nist/by_class/'
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11 test_data = 'all/all_train_data.ft'
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12 test_labels = 'all/all_train_labels.ft'
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13
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14 def read_test_data(mlp_model):
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17 #read the data
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18 h = open(data_path+test_data)
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19 i= open(data_path+test_labels)
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20 raw_test_data = ft.read(h)
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21 raw_test_labels = ft.read(i)
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22 i.close()
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23 h.close()
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24
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25 #read the model chosen
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26 a=np.load(mlp_model)
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27 W1=a['W1']
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28 W2=a['W2']
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29 b1=a['b1']
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30 b2=a['b2']
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31
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32 return (W1,b1,W2,b2,raw_test_data,raw_test_labels)
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36
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37 def get_total_test_error(everything):
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38
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39 W1=everything[0]
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40 b1=everything[1]
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41 W2=everything[2]
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42 b2=everything[3]
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43 test_data=everything[4]
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44 test_labels=everything[5]
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45 total_error_count=0
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46 total_exemple_count=0
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47
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48 nb_error_count=0
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49 nb_exemple_count=0
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51 char_error_count=0
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52 char_exemple_count=0
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53
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54 min_error_count=0
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55 min_exemple_count=0
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56
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57 maj_error_count=0
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58 maj_exemple_count=0
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59
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60 for i in range(test_labels.size):
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61 total_exemple_count = total_exemple_count +1
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62 #get activation for layer 1
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63 a0=np.dot(np.transpose(W1),np.transpose(test_data[i]/255.0)) + b1
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64 #add non linear function to layer 1 activation
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65 a0_out=np.tanh(a0)
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66
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67 #get activation for output layer
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68 a1= np.dot(np.transpose(W2),a0_out) + b2
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69 #add non linear function for output activation (softmax)
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70 a1_exp = np.exp(a1)
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71 sum_a1=np.sum(a1_exp)
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72 a1_out=a1_exp/sum_a1
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74 predicted_class=np.argmax(a1_out)
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75 wanted_class=test_labels[i]
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77 if(predicted_class!=wanted_class):
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78 total_error_count = total_error_count +1
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80 #get grouped based error
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81 #with a priori
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82 # if(wanted_class>9 and wanted_class<35):
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83 # min_exemple_count=min_exemple_count+1
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84 # predicted_class=np.argmax(a1_out[10:35])+10
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85 # if(predicted_class!=wanted_class):
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86 # min_error_count=min_error_count+1
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87 # if(wanted_class<10):
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88 # nb_exemple_count=nb_exemple_count+1
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89 # predicted_class=np.argmax(a1_out[0:10])
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90 # if(predicted_class!=wanted_class):
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91 # nb_error_count=nb_error_count+1
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92 # if(wanted_class>34):
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93 # maj_exemple_count=maj_exemple_count+1
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94 # predicted_class=np.argmax(a1_out[35:])+35
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95 # if(predicted_class!=wanted_class):
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96 # maj_error_count=maj_error_count+1
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97 #
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98 # if(wanted_class>9):
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99 # char_exemple_count=char_exemple_count+1
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100 # predicted_class=np.argmax(a1_out[10:])+10
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101 # if(predicted_class!=wanted_class):
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102 # char_error_count=char_error_count+1
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103
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105
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106 #get grouped based error
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107 #with no a priori
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108 if(wanted_class>9 and wanted_class<35):
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109 min_exemple_count=min_exemple_count+1
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110 predicted_class=np.argmax(a1_out)
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111 if(predicted_class!=wanted_class):
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112 min_error_count=min_error_count+1
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113 if(wanted_class<10):
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114 nb_exemple_count=nb_exemple_count+1
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115 predicted_class=np.argmax(a1_out)
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116 if(predicted_class!=wanted_class):
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117 nb_error_count=nb_error_count+1
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118 if(wanted_class>34):
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119 maj_exemple_count=maj_exemple_count+1
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120 predicted_class=np.argmax(a1_out)
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121 if(predicted_class!=wanted_class):
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122 maj_error_count=maj_error_count+1
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123
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124 if(wanted_class>9):
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125 char_exemple_count=char_exemple_count+1
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126 predicted_class=np.argmax(a1_out)
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127 if(predicted_class!=wanted_class):
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128 char_error_count=char_error_count+1
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130
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131 #convert to float
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132 return ( total_exemple_count,nb_exemple_count,char_exemple_count,min_exemple_count,maj_exemple_count,\
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133 total_error_count,nb_error_count,char_error_count,min_error_count,maj_error_count,\
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134 total_error_count*100.0/total_exemple_count*1.0,\
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135 nb_error_count*100.0/nb_exemple_count*1.0,\
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136 char_error_count*100.0/char_exemple_count*1.0,\
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137 min_error_count*100.0/min_exemple_count*1.0,\
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138 maj_error_count*100.0/maj_exemple_count*1.0)
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