Mercurial > ift6266
annotate baseline/mlp/mlp_get_error_from_model.py @ 599:587674030c5a
added authors and emails list
author | boulanni <nicolas_boulanger@hotmail.com> |
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date | Fri, 15 Oct 2010 14:14:06 -0400 |
parents | 9b6e0af062af |
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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/' |
237 | 11 test_data = 'all/all_train_data.ft' |
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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15 |
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16 |
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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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33 |
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34 |
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35 |
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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] |
237 | 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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50 |
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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 |
237 | 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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73 |
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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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76 |
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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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79 |
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80 #get grouped based error |
237 | 81 #with a priori |
82 # if(wanted_class>9 and wanted_class<35): | |
83 # min_exemple_count=min_exemple_count+1 | |
84 # predicted_class=np.argmax(a1_out[10:35])+10 | |
85 # if(predicted_class!=wanted_class): | |
86 # min_error_count=min_error_count+1 | |
87 # if(wanted_class<10): | |
88 # nb_exemple_count=nb_exemple_count+1 | |
89 # predicted_class=np.argmax(a1_out[0:10]) | |
90 # if(predicted_class!=wanted_class): | |
91 # nb_error_count=nb_error_count+1 | |
92 # if(wanted_class>34): | |
93 # maj_exemple_count=maj_exemple_count+1 | |
94 # predicted_class=np.argmax(a1_out[35:])+35 | |
95 # if(predicted_class!=wanted_class): | |
96 # maj_error_count=maj_error_count+1 | |
97 # | |
98 # if(wanted_class>9): | |
99 # char_exemple_count=char_exemple_count+1 | |
100 # predicted_class=np.argmax(a1_out[10:])+10 | |
101 # if(predicted_class!=wanted_class): | |
102 # char_error_count=char_error_count+1 | |
103 | |
104 | |
105 | |
106 #get grouped based error | |
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 |
237 | 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 |
237 | 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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129 |
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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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139 |
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140 |
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141 |
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