Mercurial > ift6266

annotate deep/deep_mlp/job.py @ 631:510220effb14

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author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sat, 19 Mar 2011 22:44:53 -0400
parents 75dbbe409578
children
rev   line source
626
75dbbe409578 Added code for deep mlp, experiment code to go along with it. Also added code I used to filter the P07 / PNIST07 datasets to keep only digits.
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1 #!/usr/bin/env python
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2 # coding: utf-8
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3
75dbbe409578 Added code for deep mlp, experiment code to go along with it. Also added code I used to filter the P07 / PNIST07 datasets to keep only digits.
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4 '''
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5 Launching
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6
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7 jobman sqlschedules postgres://ift6266h10@gershwin/ift6266h10_sandbox_db/mlp_dumi mlp_jobman.experiment mlp_jobman.conf
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8 'n_hidden={{500,1000,2000}}'
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9 'n_hidden_layers={{2,3}}'
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10 'train_on={{NIST,NISTP,P07}}'
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11 'train_subset={{DIGITS_ONLY,ALL}}'
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12 'learning_rate_log10={{-1.,-2.,-3.}}'
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13
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14 in mlp_jobman.conf:
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15 rng_seed=1234
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16 L1_reg=0.0
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17 L2_reg=0.0
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18 n_epochs=10
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19 minibatch_size=20
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20 '''
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21
75dbbe409578 Added code for deep mlp, experiment code to go along with it. Also added code I used to filter the P07 / PNIST07 datasets to keep only digits.
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22 import os, sys, copy, operator, time
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23 import theano
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24 import theano.tensor as T
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25 import numpy
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26 from mlp import MLP
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27 from ift6266 import datasets
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28 from pylearn.io.seriestables import *
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29 import tables
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30 from jobman.tools import DD
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31
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32 N_INPUTS = 32*32
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33 REDUCE_EVERY = 250
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34
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35 TEST_RUN = False
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36
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37 TEST_HP = DD({'n_hidden':200,
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38 'n_hidden_layers': 2,
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39 'train_on':'NIST',
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40 'train_subset':'ALL',
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41 'learning_rate_log10':-2,
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42 'rng_seed':1234,
75dbbe409578 Added code for deep mlp, experiment code to go along with it. Also added code I used to filter the P07 / PNIST07 datasets to keep only digits.
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43 'L1_reg':0.0,
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44 'L2_reg':0.0,
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45 'n_epochs':2,
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46 'minibatch_size':20})
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47
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48 ###########################################
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49 # digits datasets
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50 # nist_digits is already in NIST_PATH and in ift6266.datasets
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51 # NOTE: for these datasets the test and valid sets are wrong
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52 # (don't correspond to the training set... they're just placeholders)
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53
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54 from ift6266.datasets.defs import NIST_PATH, DATA_PATH
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55 TRANSFORMED_DIGITS_PATH = '/data/lisatmp/ift6266h10/data/transformed_digits'
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56
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57 P07_digits = FTDataSet(\
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58 train_data = [os.path.join(TRANSFORMED_DIGITS_PATH,\
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59 'data/P07_train'+str(i)+'_data.ft')\
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60 for i in range(0, 100)],
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61 train_lbl = [os.path.join(TRANSFORMED_DIGITS_PATH,\
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62 'data/P07_train'+str(i)+'_labels.ft')\
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63 for i in range(0,100)],
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64 test_data = [os.path.join(DATA_PATH,'data/P07_test_data.ft')],
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65 test_lbl = [os.path.join(DATA_PATH,'data/P07_test_labels.ft')],
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66 valid_data = [os.path.join(DATA_PATH,'data/P07_valid_data.ft')],
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67 valid_lbl = [os.path.join(DATA_PATH,'data/P07_valid_labels.ft')],
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68 indtype=theano.config.floatX, inscale=255., maxsize=None)
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69
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70 #Added PNIST
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71 PNIST07_digits = FTDataSet(train_data = [os.path.join(TRANSFORMED_DIGITS_PATH,\
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72 'PNIST07_train'+str(i)+'_data.ft')\
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73 for i in range(0,100)],
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74 train_lbl = [os.path.join(TRANSFORMED_DIGITS_PATH,\
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75 'PNIST07_train'+str(i)+'_labels.ft')\
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76 for i in range(0,100)],
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77 test_data = [os.path.join(DATA_PATH,'data/PNIST07_test_data.ft')],
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78 test_lbl = [os.path.join(DATA_PATH,'data/PNIST07_test_labels.ft')],
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79 valid_data = [os.path.join(DATA_PATH,'data/PNIST07_valid_data.ft')],
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80 valid_lbl = [os.path.join(DATA_PATH,'data/PNIST07_valid_labels.ft')],
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81 indtype=theano.config.floatX, inscale=255., maxsize=None)
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82
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83
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84 # building valid_test_datasets
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85 # - on veut des dataset_obj pour les 3 datasets
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86 # - donc juste à bâtir FTDataset(train=nimportequoi, test, valid=pNIST etc.)
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87 # - on veut dans l'array mettre des pointeurs vers la fonction either test ou valid
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88 # donc PAS dataset_obj, mais dataset_obj.train (sans les parenthèses)
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89 def build_test_valid_sets():
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90 nist_ds = datasets.nist_all()
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91 pnist_ds = datasets.PNIST07()
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92 p07_ds = datasets.nist_P07()
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93
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94 test_valid_fns = [nist_ds.test, nist_ds.valid,
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95 pnist_ds.test, pnist_ds.valid,
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96 p07_ds.test, p07_ds.valid]
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97
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98 test_valid_names = ["nist_all__test", "nist_all__valid",
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99 "NISTP__test", "NISTP__valid",
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100 "P07__test", "P07__valid"]
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101
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102 return test_valid_fns, test_valid_names
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103
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104 def add_error_series(series, error_name, hdf5_file,
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105 index_names=('minibatch_idx',), use_accumulator=False,
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106 reduce_every=250):
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107 # train
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108 series_base = ErrorSeries(error_name=error_name,
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109 table_name=error_name,
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110 hdf5_file=hdf5_file,
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111 index_names=index_names)
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112
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113 if use_accumulator:
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114 series[error_name] = \
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115 AccumulatorSeriesWrapper(base_series=series_base,
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116 reduce_every=reduce_every)
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117 else:
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118 series[error_name] = series_base
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119
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120 TEST_VALID_FNS,TEST_VALID_NAMES = None, None
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121 def compute_and_save_errors(state, mlp, series, hdf5_file, minibatch_idx):
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122 global TEST_VALID_FNS,TEST_VALID_NAMES
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123
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124 TEST_VALID_FNS,TEST_VALID_NAMES = build_test_valid_sets()
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125
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126 # if the training is on digits only, then there'll be a 100%
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127 # error on digits in the valid/test set... just ignore them
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128
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129 test_fn = theano.function([mlp.input], mlp.logRegressionLayer.y_pred)
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130
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131 test_batch_size = 100
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132 for test_ds_fn,test_ds_name in zip(TEST_VALID_FNS,TEST_VALID_NAMES):
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133 # reset error counts for every test/valid set
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134 # note: float
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135 total_errors = total_digit_errors = \
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136 total_uppercase_errors = total_lowercase_errors = 0.
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137
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138 total_all = total_lowercase = total_uppercase = total_digit = 0
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139
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140 for mb_x,mb_y in test_ds_fn(test_batch_size):
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141 digit_mask = mb_y < 10
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142 uppercase_mask = mb_y >= 36
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143 lowercase_mask = numpy.ones((len(mb_x),)) \
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144 - digit_mask - uppercase_mask
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145
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146 total_all += len(mb_x)
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147 total_digit += sum(digit_mask)
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148 total_uppercase += sum(uppercase_mask)
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149 total_lowercase += sum(lowercase_mask)
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150
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151 predictions = test_fn(mb_x)
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152
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153 all_errors = (mb_y != predictions)
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154 total_errors += sum(all_errors)
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155
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156 if len(all_errors) != len(digit_mask):
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157 print "size all", all_errors.shape, " digit", digit_mask.shape
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158 total_digit_errors += sum(numpy.multiply(all_errors, digit_mask))
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159 total_uppercase_errors += sum(numpy.multiply(all_errors, uppercase_mask))
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160 total_lowercase_errors += sum(numpy.multiply(all_errors, lowercase_mask))
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161
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162 four_errors = [float(total_errors) / total_all,
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163 float(total_digit_errors) / total_digit,
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164 float(total_lowercase_errors) / total_lowercase,
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165 float(total_uppercase_errors) / total_uppercase]
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166
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167 four_errors_names = ["all", "digits", "lower", "upper"]
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168
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169 # record stats per set
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170 print "Errors on", test_ds_name, ",".join(four_errors_names),\
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171 ":", ",".join([str(e) for e in four_errors])
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172
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173 # now in the state
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174 for err, errname in zip(four_errors, four_errors_names):
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175 error_full_name = 'error__'+test_ds_name+'_'+errname
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176 min_name = 'min_'+error_full_name
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177 minpos_name = 'minpos_'+error_full_name
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178
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179 if state.has_key(min_name):
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180 if state[min_name] > err:
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181 state[min_name] = err
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182 state[minpos_name] = pos_str
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183 else:
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184 # also create the series
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185 add_error_series(series, error_full_name, hdf5_file,
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186 index_names=('minibatch_idx',))
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187 state[min_name] = err
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188 state[minpos_name] = minibatch_idx
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189
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190 state[minpos_name] = pos_str
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191 series[error_full_name].append((minibatch_idx,), err)
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192
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193 def jobman_entrypoint(state, channel):
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194 global TEST_RUN
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195 minibatch_size = state.minibatch_size
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196
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197 print_every = 100000
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198 COMPUTE_ERROR_EVERY = 10**7 / minibatch_size # compute error every 10 million examples
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199 if TEST_RUN:
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200 print_every = 100
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201 COMPUTE_ERROR_EVERY = 1000 / minibatch_size
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202
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203 print "entrypoint, state is"
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204 print state
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205
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206 ######################
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207 # select dataset and dataset subset, plus adjust epoch num to make number
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208 # of examples seen independent of dataset
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209 # exemple: pour le cas DIGITS_ONLY, il faut changer le nombre d'époques
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210 # et pour le cas NIST pur (pas de transformations), il faut multiplier par 100
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211 # en partant car on a pas les variations
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212
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213 # compute this in terms of the P07 dataset size (=80M)
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214 MINIBATCHES_TO_SEE = state.n_epochs * 8 * (10**6) / minibatch_size
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215
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216 if state.train_on == 'NIST' and state.train_subset == 'ALL':
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217 dataset_obj = datasets.nist_all()
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218 elif state.train_on == 'NIST' and state.train_subset == 'DIGITS_ONLY':
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219 dataset_obj = datasets.nist_digits()
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220 elif state.train_on == 'NISTP' and state.train_subset == 'ALL':
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221 dataset_obj = datasets.PNIST07()
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222 elif state.train_on == 'NISTP' and state.train_subset == 'DIGITS_ONLY':
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223 dataset_obj = PNIST07_digits
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224 elif state.train_on == 'P07' and state.train_subset == 'ALL':
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225 dataset_obj = datasets.nist_P07()
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226 elif state.train_on == 'P07' and state.train_subset == 'DIGITS_ONLY':
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227 dataset_obj = datasets.P07_digits
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228
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229 dataset = dataset_obj
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230
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231 if state.train_subset == 'ALL':
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232 n_classes = 62
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233 elif state.train_subset == 'DIGITS_ONLY':
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234 n_classes = 10
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235 else:
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236 raise NotImplementedError()
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237
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238 ###############################
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239 # construct model
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240
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241 print "constructing model..."
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242 x = T.matrix('x')
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243 y = T.ivector('y')
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244
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245 rng = numpy.random.RandomState(state.rng_seed)
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246
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247 # construct the MLP class
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248 model = MLP(rng = rng, input=x, n_in=N_INPUTS,
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249 n_hidden_layers = state.n_hidden_layers,
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250 n_hidden = state.n_hidden, n_out=n_classes)
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251
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252
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253 # cost and training fn
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254 cost = T.mean(model.negative_log_likelihood(y)) \
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255 + state.L1_reg * model.L1 \
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256 + state.L2_reg * model.L2_sqr
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257
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258 print "L1, L2: ", state.L1_reg, state.L2_reg
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259
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260 gradient_nll_wrt_params = []
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261 for param in model.params:
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262 gparam = T.grad(cost, param)
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263 gradient_nll_wrt_params.append(gparam)
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264
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265 learning_rate = 10**float(state.learning_rate_log10)
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266 print "Learning rate", learning_rate
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267
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268 train_updates = {}
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269 for param, gparam in zip(model.params, gradient_nll_wrt_params):
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270 train_updates[param] = param - learning_rate * gparam
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271
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272 train_fn = theano.function([x,y], cost, updates=train_updates)
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273
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274 #######################
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275 # create series
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276 basedir = os.getcwd()
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277
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278 h5f = tables.openFile(os.path.join(basedir, "series.h5"), "w")
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279
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280 series = {}
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281 add_error_series(series, "training_error", h5f,
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282 index_names=('minibatch_idx',), use_accumulator=True,
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283 reduce_every=REDUCE_EVERY)
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284
75dbbe409578 Added code for deep mlp, experiment code to go along with it. Also added code I used to filter the P07 / PNIST07 datasets to keep only digits.
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285 ##########################
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286 # training loop
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287
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288 start_time = time.clock()
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289
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290 print "begin training..."
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291 print "will train for", MINIBATCHES_TO_SEE, "examples"
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292
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293 mb_idx = 0
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294
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295 while(mb_idx*minibatch_size<nb_max_exemples):
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296
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297 last_costs = []
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298
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299 for mb_x, mb_y in dataset.train(minibatch_size):
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300 if TEST_RUN and mb_idx > 1000:
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301 break
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302
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303 last_cost = train_fn(mb_x, mb_y)
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304 series["training_error"].append((mb_idx,), last_cost)
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305
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306 last_costs.append(last_cost)
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307 if (len(last_costs)+1) % print_every == 0:
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308 print "Mean over last", print_every, "minibatches: ", numpy.mean(last_costs)
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309 last_costs = []
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310
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311 if (mb_idx+1) % COMPUTE_ERROR_EVERY == 0:
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312 # compute errors
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313 print "computing errors on all datasets..."
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314 print "Time since training began: ", (time.clock()-start_time)/60., "minutes"
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315 compute_and_save_errors(state, model, series, h5f, mb_idx)
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316
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317 channel.save()
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318
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319 sys.stdout.flush()
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320
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321 end_time = time.clock()
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322
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323 print "-"*80
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324 print "Finished. Training took", (end_time-start_time)/60., "minutes"
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325 print state
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326
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327 def run_test():
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328 global TEST_RUN
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329 from fsml.job_management import mock_channel
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330 TEST_RUN = True
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331 jobman_entrypoint(TEST_HP, mock_channel)
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332
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333 if __name__ == '__main__':
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334 run_test()
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335