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annotate deep/deep_mlp/mlp.py @ 647:47af8a002530 tip

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changed Theano to ift6266 and remove numpy as we do not use code from numpy in this repository
author Razvan Pascanu <r.pascanu@gmail.com>
date Wed, 17 Oct 2012 09:26:14 -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 __docformat__ = 'restructedtext en'
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2
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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3 import numpy, time, cPickle, gzip, sys, os
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4
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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5 import theano
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6 import theano.tensor as T
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7
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8 from logistic_sgd import LogisticRegression, load_data
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9
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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10 class HiddenLayer(object):
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11 def __init__(self, rng, input, n_in, n_out, activation = T.tanh):
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12 print "Creating HiddenLayer with params"
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13 print locals()
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14
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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15 self.input = input
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17 W_values = numpy.asarray( rng.uniform(
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18 low = - numpy.sqrt(6./(n_in+n_out)),
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19 high = numpy.sqrt(6./(n_in+n_out)),
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20 size = (n_in, n_out)), dtype = theano.config.floatX)
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21 if activation == theano.tensor.nnet.sigmoid:
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22 W_values *= 4
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23
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24 self.W = theano.shared(value = W_values, name ='W')
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25
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26 b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
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27 self.b = theano.shared(value= b_values, name ='b')
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28
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29 self.output = activation(T.dot(input, self.W) + self.b)
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30
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31 self.params = [self.W, self.b]
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32
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33
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34 class MLP(object):
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35 def __init__(self, rng, input, n_in, n_hidden_layers, n_hidden, n_out):
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36 print "Creating MLP with params"
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37 print locals()
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38
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39 self.input = input
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40
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41 self.hiddenLayers = []
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42
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43 last_input = input
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44 last_n_out = n_in
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45 for i in range(n_hidden_layers):
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46 self.hiddenLayers.append(\
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47 HiddenLayer(rng = rng, input = last_input,
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48 n_in = last_n_out,
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49 n_out = n_hidden,
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50 activation = T.tanh))
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51 last_input = self.hiddenLayers[-1].output
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52 last_n_out = n_hidden
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53
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54 self.logRegressionLayer = LogisticRegression(
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55 input = self.hiddenLayers[-1].output,
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56 n_in = n_hidden,
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57 n_out = n_out)
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58
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59 self.L1 = abs(self.logRegressionLayer.W).sum()
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60 for h in self.hiddenLayers:
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61 self.L1 += abs(h.W).sum()
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62
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63 self.L2_sqr = (self.logRegressionLayer.W**2).sum()
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64 for h in self.hiddenLayers:
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65 self.L2_sqr += (h.W**2).sum()
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66
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67 self.negative_log_likelihood = self.logRegressionLayer.negative_log_likelihood
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68
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69 self.errors = self.logRegressionLayer.errors
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70
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71 self.params = []
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72 for hl in self.hiddenLayers:
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73 self.params += hl.params
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74 self.params += self.logRegressionLayer.params
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75
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76
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77 def test_mlp( learning_rate=0.01, L1_reg = 0.00, L2_reg = 0.0001, n_epochs=1000,
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78 dataset = '../data/mnist.pkl.gz', batch_size = 20):
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79 datasets = load_data(dataset)
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80
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81 train_set_x, train_set_y = datasets[0]
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82 valid_set_x, valid_set_y = datasets[1]
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83 test_set_x , test_set_y = datasets[2]
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84
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85 n_train_batches = train_set_x.value.shape[0] / batch_size
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86 n_valid_batches = valid_set_x.value.shape[0] / batch_size
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87 n_test_batches = test_set_x.value.shape[0] / batch_size
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88
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89 ######################
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90 # BUILD ACTUAL MODEL #
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91 ######################
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92 print '... building the model'
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93
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94 # allocate symbolic variables for the data
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95 index = T.lscalar() # index to a [mini]batch
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96 x = T.matrix('x') # the data is presented as rasterized images
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97 y = T.ivector('y') # the labels are presented as 1D vector of
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98 # [int] labels
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99
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100 rng = numpy.random.RandomState(1234)
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101
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102 # construct the MLP class
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103 classifier = MLP( rng = rng, input=x, n_in=28*28, n_hidden = 500, n_out=10)
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104
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105 # the cost we minimize during training is the negative log likelihood of
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106 # the model plus the regularization terms (L1 and L2); cost is expressed
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107 # here symbolically
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108 cost = classifier.negative_log_likelihood(y) \
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109 + L1_reg * classifier.L1 \
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110 + L2_reg * classifier.L2_sqr
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111
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112 # compiling a Theano function that computes the mistakes that are made
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113 # by the model on a minibatch
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114 test_model = theano.function(inputs = [index],
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115 outputs = classifier.errors(y),
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116 givens={
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117 x:test_set_x[index*batch_size:(index+1)*batch_size],
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118 y:test_set_y[index*batch_size:(index+1)*batch_size]})
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119
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120 validate_model = theano.function(inputs = [index],
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121 outputs = classifier.errors(y),
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122 givens={
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123 x:valid_set_x[index*batch_size:(index+1)*batch_size],
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124 y:valid_set_y[index*batch_size:(index+1)*batch_size]})
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125
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126 # compute the gradient of cost with respect to theta (sotred in params)
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127 # the resulting gradients will be stored in a list gparams
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128 gparams = []
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129 for param in classifier.params:
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130 gparam = T.grad(cost, param)
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131 gparams.append(gparam)
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132
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133
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134 # specify how to update the parameters of the model as a dictionary
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135 updates = {}
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136 # given two list the zip A = [ a1,a2,a3,a4] and B = [b1,b2,b3,b4] of
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137 # same length, zip generates a list C of same size, where each element
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138 # is a pair formed from the two lists :
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139 # C = [ (a1,b1), (a2,b2), (a3,b3) , (a4,b4) ]
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140 for param, gparam in zip(classifier.params, gparams):
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141 updates[param] = param - learning_rate*gparam
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142
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143 # compiling a Theano function `train_model` that returns the cost, but
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144 # in the same time updates the parameter of the model based on the rules
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145 # defined in `updates`
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146 train_model =theano.function( inputs = [index], outputs = cost,
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147 updates = updates,
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148 givens={
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149 x:train_set_x[index*batch_size:(index+1)*batch_size],
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150 y:train_set_y[index*batch_size:(index+1)*batch_size]})
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151
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152 ###############
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153 # TRAIN MODEL #
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154 ###############
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155 print '... training'
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156
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157 # early-stopping parameters
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158 patience = 10000 # look as this many examples regardless
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159 patience_increase = 2 # wait this much longer when a new best is
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160 # found
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161 improvement_threshold = 0.995 # a relative improvement of this much is
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162 # considered significant
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163 validation_frequency = min(n_train_batches,patience/2)
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164 # go through this many
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165 # minibatche before checking the network
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166 # on the validation set; in this case we
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167 # check every epoch
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168
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169
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170 best_params = None
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171 best_validation_loss = float('inf')
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172 best_iter = 0
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173 test_score = 0.
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174 start_time = time.clock()
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175
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176 epoch = 0
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177 done_looping = False
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178
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179 while (epoch < n_epochs) and (not done_looping):
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180 epoch = epoch + 1
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181 for minibatch_index in xrange(n_train_batches):
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182
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183 minibatch_avg_cost = train_model(minibatch_index)
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184 # iteration number
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185 iter = epoch * n_train_batches + minibatch_index
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186
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187 if (iter+1) % validation_frequency == 0:
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188 # compute zero-one loss on validation set
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189 validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
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190 this_validation_loss = numpy.mean(validation_losses)
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191
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192 print('epoch %i, minibatch %i/%i, validation error %f %%' % \
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193 (epoch, minibatch_index+1,n_train_batches, \
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194 this_validation_loss*100.))
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195
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196
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197 # if we got the best validation score until now
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198 if this_validation_loss < best_validation_loss:
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199 #improve patience if loss improvement is good enough
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200 if this_validation_loss < best_validation_loss * \
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201 improvement_threshold :
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202 patience = max(patience, iter * patience_increase)
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203
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204 best_validation_loss = this_validation_loss
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205 # test it on the test set
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206
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207 test_losses = [test_model(i) for i in xrange(n_test_batches)]
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208 test_score = numpy.mean(test_losses)
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209
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210