Mercurial > ift6266

annotate baseline/mlp/v_youssouf/mlp_nist.py @ 638:677d1b1d8158

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author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sat, 19 Mar 2011 23:11:17 -0400
parents f2dd75248483
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413
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1 """
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2 This tutorial introduces the multilayer perceptron using Theano.
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3
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4 A multilayer perceptron is a logistic regressor where
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5 instead of feeding the input to the logistic regression you insert a
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6 intermidiate layer, called the hidden layer, that has a nonlinear
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7 activation function (usually tanh or sigmoid) . One can use many such
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8 hidden layers making the architecture deep. The tutorial will also tackle
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9 the problem of MNIST digit classification.
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10
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11 .. math::
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12
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13 f(x) = G( b^{(2)} + W^{(2)}( s( b^{(1)} + W^{(1)} x))),
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14
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15 References:
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16
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17 - textbooks: "Pattern Recognition and Machine Learning" -
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18 Christopher M. Bishop, section 5
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19
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20 TODO: recommended preprocessing, lr ranges, regularization ranges (explain
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21 to do lr first, then add regularization)
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22
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23 """
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24 __docformat__ = 'restructedtext en'
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25
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26 import pdb
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27 import numpy
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28 import pylab
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29 import theano
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30 import theano.tensor as T
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31 import time
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32 import theano.tensor.nnet
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33 import pylearn
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34 import theano,pylearn.version,ift6266
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35 from pylearn.io import filetensor as ft
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36 from ift6266 import datasets
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37
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38 data_path = '/data/lisa/data/nist/by_class/'
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39
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40 class MLP(object):
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41 """Multi-Layer Perceptron Class
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42
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43 A multilayer perceptron is a feedforward artificial neural network model
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44 that has one layer or more of hidden units and nonlinear activations.
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45 Intermidiate layers usually have as activation function thanh or the
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46 sigmoid function while the top layer is a softamx layer.
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47 """
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48
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49
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50
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51 def __init__(self, input, n_in, n_hidden, n_out,learning_rate, detection_mode=0):
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52 """Initialize the parameters for the multilayer perceptron
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53
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54 :param input: symbolic variable that describes the input of the
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55 architecture (one minibatch)
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56
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57 :param n_in: number of input units, the dimension of the space in
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58 which the datapoints lie
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59
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60 :param n_hidden: number of hidden units
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61
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62 :param n_out: number of output units, the dimension of the space in
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63 which the labels lie
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64
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65 """
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66
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67 # initialize the parameters theta = (W1,b1,W2,b2) ; note that this
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68 # example contains only one hidden layer, but one can have as many
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69 # layers as he/she wishes, making the network deeper. The only
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70 # problem making the network deep this way is during learning,
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71 # backpropagation being unable to move the network from the starting
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72 # point towards; this is where pre-training helps, giving a good
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73 # starting point for backpropagation, but more about this in the
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74 # other tutorials
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75
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76 # `W1` is initialized with `W1_values` which is uniformely sampled
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77 # from -6./sqrt(n_in+n_hidden) and 6./sqrt(n_in+n_hidden)
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78 # the output of uniform if converted using asarray to dtype
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79 # theano.config.floatX so that the code is runable on GPU
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80 W1_values = numpy.asarray( numpy.random.uniform( \
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81 low = -numpy.sqrt(6./(n_in+n_hidden)), \
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82 high = numpy.sqrt(6./(n_in+n_hidden)), \
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83 size = (n_in, n_hidden)), dtype = theano.config.floatX)
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84 # `W2` is initialized with `W2_values` which is uniformely sampled
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85 # from -6./sqrt(n_hidden+n_out) and 6./sqrt(n_hidden+n_out)
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86 # the output of uniform if converted using asarray to dtype
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87 # theano.config.floatX so that the code is runable on GPU
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88 W2_values = numpy.asarray( numpy.random.uniform(
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89 low = -numpy.sqrt(6./(n_hidden+n_out)), \
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90 high= numpy.sqrt(6./(n_hidden+n_out)),\
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91 size= (n_hidden, n_out)), dtype = theano.config.floatX)
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92
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93 self.W1 = theano.shared( value = W1_values )
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94 self.b1 = theano.shared( value = numpy.zeros((n_hidden,),
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95 dtype= theano.config.floatX))
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96 self.W2 = theano.shared( value = W2_values )
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97 self.b2 = theano.shared( value = numpy.zeros((n_out,),
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98 dtype= theano.config.floatX))
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99
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100 #include the learning rate in the classifer so
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101 #we can modify it on the fly when we want
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102 lr_value=learning_rate
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103 self.lr=theano.shared(value=lr_value)
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104 # symbolic expression computing the values of the hidden layer
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105 self.hidden = T.tanh(T.dot(input, self.W1)+ self.b1)
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106
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107
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108
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109 # symbolic expression computing the values of the top layer
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110 if(detection_mode):
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111 self.p_y_given_x= T.nnet.sigmoid(T.dot(self.hidden, self.W2)+self.b2)
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112 else:
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113 self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden, self.W2)+self.b2)
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114
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115 # compute prediction as class whose probability is maximal in
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116 # symbolic form
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117 self.y_pred = T.argmax( self.p_y_given_x, axis =1)
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118 self.y_pred_num = T.argmax( self.p_y_given_x[0:9], axis =1)
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119
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120
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121
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122
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123 # L1 norm ; one regularization option is to enforce L1 norm to
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124 # be small
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125 self.L1 = abs(self.W1).sum() + abs(self.W2).sum()
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126
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127 # square of L2 norm ; one regularization option is to enforce
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128 # square of L2 norm to be small
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129 self.L2_sqr = (self.W1**2).sum() + (self.W2**2).sum()
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130
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131
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132
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133 def negative_log_likelihood(self, y):
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134 """Return the mean of the negative log-likelihood of the prediction
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135 of this model under a given target distribution.
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136
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137 .. math::
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138
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139 \frac{1}{|\mathcal{D}|}\mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
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140 \frac{1}{|\mathcal{D}|}\sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
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141 \ell (\theta=\{W,b\}, \mathcal{D})
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142
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143
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144 :param y: corresponds to a vector that gives for each example the
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145 :correct label
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146 """
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147 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
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148
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149
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150 def cross_entropy(self, y):
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151 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y]+T.sum(T.log(1-self.p_y_given_x), axis=1)-T.log(1-self.p_y_given_x)[T.arange(y.shape[0]),y])
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152
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153 def errors(self, y):
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154 """Return a float representing the number of errors in the minibatch
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155 over the total number of examples of the minibatch
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156 """
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157
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158 # check if y has same dimension of y_pred
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159 if y.ndim != self.y_pred.ndim:
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160 raise TypeError('y should have the same shape as self.y_pred',
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161 ('y', target.type, 'y_pred', self.y_pred.type))
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162 # check if y is of the correct datatype
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163 if y.dtype.startswith('int'):
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164 # the T.neq operator returns a vector of 0s and 1s, where 1
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165 # represents a mistake in prediction
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166 return T.mean(T.neq(self.y_pred, y))
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167 else:
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168 raise NotImplementedError()
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169
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170
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171 def mlp_full_nist( verbose = 1,\
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172 adaptive_lr = 0,\
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173 data_set=0,\
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174 learning_rate=0.01,\
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175 L1_reg = 0.00,\
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176 L2_reg = 0.0001,\
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177 nb_max_exemples=1000000,\
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178 batch_size=20,\
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179 nb_hidden = 30,\
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180 nb_targets = 62,
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181 tau=1e6,\
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182 lr_t2_factor=0.5,\
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183 detection_mode = 0,\
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184 reduce_label = 0):
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185
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186
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187 configuration = [learning_rate,nb_max_exemples,nb_hidden,adaptive_lr, detection_mode, reduce_label]
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188
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189 if(verbose):
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190 print(('verbose: %i') % (verbose))
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191 print(('adaptive_lr: %i') % (adaptive_lr))
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192 print(('data_set: %i') % (data_set))
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193 print(('learning_rate: %f') % (learning_rate))
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194 print(('L1_reg: %f') % (L1_reg))
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195 print(('L2_reg: %f') % (L2_reg))
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196 print(('nb_max_exemples: %i') % (nb_max_exemples))
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197 print(('batch_size: %i') % (batch_size))
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198 print(('nb_hidden: %i') % (nb_hidden))
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199 print(('nb_targets: %f') % (nb_targets))
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200 print(('tau: %f') % (tau))
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201 print(('lr_t2_factor: %f') % (lr_t2_factor))
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202 print(('detection_mode: %i') % (detection_mode))
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203 print(('reduce_label: %i') % (reduce_label))
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204
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205 # define the number of output - reduce_label : merge the lower and upper case. i.e a and A will both have label 10
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206 if(reduce_label):
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207 nb_targets = 36
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208 else:
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209 nb_targets = 62
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210
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211 #save initial learning rate if classical adaptive lr is used
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212 initial_lr=learning_rate
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213
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214 total_validation_error_list = []
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215 total_train_error_list = []
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216 learning_rate_list=[]
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217 best_training_error=float('inf');
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218
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219 if data_set==0:
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220 dataset=datasets.nist_all()
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221
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222
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223
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224
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225 ishape = (32,32) # this is the size of NIST images
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226
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227 # allocate symbolic variables for the data
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228 x = T.fmatrix() # the data is presented as rasterized images
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229 y = T.lvector() # the labels are presented as 1D vector of
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230 # [long int] labels
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231
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232
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233 # construct the logistic regression class
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234 classifier = MLP( input=x,\
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235 n_in=32*32,\
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236 n_hidden=nb_hidden,\
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237 n_out=nb_targets,
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238 learning_rate=learning_rate,
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239 detection_mode = detection_mode)
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240
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241
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242
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243
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244 # the cost we minimize during training is the negative log likelihood of
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245 # the model plus the regularization terms (L1 and L2); cost is expressed
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246 # here symbolically
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247 if(detection_mode):
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248 cost = classifier.cross_entropy(y) \
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249 + L1_reg * classifier.L1 \
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250 + L2_reg * classifier.L2_sqr
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251 else:
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252 cost = classifier.negative_log_likelihood(y) \
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253 + L1_reg * classifier.L1 \
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254 + L2_reg * classifier.L2_sqr
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255
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256 # compiling a theano function that computes the mistakes that are made by
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257 # the model on a minibatch
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258 test_model = theano.function([x,y], classifier.errors(y))
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259
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260 # compute the gradient of cost with respect to theta = (W1, b1, W2, b2)
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261 g_W1 = T.grad(cost, classifier.W1)
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262 g_b1 = T.grad(cost, classifier.b1)
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263 g_W2 = T.grad(cost, classifier.W2)
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264 g_b2 = T.grad(cost, classifier.b2)
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265
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266 # specify how to update the parameters of the model as a dictionary
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267 updates = \
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268 { classifier.W1: classifier.W1 - classifier.lr*g_W1 \
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269 , classifier.b1: classifier.b1 - classifier.lr*g_b1 \
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270 , classifier.W2: classifier.W2 - classifier.lr*g_W2 \
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271 , classifier.b2: classifier.b2 - classifier.lr*g_b2 }
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272
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273 # compiling a theano function `train_model` that returns the cost, but in
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274 # the same time updates the parameter of the model based on the rules
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275 # defined in `updates`
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276 train_model = theano.function([x, y], cost, updates = updates )
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286 #conditions for stopping the adaptation:
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287 #1) we have reached nb_max_exemples (this is rounded up to be a multiple of the train size)
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288 #2) validation error is going up twice in a row(probable overfitting)
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289
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290 # This means we no longer stop on slow convergence as low learning rates stopped
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291 # too fast.
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292
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293 #approximate number of samples in the training set
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294 #this is just to have a validation frequency
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295 #roughly proportionnal to the training set
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296 n_minibatches = 650000/batch_size
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297
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298
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299 patience =nb_max_exemples/batch_size #in units of minibatch
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300 patience_increase = 2 # wait this much longer when a new best is
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301 # found
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302 improvement_threshold = 0.995 # a relative improvement of this much is
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303 # considered significant
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304 validation_frequency = n_minibatches/4
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305
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306
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307
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308
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309
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310 best_validation_loss = float('inf')
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311 best_iter = 0
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312 test_score = 0.
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313 start_time = time.clock()
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314 time_n=0 #in unit of exemples
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315 minibatch_index=0
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316 epoch=0
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317 temp=0
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318
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319
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320
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321 if verbose == 1:
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322 print 'looking at most at %i exemples' %nb_max_exemples
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323 while(minibatch_index*batch_size<nb_max_exemples):
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324
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325 for x, y in dataset.train(batch_size):
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326
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327 if reduce_label:
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328 y[y > 35] = y[y > 35]-26
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329 minibatch_index = minibatch_index + 1
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330 if adaptive_lr==2:
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331 classifier.lr.value = tau*initial_lr/(tau+time_n)
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332
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333
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334 #train model
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335 cost_ij = train_model(x,y)
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336
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337 if (minibatch_index+1) % validation_frequency == 0:
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338
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339 #save the current learning rate
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340 learning_rate_list.append(classifier.lr.value)
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341
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342 # compute the validation error
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343 this_validation_loss = 0.
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parents:
diff changeset
344 temp=0
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parents:
diff changeset
345 for xv,yv in dataset.valid(1):
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parents:
diff changeset
346 if reduce_label:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
347 yv[yv > 35] = yv[yv > 35]-26
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
348 # sum up the errors for each minibatch
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parents:
diff changeset
349 axxa=test_model(xv,yv)
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parents:
diff changeset
350 this_validation_loss += axxa
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parents:
diff changeset
351 temp=temp+1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
352 # get the average by dividing with the number of minibatches
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parents:
diff changeset
353 this_validation_loss /= temp
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
354 #save the validation loss
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
355 total_validation_error_list.append(this_validation_loss)
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parents:
diff changeset
356 if verbose == 1:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
357 print(('epoch %i, minibatch %i, learning rate %f current validation error %f ') %
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
358 (epoch, minibatch_index+1,classifier.lr.value,
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
359 this_validation_loss*100.))
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
360
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
361 # if we got the best validation score until now
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
362 if this_validation_loss < best_validation_loss:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
363 # save best validation score and iteration number
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parents:
diff changeset
364 best_validation_loss = this_validation_loss
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parents:
diff changeset
365 best_iter = minibatch_index
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
366 # reset patience if we are going down again
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youssouf
parents:
diff changeset
367 # so we continue exploring
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parents:
diff changeset
368 patience=nb_max_exemples/batch_size
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
369 # test it on the test set
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parents:
diff changeset
370 test_score = 0.
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
371 temp =0
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
372 for xt,yt in dataset.test(batch_size):
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
373 if reduce_label:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
374 yt[yt > 35] = yt[yt > 35]-26
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parents:
diff changeset
375 test_score += test_model(xt,yt)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
376 temp = temp+1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
377 test_score /= temp
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parents:
diff changeset
378 if verbose == 1:
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parents:
diff changeset
379 print(('epoch %i, minibatch %i, test error of best '
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
380 'model %f %%') %
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
381 (epoch, minibatch_index+1,
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
382 test_score*100.))
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
383
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
384 # if the validation error is going up, we are overfitting (or oscillating)
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parents:
diff changeset
385 # stop converging but run at least to next validation
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youssouf
parents:
diff changeset
386 # to check overfitting or ocsillation
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parents:
diff changeset
387 # the saved weights of the model will be a bit off in that case
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parents:
diff changeset
388 elif this_validation_loss >= best_validation_loss:
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parents:
diff changeset
389 #calculate the test error at this point and exit
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parents:
diff changeset
390 # test it on the test set
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parents:
diff changeset
391 # however, if adaptive_lr is true, try reducing the lr to
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
392 # get us out of an oscilliation
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parents:
diff changeset
393 if adaptive_lr==1:
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parents:
diff changeset
394 classifier.lr.value=classifier.lr.value*lr_t2_factor
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
395
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
396 test_score = 0.
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parents:
diff changeset
397 #cap the patience so we are allowed one more validation error
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youssouf
parents:
diff changeset
398 #calculation before aborting
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parents:
diff changeset
399 patience = minibatch_index+validation_frequency+1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
400 temp=0
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
401 for xt,yt in dataset.test(batch_size):
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
402 if reduce_label:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
403 yt[yt > 35] = yt[yt > 35]-26
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
404
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
405 test_score += test_model(xt,yt)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
406 temp=temp+1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
407 test_score /= temp
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
408 if verbose == 1:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
409 print ' validation error is going up, possibly stopping soon'
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
410 print((' epoch %i, minibatch %i, test error of best '
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
411 'model %f %%') %
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
412 (epoch, minibatch_index+1,
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
413 test_score*100.))
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
414
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
415
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
416
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parents:
diff changeset
417
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
418 if minibatch_index>patience:
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parents:
diff changeset
419 print 'we have diverged'
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
420 break
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
421
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
422
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parents:
diff changeset
423 time_n= time_n + batch_size
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
424 epoch = epoch+1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
425 end_time = time.clock()
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
426 if verbose == 1:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
427 print(('Optimization complete. Best validation score of %f %% '
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
428 'obtained at iteration %i, with test performance %f %%') %
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
429 (best_validation_loss * 100., best_iter, test_score*100.))
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
430 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
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parents:
diff changeset
431 print minibatch_index
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
432
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
433 #save the model and the weights
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
434 numpy.savez('model.npy', config=configuration, W1=classifier.W1.value,W2=classifier.W2.value, b1=classifier.b1.value,b2=classifier.b2.value)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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diff changeset
435 numpy.savez('results.npy',config=configuration,total_train_error_list=total_train_error_list,total_validation_error_list=total_validation_error_list,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
436 learning_rate_list=learning_rate_list)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
437
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
438 return (best_training_error*100.0,best_validation_loss * 100.,test_score*100.,best_iter*batch_size,(end_time-start_time)/60)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
439
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
440 def test_error(model_file):
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
441
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
442 print((' test error on all NIST'))
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
443 # load the model
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
444 a=numpy.load(model_file)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
445 W1=a['W1']
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
446 W2=a['W2']
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
447 b1=a['b1']
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
448 b2=a['b2']
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
449 configuration=a['config']
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
450 #configuration = [learning_rate,nb_max_exemples,nb_hidden,adaptive_lr]
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
451 learning_rate = configuration[0]
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
452 nb_max_exemples = configuration[1]
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
453 nb_hidden = configuration[2]
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
454 adaptive_lr = configuration[3]
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
455
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
456 if(len(configuration) == 6):
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
457 detection_mode = configuration[4]
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
458 reduce_label = configuration[5]
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
459 else:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
460 detection_mode = 0
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
461 reduce_label = 0
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
462
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
463 # define the batch size
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
464 batch_size=20
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
465 #define the nb of target
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
466 nb_targets = 62
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
467
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
468 # create the mlp
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
469 ishape = (32,32) # this is the size of NIST images
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
470
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
471 # allocate symbolic variables for the data
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
472 x = T.fmatrix() # the data is presented as rasterized images
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
473 y = T.lvector() # the labels are presented as 1D vector of
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
474 # [long int] labels
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
475
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
476
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
477 # construct the logistic regression class
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
478 classifier = MLP( input=x,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
479 n_in=32*32,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
480 n_hidden=nb_hidden,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
481 n_out=nb_targets,
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
482 learning_rate=learning_rate,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
483 detection_mode=detection_mode)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
484
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
485
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
486 # set the weight into the model
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
487 classifier.W1.value = W1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
488 classifier.b1.value = b1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
489 classifier.W2.value = W2
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
490 classifier.b2.value = b2
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
491
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
492
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
493 # compiling a theano function that computes the mistakes that are made by
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
494 # the model on a minibatch
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
495 test_model = theano.function([x,y], classifier.errors(y))
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
496
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
497 # test it on the test set
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
498
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
499 # load NIST ALL
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
500 dataset=datasets.nist_all()
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
501 test_score = 0.
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
502 temp =0
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
503 for xt,yt in dataset.test(batch_size):
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
504 if reduce_label:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
505 yt[yt > 35] = yt[yt > 35]-26
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
506 test_score += test_model(xt,yt)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
507 temp = temp+1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
508 test_score /= temp
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
509
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
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510 print(( ' test error NIST ALL : %f %%') %(test_score*100.0))
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
511
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
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512 # load NIST DIGITS
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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513 dataset=datasets.nist_digits()
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
514 test_score = 0.
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
515 temp =0
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
516 for xt,yt in dataset.test(batch_size):
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
517 if reduce_label:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
youssouf
parents:
diff changeset
518 yt[yt > 35] = yt[yt > 35]-26
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parents:
diff changeset
519 test_score += test_model(xt,yt)
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parents:
diff changeset
520 temp = temp+1
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parents:
diff changeset
521 test_score /= temp
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522
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523 print(( ' test error NIST digits : %f %%') %(test_score*100.0))
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524
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525 # load NIST lower
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526 dataset=datasets.nist_lower()
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527 test_score = 0.
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parents:
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528 temp =0
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diff changeset
529 for xt,yt in dataset.test(batch_size):
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parents:
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530 if reduce_label:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
531 yt[yt > 35] = yt[yt > 35]-26
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parents:
diff changeset
532 test_score += test_model(xt,yt)
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parents:
diff changeset
533 temp = temp+1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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534 test_score /= temp
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
535
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parents:
diff changeset
536 print(( ' test error NIST lower : %f %%') %(test_score*100.0))
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parents:
diff changeset
537
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parents:
diff changeset
538 # load NIST upper
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parents:
diff changeset
539 dataset=datasets.nist_upper()
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parents:
diff changeset
540 test_score = 0.
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
541 temp =0
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
542 for xt,yt in dataset.test(batch_size):
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
543 if reduce_label:
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
544 yt[yt > 35] = yt[yt > 35]-26
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parents:
diff changeset
545 test_score += test_model(xt,yt)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
546 temp = temp+1
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
547 test_score /= temp
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
548
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
549 print(( ' test error NIST upper : %f %%') %(test_score*100.0))
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parents:
diff changeset
550
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
551
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parents:
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552 if __name__ == '__main__':
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parents:
diff changeset
553 '''
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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554 mlp_full_nist( verbose = 1,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
555 adaptive_lr = 1,\
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parents:
diff changeset
556 data_set=0,\
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parents:
diff changeset
557 learning_rate=0.5,\
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parents:
diff changeset
558 L1_reg = 0.00,\
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parents:
diff changeset
559 L2_reg = 0.0001,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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560 nb_max_exemples=10000000,\
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parents:
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561 batch_size=20,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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562 nb_hidden = 500,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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563 nb_targets = 62,
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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564 tau=100000,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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565 lr_t2_factor=0.5)
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parents:
diff changeset
566 '''
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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567
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
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568 test_error('model.npy.npz')
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parents:
diff changeset
569
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570 def jobman_mlp_full_nist(state,channel):
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571 (train_error,validation_error,test_error,nb_exemples,time)=mlp_full_nist(learning_rate=state.learning_rate,\
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parents:
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572 nb_max_exemples=state.nb_max_exemples,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
573 nb_hidden=state.nb_hidden,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
574 adaptive_lr=state.adaptive_lr,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
575 tau=state.tau,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
576 verbose = state.verbose,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
577 lr_t2_factor=state.lr_t2_factor,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
578 detection_mode = state.detection_mode,\
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
579 reduce_label = state.reduce_label)
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
580 state.train_error=train_error
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
581 state.validation_error=validation_error
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
582 state.test_error=test_error
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
583 state.nb_exemples=nb_exemples
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
584 state.time=time
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
585 pylearn.version.record_versions(state,[theano,ift6266,pylearn])
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
586 return channel.COMPLETE
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
587
f2dd75248483 initial commit of mlp with options for detection and 36 classes
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parents:
diff changeset
588