Mercurial > ift6266

annotate baseline_algorithms/mlp/mlp_nist.py @ 156:6f3b866c0182

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On peut maintenant launcher le pipeline avec un seed donné, résultats déterministes
author boulanni <nicolas_boulanger@hotmail.com>
date Wed, 24 Feb 2010 19:12:01 -0500
parents 8ceaaf812891
children
rev   line source
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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 from pylearn.io import filetensor as ft
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35
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36 data_path = '/data/lisa/data/nist/by_class/'
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37
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38 class MLP(object):
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39 """Multi-Layer Perceptron Class
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40
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41 A multilayer perceptron is a feedforward artificial neural network model
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42 that has one layer or more of hidden units and nonlinear activations.
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43 Intermidiate layers usually have as activation function thanh or the
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44 sigmoid function while the top layer is a softamx layer.
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45 """
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46
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47
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48
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49 def __init__(self, input, n_in, n_hidden, n_out,learning_rate):
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50 """Initialize the parameters for the multilayer perceptron
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51
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52 :param input: symbolic variable that describes the input of the
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53 architecture (one minibatch)
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54
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55 :param n_in: number of input units, the dimension of the space in
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56 which the datapoints lie
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57
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58 :param n_hidden: number of hidden units
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59
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60 :param n_out: number of output units, the dimension of the space in
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61 which the labels lie
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62
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63 """
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64
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65 # initialize the parameters theta = (W1,b1,W2,b2) ; note that this
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66 # example contains only one hidden layer, but one can have as many
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67 # layers as he/she wishes, making the network deeper. The only
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68 # problem making the network deep this way is during learning,
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69 # backpropagation being unable to move the network from the starting
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70 # point towards; this is where pre-training helps, giving a good
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71 # starting point for backpropagation, but more about this in the
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72 # other tutorials
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73
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74 # `W1` is initialized with `W1_values` which is uniformely sampled
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75 # from -6./sqrt(n_in+n_hidden) and 6./sqrt(n_in+n_hidden)
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76 # the output of uniform if converted using asarray to dtype
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77 # theano.config.floatX so that the code is runable on GPU
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78 W1_values = numpy.asarray( numpy.random.uniform( \
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79 low = -numpy.sqrt(6./(n_in+n_hidden)), \
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80 high = numpy.sqrt(6./(n_in+n_hidden)), \
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81 size = (n_in, n_hidden)), dtype = theano.config.floatX)
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82 # `W2` is initialized with `W2_values` which is uniformely sampled
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83 # from -6./sqrt(n_hidden+n_out) and 6./sqrt(n_hidden+n_out)
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84 # the output of uniform if converted using asarray to dtype
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85 # theano.config.floatX so that the code is runable on GPU
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86 W2_values = numpy.asarray( numpy.random.uniform(
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87 low = -numpy.sqrt(6./(n_hidden+n_out)), \
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88 high= numpy.sqrt(6./(n_hidden+n_out)),\
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89 size= (n_hidden, n_out)), dtype = theano.config.floatX)
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90
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91 self.W1 = theano.shared( value = W1_values )
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92 self.b1 = theano.shared( value = numpy.zeros((n_hidden,),
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93 dtype= theano.config.floatX))
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94 self.W2 = theano.shared( value = W2_values )
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95 self.b2 = theano.shared( value = numpy.zeros((n_out,),
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96 dtype= theano.config.floatX))
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97
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98 #include the learning rate in the classifer so
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99 #we can modify it on the fly when we want
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100 lr_value=learning_rate
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101 self.lr=theano.shared(value=lr_value)
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102 # symbolic expression computing the values of the hidden layer
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103 self.hidden = T.tanh(T.dot(input, self.W1)+ self.b1)
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105
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106
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107 # symbolic expression computing the values of the top layer
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108 self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden, self.W2)+self.b2)
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109
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110 # compute prediction as class whose probability is maximal in
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111 # symbolic form
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112 self.y_pred = T.argmax( self.p_y_given_x, axis =1)
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113 self.y_pred_num = T.argmax( self.p_y_given_x[0:9], axis =1)
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116
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117
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118 # L1 norm ; one regularization option is to enforce L1 norm to
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119 # be small
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120 self.L1 = abs(self.W1).sum() + abs(self.W2).sum()
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121
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122 # square of L2 norm ; one regularization option is to enforce
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123 # square of L2 norm to be small
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124 self.L2_sqr = (self.W1**2).sum() + (self.W2**2).sum()
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125
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126
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127
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128 def negative_log_likelihood(self, y):
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129 """Return the mean of the negative log-likelihood of the prediction
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130 of this model under a given target distribution.
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131
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132 .. math::
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133
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134 \frac{1}{|\mathcal{D}|}\mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
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135 \frac{1}{|\mathcal{D}|}\sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
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136 \ell (\theta=\{W,b\}, \mathcal{D})
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137
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138
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139 :param y: corresponds to a vector that gives for each example the
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140 :correct label
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141 """
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142 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
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147 def errors(self, y):
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148 """Return a float representing the number of errors in the minibatch
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149 over the total number of examples of the minibatch
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150 """
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151
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152 # check if y has same dimension of y_pred
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153 if y.ndim != self.y_pred.ndim:
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154 raise TypeError('y should have the same shape as self.y_pred',
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155 ('y', target.type, 'y_pred', self.y_pred.type))
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156 # check if y is of the correct datatype
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157 if y.dtype.startswith('int'):
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158 # the T.neq operator returns a vector of 0s and 1s, where 1
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159 # represents a mistake in prediction
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160 return T.mean(T.neq(self.y_pred, y))
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161 else:
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162 raise NotImplementedError()
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163
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164
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165 def mlp_full_nist( verbose = False,\
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166 adaptive_lr = 0,\
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167 train_data = 'all/all_train_data.ft',\
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168 train_labels = 'all/all_train_labels.ft',\
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169 test_data = 'all/all_test_data.ft',\
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170 test_labels = 'all/all_test_labels.ft',\
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171 learning_rate=0.01,\
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172 L1_reg = 0.00,\
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173 L2_reg = 0.0001,\
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174 nb_max_exemples=1000000,\
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175 batch_size=20,\
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176 nb_hidden = 500,\
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177 nb_targets = 62):
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178
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179
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180 configuration = [learning_rate,nb_max_exemples,nb_hidden,adaptive_lr]
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181
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182 total_validation_error_list = []
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183 total_train_error_list = []
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184 learning_rate_list=[]
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185 best_training_error=float('inf');
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186
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187
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188
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189 f = open(data_path+train_data)
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190 g= open(data_path+train_labels)
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191 h = open(data_path+test_data)
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192 i= open(data_path+test_labels)
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193
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194 raw_train_data = ft.read(f)
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195 raw_train_labels = ft.read(g)
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196 raw_test_data = ft.read(h)
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197 raw_test_labels = ft.read(i)
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198
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199 f.close()
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200 g.close()
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201 i.close()
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202 h.close()
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203 #create a validation set the same size as the test size
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204 #use the end of the training array for this purpose
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205 #discard the last remaining so we get a %batch_size number
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206 test_size=len(raw_test_labels)
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207 test_size = int(test_size/batch_size)
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208 test_size*=batch_size
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209 train_size = len(raw_train_data)
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210 train_size = int(train_size/batch_size)
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211 train_size*=batch_size
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212 validation_size =test_size
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213 offset = train_size-test_size
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214 if verbose == True:
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215 print 'train size = %d' %train_size
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216 print 'test size = %d' %test_size
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217 print 'valid size = %d' %validation_size
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218 print 'offset = %d' %offset
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219
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220
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221 train_set = (raw_train_data,raw_train_labels)
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222 train_batches = []
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223 for i in xrange(0, train_size-test_size, batch_size):
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224 train_batches = train_batches + \
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225 [(raw_train_data[i:i+batch_size], raw_train_labels[i:i+batch_size])]
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226
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227 test_batches = []
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228 for i in xrange(0, test_size, batch_size):
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229 test_batches = test_batches + \
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230 [(raw_test_data[i:i+batch_size], raw_test_labels[i:i+batch_size])]
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231
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232 validation_batches = []
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233 for i in xrange(0, test_size, batch_size):
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234 validation_batches = validation_batches + \
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235 [(raw_train_data[offset+i:offset+i+batch_size], raw_train_labels[offset+i:offset+i+batch_size])]
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236
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237
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238 ishape = (32,32) # this is the size of NIST images
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239
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240 # allocate symbolic variables for the data
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241 x = T.fmatrix() # the data is presented as rasterized images
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242 y = T.lvector() # the labels are presented as 1D vector of
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243 # [long int] labels
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244
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245 if verbose==True:
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246 print 'finished parsing the data'
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247 # construct the logistic regression class
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248 classifier = MLP( input=x.reshape((batch_size,32*32)),\
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249 n_in=32*32,\
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250 n_hidden=nb_hidden,\
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251 n_out=nb_targets,
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252 learning_rate=learning_rate)
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253
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254
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255
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256
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257 # the cost we minimize during training is the negative log likelihood of
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258 # the model plus the regularization terms (L1 and L2); cost is expressed
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259 # here symbolically
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260 cost = classifier.negative_log_likelihood(y) \
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261 + L1_reg * classifier.L1 \
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262 + L2_reg * classifier.L2_sqr
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263
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264 # compiling a theano function that computes the mistakes that are made by
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265 # the model on a minibatch
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266 test_model = theano.function([x,y], classifier.errors(y))
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267
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268 # compute the gradient of cost with respect to theta = (W1, b1, W2, b2)
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269 g_W1 = T.grad(cost, classifier.W1)
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270 g_b1 = T.grad(cost, classifier.b1)
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271 g_W2 = T.grad(cost, classifier.W2)
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272 g_b2 = T.grad(cost, classifier.b2)
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273
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274 # specify how to update the parameters of the model as a dictionary
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275 updates = \
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276 { classifier.W1: classifier.W1 - classifier.lr*g_W1 \
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277 , classifier.b1: classifier.b1 - classifier.lr*g_b1 \
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278 , classifier.W2: classifier.W2 - classifier.lr*g_W2 \
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279 , classifier.b2: classifier.b2 - classifier.lr*g_b2 }
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280
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281 # compiling a theano function `train_model` that returns the cost, but in
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282 # the same time updates the parameter of the model based on the rules
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283 # defined in `updates`
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284 train_model = theano.function([x, y], cost, updates = updates )
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285 n_minibatches = len(train_batches)
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286
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287
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288
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289
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290
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291
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292 #conditions for stopping the adaptation:
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293 #1) we have reached nb_max_exemples (this is rounded up to be a multiple of the train size)
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294 #2) validation error is going up twice in a row(probable overfitting)
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295
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296 # This means we no longer stop on slow convergence as low learning rates stopped
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297 # too fast.
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298
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299 # no longer relevant
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300 patience =nb_max_exemples/batch_size
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301 patience_increase = 2 # wait this much longer when a new best is
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302 # found
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303 improvement_threshold = 0.995 # a relative improvement of this much is
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304 # considered significant
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305 validation_frequency = n_minibatches/4
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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_params = None
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311 best_validation_loss = float('inf')
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312 best_iter = 0
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313 test_score = 0.
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314 start_time = time.clock()
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315 n_iter = nb_max_exemples/batch_size # nb of max times we are allowed to run through all exemples
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316 n_iter = n_iter/n_minibatches + 1 #round up
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317 n_iter=max(1,n_iter) # run at least once on short debug call
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318
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319
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320 if verbose == True:
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321 print 'looping at most %d times through the data set' %n_iter
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322 for iter in xrange(n_iter* n_minibatches):
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323
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324 # get epoch and minibatch index
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325 epoch = iter / n_minibatches
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326 minibatch_index = iter % n_minibatches
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327
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328
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329
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330 # get the minibatches corresponding to `iter` modulo
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331 # `len(train_batches)`
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332 x,y = train_batches[ minibatch_index ]
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333 # convert to float
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334 x_float = x/255.0
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335 cost_ij = train_model(x_float,y)
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336
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337 if (iter+1) % validation_frequency == 0:
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338 # compute zero-one loss on validation set
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339
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340 this_validation_loss = 0.
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341 for x,y in validation_batches:
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342 # sum up the errors for each minibatch
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343 x_float = x/255.0
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344 this_validation_loss += test_model(x_float,y)
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345 # get the average by dividing with the number of minibatches
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346 this_validation_loss /= len(validation_batches)
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347 #save the validation loss
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348 total_validation_error_list.append(this_validation_loss)
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349
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350 #get the training error rate
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351 this_train_loss=0
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352 for x,y in train_batches:
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353 # sum up the errors for each minibatch
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354 x_float = x/255.0
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355 this_train_loss += test_model(x_float,y)
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356 # get the average by dividing with the number of minibatches
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357 this_train_loss /= len(train_batches)
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358 #save the validation loss
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359 total_train_error_list.append(this_train_loss)
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360 if(this_train_loss<best_training_error):
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361 best_training_error=this_train_loss
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362
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363 if verbose == True:
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364 print('epoch %i, minibatch %i/%i, validation error %f, training error %f %%' % \
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365 (epoch, minibatch_index+1, n_minibatches, \
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366 this_validation_loss*100.,this_train_loss*100))
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367
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368
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369 #save the learning rate
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370 learning_rate_list.append(classifier.lr.value)
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371
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372
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373 # if we got the best validation score until now
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374 if this_validation_loss < best_validation_loss:
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375 # save best validation score and iteration number
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376 best_validation_loss = this_validation_loss
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377 best_iter = iter
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378 # reset patience if we are going down again
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379 # so we continue exploring
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380 patience=nb_max_exemples/batch_size
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381 # test it on the test set
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382 test_score = 0.
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383 for x,y in test_batches:
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384 x_float=x/255.0
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385 test_score += test_model(x_float,y)
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386 test_score /= len(test_batches)
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387 if verbose == True:
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388 print((' epoch %i, minibatch %i/%i, test error of best '
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389 'model %f %%') %
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390 (epoch, minibatch_index+1, n_minibatches,
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391 test_score*100.))
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392
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393 # if the validation error is going up, we are overfitting (or oscillating)
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394 # stop converging but run at least to next validation
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395 # to check overfitting or ocsillation
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396 # the saved weights of the model will be a bit off in that case
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397 elif this_validation_loss >= best_validation_loss:
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398 #calculate the test error at this point and exit
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399 # test it on the test set
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400 # however, if adaptive_lr is true, try reducing the lr to
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401 # get us out of an oscilliation
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402 if adaptive_lr==1:
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403 classifier.lr.value=classifier.lr.value/2.0
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404
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405 test_score = 0.
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406 #cap the patience so we are allowed one more validation error
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407 #calculation before aborting
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408 patience = iter+validation_frequency+1
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409 for x,y in test_batches:
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410 x_float=x/255.0
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411 test_score += test_model(x_float,y)
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412 test_score /= len(test_batches)
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413 if verbose == True:
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414 print ' validation error is going up, possibly stopping soon'
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415 print((' epoch %i, minibatch %i/%i, test error of best '
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416 'model %f %%') %
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417 (epoch, minibatch_index+1, n_minibatches,
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418 test_score*100.))
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419
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420
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421
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422
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423 if iter>patience:
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424 print 'we have diverged'
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425 break
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426
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427
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428 end_time = time.clock()
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429 if verbose == True:
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430 print(('Optimization complete. Best validation score of %f %% '
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431 'obtained at iteration %i, with test performance %f %%') %
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432 (best_validation_loss * 100., best_iter, test_score*100.))
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433 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
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434 print iter
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435
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436 #save the model and the weights
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437 numpy.savez('model.npy', config=configuration, W1=classifier.W1.value,W2=classifier.W2.value, b1=classifier.b1.value,b2=classifier.b2.value)
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438 numpy.savez('results.npy',config=configuration,total_train_error_list=total_train_error_list,total_validation_error_list=total_validation_error_list,\
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439 learning_rate_list=learning_rate_list)
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440
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441 return (best_training_error*100.0,best_validation_loss * 100.,test_score*100.,best_iter*batch_size,(end_time-start_time)/60)
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442
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443
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444 if __name__ == '__main__':
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445 mlp_full_mnist()
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446
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447 def jobman_mlp_full_nist(state,channel):
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448 (train_error,validation_error,test_error,nb_exemples,time)=mlp_full_nist(learning_rate=state.learning_rate,\
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449 nb_max_exemples=state.nb_max_exemples,\
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450 nb_hidden=state.nb_hidden,\
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451 adaptive_lr=state.adaptive_lr)
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452 state.train_error=train_error
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453 state.validation_error=validation_error
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454 state.test_error=test_error
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455 state.nb_exemples=nb_exemples
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456 state.time=time
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457 return channel.COMPLETE
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458
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459