Mercurial > ift6266

annotate baseline/mlp/mlp_nist.py @ 322:743907366476

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code clean up in progress
author xaviermuller
date Tue, 06 Apr 2010 16:00:52 -0400
parents 1e4bf5a5b46d
children 7a7615f940e8
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 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):
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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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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 self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden, self.W2)+self.b2)
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111
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112 # compute prediction as class whose probability is maximal in
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113 # symbolic form
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114 self.y_pred = T.argmax( self.p_y_given_x, axis =1)
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115 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 # L1 norm ; one regularization option is to enforce L1 norm to
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121 # be small
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122 self.L1 = abs(self.W1).sum() + abs(self.W2).sum()
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123
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124 # square of L2 norm ; one regularization option is to enforce
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125 # square of L2 norm to be small
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126 self.L2_sqr = (self.W1**2).sum() + (self.W2**2).sum()
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127
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129
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130 def negative_log_likelihood(self, y):
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131 """Return the mean of the negative log-likelihood of the prediction
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132 of this model under a given target distribution.
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133
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134 .. math::
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135
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136 \frac{1}{|\mathcal{D}|}\mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
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137 \frac{1}{|\mathcal{D}|}\sum_{i=0}^{|\mathcal{D}|} \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
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138 \ell (\theta=\{W,b\}, \mathcal{D})
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139
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140
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141 :param y: corresponds to a vector that gives for each example the
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142 :correct label
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143 """
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144 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 def errors(self, y):
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150 """Return a float representing the number of errors in the minibatch
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151 over the total number of examples of the minibatch
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152 """
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153
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154 # check if y has same dimension of y_pred
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155 if y.ndim != self.y_pred.ndim:
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156 raise TypeError('y should have the same shape as self.y_pred',
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157 ('y', target.type, 'y_pred', self.y_pred.type))
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158 # check if y is of the correct datatype
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159 if y.dtype.startswith('int'):
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160 # the T.neq operator returns a vector of 0s and 1s, where 1
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161 # represents a mistake in prediction
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162 return T.mean(T.neq(self.y_pred, y))
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163 else:
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164 raise NotImplementedError()
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165
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166
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167 def mlp_full_nist( verbose = 1,\
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168 adaptive_lr = 0,\
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169 data_set=0,\
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170 learning_rate=0.01,\
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171 L1_reg = 0.00,\
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172 L2_reg = 0.0001,\
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173 nb_max_exemples=1000000,\
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174 batch_size=20,\
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175 nb_hidden = 30,\
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176 nb_targets = 62,
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177 tau=1e6,\
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178 lr_t2_factor=0.5):
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180
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181 configuration = [learning_rate,nb_max_exemples,nb_hidden,adaptive_lr]
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182
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183 #save initial learning rate if classical adaptive lr is used
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184 initial_lr=learning_rate
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185
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186 total_validation_error_list = []
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187 total_train_error_list = []
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188 learning_rate_list=[]
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189 best_training_error=float('inf');
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190
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191 if data_set==0:
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192 dataset=datasets.nist_all()
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193
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194
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195
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196
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197 ishape = (32,32) # this is the size of NIST images
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198
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199 # allocate symbolic variables for the data
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200 x = T.fmatrix() # the data is presented as rasterized images
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201 y = T.lvector() # the labels are presented as 1D vector of
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202 # [long int] labels
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203
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204
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205 # construct the logistic regression class
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206 classifier = MLP( input=x,\
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207 n_in=32*32,\
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208 n_hidden=nb_hidden,\
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209 n_out=nb_targets,
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210 learning_rate=learning_rate)
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211
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212
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213
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214
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215 # the cost we minimize during training is the negative log likelihood of
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216 # the model plus the regularization terms (L1 and L2); cost is expressed
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217 # here symbolically
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218 cost = classifier.negative_log_likelihood(y) \
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219 + L1_reg * classifier.L1 \
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220 + L2_reg * classifier.L2_sqr
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221
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222 # compiling a theano function that computes the mistakes that are made by
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223 # the model on a minibatch
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224 test_model = theano.function([x,y], classifier.errors(y))
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225
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226 # compute the gradient of cost with respect to theta = (W1, b1, W2, b2)
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227 g_W1 = T.grad(cost, classifier.W1)
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228 g_b1 = T.grad(cost, classifier.b1)
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229 g_W2 = T.grad(cost, classifier.W2)
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230 g_b2 = T.grad(cost, classifier.b2)
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231
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232 # specify how to update the parameters of the model as a dictionary
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233 updates = \
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234 { classifier.W1: classifier.W1 - classifier.lr*g_W1 \
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235 , classifier.b1: classifier.b1 - classifier.lr*g_b1 \
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236 , classifier.W2: classifier.W2 - classifier.lr*g_W2 \
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237 , classifier.b2: classifier.b2 - classifier.lr*g_b2 }
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238
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239 # compiling a theano function `train_model` that returns the cost, but in
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240 # the same time updates the parameter of the model based on the rules
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241 # defined in `updates`
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242 train_model = theano.function([x, y], cost, updates = updates )
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243
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244
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245
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246
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247
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248
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249
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250
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251
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252 #conditions for stopping the adaptation:
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253 #1) we have reached nb_max_exemples (this is rounded up to be a multiple of the train size)
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254 #2) validation error is going up twice in a row(probable overfitting)
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255
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256 # This means we no longer stop on slow convergence as low learning rates stopped
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257 # too fast.
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258
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259 #approximate number of samples in the training set
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260 #this is just to have a validation frequency
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261 #roughly proportionnal to the training set
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262 n_minibatches = 650000/batch_size
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263
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264
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265 patience =nb_max_exemples/batch_size #in units of minibatch
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266 patience_increase = 2 # wait this much longer when a new best is
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267 # found
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268 improvement_threshold = 0.995 # a relative improvement of this much is
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269 # considered significant
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270 validation_frequency = n_minibatches/4
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271
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272
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273
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274
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275
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276 best_validation_loss = float('inf')
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277 best_iter = 0
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278 test_score = 0.
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279 start_time = time.clock()
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280 time_n=0 #in unit of exemples
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281 minibatch_index=0
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282 epoch=0
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283 temp=0
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284
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285
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286
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287 if verbose == 1:
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288 print 'looking at most at %i exemples' %nb_max_exemples
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289 while(minibatch_index*batch_size<nb_max_exemples):
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290
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291 for x, y in dataset.train(batch_size):
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292
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293
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294 minibatch_index = minibatch_index + 1
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295 if adaptive_lr==2:
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296 classifier.lr.value = tau*initial_lr/(tau+time_n)
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297
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298
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299 #train model
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300 cost_ij = train_model(x,y)
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301
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302 if (minibatch_index+1) % validation_frequency == 0:
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303
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304 #save the current learning rate
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305 learning_rate_list.append(classifier.lr.value)
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306
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307 # compute the validation error
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308 this_validation_loss = 0.
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309 temp=0
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310 for xv,yv in dataset.valid(1):
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311 # sum up the errors for each minibatch
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312 axxa=test_model(xv,yv)
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313 this_validation_loss += axxa
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314 temp=temp+1
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315 # get the average by dividing with the number of minibatches
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316 this_validation_loss /= temp
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317 #save the validation loss
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318 total_validation_error_list.append(this_validation_loss)
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319 if verbose == 1:
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320 print(('epoch %i, minibatch %i, learning rate %f current validation error %f ') %
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321 (epoch, minibatch_index+1,classifier.lr.value,
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322 this_validation_loss*100.))
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323
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324 # if we got the best validation score until now
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325 if this_validation_loss < best_validation_loss:
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326 # save best validation score and iteration number
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327 best_validation_loss = this_validation_loss
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328 best_iter = minibatch_index
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329 # reset patience if we are going down again
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330 # so we continue exploring
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331 patience=nb_max_exemples/batch_size
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332 # test it on the test set
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333 test_score = 0.
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334 temp =0
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335 for xt,yt in dataset.test(batch_size):
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336 test_score += test_model(xt,yt)
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337 temp = temp+1
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338 test_score /= temp
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339 if verbose == 1:
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340 print(('epoch %i, minibatch %i, test error of best '
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341 'model %f %%') %
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342 (epoch, minibatch_index+1,
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343 test_score*100.))
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344
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345 # if the validation error is going up, we are overfitting (or oscillating)
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346 # stop converging but run at least to next validation
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347 # to check overfitting or ocsillation
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348 # the saved weights of the model will be a bit off in that case
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349 elif this_validation_loss >= best_validation_loss:
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350 #calculate the test error at this point and exit
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351 # test it on the test set
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352 # however, if adaptive_lr is true, try reducing the lr to
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353 # get us out of an oscilliation
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354 if adaptive_lr==1:
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355 classifier.lr.value=classifier.lr.value*lr_t2_factor
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356
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357 test_score = 0.
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358 #cap the patience so we are allowed one more validation error
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359 #calculation before aborting
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360 patience = minibatch_index+validation_frequency+1
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361 temp=0
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362 for xt,yt in dataset.test(batch_size):
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363 test_score += test_model(xt,yt)
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364 temp=temp+1
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365 test_score /= temp
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366 if verbose == 1:
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367 print ' validation error is going up, possibly stopping soon'
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368 print((' epoch %i, minibatch %i, test error of best '
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369 'model %f %%') %
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370 (epoch, minibatch_index+1,
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371 test_score*100.))
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372
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373
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374
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375
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376 if minibatch_index>patience:
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377 print 'we have diverged'
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378 break
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379
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380
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381 time_n= time_n + batch_size
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382 epoch = epoch+1
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383 end_time = time.clock()
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384 if verbose == 1:
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385 print(('Optimization complete. Best validation score of %f %% '
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386 'obtained at iteration %i, with test performance %f %%') %
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387 (best_validation_loss * 100., best_iter, test_score*100.))
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388 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
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389 print minibatch_index
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390
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391 #save the model and the weights
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392 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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393 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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394 learning_rate_list=learning_rate_list)
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395
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396 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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397
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398
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399 if __name__ == '__main__':
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400 mlp_full_mnist()
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401
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402 def jobman_mlp_full_nist(state,channel):
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403 (train_error,validation_error,test_error,nb_exemples,time)=mlp_full_nist(learning_rate=state.learning_rate,\
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404 nb_max_exemples=state.nb_max_exemples,\
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405 nb_hidden=state.nb_hidden,\
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406 adaptive_lr=state.adaptive_lr,\
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407 tau=state.tau,\
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408 verbose = state.verbose,\
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409 train_data = state.train_data,\
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410 train_labels = state.train_labels,\
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411 test_data = state.test_data,\
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412 test_labels = state.test_labels,\
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413 lr_t2_factor=state.lr_t2_factor)
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414 state.train_error=train_error
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415 state.validation_error=validation_error
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416 state.test_error=test_error
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417 state.nb_exemples=nb_exemples
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418 state.time=time
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419 pylearn.version.record_versions(state,[theano,ift6266,pylearn])
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420 return channel.COMPLETE
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421
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422