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annotate code_tutoriel/SdA.py @ 266:1e4e60ddadb1

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Merge. Ah, et dans le dernier commit, j'avais oublié de mentionner que j'ai ajouté du code pour gérer l'isolation de différents clones pour rouler des expériences et modifier le code en même temps.
author fsavard
date Fri, 19 Mar 2010 10:56:16 -0400
parents 4bc5eeec6394
children
rev   line source
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1 """
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2 This tutorial introduces stacked denoising auto-encoders (SdA) using Theano.
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3
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4 Denoising autoencoders are the building blocks for SdA.
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5 They are based on auto-encoders as the ones used in Bengio et al. 2007.
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6 An autoencoder takes an input x and first maps it to a hidden representation
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7 y = f_{\theta}(x) = s(Wx+b), parameterized by \theta={W,b}. The resulting
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8 latent representation y is then mapped back to a "reconstructed" vector
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9 z \in [0,1]^d in input space z = g_{\theta'}(y) = s(W'y + b'). The weight
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10 matrix W' can optionally be constrained such that W' = W^T, in which case
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11 the autoencoder is said to have tied weights. The network is trained such
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12 that to minimize the reconstruction error (the error between x and z).
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13
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14 For the denosing autoencoder, during training, first x is corrupted into
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15 \tilde{x}, where \tilde{x} is a partially destroyed version of x by means
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16 of a stochastic mapping. Afterwards y is computed as before (using
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17 \tilde{x}), y = s(W\tilde{x} + b) and z as s(W'y + b'). The reconstruction
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18 error is now measured between z and the uncorrupted input x, which is
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19 computed as the cross-entropy :
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20 - \sum_{k=1}^d[ x_k \log z_k + (1-x_k) \log( 1-z_k)]
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21
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22
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23 References :
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24 - P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol: Extracting and
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25 Composing Robust Features with Denoising Autoencoders, ICML'08, 1096-1103,
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26 2008
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27 - Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise
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28 Training of Deep Networks, Advances in Neural Information Processing
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29 Systems 19, 2007
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30
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31 """
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32
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33 import numpy, time, cPickle, gzip
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34
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35 import theano
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36 import theano.tensor as T
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37 from theano.tensor.shared_randomstreams import RandomStreams
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38
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39 from logistic_sgd import LogisticRegression, load_data
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40 from mlp import HiddenLayer
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41 from dA import dA
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42
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43
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44
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45 class SdA(object):
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46 """Stacked denoising auto-encoder class (SdA)
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47
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48 A stacked denoising autoencoder model is obtained by stacking several
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49 dAs. The hidden layer of the dA at layer `i` becomes the input of
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50 the dA at layer `i+1`. The first layer dA gets as input the input of
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51 the SdA, and the hidden layer of the last dA represents the output.
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52 Note that after pretraining, the SdA is dealt with as a normal MLP,
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53 the dAs are only used to initialize the weights.
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54 """
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55
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56 def __init__(self, numpy_rng, theano_rng = None, n_ins = 784,
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57 hidden_layers_sizes = [500,500], n_outs = 10,
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58 corruption_levels = [0.1, 0.1]):
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59 """ This class is made to support a variable number of layers.
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60
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61 :type numpy_rng: numpy.random.RandomState
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62 :param numpy_rng: numpy random number generator used to draw initial
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63 weights
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64
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65 :type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
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66 :param theano_rng: Theano random generator; if None is given one is
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67 generated based on a seed drawn from `rng`
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68
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69 :type n_ins: int
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70 :param n_ins: dimension of the input to the sdA
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71
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72 :type n_layers_sizes: list of ints
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73 :param n_layers_sizes: intermidiate layers size, must contain
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74 at least one value
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75
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76 :type n_outs: int
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77 :param n_outs: dimension of the output of the network
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78
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79 :type corruption_levels: list of float
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80 :param corruption_levels: amount of corruption to use for each
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81 layer
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82 """
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83
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84 self.sigmoid_layers = []
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85 self.dA_layers = []
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86 self.params = []
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87 self.n_layers = len(hidden_layers_sizes)
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88
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89 assert self.n_layers > 0
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90
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91 if not theano_rng:
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92 theano_rng = RandomStreams(numpy_rng.randint(2**30))
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93 # allocate symbolic variables for the data
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94 self.x = T.matrix('x') # the data is presented as rasterized images
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95 self.y = T.ivector('y') # the labels are presented as 1D vector of
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96 # [int] labels
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97
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98 # The SdA is an MLP, for which all weights of intermidiate layers
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99 # are shared with a different denoising autoencoders
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100 # We will first construct the SdA as a deep multilayer perceptron,
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101 # and when constructing each sigmoidal layer we also construct a
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102 # denoising autoencoder that shares weights with that layer
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103 # During pretraining we will train these autoencoders (which will
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104 # lead to chainging the weights of the MLP as well)
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105 # During finetunining we will finish training the SdA by doing
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106 # stochastich gradient descent on the MLP
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107
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108 for i in xrange( self.n_layers ):
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109 # construct the sigmoidal layer
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110
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111 # the size of the input is either the number of hidden units of
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112 # the layer below or the input size if we are on the first layer
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113 if i == 0 :
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114 input_size = n_ins
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115 else:
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116 input_size = hidden_layers_sizes[i-1]
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117
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118 # the input to this layer is either the activation of the hidden
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119 # layer below or the input of the SdA if you are on the first
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120 # layer
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121 if i == 0 :
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122 layer_input = self.x
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123 else:
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124 layer_input = self.sigmoid_layers[-1].output
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125
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126 sigmoid_layer = HiddenLayer(rng = numpy_rng,
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127 input = layer_input,
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128 n_in = input_size,
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129 n_out = hidden_layers_sizes[i],
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130 activation = T.nnet.sigmoid)
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131 # add the layer to our list of layers
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132 self.sigmoid_layers.append(sigmoid_layer)
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133 # its arguably a philosophical question...
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134 # but we are going to only declare that the parameters of the
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135 # sigmoid_layers are parameters of the StackedDAA
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136 # the visible biases in the dA are parameters of those
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137 # dA, but not the SdA
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138 self.params.extend(sigmoid_layer.params)
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139
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140 # Construct a denoising autoencoder that shared weights with this
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141 # layer
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142 dA_layer = dA(numpy_rng = numpy_rng, theano_rng = theano_rng, input = layer_input,
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143 n_visible = input_size,
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144 n_hidden = hidden_layers_sizes[i],
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145 W = sigmoid_layer.W, bhid = sigmoid_layer.b)
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146 self.dA_layers.append(dA_layer)
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147
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148
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149 # We now need to add a logistic layer on top of the MLP
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150 self.logLayer = LogisticRegression(\
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151 input = self.sigmoid_layers[-1].output,\
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152 n_in = hidden_layers_sizes[-1], n_out = n_outs)
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153
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154 self.params.extend(self.logLayer.params)
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155 # construct a function that implements one step of finetunining
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156
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157 # compute the cost for second phase of training,
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158 # defined as the negative log likelihood
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159 self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
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160 # compute the gradients with respect to the model parameters
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161 # symbolic variable that points to the number of errors made on the
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162 # minibatch given by self.x and self.y
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163 self.errors = self.logLayer.errors(self.y)
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164
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165 def pretraining_functions(self, train_set_x, batch_size):
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166 ''' Generates a list of functions, each of them implementing one
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167 step in trainnig the dA corresponding to the layer with same index.
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168 The function will require as input the minibatch index, and to train
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169 a dA you just need to iterate, calling the corresponding function on
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170 all minibatch indexes.
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171
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172 :type train_set_x: theano.tensor.TensorType
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173 :param train_set_x: Shared variable that contains all datapoints used
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174 for training the dA
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175
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176 :type batch_size: int
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177 :param batch_size: size of a [mini]batch
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178
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179 :type learning_rate: float
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180 :param learning_rate: learning rate used during training for any of
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181 the dA layers
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182 '''
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183
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184 # index to a [mini]batch
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185 index = T.lscalar('index') # index to a minibatch
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186 corruption_level = T.scalar('corruption') # amount of corruption to use
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187 learning_rate = T.scalar('lr') # learning rate to use
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188 # number of batches
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189 n_batches = train_set_x.value.shape[0] / batch_size
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190 # begining of a batch, given `index`
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191 batch_begin = index * batch_size
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192 # ending of a batch given `index`
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193 batch_end = batch_begin+batch_size
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194
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195 pretrain_fns = []
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196 for dA in self.dA_layers:
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197 # get the cost and the updates list
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198 cost,updates = dA.get_cost_updates( corruption_level, learning_rate)
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199 # compile the theano function
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200 fn = theano.function( inputs = [index,
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201 theano.Param(corruption_level, default = 0.2),
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202 theano.Param(learning_rate, default = 0.1)],
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203 outputs = cost,
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204 updates = updates,
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205 givens = {self.x :train_set_x[batch_begin:batch_end]})
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206 # append `fn` to the list of functions
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207 pretrain_fns.append(fn)
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208
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209 return pretrain_fns
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210
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211
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212 def build_finetune_functions(self, datasets, batch_size, learning_rate):
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213 '''Generates a function `train` that implements one step of
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214 finetuning, a function `validate` that computes the error on
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215 a batch from the validation set, and a function `test` that
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216 computes the error on a batch from the testing set
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217
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218 :type datasets: list of pairs of theano.tensor.TensorType
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219 :param datasets: It is a list that contain all the datasets;
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220 the has to contain three pairs, `train`,
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221 `valid`, `test` in this order, where each pair
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222 is formed of two Theano variables, one for the
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223 datapoints, the other for the labels
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224
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225 :type batch_size: int
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226 :param batch_size: size of a minibatch
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227
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228 :type learning_rate: float
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229 :param learning_rate: learning rate used during finetune stage
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230 '''
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231
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232 (train_set_x, train_set_y) = datasets[0]
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233 (valid_set_x, valid_set_y) = datasets[1]
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234 (test_set_x , test_set_y ) = datasets[2]
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235
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236 # compute number of minibatches for training, validation and testing
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237 n_valid_batches = valid_set_x.value.shape[0] / batch_size
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238 n_test_batches = test_set_x.value.shape[0] / batch_size
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239
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240 index = T.lscalar('index') # index to a [mini]batch
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241
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242 # compute the gradients with respect to the model parameters
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243 gparams = T.grad(self.finetune_cost, self.params)
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244
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245 # compute list of fine-tuning updates
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246 updates = {}
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247 for param, gparam in zip(self.params, gparams):
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248 updates[param] = param - gparam*learning_rate
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249
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250 train_fn = theano.function(inputs = [index],
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251 outputs = self.finetune_cost,
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252 updates = updates,
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253 givens = {
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254 self.x : train_set_x[index*batch_size:(index+1)*batch_size],
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255 self.y : train_set_y[index*batch_size:(index+1)*batch_size]})
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256
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257 test_score_i = theano.function([index], self.errors,
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258 givens = {
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259 self.x: test_set_x[index*batch_size:(index+1)*batch_size],
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260 self.y: test_set_y[index*batch_size:(index+1)*batch_size]})
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261
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262 valid_score_i = theano.function([index], self.errors,
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263 givens = {
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264 self.x: valid_set_x[index*batch_size:(index+1)*batch_size],
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265 self.y: valid_set_y[index*batch_size:(index+1)*batch_size]})
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266
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267 # Create a function that scans the entire validation set
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268 def valid_score():
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269 return [valid_score_i(i) for i in xrange(n_valid_batches)]
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270
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271 # Create a function that scans the entire test set
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272 def test_score():
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273 return [test_score_i(i) for i in xrange(n_test_batches)]
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274
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275 return train_fn, valid_score, test_score
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276
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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 def test_SdA( finetune_lr = 0.1, pretraining_epochs = 15, \
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283 pretrain_lr = 0.1, training_epochs = 1000, \
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284 dataset='mnist.pkl.gz'):
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285 """
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286 Demonstrates how to train and test a stochastic denoising autoencoder.
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287
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288 This is demonstrated on MNIST.
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289
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290 :type learning_rate: float
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291 :param learning_rate: learning rate used in the finetune stage
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292 (factor for the stochastic gradient)
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293
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294 :type pretraining_epochs: int
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295 :param pretraining_epochs: number of epoch to do pretraining
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296
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297 :type pretrain_lr: float
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298 :param pretrain_lr: learning rate to be used during pre-training
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299
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300 :type n_iter: int
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301 :param n_iter: maximal number of iterations ot run the optimizer
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302
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303 :type dataset: string
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304 :param dataset: path the the pickled dataset
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305
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306 """
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307
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308 datasets = load_data(dataset)
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309
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310 train_set_x, train_set_y = datasets[0]
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311 valid_set_x, valid_set_y = datasets[1]
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312 test_set_x , test_set_y = datasets[2]
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313
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314
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315 batch_size = 20 # size of the minibatch
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316
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317 # compute number of minibatches for training, validation and testing
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318 n_train_batches = train_set_x.value.shape[0] / batch_size
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319
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320 # numpy random generator
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321 numpy_rng = numpy.random.RandomState(123)
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322 print '... building the model'
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323 # construct the stacked denoising autoencoder class
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324 sda = SdA( numpy_rng = numpy_rng, n_ins = 28*28,
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325 hidden_layers_sizes = [1000,1000,1000],
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326 n_outs = 10)
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327
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328
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329 #########################
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330 # PRETRAINING THE MODEL #
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331 #########################
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332 print '... getting the pretraining functions'
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333 pretraining_fns = sda.pretraining_functions(
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334 train_set_x = train_set_x,
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335 batch_size = batch_size )
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336
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337 print '... pre-training the model'
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338 start_time = time.clock()
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339 ## Pre-train layer-wise
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340 for i in xrange(sda.n_layers):
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341 # go through pretraining epochs
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342 for epoch in xrange(pretraining_epochs):
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343 # go through the training set
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344 c = []
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345 for batch_index in xrange(n_train_batches):
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346 c.append( pretraining_fns[i](index = batch_index,
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347 corruption = 0.2, lr = pretrain_lr ) )
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348 print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),numpy.mean(c)
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349
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350 end_time = time.clock()
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351
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352 print ('Pretraining took %f minutes' %((end_time-start_time)/60.))
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353
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354 ########################
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355 # FINETUNING THE MODEL #
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356 ########################
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357
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358 # get the training, validation and testing function for the model
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359 print '... getting the finetuning functions'
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360 train_fn, validate_model, test_model = sda.build_finetune_functions (
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361 datasets = datasets, batch_size = batch_size,
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362 learning_rate = finetune_lr)
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363
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364 print '... finetunning the model'
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365 # early-stopping parameters
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366 patience = 10000 # look as this many examples regardless
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367 patience_increase = 2. # wait this much longer when a new best is
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368 # found
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369 improvement_threshold = 0.995 # a relative improvement of this much is
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370 # considered significant
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371 validation_frequency = min(n_train_batches, patience/2)
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372 # go through this many
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373 # minibatche before checking the network
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374 # on the validation set; in this case we
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375 # check every epoch
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376
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377
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378 best_params = None
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379 best_validation_loss = float('inf')
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380 test_score = 0.
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381 start_time = time.clock()
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382
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383 done_looping = False
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384 epoch = 0
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385
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386 while (epoch < training_epochs) and (not done_looping):
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387 epoch = epoch + 1
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388 for minibatch_index in xrange(n_train_batches):
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389
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390 minibatch_avg_cost = train_fn(minibatch_index)
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391 iter = epoch * n_train_batches + minibatch_index
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392
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393 if (iter+1) % validation_frequency == 0:
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394
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395 validation_losses = validate_model()
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396 this_validation_loss = numpy.mean(validation_losses)
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Dumitru Erhan <dumitru.erhan@gmail.com>
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397 print('epoch %i, minibatch %i/%i, validation error %f %%' % \
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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398 (epoch, minibatch_index+1, n_train_batches, \
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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399 this_validation_loss*100.))
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Dumitru Erhan <dumitru.erhan@gmail.com>
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400
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Dumitru Erhan <dumitru.erhan@gmail.com>
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401
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Dumitru Erhan <dumitru.erhan@gmail.com>
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402 # if we got the best validation score until now
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Dumitru Erhan <dumitru.erhan@gmail.com>
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403 if this_validation_loss < best_validation_loss:
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Dumitru Erhan <dumitru.erhan@gmail.com>
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404
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Dumitru Erhan <dumitru.erhan@gmail.com>
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405 #improve patience if loss improvement is good enough
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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406 if this_validation_loss < best_validation_loss * \
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Dumitru Erhan <dumitru.erhan@gmail.com>
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407 improvement_threshold :
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Dumitru Erhan <dumitru.erhan@gmail.com>
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408 patience = max(patience, iter * patience_increase)
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Dumitru Erhan <dumitru.erhan@gmail.com>
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409
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Dumitru Erhan <dumitru.erhan@gmail.com>
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410 # save best validation score and iteration number
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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411 best_validation_loss = this_validation_loss
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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412 best_iter = iter
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Dumitru Erhan <dumitru.erhan@gmail.com>
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413
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Dumitru Erhan <dumitru.erhan@gmail.com>
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414 # test it on the test set
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Dumitru Erhan <dumitru.erhan@gmail.com>
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415 test_losses = test_model()
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Dumitru Erhan <dumitru.erhan@gmail.com>
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416 test_score = numpy.mean(test_losses)
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Dumitru Erhan <dumitru.erhan@gmail.com>
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417 print((' epoch %i, minibatch %i/%i, test error of best '
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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418 'model %f %%') %
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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419 (epoch, minibatch_index+1, n_train_batches,
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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420 test_score*100.))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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421
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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422
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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423 if patience <= iter :
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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424 done_looping = True
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Dumitru Erhan <dumitru.erhan@gmail.com>
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425 break
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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426
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Dumitru Erhan <dumitru.erhan@gmail.com>
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427 end_time = time.clock()
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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428 print(('Optimization complete with best validation score of %f %%,'
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
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429 'with test performance %f %%') %
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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430 (best_validation_loss * 100., test_score*100.))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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431 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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432
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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433
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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434
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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435
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
diff changeset
436
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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437
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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438 if __name__ == '__main__':
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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439 test_SdA()
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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440
4bc5eeec6394 Updating the tutorial code to the latest revisions.
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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441