Mercurial > ift6266

annotate baseline/deep_mlp/deepmlp.py @ 239:42005ec87747

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Mergé (manuellement) les changements de Sylvain pour utiliser le code de dataset d'Arnaud, à cette différence près que je n'utilse pas les givens. J'ai probablement une approche différente pour limiter la taille du dataset dans mon débuggage, aussi.
author fsavard
date Mon, 15 Mar 2010 18:30:21 -0400
parents 89a725d332ae
children
rev   line source
21
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1 #
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2
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3 import numpy, cPickle, gzip
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4
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5
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6 import theano
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7 import theano.tensor as T
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8
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9 import time
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10
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11 import theano.tensor.nnet
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12
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13 class MLP(object):
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14 """Multi-Layer Perceptron Class
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16 A multilayer perceptron is a feedforward artificial neural network model
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17 that has one layer or more of hidden units and nonlinear activations.
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18 Intermidiate layers usually have as activation function thanh or the
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19 sigmoid function while the top layer is a softamx layer.
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20 """
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23
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24 def __init__(self, input, n_in, n_hidden, n_out):
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25 """Initialize the parameters for the multilayer perceptron
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27 :param input: symbolic variable that describes the input of the
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28 architecture (one minibatch)
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29
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30 :param n_in: number of input units, the dimension of the space in
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31 which the datapoints lie
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32
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33 :param n_hidden: List representing the number of units for each
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34 hidden layer
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35
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36 #:param n_layer: Number of hidden layers
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37
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38 :param n_out: number of output units, the dimension of the space in
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39 which the labels lie
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40
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41 """
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42
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43 # initialize the parameters theta = (W,b) ; Here W and b are lists
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44 # where W[i] and b[i] represent the parameters and the bias vector
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45 # of the i-th layer.
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46 n_layer=len(n_hidden)
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47 W_values=[]
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48 b_values=[]
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49 self.W=[]
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50 self.b=[]
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51
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52 # We first initialize the matrix W[0] and b[0] that represent the parameters
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53 # from the input to the first hidden layer
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54 W_values.append(numpy.asarray( numpy.random.uniform( \
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55 low = -numpy.sqrt(6./(n_in+n_hidden[0])), \
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56 high = numpy.sqrt(6./(n_in+n_hidden[0])), \
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57 size = (n_in, n_hidden[0])), dtype = theano.config.floatX))
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58 self.W.append(theano.shared( value = W_values[0] ))
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59 self.b.append(theano.shared( value = numpy.zeros((n_hidden[0],),
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60 dtype= theano.config.floatX)))
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61
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62 # We initialize the parameters between all consecutive hidden layers
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63 for i in range(1,n_layer):
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64 # Each `W[i]` is initialized with `W_values[i]` which is uniformely sampled
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65 # from -6./sqrt(n_hidden[i]+n_hidden[i+1]) and 6./sqrt(n_hidden[i]+n_hidden[i+1])
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66 # the output of uniform if converted using asarray to dtype
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67 # theano.config.floatX so that the code is runable on GPU
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68 W_values.append(numpy.asarray( numpy.random.uniform( \
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69 low = -numpy.sqrt(6./(n_hidden[i-1]+n_hidden[i])), \
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70 high = numpy.sqrt(6./(n_hidden[i-1]+n_hidden[i])), \
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71 size = (n_hidden[i-1], n_hidden[i])), dtype = theano.config.floatX))
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72 self.W.append(theano.shared( value = W_values[i] ))
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73 self.b.append(theano.shared( value = numpy.zeros((n_hidden[i],),
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74 dtype= theano.config.floatX)))
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75
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76 # We initialize the matrix W[n_layer] and b[n_layer] that represent
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77 # the parameters from the last hidden layer to the output layer using the
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78 # same uniform sampling.
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79 W_values.append(numpy.asarray( numpy.random.uniform(
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80 low = -numpy.sqrt(6./(n_hidden[n_layer-1]+n_out)), \
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81 high= numpy.sqrt(6./(n_hidden[n_layer-1]+n_out)),\
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82 size= (n_hidden[n_layer-1], n_out)), dtype = theano.config.floatX))
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83 self.W.append(theano.shared( value = W_values[n_layer]))
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84 self.b.append(theano.shared( value = numpy.zeros((n_out,),
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85 dtype= theano.config.floatX)))
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86
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87 # List of the symbolic expressions computing the values each hidden layer
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88 self.hidden = []
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89
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90 # Symbolic expression of the first hidden layer
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91 self.hidden.append(T.tanh(T.dot(input, self.W[0])+ self.b[0]))
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92 for i in range(1,n_layer):
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93 # Symbolic expression of the i-th hidden layer
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94 self.hidden.append(T.tanh(T.dot(self.hidden[i-1], self.W[i])+ self.b[i]))
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95
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96 # symbolic expression computing the values of the top layer
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97 self.p_y_given_x= T.nnet.softmax(T.dot(self.hidden[n_layer-1], self.W[n_layer])+self.b[n_layer])
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98
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99 # compute prediction as class whose probability is maximal in
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100 # symbolic form
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101 self.y_pred = T.argmax( self.p_y_given_x, axis =1)
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102
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103 # L1 norm ; one regularization option is to enforce L1 norm to
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104 # be small
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cb47cbc95a21 I fixed a bug in the computation of L1 and L2 regularizations
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105 self.L1=abs(self.W[0]).sum()
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106 self.L2_sqr=abs(self.W[0]).sum()
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107 for i in range(1,n_layer+1):
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108 self.L1 += abs(self.W[i]).sum()
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109 # square of L2 norm ; one regularization option is to enforce
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110 # square of L2 norm to be small
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111 for i in range(n_layer+1):
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112 self.L2_sqr += abs(self.W[i]**2).sum()
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113
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114 def negative_log_likelihood(self, y):
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115 return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
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116
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117 def errors(self, y):
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118 """Return a float representing the number of errors in the minibatch
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119 over the total number of examples of the minibatch
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120 """
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121
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122 # check if y has same dimension of y_pred
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123 if y.ndim != self.y_pred.ndim:
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124 raise TypeError('y should have the same shape as self.y_pred',
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125 ('y', target.type, 'y_pred', self.y_pred.type))
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126 # check if y is of the correct datatype
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127 if y.dtype.startswith('int'):
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128 # the T.neq operator returns a vector of 0s and 1s, where 1
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129 # represents a mistake in prediction
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130 return T.mean(T.neq(self.y_pred, y))
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131 else:
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132 raise NotImplementedError()
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133 def sgd_optimization_mnist( learning_rate=0.01, L1_reg = 0.00, \
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134 L2_reg = 0.0001, n_iter=100,n_hidden=[200,100,90,80,70]):
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135 """
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136 Demonstrate stochastic gradient descent optimization for a multilayer
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137 perceptron
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138
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139 This is demonstrated on MNIST.
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140
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141 :param learning_rate: learning rate used (factor for the stochastic
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142 gradient
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143
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144 :param L1_reg: L1-norm's weight when added to the cost (see
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145 regularization)
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146
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147 :param L2_reg: L2-norm's weight when added to the cost (see
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148 regularization)
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149
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150 :param n_iter: maximal number of iterations ot run the optimizer
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151
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152 """
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153
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154 # Load the dataset
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155 f = gzip.open('mnist.pkl.gz','rb')
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156 train_set, valid_set, test_set = cPickle.load(f)
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157 f.close()
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158
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159 # make minibatches of size 20
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160 batch_size = 20 # sized of the minibatch
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161
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162 # Dealing with the training set
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163 # get the list of training images (x) and their labels (y)
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164 (train_set_x, train_set_y) = train_set
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165
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166 # initialize the list of training minibatches with empty list
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167 train_batches = []
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168 for i in xrange(0, len(train_set_x), batch_size):
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169 # add to the list of minibatches the minibatch starting at
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170 # position i, ending at position i+batch_size
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171 # a minibatch is a pair ; the first element of the pair is a list
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172 # of datapoints, the second element is the list of corresponding
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173 # labels
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174 train_batches = train_batches + \
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175 [(train_set_x[i:i+batch_size], train_set_y[i:i+batch_size])]
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176
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177 # Dealing with the validation set
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178 (valid_set_x, valid_set_y) = valid_set
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179 # initialize the list of validation minibatches
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180 valid_batches = []
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181 for i in xrange(0, len(valid_set_x), batch_size):
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182 valid_batches = valid_batches + \
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183 [(valid_set_x[i:i+batch_size], valid_set_y[i:i+batch_size])]
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184
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185 # Dealing with the testing set
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186 (test_set_x, test_set_y) = test_set
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187 # initialize the list of testing minibatches
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188 test_batches = []
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189 for i in xrange(0, len(test_set_x), batch_size):
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190 test_batches = test_batches + \
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191 [(test_set_x[i:i+batch_size], test_set_y[i:i+batch_size])]
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192
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193
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194 ishape = (28,28) # this is the size of MNIST images
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195
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196 # allocate symbolic variables for the data
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197 x = T.fmatrix() # the data is presented as rasterized images
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198 y = T.lvector() # the labels are presented as 1D vector of
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199 # [long int] labels
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200
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201 # construct the logistic regression class
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202 classifier = MLP( input=x.reshape((batch_size,28*28)),\
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203 n_in=28*28, n_hidden=n_hidden, n_out=10)
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204
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205 # the cost we minimize during training is the negative log likelihood of
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206 # the model plus the regularization terms (L1 and L2); cost is expressed
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207 # here symbolically
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208 cost = classifier.negative_log_likelihood(y) \
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209 + L1_reg * classifier.L1 \
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210 + L2_reg * classifier.L2_sqr
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211
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212 # compiling a theano function that computes the mistakes that are made by
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213 # the model on a minibatch
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214 test_model = theano.function([x,y], classifier.errors(y))
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215 g_W=[]
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216 g_b=[]
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217 # compute the gradient of cost with respect to theta = (W1, b1, W2, b2)
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218 for i in range(len(n_hidden)+1):
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219 g_W.append(T.grad(cost, classifier.W[i]))
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220 g_b.append(T.grad(cost, classifier.b[i]))
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221
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222
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223 # specify how to update the parameters of the model as a dictionary
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224 updates={}
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225 for i in range(len(n_hidden)+1):
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226 updates[classifier.W[i]]= classifier.W[i] - learning_rate*g_W[i]
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227 updates[classifier.b[i]]= classifier.b[i] - learning_rate*g_b[i]
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228 # compiling a theano function `train_model` that returns the cost, but in
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229 # the same time updates the parameter of the model based on the rules
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230 # defined in `updates`
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231 train_model = theano.function([x, y], cost, updates = updates )
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232 n_minibatches = len(train_batches)
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233
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234 # early-stopping parameters
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235 patience = 10000 # look as this many examples regardless
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236 patience_increase = 2 # wait this much longer when a new best is
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237 # found
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238 improvement_threshold = 0.995 # a relative improvement of this much is
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239 # considered significant
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240 validation_frequency = n_minibatches # go through this many
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241 # minibatche before checking the network
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242 # on the validation set; in this case we
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243 # check every epoch
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244
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245
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246 best_params = None
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247 best_validation_loss = float('inf')
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248 best_iter = 0
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249 test_score = 0.
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250 start_time = time.clock()
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251 # have a maximum of `n_iter` iterations through the entire dataset
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252 for iter in xrange(n_iter* n_minibatches):
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253
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254 # get epoch and minibatch index
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255 epoch = iter / n_minibatches
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256 minibatch_index = iter % n_minibatches
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257
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258 # get the minibatches corresponding to `iter` modulo
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259 # `len(train_batches)`
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260 x,y = train_batches[ minibatch_index ]
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261 cost_ij = train_model(x,y)
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262
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263 if (iter+1) % validation_frequency == 0:
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264 # compute zero-one loss on validation set
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265 this_validation_loss = 0.
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266 for x,y in valid_batches:
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267 # sum up the errors for each minibatch
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268 this_validation_loss += test_model(x,y)
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269 # get the average by dividing with the number of minibatches
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270 this_validation_loss /= len(valid_batches)
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271
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272 print('epoch %i, minibatch %i/%i, validation error %f %%' % \
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273 (epoch, minibatch_index+1, n_minibatches, \
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274 this_validation_loss*100.))
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275
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276
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277 # if we got the best validation score until now
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278 if this_validation_loss < best_validation_loss:
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279
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280 #improve patience if loss improvement is good enough
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281 if this_validation_loss < best_validation_loss * \
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282 improvement_threshold :
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283 patience = max(patience, iter * patience_increase)
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284
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285 # save best validation score and iteration number
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286 best_validation_loss = this_validation_loss
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287 best_iter = iter
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288
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289 # test it on the test set
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290 test_score = 0.
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291 for x,y in test_batches:
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292 test_score += test_model(x,y)
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293 test_score /= len(test_batches)
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294 print((' epoch %i, minibatch %i/%i, test error of best '
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295 'model %f %%') %
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296 (epoch, minibatch_index+1, n_minibatches,
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297 test_score*100.))
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298
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299 if patience <= iter :
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300 break
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301
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302 end_time = time.clock()
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303 print(('Optimization complete. Best validation score of %f %% '
afdd41db8152 Initial commit of the multiple hidden layer perceptron
Owner <salahmeister@gmail.com>
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304 'obtained at iteration %i, with test performance %f %%') %
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Owner <salahmeister@gmail.com>
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305 (best_validation_loss * 100., best_iter, test_score*100.))
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306 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
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Owner <salahmeister@gmail.com>
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307 #test on NIST (you need pylearn and access to NIST to do that)
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308 if __name__ == '__main__':
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Owner <salahmeister@gmail.com>
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309 sgd_optimization_mnist()
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Owner <salahmeister@gmail.com>
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310