Mercurial > ift6266

annotate deep/convolutional_dae/stacked_convolutional_dae.py @ 211:476da2ba6a12

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Add nist_P07 datasets to the predefs.
author Arnaud Bergeron <abergeron@gmail.com>
date Wed, 10 Mar 2010 09:56:02 -0500
parents 3f2cc90ad51c
children 334d2444000d
rev   line source
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128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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1 import numpy
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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2 import theano
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3 import time
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4 import theano.tensor as T
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5 from theano.tensor.shared_randomstreams import RandomStreams
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6 import theano.sandbox.softsign
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7
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8 from theano.tensor.signal import downsample
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9 from theano.tensor.nnet import conv
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10
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11 from ift6266 import datasets
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13 from ift6266.baseline.log_reg.log_reg import LogisticRegression
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15 class SigmoidalLayer(object):
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16 def __init__(self, rng, input, n_in, n_out):
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128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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18 self.input = input
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19
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20 W_values = numpy.asarray( rng.uniform( \
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21 low = -numpy.sqrt(6./(n_in+n_out)), \
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22 high = numpy.sqrt(6./(n_in+n_out)), \
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23 size = (n_in, n_out)), dtype = theano.config.floatX)
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24 self.W = theano.shared(value = W_values)
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25
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26 b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
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27 self.b = theano.shared(value= b_values)
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29 self.output = T.tanh(T.dot(input, self.W) + self.b)
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30 self.params = [self.W, self.b]
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31
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32 class dA_conv(object):
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33
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34 def __init__(self, input, filter_shape, corruption_level = 0.1,
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35 shared_W = None, shared_b = None, image_shape = None,
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36 poolsize = (2,2)):
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38 theano_rng = RandomStreams()
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39
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40 fan_in = numpy.prod(filter_shape[1:])
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41 fan_out = filter_shape[0] * numpy.prod(filter_shape[2:])
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42
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43 center = theano.shared(value = 1, name="center")
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44 scale = theano.shared(value = 2, name="scale")
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45
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46 if shared_W != None and shared_b != None :
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47 self.W = shared_W
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48 self.b = shared_b
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49 else:
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50 initial_W = numpy.asarray( numpy.random.uniform(
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51 low = -numpy.sqrt(6./(fan_in+fan_out)),
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52 high = numpy.sqrt(6./(fan_in+fan_out)),
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53 size = filter_shape), dtype = theano.config.floatX)
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54 initial_b = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
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55 self.W = theano.shared(value = initial_W, name = "W")
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56 self.b = theano.shared(value = initial_b, name = "b")
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58
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59 initial_b_prime= numpy.zeros((filter_shape[1],))
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60
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61 self.W_prime=T.dtensor4('W_prime')
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62
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63 self.b_prime = theano.shared(value = initial_b_prime, name = "b_prime")
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64
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65 self.x = input
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66
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67 self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level) * self.x
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68
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69 conv1_out = conv.conv2d(self.tilde_x, self.W, filter_shape=filter_shape,
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70 image_shape=image_shape, border_mode='valid')
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73 self.y = T.tanh(conv1_out + self.b.dimshuffle('x', 0, 'x', 'x'))
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75
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76 da_filter_shape = [ filter_shape[1], filter_shape[0], filter_shape[2],\
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77 filter_shape[3] ]
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78 initial_W_prime = numpy.asarray( numpy.random.uniform( \
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79 low = -numpy.sqrt(6./(fan_in+fan_out)), \
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80 high = numpy.sqrt(6./(fan_in+fan_out)), \
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81 size = da_filter_shape), dtype = theano.config.floatX)
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82 self.W_prime = theano.shared(value = initial_W_prime, name = "W_prime")
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83
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84 conv2_out = conv.conv2d(self.y, self.W_prime,
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85 filter_shape = da_filter_shape,
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86 border_mode='full')
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87
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88 self.z = (T.tanh(conv2_out + self.b_prime.dimshuffle('x', 0, 'x', 'x'))+center) / scale
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89
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90 scaled_x = (self.x + center) / scale
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91
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92 self.L = - T.sum( scaled_x*T.log(self.z) + (1-scaled_x)*T.log(1-self.z), axis=1 )
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93
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94 self.cost = T.mean(self.L)
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95
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96 self.params = [ self.W, self.b, self.b_prime ]
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97
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98 class LeNetConvPoolLayer(object):
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99 def __init__(self, rng, input, filter_shape, image_shape=None, poolsize=(2,2)):
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100 self.input = input
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101
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102 W_values = numpy.zeros(filter_shape, dtype=theano.config.floatX)
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103 self.W = theano.shared(value=W_values)
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104
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105 b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
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106 self.b = theano.shared(value=b_values)
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107
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108 conv_out = conv.conv2d(input, self.W,
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109 filter_shape=filter_shape, image_shape=image_shape)
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112 fan_in = numpy.prod(filter_shape[1:])
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113 fan_out = filter_shape[0] * numpy.prod(filter_shape[2:]) / numpy.prod(poolsize)
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114
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115 W_bound = numpy.sqrt(6./(fan_in + fan_out))
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116 self.W.value = numpy.asarray(
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117 rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
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118 dtype = theano.config.floatX)
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120
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121 pooled_out = downsample.max_pool2D(conv_out, poolsize, ignore_border=True)
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122
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123 self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
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124 self.params = [self.W, self.b]
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125
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126
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127 class SdA():
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128 def __init__(self, input, n_ins_mlp, conv_hidden_layers_sizes,
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129 mlp_hidden_layers_sizes, corruption_levels, rng, n_out,
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130 pretrain_lr, finetune_lr):
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132 self.layers = []
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133 self.pretrain_functions = []
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134 self.params = []
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135 self.conv_n_layers = len(conv_hidden_layers_sizes)
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136 self.mlp_n_layers = len(mlp_hidden_layers_sizes)
200
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137
138
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138 self.x = T.dmatrix('x') # the data is presented as rasterized images
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139 self.y = T.ivector('y') # the labels are presented as 1D vector of
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140
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141 for i in xrange( self.conv_n_layers ):
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142 filter_shape=conv_hidden_layers_sizes[i][0]
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143 image_shape=conv_hidden_layers_sizes[i][1]
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144 max_poolsize=conv_hidden_layers_sizes[i][2]
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145
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146 if i == 0 :
200
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147 layer_input=self.x.reshape((self.x.shape[0], 1, 32, 32))
138
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148 else:
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149 layer_input=self.layers[-1].output
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150
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151 layer = LeNetConvPoolLayer(rng, input=layer_input,
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152 image_shape=image_shape,
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153 filter_shape=filter_shape,
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154 poolsize=max_poolsize)
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155 print 'Convolutional layer', str(i+1), 'created'
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156
138
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157 self.layers += [layer]
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158 self.params += layer.params
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159
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160 da_layer = dA_conv(corruption_level = corruption_levels[0],
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161 input = layer_input,
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162 shared_W = layer.W, shared_b = layer.b,
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163 filter_shape = filter_shape,
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164 image_shape = image_shape )
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165
138
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166 gparams = T.grad(da_layer.cost, da_layer.params)
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167
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168 updates = {}
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169 for param, gparam in zip(da_layer.params, gparams):
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170 updates[param] = param - gparam * pretrain_lr
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171
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172 update_fn = theano.function([self.x], da_layer.cost, updates = updates)
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173
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174 self.pretrain_functions += [update_fn]
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175
138
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176 for i in xrange( self.mlp_n_layers ):
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177 if i == 0 :
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178 input_size = n_ins_mlp
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179 else:
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180 input_size = mlp_hidden_layers_sizes[i-1]
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181
138
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182 if i == 0 :
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183 if len( self.layers ) == 0 :
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184 layer_input=self.x
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185 else :
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186 layer_input = self.layers[-1].output.flatten(2)
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187 else:
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188 layer_input = self.layers[-1].output
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189
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190 layer = SigmoidalLayer(rng, layer_input, input_size,
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191 mlp_hidden_layers_sizes[i] )
200
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192
138
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193 self.layers += [layer]
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194 self.params += layer.params
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195
200
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196 print 'MLP layer', str(i+1), 'created'
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197
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198 self.logLayer = LogisticRegression(input=self.layers[-1].output, \
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199 n_in=mlp_hidden_layers_sizes[-1], n_out=n_out)
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200 self.params += self.logLayer.params
200
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201
138
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202 cost = self.logLayer.negative_log_likelihood(self.y)
200
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203
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204 gparams = T.grad(cost, self.params)
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205
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206 updates = {}
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207 for param,gparam in zip(self.params, gparams):
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208 updates[param] = param - gparam*finetune_lr
200
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209
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210 self.finetune = theano.function([self.x, self.y], cost, updates = updates)
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211
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212 self.errors = self.logLayer.errors(self.y)
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213
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214 def sgd_optimization_mnist( learning_rate=0.1, pretraining_epochs = 2, \
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215 pretrain_lr = 0.01, training_epochs = 1000, \
200
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216 dataset=datasets.nist_digits):
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217
138
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218 batch_size = 500 # size of the minibatch
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219
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220 # allocate symbolic variables for the data
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221 index = T.lscalar() # index to a [mini]batch
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222 x = T.matrix('x') # the data is presented as rasterized images
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223 y = T.ivector('y') # the labels are presented as 1d vector of
200
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224 # [int] labels
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225 layer0_input = x.reshape((x.shape[0],1,32,32))
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226
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227
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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228 # Setup the convolutional layers with their DAs(add as many as you want)
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229 corruption_levels = [ 0.2, 0.2, 0.2]
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230 rng = numpy.random.RandomState(1234)
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231 ker1=2
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232 ker2=2
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233 conv_layers=[]
200
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234 conv_layers.append([[ker1,1,5,5], None, [2,2] ])
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235 conv_layers.append([[ker2,ker1,5,5], None, [2,2] ])
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236
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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237 # Setup the MLP layers of the network
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238 mlp_layers=[500]
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239
200
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240 network = SdA(input = layer0_input, n_ins_mlp = ker2*4*4,
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241 conv_hidden_layers_sizes = conv_layers,
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242 mlp_hidden_layers_sizes = mlp_layers,
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243 corruption_levels = corruption_levels , n_out = 10,
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244 rng = rng , pretrain_lr = pretrain_lr ,
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245 finetune_lr = learning_rate )
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246
200
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247 test_model = theano.function([network.x, network.y], network.errors)
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248
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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249 start_time = time.clock()
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250 for i in xrange(len(network.layers)-len(mlp_layers)):
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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251 for epoch in xrange(pretraining_epochs):
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252 for x, y in dataset.train(batch_size):
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253 c = network.pretrain_functions[i](x)
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254 print 'pre-training convolution layer %i, epoch %d, cost '%(i,epoch), c
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255
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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256 patience = 10000 # look as this many examples regardless
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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257 patience_increase = 2. # WAIT THIS MUCH LONGER WHEN A NEW BEST IS
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258 # FOUND
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259 improvement_threshold = 0.995 # a relative improvement of this much is
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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260
200
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261 validation_frequency = patience/2
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262
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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263 best_params = None
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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264 best_validation_loss = float('inf')
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265 test_score = 0.
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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266 start_time = time.clock()
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267
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268 done_looping = False
128507ac4edf Initial commit for the stacked convolutional denoising autoencoders
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269 epoch = 0
200
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270 iter = 0
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271
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272 while (epoch < training_epochs) and (not done_looping):
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273 epoch = epoch + 1
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274 for x, y in dataset.train(batch_size):
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275
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276 cost_ij = network.finetune(x, y)
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277 iter += 1
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278
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279 if iter % validation_frequency == 0:
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280 validation_losses = [test_model(xv, yv) for xv, yv in dataset.valid(batch_size)]
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281 this_validation_loss = numpy.mean(validation_losses)
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282 print('epoch %i, iter %i, validation error %f %%' % \
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283 (epoch, iter, this_validation_loss*100.))
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284
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285 # if we got the best validation score until now
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286 if this_validation_loss < best_validation_loss:
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287
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288 #improve patience if loss improvement is good enough
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289 if this_validation_loss < best_validation_loss * \
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290 improvement_threshold :
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291 patience = max(patience, iter * patience_increase)
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292
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293 # save best validation score and iteration number
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294 best_validation_loss = this_validation_loss
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295 best_iter = iter
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296
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297 # test it on the test set
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298 test_losses = [test_model(xt, yt) for xt, yt in dataset.test(batch_size)]
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299 test_score = numpy.mean(test_losses)
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300 print((' epoch %i, iter %i, test error of best '
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301 'model %f %%') %
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302 (epoch, iter, test_score*100.))
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303
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304 if patience <= iter :
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305 done_looping = True
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306 break
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307
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308 end_time = time.clock()
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309 print(('Optimization complete with best validation score of %f %%,'
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310 'with test performance %f %%') %
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311 (best_validation_loss * 100., test_score*100.))
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312 print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
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313
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314 if __name__ == '__main__':
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315 sgd_optimization_mnist()
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316