Mercurial > ift6266

annotate deep/crbm/crbm.py @ 430:5777b5041ac9

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fixed error computation for 36 classes
author Dumitru Erhan <dumitru.erhan@gmail.com>
date Sun, 02 May 2010 22:26:19 -0400
parents 8d116d4a7593
children
rev   line source
337
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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1 import sys
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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2 import os, os.path
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3
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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4 import numpy
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5
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6 import theano
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7
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8 USING_GPU = "gpu" in theano.config.device
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9
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10 import theano.tensor as T
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11 from theano.tensor.nnet import conv, sigmoid
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12
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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13 if not USING_GPU:
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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14 from theano.tensor.shared_randomstreams import RandomStreams
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15 else:
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16 from theano.sandbox.rng_mrg import MRG_RandomStreams
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17
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18 _PRINT_GRAPHS = True
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19
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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20 def _init_conv_biases(num_filters, varname, rng=numpy.random):
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21 b_shp = (num_filters,)
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22 b = theano.shared( numpy.asarray(
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23 rng.uniform(low=-.5, high=.5, size=b_shp),
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24 dtype=theano.config.floatX), name=varname)
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25 return b
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26
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27 def _init_conv_weights(conv_params, varname, rng=numpy.random):
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28 cp = conv_params
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29
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30 # initialize shared variable for weights.
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31 w_shp = conv_params.as_conv2d_shape_tuple()
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32 w_bound = numpy.sqrt(cp.num_input_planes * \
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33 cp.height_filters * cp.width_filters)
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34 W = theano.shared( numpy.asarray(
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35 rng.uniform(
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36 low=-1.0 / w_bound,
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37 high=1.0 / w_bound,
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38 size=w_shp),
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39 dtype=theano.config.floatX), name=varname)
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40
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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41 return W
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42
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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43 # Shape of W for conv2d
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44 class ConvolutionParams:
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45 def __init__(self, num_filters, num_input_planes, height_filters, width_filters):
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46 self.num_filters = num_filters
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47 self.num_input_planes = num_input_planes
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48 self.height_filters = height_filters
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49 self.width_filters = width_filters
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50
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51 def as_conv2d_shape_tuple(self):
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52 cp = self
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53 return (cp.num_filters, cp.num_input_planes,
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54 cp.height_filters, cp.width_filters)
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55
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56 class CRBM:
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57 def __init__(self, minibatch_size, image_size, conv_params,
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58 learning_rate, sparsity_lambda, sparsity_p):
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59 '''
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60 Parameters
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61 ----------
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62 image_size
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63 height, width
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64 '''
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65 self.minibatch_size = minibatch_size
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66 self.image_size = image_size
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67 self.conv_params = conv_params
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68
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69 '''
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70 Dimensions:
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71 0- minibatch
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72 1- plane/color
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73 2- y (rows)
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74 3- x (cols)
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75 '''
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76 self.x = T.tensor4('x')
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77 self.h = T.tensor4('h')
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78
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79 self.lr = theano.shared(numpy.asarray(learning_rate,
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80 dtype=theano.config.floatX))
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81 self.sparsity_lambda = \
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82 theano.shared( \
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83 numpy.asarray( \
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84 sparsity_lambda,
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85 dtype=theano.config.floatX))
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86 self.sparsity_p = \
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87 theano.shared( \
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88 numpy.asarray(sparsity_p, \
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89 dtype=theano.config.floatX))
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90
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91 self.numpy_rng = numpy.random.RandomState(1234)
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92
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93 if not USING_GPU:
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94 self.theano_rng = RandomStreams(self.numpy_rng.randint(2**30))
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95 else:
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96 self.theano_rng = MRG_RandomStreams(234, use_cuda=True)
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97
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98 self._init_params()
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99 self._init_functions()
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100
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101 def _get_visibles_shape(self):
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102 imsz = self.image_size
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103 return (self.minibatch_size,
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104 self.conv_params.num_input_planes,
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105 imsz[0], imsz[1])
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106
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107 def _get_hiddens_shape(self):
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108 cp = self.conv_params
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109 imsz = self.image_size
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110 wf, hf = cp.height_filters, cp.width_filters
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111 return (self.minibatch_size, cp.num_filters,
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112 imsz[0] - hf + 1, imsz[1] - wf + 1)
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113
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114 def _init_params(self):
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115 cp = self.conv_params
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116
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117 self.W = _init_conv_weights(cp, 'W')
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118 self.b_h = _init_conv_biases(cp.num_filters, 'b_h')
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119 '''
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120 Lee09 mentions "all visible units share a single bias c"
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121 but for upper layers it's pretty clear we need one
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122 per plane, by symmetry
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123 '''
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124 self.b_x = _init_conv_biases(cp.num_input_planes, 'b_x')
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125
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126 self.params = [self.W, self.b_h, self.b_x]
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127
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128 # flip filters horizontally and vertically
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129 W_flipped = self.W[:, :, ::-1, ::-1]
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130 # also have to invert the filters/num_planes
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131 self.W_tilde = W_flipped.dimshuffle(1,0,2,3)
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132
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133 '''
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134 I_up and I_down come from the symbol used in the
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135 Lee 2009 CRBM paper
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136 '''
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137 def _I_up(self, visibles_mb):
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138 '''
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139 output of conv is features maps of size
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140 image_size - filter_size + 1
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141 The dimshuffle serves to broadcast b_h so that it
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142 corresponds to output planes
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143 '''
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144 fshp = self.conv_params.as_conv2d_shape_tuple()
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145 return conv.conv2d(visibles_mb, self.W,
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146 filter_shape=fshp) + \
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147 self.b_h.dimshuffle('x',0,'x','x')
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148
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149 def _I_down(self, hiddens_mb):
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150 '''
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151 notice border_mode='full'... we want to get
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152 back the original size
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153 so we get feature_map_size + filter_size - 1
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154 The dimshuffle serves to broadcast b_x so that
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155 it corresponds to output planes
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156 '''
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157 fshp = list(self.conv_params.as_conv2d_shape_tuple())
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158 # num_filters and num_planes swapped
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159 fshp[0], fshp[1] = fshp[1], fshp[0]
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160 return conv.conv2d(hiddens_mb, self.W_tilde,
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161 border_mode='full',filter_shape=tuple(fshp)) + \
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162 self.b_x.dimshuffle('x',0,'x','x')
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163
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164 def _mean_free_energy(self, visibles_mb):
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165 '''
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166 visibles_mb is mb_size x num_planes x h x w
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167
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168 we want to match the summed input planes
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169 (second dimension, first is mb index)
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170 to respective bias terms for the visibles
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171 The dimshuffle isn't really necessary,
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172 but I put it there for clarity.
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173 '''
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174 vbias_term = \
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175 self.b_x.dimshuffle('x',0) * \
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176 T.sum(visibles_mb,axis=[2,3])
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177 # now sum over term per planes, get one free energy
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178 # contribution per element of minibatch
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179 vbias_term = - T.sum(vbias_term, axis=1)
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180
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181 '''
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182 Here it's a bit more complex, a few points:
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183 - The usual free energy, in the fully connected case,
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184 is a sum over all hiddens.
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185 We do the same thing here, but each unit has limited
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186 connectivity and there's weight reuse.
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187 Therefore we only need to first do the convolutions
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188 (with I_up) which gives us
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189 what would normally be the Wx+b_h for each hidden.
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190 Once we have this,
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191 we take the log(1+exp(sum for this hidden)) elemwise
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192 for each hidden,
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193 then we sum for all hiddens in one example of the minibatch.
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194
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195 - Notice that we reuse the same b_h everywhere instead of
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196 using one b per hidden,
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197 so the broadcasting for b_h done in I_up is all right.
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198
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199 That sum is over all hiddens, so all filters
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200 (planes of hiddens), x, and y.
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201 In the end we get one free energy contribution per
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202 example of the minibatch.
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203 '''
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204 softplused = T.log(1.0+T.exp(self._I_up(visibles_mb)))
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205 # h_sz = self._get_hiddens_shape()
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206 # this simplifies the sum
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207 # num_hiddens = h_sz[1] * h_sz[2] * h_sz[3]
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208 # reshaped = T.reshape(softplused,
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209 # (self.minibatch_size, num_hiddens))
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210
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211 # this is because the 0,1,1,1 sum pattern is not
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212 # implemented on gpu, but the 1,0,1,1 pattern is
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213 dimshuffled = softplused.dimshuffle(1,0,2,3)
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214 xh_and_hbias_term = - T.sum(dimshuffled, axis=[0,2,3])
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215
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216 '''
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217 both bias_term and vbias_term end up with one
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218 contributor to free energy per minibatch
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219 so we mean over minibatches
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220 '''
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221 return T.mean(vbias_term + xh_and_hbias_term)
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222
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223 def _init_functions(self):
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224 # propup
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225 # b_h is broadcasted keeping in mind we want it to
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226 # correspond to each new plane (corresponding to filters)
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227 I_up = self._I_up(self.x)
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228 # expected values for the distributions for each hidden
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229 E_h_given_x = sigmoid(I_up)
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230 # might be needed if we ever want a version where we
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231 # take expectations instead of samples for CD learning
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232 self.E_h_given_x_func = theano.function([self.x], E_h_given_x)
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233
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234 if _PRINT_GRAPHS:
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235 print "----------------------\nE_h_given_x_func"
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236 theano.printing.debugprint(self.E_h_given_x_func)
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237
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diff changeset
238 h_sample_given_x = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
239 self.theano_rng.binomial( \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
240 size = self._get_hiddens_shape(),
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
241 n = 1,
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
242 p = E_h_given_x,
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
243 dtype = theano.config.floatX)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
244
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
245 self.h_sample_given_x_func = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
246 theano.function([self.x],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
247 h_sample_given_x)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
248
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
249 if _PRINT_GRAPHS:
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
250 print "----------------------\nh_sample_given_x_func"
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
251 theano.printing.debugprint(self.h_sample_given_x_func)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
252
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
253 # propdown
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
254 I_down = self._I_down(self.h)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
255 E_x_given_h = sigmoid(I_down)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
256 self.E_x_given_h_func = theano.function([self.h], E_x_given_h)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
257
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
258 if _PRINT_GRAPHS:
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
259 print "----------------------\nE_x_given_h_func"
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
260 theano.printing.debugprint(self.E_x_given_h_func)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
261
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
262 x_sample_given_h = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
263 self.theano_rng.binomial( \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
264 size = self._get_visibles_shape(),
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
265 n = 1,
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
266 p = E_x_given_h,
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
267 dtype = theano.config.floatX)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
268
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
269 self.x_sample_given_h_func = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
270 theano.function([self.h],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
271 x_sample_given_h)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
272
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
273 if _PRINT_GRAPHS:
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
274 print "----------------------\nx_sample_given_h_func"
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
275 theano.printing.debugprint(self.x_sample_given_h_func)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
276
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
277 ##############################################
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
278 # cd update done by grad of free energy
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
279
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
280 x_tilde = T.tensor4('x_tilde')
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
281 cd_update_cost = self._mean_free_energy(self.x) - \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
282 self._mean_free_energy(x_tilde)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
283
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
284 cd_grad = T.grad(cd_update_cost, self.params)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
285 # This is NLL minimization so we use a -
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
286 cd_updates = {self.W: self.W - self.lr * cd_grad[0],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
287 self.b_h: self.b_h - self.lr * cd_grad[1],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
288 self.b_x: self.b_x - self.lr * cd_grad[2]}
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
289
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
290 cd_returned = [cd_update_cost,
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
291 cd_grad[0], cd_grad[1], cd_grad[2],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
292 self.lr * cd_grad[0],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
293 self.lr * cd_grad[1],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
294 self.lr * cd_grad[2]]
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
295 self.cd_return_desc = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
296 ['cd_update_cost',
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
297 'cd_grad_W', 'cd_grad_b_h', 'cd_grad_b_x',
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
298 'lr_times_cd_grad_W',
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
299 'lr_times_cd_grad_b_h',
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
300 'lr_times_cd_grad_b_x']
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
301
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
302 self.cd_update_function = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
303 theano.function([self.x, x_tilde],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
304 cd_returned, updates=cd_updates)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
305
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
306 if _PRINT_GRAPHS:
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
307 print "----------------------\ncd_update_function"
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
308 theano.printing.debugprint(self.cd_update_function)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
309
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
310 ##############
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
311 # sparsity update, based on grad for b_h only
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
312
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
313 '''
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
314 This mean returns an array of shape
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
315 (num_hiddens_planes, feature_map_height, feature_map_width)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
316 (so it's a mean over each unit's activation)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
317 '''
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
318 mean_expected_activation = T.mean(E_h_given_x, axis=0)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
319 # sparsity_p is broadcasted everywhere
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
320 sparsity_update_cost = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
321 T.sqr(self.sparsity_p - mean_expected_activation)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
322 sparsity_update_cost = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
323 T.sum(T.sum(T.sum( \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
324 sparsity_update_cost, axis=2), axis=1), axis=0)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
325 sparsity_grad = T.grad(sparsity_update_cost, [self.W, self.b_h])
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
326
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
327 sparsity_returned = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
328 [sparsity_update_cost,
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
329 sparsity_grad[0], sparsity_grad[1],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
330 self.sparsity_lambda * self.lr * sparsity_grad[0],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
331 self.sparsity_lambda * self.lr * sparsity_grad[1]]
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
332 self.sparsity_return_desc = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
333 ['sparsity_update_cost',
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
334 'sparsity_grad_W',
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
335 'sparsity_grad_b_h',
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
336 'lambda_lr_times_sparsity_grad_W',
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
337 'lambda_lr_times_sparsity_grad_b_h']
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
338
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
339 # gradient _descent_ so we use a -
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
340 sparsity_update = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
341 {self.b_h: self.b_h - \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
342 self.sparsity_lambda * self.lr * sparsity_grad[1],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
343 self.W: self.W - \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
344 self.sparsity_lambda * self.lr * sparsity_grad[0]}
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
345 self.sparsity_update_function = \
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
346 theano.function([self.x],
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
347 sparsity_returned, updates=sparsity_update)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
348
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
349 if _PRINT_GRAPHS:
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
350 print "----------------------\nsparsity_update_function"
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
351 theano.printing.debugprint(self.sparsity_update_function)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
352
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
353 def CD_step(self, x):
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
354 h1 = self.h_sample_given_x_func(x)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
fsavard
parents:
diff changeset
355 x2 = self.x_sample_given_h_func(h1)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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356 return self.cd_update_function(x, x2)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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357
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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358 def sparsity_step(self, x):
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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359 return self.sparsity_update_function(x)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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360
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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361 # these two also operate on minibatches
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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362
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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363 def random_gibbs_samples(self, num_updown_steps):
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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364 start_x = self.numpy_rng.rand(*self._get_visibles_shape())
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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365 return self.gibbs_samples_from(start_x, num_updown_steps)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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366
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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367 def gibbs_samples_from(self, start_x, num_updown_steps):
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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368 x_sample = start_x
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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369 for i in xrange(num_updown_steps):
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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370 h_sample = self.h_sample_given_x_func(x_sample)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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371 x_sample = self.x_sample_given_h_func(h_sample)
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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372 return x_sample
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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373
8d116d4a7593 Added convolutional RBM (ala Lee09) code, imported from my working dir elsewhere. Seems to work for one layer. No subsampling yet.
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374