Mercurial > ift6266

annotate deep/crbm/mnist_crbm.py @ 359:969ad25e78cc

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Fichier config.py.example supprimé je ne sais pas pourquoi ?! Enfin je le réajoute
author fsavard
date Thu, 22 Apr 2010 10:19:07 -0400
parents 8d116d4a7593
children ffbf0e41bcee
rev   line source
337
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1 #!/usr/bin/python
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2
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3 import sys
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4 import os, os.path
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5
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6 import numpy as N
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7
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8 import theano
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9 import theano.tensor as T
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10
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11 from crbm import CRBM, ConvolutionParams
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12
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13 from pylearn.datasets import MNIST
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14 from pylearn.io.image_tiling import tile_raster_images
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15
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16 import Image
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17
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18 from pylearn.io.seriestables import *
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19 import tables
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20
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21 IMAGE_OUTPUT_DIR = 'img/'
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22
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23 REDUCE_EVERY = 100
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24
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25 def filename_from_time(suffix):
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26 import datetime
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27 return str(datetime.datetime.now()) + suffix + ".png"
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28
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29 # Just a shortcut for a common case where we need a few
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30 # related Error (float) series
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31 def get_accumulator_series_array( \
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32 hdf5_file, group_name, series_names,
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33 reduce_every,
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34 index_names=('epoch','minibatch'),
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35 stdout_too=True,
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36 skip_hdf5_append=False):
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37 all_series = []
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38
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39 hdf5_file.createGroup('/', group_name)
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40
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41 other_targets = []
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42 if stdout_too:
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43 other_targets = [StdoutAppendTarget()]
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44
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45 for sn in series_names:
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46 series_base = \
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47 ErrorSeries(error_name=sn,
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48 table_name=sn,
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49 hdf5_file=hdf5_file,
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50 hdf5_group='/'+group_name,
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51 index_names=index_names,
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52 other_targets=other_targets,
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53 skip_hdf5_append=skip_hdf5_append)
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54
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55 all_series.append( \
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56 AccumulatorSeriesWrapper( \
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57 base_series=series_base,
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58 reduce_every=reduce_every))
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59
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60 ret_wrapper = SeriesArrayWrapper(all_series)
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61
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62 return ret_wrapper
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63
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64 class MnistCrbm(object):
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65 def __init__(self):
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66 self.mnist = MNIST.full()#first_10k()
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67
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68 self.cp = ConvolutionParams( \
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69 num_filters=40,
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70 num_input_planes=1,
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71 height_filters=12,
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72 width_filters=12)
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73
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74 self.image_size = (28,28)
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75
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76 self.minibatch_size = 10
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77
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78 self.lr = 0.01
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79 self.sparsity_lambda = 1.0
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80 # about 1/num_filters, so only one filter active at a time
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81 # 40 * 0.05 = ~2 filters active for any given pixel
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82 self.sparsity_p = 0.05
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83
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84 self.crbm = CRBM( \
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85 minibatch_size=self.minibatch_size,
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86 image_size=self.image_size,
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87 conv_params=self.cp,
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88 learning_rate=self.lr,
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89 sparsity_lambda=self.sparsity_lambda,
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90 sparsity_p=self.sparsity_p)
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91
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92 self.num_epochs = 10
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93
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94 self.init_series()
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95
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96 def init_series(self):
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97
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98 series = {}
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99
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100 basedir = os.getcwd()
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101
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102 h5f = tables.openFile(os.path.join(basedir, "series.h5"), "w")
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103
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104 cd_series_names = self.crbm.cd_return_desc
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105 series['cd'] = \
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106 get_accumulator_series_array( \
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107 h5f, 'cd', cd_series_names,
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108 REDUCE_EVERY,
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109 stdout_too=True)
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110
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111 sparsity_series_names = self.crbm.sparsity_return_desc
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112 series['sparsity'] = \
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113 get_accumulator_series_array( \
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114 h5f, 'sparsity', sparsity_series_names,
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115 REDUCE_EVERY,
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116 stdout_too=True)
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117
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118 # so first we create the names for each table, based on
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119 # position of each param in the array
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120 params_stdout = StdoutAppendTarget("\n------\nParams")
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121 series['params'] = SharedParamsStatisticsWrapper(
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122 new_group_name="params",
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123 base_group="/",
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124 arrays_names=['W','b_h','b_x'],
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125 hdf5_file=h5f,
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126 index_names=('epoch','minibatch'),
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127 other_targets=[params_stdout])
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128
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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129 self.series = series
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130
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131 def train(self):
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132 num_minibatches = len(self.mnist.train.x) / self.minibatch_size
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133
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134 print_every = 1000
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135 visualize_every = 5000
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136 gibbs_steps_from_random = 1000
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137
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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138 for epoch in xrange(self.num_epochs):
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139 for mb_index in xrange(num_minibatches):
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140 mb_x = self.mnist.train.x \
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141 [mb_index : mb_index+self.minibatch_size]
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142 mb_x = mb_x.reshape((self.minibatch_size, 1, 28, 28))
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143
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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144 #E_h = crbm.E_h_given_x_func(mb_x)
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145 #print "Shape of E_h", E_h.shape
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146
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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147 cd_return = self.crbm.CD_step(mb_x)
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148 sp_return = self.crbm.sparsity_step(mb_x)
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149
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150 self.series['cd'].append( \
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151 (epoch, mb_index), cd_return)
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diff changeset
152 self.series['sparsity'].append( \
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153 (epoch, mb_index), sp_return)
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154
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155 total_idx = epoch*num_minibatches + mb_index
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156
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diff changeset
157 if (total_idx+1) % REDUCE_EVERY == 0:
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diff changeset
158 self.series['params'].append( \
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diff changeset
159 (epoch, mb_index), self.crbm.params)
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160
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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diff changeset
161 if total_idx % visualize_every == 0:
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diff changeset
162 self.visualize_gibbs_result(\
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diff changeset
163 mb_x, gibbs_steps_from_random)
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164 self.visualize_gibbs_result(mb_x, 1)
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165 self.visualize_filters()
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166
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167 def visualize_gibbs_result(self, start_x, gibbs_steps):
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168 # Run minibatch_size chains for gibbs_steps
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169 x_samples = None
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diff changeset
170 if not start_x is None:
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171 x_samples = self.crbm.gibbs_samples_from(start_x, gibbs_steps)
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172 else:
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173 x_samples = self.crbm.random_gibbs_samples(gibbs_steps)
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174 x_samples = x_samples.reshape((self.minibatch_size, 28*28))
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175
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176 tile = tile_raster_images(x_samples, self.image_size,
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177 (1, self.minibatch_size), output_pixel_vals=True)
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178
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179 filepath = os.path.join(IMAGE_OUTPUT_DIR,
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180 filename_from_time("gibbs"))
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181 img = Image.fromarray(tile)
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182 img.save(filepath)
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183
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184 print "Result of running Gibbs", \
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185 gibbs_steps, "times outputed to", filepath
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186
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187 def visualize_filters(self):
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188 cp = self.cp
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189
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190 # filter size
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191 fsz = (cp.height_filters, cp.width_filters)
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192 tile_shape = (cp.num_filters, cp.num_input_planes)
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193
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diff changeset
194 filters_flattened = self.crbm.W.value.reshape(
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diff changeset
195 (tile_shape[0]*tile_shape[1],
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diff changeset
196 fsz[0]*fsz[1]))
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diff changeset
197
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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diff changeset
198 tile = tile_raster_images(filters_flattened, fsz,
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diff changeset
199 tile_shape, output_pixel_vals=True)
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diff changeset
200
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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diff changeset
201 filepath = os.path.join(IMAGE_OUTPUT_DIR,
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diff changeset
202 filename_from_time("filters"))
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diff changeset
203 img = Image.fromarray(tile)
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diff changeset
204 img.save(filepath)
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diff changeset
205
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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diff changeset
206 print "Filters (as images) outputed to", filepath
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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diff changeset
207 print "b_h is", self.crbm.b_h.value
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diff changeset
208
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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diff changeset
209
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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diff changeset
210
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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diff changeset
211
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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diff changeset
212 if __name__ == '__main__':
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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diff changeset
213 mc = MnistCrbm()
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diff changeset
214 mc.train()
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diff changeset
215