Mercurial > ift6266
annotate deep/crbm/mnist_crbm.py @ 350:625c0c3fcbdb
Amelioration de l'efficacite de la sauvegarde des parametres
author | SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca> |
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date | Tue, 20 Apr 2010 16:38:13 -0400 |
parents | 8d116d4a7593 |
children | ffbf0e41bcee |
rev | line source |
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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 |
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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 |
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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 |
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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 |
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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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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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157 if (total_idx+1) % REDUCE_EVERY == 0: |
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158 self.series['params'].append( \ |
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159 (epoch, mb_index), self.crbm.params) |
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160 |
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161 if total_idx % visualize_every == 0: |
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162 self.visualize_gibbs_result(\ |
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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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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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194 filters_flattened = self.crbm.W.value.reshape( |
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195 (tile_shape[0]*tile_shape[1], |
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196 fsz[0]*fsz[1])) |
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197 |
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198 tile = tile_raster_images(filters_flattened, fsz, |
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199 tile_shape, output_pixel_vals=True) |
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200 |
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201 filepath = os.path.join(IMAGE_OUTPUT_DIR, |
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202 filename_from_time("filters")) |
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203 img = Image.fromarray(tile) |
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204 img.save(filepath) |
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205 |
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206 print "Filters (as images) outputed to", filepath |
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207 print "b_h is", self.crbm.b_h.value |
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208 |
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209 |
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210 |
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211 |
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212 if __name__ == '__main__': |
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213 mc = MnistCrbm() |
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214 mc.train() |
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215 |