Mercurial > ift6266

annotate deep/crbm/mnist_crbm.py @ 340:523e7b87c521

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Corrected a few bugs, no new features. Supposedly ready to run on cluster.
author fsavard
date Sun, 18 Apr 2010 11:39:24 -0400
parents ffbf0e41bcee
children b25ad1670ff7
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
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5 # do this before importing custom modules
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6 from mnist_config import *
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7
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8 if not (len(sys.argv) > 1 and sys.argv[1] in \
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9 ('test_jobman_entrypoint', 'run_local')):
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10 # in those cases don't use isolated code, use dev code
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11 isolate_code()
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12
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13 import os, os.path
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14
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15 import numpy as N
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16
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17 import theano
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18 import theano.tensor as T
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19
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20 from crbm import CRBM, ConvolutionParams
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21
340
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22 import pylearn, pylearn.version
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23 from pylearn.datasets import MNIST
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24 from pylearn.io.image_tiling import tile_raster_images
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25
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26 import Image
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27
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28 from pylearn.io.seriestables import *
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29 import tables
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30
340
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31 import ift6266
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32
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33 import utils
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34
340
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35 if not os.path.exists(IMAGE_OUTPUT_DIR):
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36 os.mkdir(IMAGE_OUTPUT_DIR)
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37 elif os.path.isfile(IMAGE_OUTPUT_DIR):
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38 print "IMAGE_OUTPUT_DIR is not a directory!"
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39 sys.exit(1)
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40
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41 #def filename_from_time(suffix):
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42 # import datetime
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43 # return str(datetime.datetime.now()) + suffix + ".png"
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44
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45 def jobman_entrypoint(state, channel):
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46 # record mercurial versions of each package
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47 pylearn.version.record_versions(state,[theano,ift6266,pylearn])
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48 channel.save()
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49
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50 crbm = MnistCrbm(state)
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51 crbm.train()
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52
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53 return channel.COMPLETE
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54
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55 class MnistCrbm(object):
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56 def __init__(self, state):
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57 self.state = state
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58
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59 if TEST_CONFIG:
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60 self.mnist = MNIST.first_1k()
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61 print "Test config, so loaded MNIST first 1000"
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62 else:
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63 self.mnist = MNIST.full()#first_10k()
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64 print "Loaded MNIST full"
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65
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66 self.cp = ConvolutionParams( \
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67 num_filters=state.num_filters,
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68 num_input_planes=1,
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69 height_filters=state.filter_size,
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70 width_filters=state.filter_size)
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71
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72 self.image_size = (28,28)
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73
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74 self.minibatch_size = state.minibatch_size
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75
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76 self.lr = state.learning_rate
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77 self.sparsity_lambda = state.sparsity_lambda
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78 # about 1/num_filters, so only one filter active at a time
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79 # 40 * 0.05 = ~2 filters active for any given pixel
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80 self.sparsity_p = state.sparsity_p
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81
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82 self.crbm = CRBM( \
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83 minibatch_size=self.minibatch_size,
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84 image_size=self.image_size,
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85 conv_params=self.cp,
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86 learning_rate=self.lr,
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87 sparsity_lambda=self.sparsity_lambda,
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88 sparsity_p=self.sparsity_p)
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89
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90 self.num_epochs = state.num_epochs
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91
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92 self.init_series()
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93
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94 def init_series(self):
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95 series = {}
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96
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97 basedir = os.getcwd()
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98
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99 h5f = tables.openFile(os.path.join(basedir, "series.h5"), "w")
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100
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101 cd_series_names = self.crbm.cd_return_desc
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102 series['cd'] = \
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103 utils.get_accumulator_series_array( \
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104 h5f, 'cd', cd_series_names,
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105 REDUCE_EVERY,
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106 stdout_too=SERIES_STDOUT_TOO)
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107
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108 sparsity_series_names = self.crbm.sparsity_return_desc
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109 series['sparsity'] = \
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110 utils.get_accumulator_series_array( \
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111 h5f, 'sparsity', sparsity_series_names,
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112 REDUCE_EVERY,
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113 stdout_too=SERIES_STDOUT_TOO)
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114
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115 # so first we create the names for each table, based on
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116 # position of each param in the array
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117 params_stdout = []
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118 if SERIES_STDOUT_TOO:
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119 params_stdout = [StdoutAppendTarget()]
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120 series['params'] = SharedParamsStatisticsWrapper(
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121 new_group_name="params",
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122 base_group="/",
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123 arrays_names=['W','b_h','b_x'],
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124 hdf5_file=h5f,
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125 index_names=('epoch','minibatch'),
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126 other_targets=params_stdout)
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127
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128 self.series = series
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129
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130 def train(self):
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131 num_minibatches = len(self.mnist.train.x) / self.minibatch_size
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132
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133 for epoch in xrange(self.num_epochs):
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134 for mb_index in xrange(num_minibatches):
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135 mb_x = self.mnist.train.x \
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136 [mb_index : mb_index+self.minibatch_size]
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137 mb_x = mb_x.reshape((self.minibatch_size, 1, 28, 28))
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138
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139 #E_h = crbm.E_h_given_x_func(mb_x)
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140 #print "Shape of E_h", E_h.shape
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141
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142 cd_return = self.crbm.CD_step(mb_x)
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143 sp_return = self.crbm.sparsity_step(mb_x)
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144
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145 self.series['cd'].append( \
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146 (epoch, mb_index), cd_return)
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147 self.series['sparsity'].append( \
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148 (epoch, mb_index), sp_return)
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149
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150 total_idx = epoch*num_minibatches + mb_index
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151
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152 if (total_idx+1) % REDUCE_EVERY == 0:
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153 self.series['params'].append( \
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154 (epoch, mb_index), self.crbm.params)
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155
339
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156 if total_idx % VISUALIZE_EVERY == 0:
337
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157 self.visualize_gibbs_result(\
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158 mb_x, GIBBS_STEPS_IN_VIZ_CHAIN,
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159 "gibbs_chain_"+str(epoch)+"_"+str(mb_index))
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160 self.visualize_gibbs_result(mb_x, 1,
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161 "gibbs_1_"+str(epoch)+"_"+str(mb_index))
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162 self.visualize_filters(
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163 "filters_"+str(epoch)+"_"+str(mb_index))
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164 if TEST_CONFIG:
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165 # do a single epoch for cluster tests config
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166 break
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167
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168 if SAVE_PARAMS:
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169 utils.save_params(self.crbm.params, "params.pkl")
337
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170
339
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171 def visualize_gibbs_result(self, start_x, gibbs_steps, filename):
337
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172 # Run minibatch_size chains for gibbs_steps
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173 x_samples = None
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174 if not start_x is None:
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175 x_samples = self.crbm.gibbs_samples_from(start_x, gibbs_steps)
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176 else:
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177 x_samples = self.crbm.random_gibbs_samples(gibbs_steps)
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178 x_samples = x_samples.reshape((self.minibatch_size, 28*28))
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179
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180 tile = tile_raster_images(x_samples, self.image_size,
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181 (1, self.minibatch_size), output_pixel_vals=True)
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182
339
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183 filepath = os.path.join(IMAGE_OUTPUT_DIR, filename+".png")
337
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184 img = Image.fromarray(tile)
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185 img.save(filepath)
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186
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187 print "Result of running Gibbs", \
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188 gibbs_steps, "times outputed to", filepath
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189
339
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190 def visualize_filters(self, filename):
337
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191 cp = self.cp
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192
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193 # filter size
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194 fsz = (cp.height_filters, cp.width_filters)
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195 tile_shape = (cp.num_filters, cp.num_input_planes)
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196
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197 filters_flattened = self.crbm.W.value.reshape(
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198 (tile_shape[0]*tile_shape[1],
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199 fsz[0]*fsz[1]))
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200
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201 tile = tile_raster_images(filters_flattened, fsz,
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202 tile_shape, output_pixel_vals=True)
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203
339
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204 filepath = os.path.join(IMAGE_OUTPUT_DIR, filename+".png")
337
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205 img = Image.fromarray(tile)
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206 img.save(filepath)
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207
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208 print "Filters (as images) outputed to", filepath
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209
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210
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211
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212 if __name__ == '__main__':
339
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213 args = sys.argv[1:]
337
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214
339
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diff changeset
215 if len(args) == 0:
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216 print "Bad usage"
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217 elif args[0] == 'jobman_insert':
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218 utils.jobman_insert_job_vals(JOBDB, EXPERIMENT_PATH, JOB_VALS)
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219 elif args[0] == 'test_jobman_entrypoint':
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diff changeset
220 chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
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221 jobman_entrypoint(DEFAULT_STATE, chanmock)
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222 elif args[0] == 'run_default':
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223 mc = MnistCrbm(DEFAULT_STATE)
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224 mc.train()
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225
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226