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annotate pylearn/algorithms/tests/test_mcRBM.py @ 1524:9d21919e2332

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autopep8
author Frederic Bastien <nouiz@nouiz.org>
date Fri, 02 Nov 2012 13:02:18 -0400
parents 7f166d01bf8e
children 9c24a2bdbe90
rev   line source
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1 import sys
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2
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3 import numpy
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4 import theano
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5 from theano import tensor
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6
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7 from pylearn.algorithms.mcRBM import mcRBM, mcRBMTrainer, mcRBM_withP, l2
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8 #import pylearn.datasets.cifar10
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9 import pylearn.dataset_ops.cifar10
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10 from pylearn.shared.layers.logreg import LogisticRegression
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11 from pylearn.io import image_tiling
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12 import pylearn.dataset_ops.image_patches
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13
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14
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15 def _default_rbm_alloc(n_I, n_K=256, n_J=100):
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16 return mcRBM.alloc(n_I, n_K, n_J)
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17
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18
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19 def _default_trainer_alloc(rbm, train_batch, batchsize, initial_lr_per_example,
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20 l1_penalty, l1_penalty_start, persistent_chains):
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21 return mcRBMTrainer.alloc(rbm, train_batch, batchsize,
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22 l1_penalty=l1_penalty,
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23 l1_penalty_start=l1_penalty_start,
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24 persistent_chains=persistent_chains)
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25
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26
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27 def test_reproduce_ranzato_hinton_2010(
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28 dataset='MAR', as_unittest=True, n_train_iters=5000,
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29 rbm_alloc=_default_rbm_alloc, trainer_alloc=_default_trainer_alloc,
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30 lr_per_example=.075,
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31 l1_penalty=1e-3,
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32 l1_penalty_start=1000,
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33 persistent_chains=True,
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34 ):
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35
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36 batchsize = 128
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37
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38 if dataset == 'MAR':
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39 n_vis = 105
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40 n_patches = 10240
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41 epoch_size = n_patches
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42 elif dataset == 'cifar10patches8x8':
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43 R, C = 8, 8 # the size of image patches
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44 n_vis = 96 # pca components
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45 epoch_size = batchsize * 500
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46 n_patches = epoch_size * 20
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47 elif dataset == 'tinyimages_patches':
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48 R, C = 8, 8
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49 n_vis = 81
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50 epoch_size = batchsize * 500
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51 n_patches = epoch_size * 20
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52 else:
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53 R, C = 16, 16 # the size of image patches
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54 n_vis = R * C
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55 n_patches = 100000
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56 epoch_size = n_patches
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57
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58 def l2(X):
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59 return numpy.sqrt((X ** 2).sum())
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60
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61 if dataset == 'MAR':
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62 tile = pylearn.dataset_ops.image_patches.save_filters_of_ranzato_hinton_2010
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63 elif dataset == 'cifar10patches8x8':
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64 def tile(X, fname):
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65 _img = pylearn.datasets.cifar10.tile_rasterized_examples(
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66 pylearn.preprocessing.pca.pca_whiten_inverse(
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67 pylearn.dataset_ops.cifar10.random_cifar_patches_pca(
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68 n_vis, None, 'float32', n_patches, R, C,),
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69 X),
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70 img_shape=(R, C))
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71 image_tiling.save_tiled_raster_images(_img, fname)
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72 elif dataset == 'tinyimages_patches':
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73 tile = pylearn.dataset_ops.tinyimages.save_filters
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74 else:
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75 def tile(X, fname):
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76 _img = image_tiling.tile_raster_images(X,
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77 img_shape=(R, C),
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78 min_dynamic_range=1e-2)
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79 image_tiling.save_tiled_raster_images(_img, fname)
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80
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81 batch_idx = tensor.iscalar()
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82 batch_range = batch_idx * batchsize + numpy.arange(batchsize)
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83
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84 if dataset == 'MAR':
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85 train_batch = pylearn.dataset_ops.image_patches.ranzato_hinton_2010_op(batch_range)
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86 elif dataset == 'cifar10patches8x8':
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87 train_batch = pylearn.dataset_ops.cifar10.cifar10_patches(
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88 batch_range, 'train', n_patches=n_patches, patch_size=(R, C),
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89 pca_components=n_vis)
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90 elif dataset == 'tinyimages_patches':
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91 train_batch = pylearn.dataset_ops.tinyimages.tinydataset_op(batch_range)
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92 else:
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93 train_batch = pylearn.dataset_ops.image_patches.image_patches(
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94 s_idx=(batch_idx * batchsize + numpy.arange(batchsize)),
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95 dims=(n_patches, R, C),
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96 center=True,
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97 unitvar=True,
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98 dtype=theano.config.floatX,
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99 rasterized=True)
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100
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101 if not as_unittest:
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102 imgs_fn = theano.function([batch_idx], outputs=train_batch)
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103
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104 trainer = trainer_alloc(
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105 rbm_alloc(n_I=n_vis),
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106 train_batch,
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107 batchsize,
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108 initial_lr_per_example=lr_per_example,
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109 l1_penalty=l1_penalty,
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110 l1_penalty_start=l1_penalty_start,
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111 persistent_chains=persistent_chains)
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112 rbm = trainer.rbm
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113
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114 if persistent_chains:
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115 grads = trainer.contrastive_grads()
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116 learn_fn = theano.function([batch_idx],
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117 outputs=[grads[0].norm(2), grads[0].norm(2), grads[1].norm(2)],
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118 updates=trainer.cd_updates())
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119 else:
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120 learn_fn = theano.function([batch_idx], outputs=[],
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121 updates=trainer.cd_updates())
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122
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123 if persistent_chains:
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124 smplr = trainer.sampler
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125 else:
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126 smplr = trainer._last_cd1_sampler
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127
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128 if dataset == 'cifar10patches8x8':
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129 cPickle.dump(
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130 pylearn.dataset_ops.cifar10.random_cifar_patches_pca(
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131 n_vis, None, 'float32', n_patches, R, C,),
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132 open('test_mcRBM.pca.pkl', 'w'))
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133
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134 print "Learning..."
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James Bergstra <bergstrj@iro.umontreal.ca>
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135 last_epoch = -1
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136 for jj in xrange(n_train_iters):
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137 epoch = jj * batchsize / epoch_size
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138
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139 print_jj = epoch != last_epoch
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140 last_epoch = epoch
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141
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142 if as_unittest and epoch == 5:
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143 U = rbm.U.get_value(borrow=True)
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144 W = rbm.W.get_value(borrow=True)
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145
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146 def allclose(a, b):
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147 return numpy.allclose(a, b, rtol=1.01, atol=1e-3)
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148 print ""
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149 print "--------------"
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150 print "assert allclose(l2(U), %f)" % l2(U)
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151 print "assert allclose(l2(W), %f)" % l2(W)
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152 print "assert allclose(U.min(), %f)" % U.min()
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153 print "assert allclose(U.max(), %f)" % U.max()
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154 print "assert allclose(W.min(),%f)" % W.min()
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155 print "assert allclose(W.max(), %f)" % W.max()
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156 print "--------------"
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parents:
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157
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158 assert allclose(l2(U), 21.351664)
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parents:
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159 assert allclose(l2(W), 6.275828)
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160 assert allclose(U.min(), -1.176703)
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161 assert allclose(U.max(), 0.859802)
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162 assert allclose(W.min(), -0.223128)
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163 assert allclose(W.max(), 0.227558)
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parents:
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164
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parents:
diff changeset
165 break
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parents:
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166
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167 if print_jj:
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168 if not as_unittest:
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169 tile(imgs_fn(jj), "imgs_%06i.png" % jj)
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170 if persistent_chains:
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171 tile(smplr.positions.value, "sample_%06i.png" % jj)
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172 tile(rbm.U.value.T, "U_%06i.png" % jj)
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173 tile(rbm.W.value.T, "W_%06i.png" % jj)
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174
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175 print 'saving samples', jj, 'epoch', jj / (epoch_size / batchsize)
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176
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177 print 'l2(U)', l2(rbm.U.get_value(borrow=True)),
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178 print 'l2(W)', l2(rbm.W.get_value(borrow=True)),
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179 print 'l1_penalty',
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180 try:
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181 print trainer.effective_l1_penalty.get_value(borrow=True)
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182 except:
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183 print trainer.effective_l1_penalty
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184
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185 print 'U min max', rbm.U.get_value(borrow=True).min(), rbm.U.get_value(borrow=True).max(),
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186 print 'W min max', rbm.W.get_value(borrow=True).min(), rbm.W.get_value(borrow=True).max(),
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187 print 'a min max', rbm.a.get_value(borrow=True).min(), rbm.a.get_value(borrow=True).max(),
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188 print 'b min max', rbm.b.get_value(borrow=True).min(), rbm.b.get_value(borrow=True).max(),
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189 print 'c min max', rbm.c.get_value(borrow=True).min(), rbm.c.get_value(borrow=True).max()
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190
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191 if persistent_chains:
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192 print 'parts min', smplr.positions.get_value(borrow=True).min(),
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193 print 'max', smplr.positions.get_value(borrow=True).max(),
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194 print 'HMC step', smplr.stepsize.get_value(borrow=True),
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195 print 'arate', smplr.avg_acceptance_rate.get_value(borrow=True)
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196
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197 l2_of_Ugrad = learn_fn(jj)
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198
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199 if persistent_chains and print_jj:
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200 print 'l2(U_grad)', float(l2_of_Ugrad[0]),
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201 print 'l2(U_inc)', float(l2_of_Ugrad[1]),
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parents:
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202 print 'l2(W_inc)', float(l2_of_Ugrad[2]),
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parents:
diff changeset
203 #print 'FE+', float(l2_of_Ugrad[2]),
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parents:
diff changeset
204 #print 'FE+[0]', float(l2_of_Ugrad[3]),
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205 #print 'FE+[1]', float(l2_of_Ugrad[4]),
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parents:
diff changeset
206 #print 'FE+[2]', float(l2_of_Ugrad[5]),
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parents:
diff changeset
207 #print 'FE+[3]', float(l2_of_Ugrad[6])
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parents:
diff changeset
208
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209 if not as_unittest:
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diff changeset
210 if jj % 2000 == 0:
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211 print ''
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212 print 'Saving rbm...'
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213 cPickle.dump(rbm, open('mcRBM.rbm.%06i.pkl' % jj, 'w'), -1)
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diff changeset
214 if persistent_chains:
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diff changeset
215 print 'Saving sampler...'
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diff changeset
216 cPickle.dump(smplr, open(
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diff changeset
217 'mcRBM.smplr.%06i.pkl' % jj, 'w'), -1)
1000
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parents:
diff changeset
218
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diff changeset
219 if not as_unittest:
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diff changeset
220 return rbm, smplr
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diff changeset
221
1323
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diff changeset
222
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diff changeset
223 def run_classif_experiment(checkpoint):
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diff changeset
224
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225 R, C = 8, 8
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226 n_vis = 74
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diff changeset
227 # PRETRAIN
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228 #
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229 # extract 1 million 8x8 patches from TinyImages
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diff changeset
230 # pre-process them the right way
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231 # find 74 dims of PCA
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diff changeset
232 # filter patches through PCA
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diff changeset
233 whitened_patches, pca_dct = pylearn.dataset_ops.tinyimages.main(
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parents: 1512
diff changeset
234 n_imgs=100000, max_components=n_vis, seed=234)
1323
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diff changeset
235 #
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diff changeset
236 # Set up mcRBM Trainer
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diff changeset
237 # Initialize P using topological 3x3 overlapping patches thing
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238 # start learning P matrix after 2 passes through dataset
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239 #
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240 rbm_filename = 'mcRBM.rbm.%06i.pkl' % 46000
1323
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parents: 1286
diff changeset
241 try:
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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parents: 1286
diff changeset
242 open(rbm_filename).close()
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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diff changeset
243 load_mcrbm = True
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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parents: 1286
diff changeset
244 except:
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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parents: 1286
diff changeset
245 load_mcrbm = False
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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parents: 1286
diff changeset
246
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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diff changeset
247 if load_mcrbm:
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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parents: 1286
diff changeset
248 print 'loading mcRBM from file', rbm_filename
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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parents: 1286
diff changeset
249 rbm = cPickle.load(open(rbm_filename))
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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parents: 1286
diff changeset
250
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
251 else:
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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diff changeset
252 print "Training mcRBM"
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diff changeset
253 batchsize = 128
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diff changeset
254 epoch_size = len(whitened_patches)
1323
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diff changeset
255 tile = pylearn.dataset_ops.tinyimages.save_filters
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256 train_batch = theano.tensor.matrix()
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257 trainer = mcRBMTrainer.alloc_for_P(
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258 rbm=mcRBM_withP.alloc_topo_P(n_I=n_vis, n_J=81),
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259 visible_batch=train_batch,
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260 batchsize=batchsize,
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261 initial_lr_per_example=0.05,
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262 l1_penalty=1e-3,
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263 l1_penalty_start=sys.maxint,
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264 p_training_start=2 * epoch_size // batchsize,
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265 persistent_chains=False)
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266 rbm = trainer.rbm
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267 learn_fn = theano.function([train_batch], outputs=[],
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268 updates=trainer.cd_updates())
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269 smplr = trainer._last_cd1_sampler
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270
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271 ii = 0
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272 for i_epoch in range(6):
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273 for i_batch in xrange(epoch_size // batchsize):
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274 batch_vals = whitened_patches[i_batch *
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275 batchsize:(i_batch + 1) * batchsize]
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276 learn_fn(batch_vals)
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277
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278 if (ii % 1000) == 0:
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279 #tile(imgs_fn(ii), "imgs_%06i.png"%ii)
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280 tile(rbm.U.value.T, "U_%06i.png" % ii)
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281 tile(rbm.W.value.T, "W_%06i.png" % ii)
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282
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283 print 'saving samples', ii, 'epoch', i_epoch, i_batch
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284
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285 print 'l2(U)', l2(rbm.U.get_value(borrow=True)),
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286 print 'l2(W)', l2(rbm.W.get_value(borrow=True)),
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287 print 'l1_penalty',
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288 try:
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289 print trainer.effective_l1_penalty.get_value(borrow=True)
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290 except:
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291 print trainer.effective_l1_penalty
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292
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293 print 'U min max', rbm.U.get_value(
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294 borrow=True).min(), rbm.U.get_value(borrow=True).max(),
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295 print 'W min max', rbm.W.get_value(
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296 borrow=True).min(), rbm.W.get_value(borrow=True).max(),
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297 print 'a min max', rbm.a.get_value(
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298 borrow=True).min(), rbm.a.get_value(borrow=True).max(),
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299 print 'b min max', rbm.b.get_value(
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300 borrow=True).min(), rbm.b.get_value(borrow=True).max(),
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301 print 'c min max', rbm.c.get_value(
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302 borrow=True).min(), rbm.c.get_value(borrow=True).max()
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303
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304 print 'HMC step', smplr.stepsize.get_value(borrow=True),
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305 print 'arate', smplr.avg_acceptance_rate.get_value(borrow=True)
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306 print 'P min max', rbm.P.get_value(
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307 borrow=True).min(), rbm.P.get_value(borrow=True).max(),
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308 print 'P_lr', trainer.p_lr.get_value(borrow=True)
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309 print ''
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310 print 'Saving rbm...'
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311 cPickle.dump(rbm, open('mcRBM.rbm.%06i.pkl' % ii, 'w'), -1)
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312
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313 ii += 1
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314
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315 # extract convolutional features from the CIFAR10 data
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316 feat_filename = 'mcrbm_features.npy'
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317 feat_filename = 'cifar10.features.46000.npy'
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318 try:
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319 open(feat_filename).close()
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320 load_features = True
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321 except:
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322 load_features = False
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323
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324 if load_features:
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325 print 'Loading features from', feat_filename
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326 all_features = numpy.load(feat_filename, mmap_mode='r')
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327 else:
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328 batchsize = 100
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329 feat_idx = tensor.lscalar()
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330 feat_idx_range = feat_idx * batchsize + tensor.arange(batchsize)
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diff changeset
331 train_batch_x, train_batch_y = pylearn.dataset_ops.cifar10.cifar10(
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diff changeset
332 feat_idx_range,
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333 split='all',
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334 dtype='uint8',
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335 rasterized=False,
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336 color='rgb')
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337
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338 WINDOW_SIZE = 8
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339 WINDOW_STRIDE = 4
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diff changeset
340
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341 # put these into shared vars because support for big matrix
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342 # constants is bad, (comparing them is slow)
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343 pca_eigvecs = theano.shared(pca_dct['eig_vecs'].astype('float32'))
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344 pca_eigvals = theano.shared(pca_dct['eig_vals'].astype('float32'))
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345 pca_mean = theano.shared(pca_dct['mean'].astype('float32'))
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346
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diff changeset
347 def theano_pca_whiten(X):
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348 #copying preprepcessing.pca.pca_whiten
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349 return tensor.true_div(
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350 tensor.dot(X - pca_mean, pca_eigvecs),
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351 tensor.sqrt(pca_eigvals) + 1e-8)
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diff changeset
352
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353 h_list = []
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354 g_list = []
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355 for r_offset in range(0, 32 - WINDOW_SIZE + 1, WINDOW_STRIDE):
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356 for c_offset in range(0, 32 - WINDOW_SIZE + 1, WINDOW_STRIDE):
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diff changeset
357 window = train_batch_x[:, r_offset:r_offset + WINDOW_SIZE,
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358 c_offset:c_offset + WINDOW_SIZE, :]
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diff changeset
359 assert window.dtype == 'uint8'
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diff changeset
360
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diff changeset
361 #rasterize the patches
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diff changeset
362 raster_window = tensor.flatten(tensor.cast(
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diff changeset
363 window, 'float32'), 2)
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diff changeset
364
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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diff changeset
365 #subtract off the mean of each image
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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diff changeset
366 raster_window = raster_window - raster_window.mean(axis=1).reshape((batchsize,1))
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diff changeset
367
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diff changeset
368 h, g = rbm.expected_h_g_given_v(
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diff changeset
369 theano_pca_whiten(raster_window))
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diff changeset
370
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diff changeset
371 h_list.append(h)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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diff changeset
372 g_list.append(g)
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diff changeset
373
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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diff changeset
374 hg = tensor.concatenate(h_list + g_list, axis=1)
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diff changeset
375
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diff changeset
376 feat_fn = theano.function([feat_idx], hg)
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diff changeset
377 features = numpy.empty((60000, 11025), dtype='float32')
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diff changeset
378 for i in xrange(60000 // batchsize):
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diff changeset
379 if i % 100 == 0:
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380 print("feature batch %i" % i)
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381 features[i * batchsize:(i + 1) * batchsize] = feat_fn(i)
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diff changeset
382
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383 print("saving features to %s" % feat_filename)
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diff changeset
384 numpy.save(feat_filename, features)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
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diff changeset
385 all_features = features
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
386 del features
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
387
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
388 # CLASSIFY FEATURES
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
389 if 0:
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
390 # nothing to load
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
391 pass
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
392 else:
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
393 batchsize = 100
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
394
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
395 if feat_filename.startswith('cifar'):
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
396 learnrate = 0.002
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
397 l1_regularization = 0.004
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
398 anneal_epoch = 100
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
399 n_epochs = 500
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
400 else:
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
401 learnrate = 0.005
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
402 l1_regularization = 0.004
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
403 n_epochs = 100
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
404 anneal_epoch = 20
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
405
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
406 x_i = tensor.matrix()
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
407 y_i = tensor.ivector()
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
408 lr = tensor.scalar()
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
409 #l1_regularization = float(sys.argv[1]) #1.e-3
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
410 #l2_regularization = float(sys.argv[2]) #1.e-3*0
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
411
1509
b709f6b53b17 auto fix white space.
Frederic Bastien <nouiz@nouiz.org>
parents: 1508
diff changeset
412 feature_logreg = LogisticRegression.new(x_i,
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
413 n_in=11025, n_out=10,
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
414 dtype=x_i.dtype)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
415
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
416 # marc'aurelio does this...
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
417 feature_logreg.w.value = numpy.random.RandomState(44).randn(11025,
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
418 10) * .02
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
419
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
420 traincost = feature_logreg.nll(y_i).sum()
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
421 traincost = traincost + abs(feature_logreg.w).sum() * l1_regularization
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
422 #traincost = traincost + (feature_logreg.w**2).sum() * l2_regularization
1509
b709f6b53b17 auto fix white space.
Frederic Bastien <nouiz@nouiz.org>
parents: 1508
diff changeset
423 train_logreg_fn = theano.function([x_i, y_i, lr],
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
424 [feature_logreg.nll(y_i).mean(),
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
425 feature_logreg.errors(y_i).mean()],
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
426 updates=pylearn.gd.sgd.sgd_updates(
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
427 params=feature_logreg.params,
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
428 grads=tensor.grad(traincost, feature_logreg.params),
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
429 stepsizes=[lr, lr / 10.]))
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
430
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
431 all_labels = pylearn.dataset_ops.cifar10.all_data_labels('uint8')[1]
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
432 pylearn.dataset_ops.cifar10.all_data_labels.forget()
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
433 # clear memo cache
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
434 assert len(all_labels) == 60000
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
435 if 0:
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
436 print "Using validation set"
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
437 train_labels = all_labels[:40000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
438 valid_labels = all_labels[40000:50000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
439 test_labels = all_labels[50000:60000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
440 train_features = all_features[:40000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
441 valid_features = all_features[40000:50000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
442 test_features = all_features[50000:60000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
443 else:
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
444 print "NOT USING validation set"
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
445 train_labels = all_labels[:50000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
446 valid_labels = None
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
447 test_labels = all_labels[50000:60000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
448 train_features = all_features[:50000]
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
449 valid_features = None
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
450 test_features = all_features[50000:60000]
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
451
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
452 if 1:
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
453 print "Computing mean and std.dev"
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
454 train_mean = train_features.mean(axis=0)
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
455 train_std = train_features.std(axis=0) + 1e-4
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
456 preproc = lambda x: (x - train_mean) / (0.1 + train_std)
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
457 else:
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
458 print "Not centering data"
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
459 preproc = lambda x: x
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
460
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
461 for epoch in xrange(n_epochs):
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
462 print 'epoch', epoch
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
463 # validate
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
464 # Marc'Aurelio, you crazy!!
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
465 # the division by batchsize is done in the cost function
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
466 e_lr = learnrate / (batchsize * max(1.0, numpy.floor(
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
467 max(1., epoch / float(anneal_epoch)) - 2)))
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
468
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
469 if valid_features is not None:
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
470 l01s = []
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
471 nlls = []
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
472 for i in xrange(10000 / batchsize):
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
473 x_i = valid_features[i * batchsize:(i + 1) * batchsize]
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
474 y_i = valid_labels[i * batchsize:(i + 1) * batchsize]
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
475
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
476 #lr=0.0 -> no learning, safe for validation set
1509
b709f6b53b17 auto fix white space.
Frederic Bastien <nouiz@nouiz.org>
parents: 1508
diff changeset
477 nll, l01 = train_logreg_fn(preproc(x_i), y_i, 0.0)
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
478 nlls.append(nll)
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
479 l01s.append(l01)
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
480 print 'validate log_reg', numpy.mean(nlls), numpy.mean(l01s)
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
481
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
482 # test
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
483
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
484 l01s = []
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
485 nlls = []
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
486 for i in xrange(len(test_features) // batchsize):
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
487 x_i = test_features[i * batchsize:(i + 1) * batchsize]
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
488 y_i = test_labels[i * batchsize:(i + 1) * batchsize]
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
489
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
490 #lr=0.0 -> no learning, safe for validation set
1509
b709f6b53b17 auto fix white space.
Frederic Bastien <nouiz@nouiz.org>
parents: 1508
diff changeset
491 nll, l01 = train_logreg_fn(preproc(x_i), y_i, 0.0)
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
492 nlls.append(nll)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
493 l01s.append(l01)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
494 print 'test log_reg', numpy.mean(nlls), numpy.mean(l01s)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
495
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
496 #train
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
497 l01s = []
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
498 nlls = []
1524
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
499 for i in xrange(len(train_features) // batchsize):
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
500 x_i = train_features[i * batchsize:(i + 1) * batchsize]
9d21919e2332 autopep8
Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
501 y_i = train_labels[i * batchsize:(i + 1) * batchsize]
1333
c7b2da4e2df6 modifs to test_mcRBM to reproduce mcRBM classif results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1323
diff changeset
502 nll, l01 = train_logreg_fn(preproc(x_i), y_i, e_lr)
1323
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
503 nlls.append(nll)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
504 l01s.append(l01)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
505 print 'train log_reg', numpy.mean(nlls), numpy.mean(l01s)
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
506
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
507
1284
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
508 import pickle as cPickle
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
509 #import cPickle
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
510 if __name__ == '__main__':
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Frederic Bastien <nouiz@nouiz.org>
parents: 1508
diff changeset
511 if 0:
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1817485d586d mcRBM - many changes incl. adding support for pooling matrix
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parents: 1273
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512 #learning 16 x 16 pinwheel filters from official cifar patches (MAR)
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parents: 1512
diff changeset
513 rbm, smplr = test_reproduce_ranzato_hinton_2010(
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parents: 1273
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514 as_unittest=False,
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
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parents: 1273
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515 n_train_iters=5000,
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Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
516 rbm_alloc=lambda n_I: mcRBM_withP.alloc_topo_P(n_I, n_J=81),
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parents: 1273
diff changeset
517 trainer_alloc=mcRBMTrainer.alloc_for_P,
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
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parents: 1273
diff changeset
518 dataset='MAR'
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
519 )
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
520
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
521 if 0:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1284
diff changeset
522 # pretraining settings
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Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
523 rbm, smplr = test_reproduce_ranzato_hinton_2010(
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
524 as_unittest=False,
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
525 n_train_iters=60000,
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Frederic Bastien <nouiz@nouiz.org>
parents: 1512
diff changeset
526 rbm_alloc=lambda n_I: mcRBM_withP.alloc_topo_P(n_I, n_J=81),
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parents: 1273
diff changeset
527 trainer_alloc=mcRBMTrainer.alloc_for_P,
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
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parents: 1273
diff changeset
528 lr_per_example=0.05,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1284
diff changeset
529 dataset='tinyimages_patches',
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parents: 1273
diff changeset
530 l1_penalty=1e-3,
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
531 l1_penalty_start=30000,
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
532 #l1_penalty_start=350, #DEBUG
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
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parents: 1273
diff changeset
533 persistent_chains=False
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
534 )
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
535
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
536 if 1:
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
537 def checkpoint():
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
538 return checkpoint
d6726417cf57 adding training script for test_mcRBM to reproduce classification results
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1286
diff changeset
539 run_classif_experiment(checkpoint=checkpoint)