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annotate pylearn/algorithms/tests/test_mcRBM.py @ 1286:8905186b176c

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test_mcRBM - added code to iterate over tinyimages
author James Bergstra <bergstrj@iro.umontreal.ca>
date Thu, 23 Sep 2010 19:12:52 -0400
parents 1817485d586d
children d6726417cf57
rev   line source
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1 from pylearn.algorithms.mcRBM import *
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2 import pylearn.datasets.cifar10
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3 import pylearn.dataset_ops.tinyimages
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4
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5 import pylearn.dataset_ops.cifar10
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6
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7 def _default_rbm_alloc(n_I, n_K=256, n_J=100):
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8 return mcRBM.alloc(n_I, n_K, n_J)
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9
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10 def _default_trainer_alloc(rbm, train_batch, batchsize, l1_penalty, l1_penalty_start):
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11 return mcRBMTrainer.alloc(rbm, train_batch, batchsize, l1_penalty=l1_penalty,
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12 l1_penalty_start=l1_penalty_start,persistent_chains=persistent_chains)
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13
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14
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15 def test_reproduce_ranzato_hinton_2010(dataset='MAR', as_unittest=True, n_train_iters=5000,
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16 rbm_alloc=_default_rbm_alloc, trainer_alloc=_default_trainer_alloc,
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17 lr_per_example=.075,
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18 l1_penalty=1e-3,
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19 l1_penalty_start=1000,
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20 persistent_chains=True,
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21 ):
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22
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23 batchsize = 128
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24
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25 if dataset == 'MAR':
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26 n_vis=105
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27 n_patches=10240
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28 epoch_size=n_patches
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29 elif dataset=='cifar10patches8x8':
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30 R,C= 8,8 # the size of image patches
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31 n_vis=96 # pca components
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32 epoch_size=batchsize*500
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33 n_patches=epoch_size*20
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34 elif dataset=='tinyimages_patches':
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35 R,C=8,8
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36 n_vis=81
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37 epoch_size=batchsize*500
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38 n_patches=epoch_size*20
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39 else:
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40 R,C= 16,16 # the size of image patches
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41 n_vis=R*C
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42 n_patches=100000
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43 epoch_size=n_patches
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44
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45 def l2(X):
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46 return numpy.sqrt((X**2).sum())
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47
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48 if dataset == 'MAR':
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49 tile = pylearn.dataset_ops.image_patches.save_filters_of_ranzato_hinton_2010
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50 elif dataset == 'cifar10patches8x8':
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51 def tile(X, fname):
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52 _img = pylearn.datasets.cifar10.tile_rasterized_examples(
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53 pylearn.preprocessing.pca.pca_whiten_inverse(
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54 pylearn.dataset_ops.cifar10.random_cifar_patches_pca(
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55 n_vis, None, 'float32', n_patches, R, C,),
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56 X),
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57 img_shape=(R,C))
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58 image_tiling.save_tiled_raster_images(_img, fname)
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59 elif dataset == 'tinyimages_patches':
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60 tile = pylearn.dataset_ops.tinyimages.save_filters
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61 else:
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62 def tile(X, fname):
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63 _img = image_tiling.tile_raster_images(X,
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64 img_shape=(R,C),
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65 min_dynamic_range=1e-2)
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66 image_tiling.save_tiled_raster_images(_img, fname)
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67
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68 batch_idx = TT.iscalar()
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69 batch_range =batch_idx * batchsize + np.arange(batchsize)
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70
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71 if dataset == 'MAR':
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72 train_batch = pylearn.dataset_ops.image_patches.ranzato_hinton_2010_op(batch_range)
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73 elif dataset == 'cifar10patches8x8':
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74 train_batch = pylearn.dataset_ops.cifar10.cifar10_patches(
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75 batch_range, 'train', n_patches=n_patches, patch_size=(R,C),
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76 pca_components=n_vis)
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77 elif dataset == 'tinyimages_patches':
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78 train_batch = pylearn.dataset_ops.tinyimages.tinydataset_op(batch_range)
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79 else:
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80 train_batch = pylearn.dataset_ops.image_patches.image_patches(
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81 s_idx = (batch_idx * batchsize + np.arange(batchsize)),
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82 dims = (n_patches,R,C),
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83 center=True,
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84 unitvar=True,
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85 dtype=floatX,
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86 rasterized=True)
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87
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88 if not as_unittest:
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89 imgs_fn = function([batch_idx], outputs=train_batch)
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90
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91 trainer = trainer_alloc(
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92 rbm_alloc(n_I=n_vis),
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93 train_batch,
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94 batchsize,
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95 initial_lr_per_example=lr_per_example,
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96 l1_penalty=l1_penalty,
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97 l1_penalty_start=l1_penalty_start,
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98 persistent_chains=persistent_chains)
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99 rbm=trainer.rbm
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100
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101 if persistent_chains:
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102 grads = trainer.contrastive_grads()
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103 learn_fn = function([batch_idx],
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104 outputs=[grads[0].norm(2), grads[0].norm(2), grads[1].norm(2)],
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105 updates=trainer.cd_updates())
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106 else:
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107 learn_fn = function([batch_idx], outputs=[], updates=trainer.cd_updates())
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108
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109 if persistent_chains:
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110 smplr = trainer.sampler
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111 else:
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112 smplr = trainer._last_cd1_sampler
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113
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114 if dataset == 'cifar10patches8x8':
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115 cPickle.dump(
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116 pylearn.dataset_ops.cifar10.random_cifar_patches_pca(
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117 n_vis, None, 'float32', n_patches, R, C,),
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118 open('test_mcRBM.pca.pkl','w'))
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119
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120 print "Learning..."
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121 last_epoch = -1
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
122 for jj in xrange(n_train_iters):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
123 epoch = jj*batchsize / epoch_size
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
124
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
125 print_jj = epoch != last_epoch
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
126 last_epoch = epoch
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
127
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
128 if as_unittest and epoch == 5:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
129 U = rbm.U.value
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
130 W = rbm.W.value
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
131 def allclose(a,b):
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parents:
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132 return numpy.allclose(a,b,rtol=1.01,atol=1e-3)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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133 print ""
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
134 print "--------------"
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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135 print "assert allclose(l2(U), %f)"%l2(U)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
136 print "assert allclose(l2(W), %f)"%l2(W)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
137 print "assert allclose(U.min(), %f)"%U.min()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
138 print "assert allclose(U.max(), %f)"%U.max()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
139 print "assert allclose(W.min(),%f)"%W.min()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
140 print "assert allclose(W.max(), %f)"%W.max()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
141 print "--------------"
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
142
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
143 assert allclose(l2(U), 21.351664)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
144 assert allclose(l2(W), 6.275828)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
145 assert allclose(U.min(), -1.176703)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
146 assert allclose(U.max(), 0.859802)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
147 assert allclose(W.min(),-0.223128)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
148 assert allclose(W.max(), 0.227558 )
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
149
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
150 break
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
151
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
152 if print_jj:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
153 if not as_unittest:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
154 tile(imgs_fn(jj), "imgs_%06i.png"%jj)
1284
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diff changeset
155 if persistent_chains:
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parents: 1273
diff changeset
156 tile(smplr.positions.value, "sample_%06i.png"%jj)
1000
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
157 tile(rbm.U.value.T, "U_%06i.png"%jj)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
158 tile(rbm.W.value.T, "W_%06i.png"%jj)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
159
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
160 print 'saving samples', jj, 'epoch', jj/(epoch_size/batchsize)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
161
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
162 print 'l2(U)', l2(rbm.U.value),
1284
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parents: 1273
diff changeset
163 print 'l2(W)', l2(rbm.W.value),
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parents: 1273
diff changeset
164 print 'l1_penalty',
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parents: 1273
diff changeset
165 try:
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parents: 1273
diff changeset
166 print trainer.effective_l1_penalty.value
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parents: 1273
diff changeset
167 except:
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diff changeset
168 print trainer.effective_l1_penalty
1000
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
169
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
170 print 'U min max', rbm.U.value.min(), rbm.U.value.max(),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
171 print 'W min max', rbm.W.value.min(), rbm.W.value.max(),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
172 print 'a min max', rbm.a.value.min(), rbm.a.value.max(),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
173 print 'b min max', rbm.b.value.min(), rbm.b.value.max(),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
174 print 'c min max', rbm.c.value.min(), rbm.c.value.max()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
175
1284
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diff changeset
176 if persistent_chains:
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parents: 1273
diff changeset
177 print 'parts min', smplr.positions.value.min(),
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parents: 1273
diff changeset
178 print 'max',smplr.positions.value.max(),
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parents: 1273
diff changeset
179 print 'HMC step', smplr.stepsize.value,
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parents: 1273
diff changeset
180 print 'arate', smplr.avg_acceptance_rate.value
1000
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parents:
diff changeset
181
1267
075c193afd1b refactoring mcRBM
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1000
diff changeset
182
1284
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diff changeset
183 l2_of_Ugrad = learn_fn(jj)
1000
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parents:
diff changeset
184
1284
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diff changeset
185 if persistent_chains and print_jj:
1000
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
186 print 'l2(U_grad)', float(l2_of_Ugrad[0]),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
187 print 'l2(U_inc)', float(l2_of_Ugrad[1]),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
188 print 'l2(W_inc)', float(l2_of_Ugrad[2]),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
189 #print 'FE+', float(l2_of_Ugrad[2]),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
190 #print 'FE+[0]', float(l2_of_Ugrad[3]),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
191 #print 'FE+[1]', float(l2_of_Ugrad[4]),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
192 #print 'FE+[2]', float(l2_of_Ugrad[5]),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
193 #print 'FE+[3]', float(l2_of_Ugrad[6])
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parents:
diff changeset
194
1284
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diff changeset
195 if not as_unittest:
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parents: 1273
diff changeset
196 if jj % 2000 == 0:
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
197 print ''
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parents: 1273
diff changeset
198 print 'Saving rbm...'
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
199 cPickle.dump(rbm, open('mcRBM.rbm.%06i.pkl'%jj, 'w'), -1)
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parents: 1273
diff changeset
200 if persistent_chains:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
201 print 'Saving sampler...'
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
202 cPickle.dump(smplr, open('mcRBM.smplr.%06i.pkl'%jj, 'w'), -1)
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parents: 1273
diff changeset
203
1000
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
204
1273
7bb5dd98e671 mcRBM - added hack to main script to pickle final model and sampler
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1272
diff changeset
205 if not as_unittest:
7bb5dd98e671 mcRBM - added hack to main script to pickle final model and sampler
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1272
diff changeset
206 return rbm, smplr
1284
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parents: 1273
diff changeset
207
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
208 import pickle as cPickle
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parents: 1273
diff changeset
209 #import cPickle
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parents: 1273
diff changeset
210 if __name__ == '__main__':
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
211 if 0:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
212 #learning 16 x 16 pinwheel filters from official cifar patches (MAR)
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parents: 1273
diff changeset
213 rbm,smplr = test_reproduce_ranzato_hinton_2010(
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
214 as_unittest=False,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
215 n_train_iters=5000,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
216 rbm_alloc=lambda n_I : mcRBM_withP.alloc_topo_P(n_I, n_J=81),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
217 trainer_alloc=mcRBMTrainer.alloc_for_P,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
218 dataset='MAR'
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
219 )
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
220
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
221 if 1:
1286
8905186b176c test_mcRBM - added code to iterate over tinyimages
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1284
diff changeset
222 # pretraining settings
1284
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parents: 1273
diff changeset
223 rbm,smplr = test_reproduce_ranzato_hinton_2010(
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
224 as_unittest=False,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
225 n_train_iters=60000,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
226 rbm_alloc=lambda n_I : mcRBM_withP.alloc_topo_P(n_I, n_J=81),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
227 trainer_alloc=mcRBMTrainer.alloc_for_P,
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parents: 1273
diff changeset
228 lr_per_example=0.05,
1286
8905186b176c test_mcRBM - added code to iterate over tinyimages
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1284
diff changeset
229 dataset='tinyimages_patches',
1284
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parents: 1273
diff changeset
230 l1_penalty=1e-3,
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
231 l1_penalty_start=30000,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
232 #l1_penalty_start=350, #DEBUG
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
233 persistent_chains=False
1817485d586d mcRBM - many changes incl. adding support for pooling matrix
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1273
diff changeset
234 )