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annotate pylearn/algorithms/tests/test_mcRBM.py @ 1273:7bb5dd98e671

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mcRBM - added hack to main script to pickle final model and sampler
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 08 Sep 2010 13:05:02 -0400
parents ba25c6e4f55d
children 1817485d586d
rev   line source
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d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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1 from pylearn.algorithms.mcRBM import *
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2
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7bb5dd98e671 mcRBM - added hack to main script to pickle final model and sampler
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parents: 1272
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3 def test_reproduce_ranzato_hinton_2010(dataset='MAR', as_unittest=True, n_train_iters=5000):
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4 dataset='MAR'
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James Bergstra <bergstrj@iro.umontreal.ca>
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5 if dataset == 'MAR':
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James Bergstra <bergstrj@iro.umontreal.ca>
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6 n_vis=105
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James Bergstra <bergstrj@iro.umontreal.ca>
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7 n_patches=10240
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James Bergstra <bergstrj@iro.umontreal.ca>
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8 else:
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James Bergstra <bergstrj@iro.umontreal.ca>
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9 R,C= 16,16 # the size of image patches
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James Bergstra <bergstrj@iro.umontreal.ca>
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10 n_vis=R*C
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James Bergstra <bergstrj@iro.umontreal.ca>
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11 n_patches=100000
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James Bergstra <bergstrj@iro.umontreal.ca>
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12
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James Bergstra <bergstrj@iro.umontreal.ca>
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13 n_burnin_steps=10000
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14
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15
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16 l1_penalty=1e-3
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James Bergstra <bergstrj@iro.umontreal.ca>
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17 no_l1_epochs = 10
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James Bergstra <bergstrj@iro.umontreal.ca>
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18 effective_l1_penalty=0.0
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James Bergstra <bergstrj@iro.umontreal.ca>
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19
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James Bergstra <bergstrj@iro.umontreal.ca>
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20 epoch_size=n_patches
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James Bergstra <bergstrj@iro.umontreal.ca>
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21 batchsize = 128
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James Bergstra <bergstrj@iro.umontreal.ca>
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22 lr = 0.075 / batchsize
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James Bergstra <bergstrj@iro.umontreal.ca>
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23 s_lr = TT.scalar()
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James Bergstra <bergstrj@iro.umontreal.ca>
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24 n_K=256
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James Bergstra <bergstrj@iro.umontreal.ca>
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25 n_J=100
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James Bergstra <bergstrj@iro.umontreal.ca>
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26
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James Bergstra <bergstrj@iro.umontreal.ca>
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27 def l2(X):
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James Bergstra <bergstrj@iro.umontreal.ca>
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28 return numpy.sqrt((X**2).sum())
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29 if dataset == 'MAR':
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James Bergstra <bergstrj@iro.umontreal.ca>
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30 tile = pylearn.dataset_ops.image_patches.save_filters_of_ranzato_hinton_2010
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31 else:
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32 def tile(X, fname):
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James Bergstra <bergstrj@iro.umontreal.ca>
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33 _img = image_tiling.tile_raster_images(X,
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34 img_shape=(R,C),
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35 min_dynamic_range=1e-2)
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36 image_tiling.save_tiled_raster_images(_img, fname)
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37
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James Bergstra <bergstrj@iro.umontreal.ca>
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38 batch_idx = TT.iscalar()
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39
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40 if dataset == 'MAR':
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James Bergstra <bergstrj@iro.umontreal.ca>
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41 train_batch = pylearn.dataset_ops.image_patches.ranzato_hinton_2010_op(batch_idx * batchsize + np.arange(batchsize))
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42 else:
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43 train_batch = pylearn.dataset_ops.image_patches.image_patches(
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44 s_idx = (batch_idx * batchsize + np.arange(batchsize)),
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45 dims = (n_patches,R,C),
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James Bergstra <bergstrj@iro.umontreal.ca>
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46 center=True,
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James Bergstra <bergstrj@iro.umontreal.ca>
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47 unitvar=True,
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James Bergstra <bergstrj@iro.umontreal.ca>
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48 dtype=floatX,
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James Bergstra <bergstrj@iro.umontreal.ca>
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49 rasterized=True)
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50
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51 if not as_unittest:
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James Bergstra <bergstrj@iro.umontreal.ca>
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52 imgs_fn = function([batch_idx], outputs=train_batch)
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53
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54 trainer = mcRBMTrainer.alloc(
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55 mcRBM.alloc(n_I=n_vis, n_K=n_K, n_J=n_J),
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56 train_batch,
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57 batchsize, l1_penalty=TT.scalar())
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58 rbm=trainer.rbm
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59 smplr = trainer.sampler
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60
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61 grads = trainer.contrastive_grads()
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62 learn_fn = function([batch_idx, trainer.l1_penalty],
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63 outputs=[grads[0].norm(2), grads[0].norm(2), grads[1].norm(2)],
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64 updates=trainer.cd_updates())
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65
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James Bergstra <bergstrj@iro.umontreal.ca>
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66 print "Learning..."
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James Bergstra <bergstrj@iro.umontreal.ca>
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67 last_epoch = -1
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James Bergstra <bergstrj@iro.umontreal.ca>
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68 for jj in xrange(n_train_iters):
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69 epoch = jj*batchsize / epoch_size
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70
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James Bergstra <bergstrj@iro.umontreal.ca>
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71 print_jj = epoch != last_epoch
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James Bergstra <bergstrj@iro.umontreal.ca>
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72 last_epoch = epoch
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James Bergstra <bergstrj@iro.umontreal.ca>
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73
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James Bergstra <bergstrj@iro.umontreal.ca>
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74 if as_unittest and epoch == 5:
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James Bergstra <bergstrj@iro.umontreal.ca>
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75 U = rbm.U.value
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James Bergstra <bergstrj@iro.umontreal.ca>
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76 W = rbm.W.value
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James Bergstra <bergstrj@iro.umontreal.ca>
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77 def allclose(a,b):
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James Bergstra <bergstrj@iro.umontreal.ca>
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78 return numpy.allclose(a,b,rtol=1.01,atol=1e-3)
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James Bergstra <bergstrj@iro.umontreal.ca>
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79 print ""
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James Bergstra <bergstrj@iro.umontreal.ca>
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80 print "--------------"
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James Bergstra <bergstrj@iro.umontreal.ca>
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81 print "assert allclose(l2(U), %f)"%l2(U)
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James Bergstra <bergstrj@iro.umontreal.ca>
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82 print "assert allclose(l2(W), %f)"%l2(W)
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James Bergstra <bergstrj@iro.umontreal.ca>
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83 print "assert allclose(U.min(), %f)"%U.min()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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84 print "assert allclose(U.max(), %f)"%U.max()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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85 print "assert allclose(W.min(),%f)"%W.min()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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86 print "assert allclose(W.max(), %f)"%W.max()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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87 print "--------------"
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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88
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James Bergstra <bergstrj@iro.umontreal.ca>
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89 assert allclose(l2(U), 21.351664)
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James Bergstra <bergstrj@iro.umontreal.ca>
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90 assert allclose(l2(W), 6.275828)
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James Bergstra <bergstrj@iro.umontreal.ca>
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91 assert allclose(U.min(), -1.176703)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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92 assert allclose(U.max(), 0.859802)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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93 assert allclose(W.min(),-0.223128)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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94 assert allclose(W.max(), 0.227558 )
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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95
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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96 break
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James Bergstra <bergstrj@iro.umontreal.ca>
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97
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James Bergstra <bergstrj@iro.umontreal.ca>
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98 if print_jj:
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James Bergstra <bergstrj@iro.umontreal.ca>
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99 if not as_unittest:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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100 tile(imgs_fn(jj), "imgs_%06i.png"%jj)
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101 tile(smplr.positions.value, "sample_%06i.png"%jj)
1000
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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102 tile(rbm.U.value.T, "U_%06i.png"%jj)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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103 tile(rbm.W.value.T, "W_%06i.png"%jj)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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104
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James Bergstra <bergstrj@iro.umontreal.ca>
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105 print 'saving samples', jj, 'epoch', jj/(epoch_size/batchsize)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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106
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James Bergstra <bergstrj@iro.umontreal.ca>
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107 print 'l2(U)', l2(rbm.U.value),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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108 print 'l2(W)', l2(rbm.W.value)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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109
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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110 print 'U min max', rbm.U.value.min(), rbm.U.value.max(),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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111 print 'W min max', rbm.W.value.min(), rbm.W.value.max(),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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112 print 'a min max', rbm.a.value.min(), rbm.a.value.max(),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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113 print 'b min max', rbm.b.value.min(), rbm.b.value.max(),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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114 print 'c min max', rbm.c.value.min(), rbm.c.value.max()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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115
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116 print 'parts min', smplr.positions.value.min(),
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117 print 'max',smplr.positions.value.max(),
1000
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James Bergstra <bergstrj@iro.umontreal.ca>
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118 print 'HMC step', smplr.stepsize,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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119 print 'arate', smplr.avg_acceptance_rate
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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120
1267
075c193afd1b refactoring mcRBM
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1000
diff changeset
121
1272
ba25c6e4f55d mcRBM working with whole learning algo in theano
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1267
diff changeset
122 l2_of_Ugrad = learn_fn(jj, effective_l1_penalty)
1000
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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123
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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124 if print_jj:
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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125 print 'l2(U_grad)', float(l2_of_Ugrad[0]),
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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126 print 'l2(U_inc)', float(l2_of_Ugrad[1]),
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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127 print 'l2(W_inc)', float(l2_of_Ugrad[2]),
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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128 #print 'FE+', float(l2_of_Ugrad[2]),
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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129 #print 'FE+[0]', float(l2_of_Ugrad[3]),
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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130 #print 'FE+[1]', float(l2_of_Ugrad[4]),
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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131 #print 'FE+[2]', float(l2_of_Ugrad[5]),
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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132 #print 'FE+[3]', float(l2_of_Ugrad[6])
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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133
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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134 if jj == no_l1_epochs * epoch_size/batchsize:
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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135 print "Activating L1 weight decay"
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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136 effective_l1_penalty = 1e-3
d4a14c6c36e0 mcRBM - post code-review #1 with Guillaume
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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137
1273
7bb5dd98e671 mcRBM - added hack to main script to pickle final model and sampler
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1272
diff changeset
138 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
139 return rbm, smplr