Mercurial > pylearn
annotate sandbox/rbm/model.py @ 402:ffdd2c199f2a
* Added momentum.
* Added deterministic_reconstruction_error.
| author | Joseph Turian <turian@gmail.com> |
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| date | Wed, 09 Jul 2008 15:27:12 -0400 |
| parents | 217c8789284b |
| children | be4209cd568f |
| rev | line source |
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1 """ |
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2 The model for an autoassociator for sparse inputs, using Ronan Collobert + Jason |
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3 Weston's sampling trick (2008). |
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4 """ |
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5 |
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6 import parameters |
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7 |
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8 import globals |
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9 from globals import LR |
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10 |
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11 import numpy |
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12 from numpy import dot |
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13 import random |
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14 random.seed(globals.SEED) |
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15 |
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16 import pylearn.nnet_ops |
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17 import pylearn.sparse_instance |
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18 |
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19 def sigmoid(v): |
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20 """ |
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21 @todo: Move to pylearn.more_numpy |
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22 @todo: Fix to avoid floating point overflow. |
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23 """ |
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24 # if x < -30.0: return 0.0 |
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25 # if x > 30.0: return 1.0 |
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26 return 1.0 / (1.0 + numpy.exp(-v)) |
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27 |
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28 def sample(v): |
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29 """ |
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30 @todo: Move to pylearn.more_numpy |
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31 """ |
| 396 | 32 assert len(v.shape) == 2 |
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33 x = numpy.zeros(v.shape) |
| 396 | 34 for j in range(v.shape[0]): |
| 35 for i in range(v.shape[1]): | |
| 36 assert v[j][i] >= 0 and v[j][i] <= 1 | |
| 37 if random.random() < v[j][i]: x[j][i] = 1 | |
| 38 else: x[j][i] = 0 | |
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39 return x |
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40 |
| 398 | 41 def crossentropy(output, target): |
| 42 """ | |
| 43 Compute the crossentropy of binary output wrt binary target. | |
| 44 @note: We do not sum, crossentropy is computed by component. | |
| 45 @todo: Rewrite as a scalar, and then broadcast to tensor. | |
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46 @todo: Move to pylearn.more_numpy |
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47 @todo: Fix to avoid floating point overflow. |
| 398 | 48 """ |
| 49 return -(target * numpy.log(output) + (1 - target) * numpy.log(1 - output)) | |
| 50 | |
| 51 | |
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52 class Model: |
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53 """ |
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54 @todo: input dimensions should be stored here! not as a global. |
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parents:
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55 """ |
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56 def __init__(self): |
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57 self.parameters = parameters.Parameters(randomly_initialize=True) |
| 402 | 58 self.prev_dw = 0 |
| 59 self.prev_db = 0 | |
| 60 self.prev_dc = 0 | |
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61 |
| 402 | 62 def deterministic_reconstruction(self, v0): |
| 63 """ | |
| 64 One up-down cycle, but a mean-field approximation (no sampling). | |
| 65 """ | |
| 66 q = sigmoid(self.parameters.b + dot(v0, self.parameters.w)) | |
| 67 p = sigmoid(self.parameters.c + dot(q, self.parameters.w.T)) | |
| 401 | 68 return p |
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69 |
| 402 | 70 def deterministic_reconstruction_error(self, v0): |
| 71 """ | |
| 72 @note: According to Yoshua, -log P(V1 = v0 | tilde(h)(v0)). | |
| 73 """ | |
| 74 return crossentropy(self.deterministic_reconstruction(v0), v0) | |
| 75 | |
| 399 | 76 def update(self, instances): |
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77 """ |
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78 Update the L{Model} using one training instance. |
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79 @param instance: A dict from feature index to (non-zero) value. |
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80 @todo: Should assert that nonzero_indices and zero_indices |
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81 are correct (i.e. are truly nonzero/zero). |
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82 """ |
| 399 | 83 minibatch = len(instances) |
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84 v0 = pylearn.sparse_instance.to_vector(instances, globals.INPUT_DIMENSION) |
| 402 | 85 print "old XENT:", numpy.sum(self.deterministic_reconstruction_error(v0)) |
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86 q0 = sigmoid(self.parameters.b + dot(v0, self.parameters.w)) |
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87 h0 = sample(q0) |
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88 p0 = sigmoid(self.parameters.c + dot(h0, self.parameters.w.T)) |
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89 v1 = sample(p0) |
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90 q1 = sigmoid(self.parameters.b + dot(v1, self.parameters.w)) |
| 402 | 91 |
| 92 dw = LR * (dot(v0.T, h0) - dot(v1.T, q1)) / minibatch + globals.MOMENTUM * self.prev_dw | |
| 93 db = LR * numpy.sum(h0 - q1, axis=0) / minibatch + globals.MOMENTUM * self.prev_db | |
| 94 dc = LR * numpy.sum(v0 - v1, axis=0) / minibatch + globals.MOMENTUM * self.prev_dc | |
| 95 | |
| 96 self.parameters.w += dw | |
| 97 self.parameters.b += db | |
| 98 self.parameters.c += dc | |
| 99 | |
| 100 self.last_dw = dw | |
| 101 self.last_db = db | |
| 102 self.last_dc = dc | |
| 103 | |
| 104 print "new XENT:", numpy.sum(self.deterministic_reconstruction_error(v0)) | |
| 105 | |
| 106 # print | |
| 399 | 107 # print "v[0]:", v0 |
| 108 # print "Q(h[0][i] = 1 | v[0]):", q0 | |
| 109 # print "h[0]:", h0 | |
| 110 # print "P(v[1][j] = 1 | h[0]):", p0 | |
| 402 | 111 # print "XENT(P(v[1][j] = 1 | h[0]) | v0):", numpy.sum(crossentropy(p0, v0)) |
| 399 | 112 # print "v[1]:", v1 |
| 113 # print "Q(h[1][i] = 1 | v[1]):", q1 | |
| 402 | 114 # |
| 399 | 115 # print |
| 116 # print v0.T.shape | |
| 117 # print h0.shape | |
| 118 # print dot(v0.T, h0).shape | |
| 119 # print self.parameters.w.shape | |
| 402 | 120 # self.parameters.w += LR * (dot(v0.T, h0) - dot(v1.T, q1)) / minibatch |
| 399 | 121 # print |
| 122 # print h0.shape | |
| 123 # print q1.shape | |
| 124 # print self.parameters.b.shape | |
| 402 | 125 # self.parameters.b += LR * numpy.sum(h0 - q1, axis=0) / minibatch |
| 399 | 126 # print v0.shape, v1.shape |
| 127 # print | |
| 128 # print self.parameters.c.shape | |
| 402 | 129 # self.parameters.c += LR * numpy.sum(v0 - v1, axis=0) / minibatch |
| 398 | 130 # print self.parameters |