Mercurial > pylearn
annotate sandbox/rbm/model.py @ 494:02a331ba833b
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| author | Joseph Turian <turian@gmail.com> |
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| date | Tue, 28 Oct 2008 11:40:56 -0400 |
| parents | 4f61201fa9a9 |
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| 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 numpy |
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9 from numpy import dot |
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10 import random |
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11 |
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12 import pylearn.nnet_ops |
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13 import pylearn.sparse_instance |
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14 |
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15 def sigmoid(v): |
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16 """ |
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17 @todo: Move to pylearn.more_numpy |
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18 @todo: Fix to avoid floating point overflow. |
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19 """ |
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20 # if x < -30.0: return 0.0 |
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21 # if x > 30.0: return 1.0 |
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22 return 1.0 / (1.0 + numpy.exp(-v)) |
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23 |
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24 def sample(v): |
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25 """ |
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26 @todo: Move to pylearn.more_numpy |
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27 """ |
| 396 | 28 assert len(v.shape) == 2 |
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29 x = numpy.zeros(v.shape) |
| 396 | 30 for j in range(v.shape[0]): |
| 31 for i in range(v.shape[1]): | |
| 32 assert v[j][i] >= 0 and v[j][i] <= 1 | |
| 33 if random.random() < v[j][i]: x[j][i] = 1 | |
| 34 else: x[j][i] = 0 | |
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35 return x |
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36 |
| 398 | 37 def crossentropy(output, target): |
| 38 """ | |
| 39 Compute the crossentropy of binary output wrt binary target. | |
| 40 @note: We do not sum, crossentropy is computed by component. | |
| 41 @todo: Rewrite as a scalar, and then broadcast to tensor. | |
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42 @todo: Move to pylearn.more_numpy |
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43 @todo: Fix to avoid floating point overflow. |
| 398 | 44 """ |
| 45 return -(target * numpy.log(output) + (1 - target) * numpy.log(1 - output)) | |
| 46 | |
| 47 | |
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48 class Model: |
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49 """ |
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50 @todo: input dimensions should be stored here! not as a global. |
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51 """ |
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52 def __init__(self, input_dimension, hidden_dimension, learning_rate = 0.1, momentum = 0.9, weight_decay = 0.0002, random_seed = 666): |
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53 self.input_dimension = input_dimension |
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54 self.hidden_dimension = hidden_dimension |
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55 self.learning_rate = learning_rate |
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56 self.momentum = momentum |
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57 self.weight_decay = weight_decay |
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58 self.random_seed = random_seed |
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59 |
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60 random.seed(random_seed) |
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61 |
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62 self.parameters = parameters.Parameters(input_dimension=self.input_dimension, hidden_dimension=self.hidden_dimension, randomly_initialize=True, random_seed=self.random_seed) |
| 402 | 63 self.prev_dw = 0 |
| 64 self.prev_db = 0 | |
| 65 self.prev_dc = 0 | |
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66 |
| 402 | 67 def deterministic_reconstruction(self, v0): |
| 68 """ | |
| 69 One up-down cycle, but a mean-field approximation (no sampling). | |
| 70 """ | |
| 71 q = sigmoid(self.parameters.b + dot(v0, self.parameters.w)) | |
| 72 p = sigmoid(self.parameters.c + dot(q, self.parameters.w.T)) | |
| 401 | 73 return p |
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74 |
| 402 | 75 def deterministic_reconstruction_error(self, v0): |
| 76 """ | |
| 77 @note: According to Yoshua, -log P(V1 = v0 | tilde(h)(v0)). | |
| 78 """ | |
| 79 return crossentropy(self.deterministic_reconstruction(v0), v0) | |
| 80 | |
| 399 | 81 def update(self, instances): |
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82 """ |
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83 Update the L{Model} using one training instance. |
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84 @param instance: A dict from feature index to (non-zero) value. |
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85 @todo: Should assert that nonzero_indices and zero_indices |
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86 are correct (i.e. are truly nonzero/zero). |
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87 @todo: Multiply L{self.weight_decay} by L{self.learning_rate}, as done in Semantic Hashing? |
| 405 | 88 @todo: Decay the biases too? |
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89 """ |
| 399 | 90 minibatch = len(instances) |
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91 v0 = pylearn.sparse_instance.to_vector(instances, self.input_dimension) |
| 410 | 92 print "old XENT per instance:", numpy.sum(self.deterministic_reconstruction_error(v0))/minibatch |
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93 q0 = sigmoid(self.parameters.b + dot(v0, self.parameters.w)) |
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94 h0 = sample(q0) |
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95 p0 = sigmoid(self.parameters.c + dot(h0, self.parameters.w.T)) |
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96 v1 = sample(p0) |
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97 q1 = sigmoid(self.parameters.b + dot(v1, self.parameters.w)) |
| 402 | 98 |
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99 dw = self.learning_rate * (dot(v0.T, h0) - dot(v1.T, q1)) / minibatch + self.momentum * self.prev_dw |
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100 db = self.learning_rate * numpy.sum(h0 - q1, axis=0) / minibatch + self.momentum * self.prev_db |
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101 dc = self.learning_rate * numpy.sum(v0 - v1, axis=0) / minibatch + self.momentum * self.prev_dc |
| 402 | 102 |
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103 self.parameters.w *= (1 - self.weight_decay) |
| 405 | 104 |
| 402 | 105 self.parameters.w += dw |
| 106 self.parameters.b += db | |
| 107 self.parameters.c += dc | |
| 108 | |
| 109 self.last_dw = dw | |
| 110 self.last_db = db | |
| 111 self.last_dc = dc | |
| 112 | |
| 410 | 113 print "new XENT per instance:", numpy.sum(self.deterministic_reconstruction_error(v0))/minibatch |
| 402 | 114 |
| 115 # print | |
| 399 | 116 # print "v[0]:", v0 |
| 117 # print "Q(h[0][i] = 1 | v[0]):", q0 | |
| 118 # print "h[0]:", h0 | |
| 119 # print "P(v[1][j] = 1 | h[0]):", p0 | |
| 402 | 120 # print "XENT(P(v[1][j] = 1 | h[0]) | v0):", numpy.sum(crossentropy(p0, v0)) |
| 399 | 121 # print "v[1]:", v1 |
| 122 # print "Q(h[1][i] = 1 | v[1]):", q1 | |
| 402 | 123 # |
| 399 | 124 # print |
| 125 # print v0.T.shape | |
| 126 # print h0.shape | |
| 127 # print dot(v0.T, h0).shape | |
| 128 # print self.parameters.w.shape | |
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129 # self.parameters.w += self.learning_rate * (dot(v0.T, h0) - dot(v1.T, q1)) / minibatch |
| 399 | 130 # print |
| 131 # print h0.shape | |
| 132 # print q1.shape | |
| 133 # print self.parameters.b.shape | |
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134 # self.parameters.b += self.learning_rate * numpy.sum(h0 - q1, axis=0) / minibatch |
| 399 | 135 # print v0.shape, v1.shape |
| 136 # print | |
| 137 # print self.parameters.c.shape | |
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138 # self.parameters.c += self.learning_rate * numpy.sum(v0 - v1, axis=0) / minibatch |
| 398 | 139 # print self.parameters |