Mercurial > pylearn

comparison sandbox/rbm/model.py @ 399:8796b91a9f09

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
RBM minibatch works
author Joseph Turian <turian@gmail.com>
date Tue, 08 Jul 2008 21:42:21 -0400
parents 6e55ccb7e2bf
children 269d5c5a4209
comparison
equal deleted inserted replaced
398:6e55ccb7e2bf 399:8796b91a9f09
41 41
42 class Model: 42 class Model:
43 def __init__(self): 43 def __init__(self):
44 self.parameters = parameters.Parameters(randomly_initialize=True) 44 self.parameters = parameters.Parameters(randomly_initialize=True)
45 45
46 def update(self, instance): 46 def update(self, instances):
47 """ 47 """
48 Update the L{Model} using one training instance. 48 Update the L{Model} using one training instance.
49 @param instance: A dict from feature index to (non-zero) value. 49 @param instance: A dict from feature index to (non-zero) value.
50 @todo: Should assert that nonzero_indices and zero_indices 50 @todo: Should assert that nonzero_indices and zero_indices
51 are correct (i.e. are truly nonzero/zero). 51 are correct (i.e. are truly nonzero/zero).
52 """ 52 """
53 v0 = numpy.zeros((1, globals.INPUT_DIMENSION)) 53 v0 = numpy.zeros((len(instances), globals.INPUT_DIMENSION))
54 for idx in instance.keys(): 54 minibatch = len(instances)
55 v0[0][idx] = instance[idx] 55 for i in range(minibatch):
56 for idx in instances[i].keys():
57 v0[i][idx] = instances[i][idx]
56 58
57 q0 = sigmoid(self.parameters.b + dot(v0, self.parameters.w)) 59 q0 = sigmoid(self.parameters.b + dot(v0, self.parameters.w))
58 h0 = sample(q0) 60 h0 = sample(q0)
59 p0 = sigmoid(self.parameters.c + dot(h0, self.parameters.w.T)) 61 p0 = sigmoid(self.parameters.c + dot(h0, self.parameters.w.T))
60 v1 = sample(p0) 62 v1 = sample(p0)
61 q1 = sigmoid(self.parameters.b + dot(v1, self.parameters.w)) 63 q1 = sigmoid(self.parameters.b + dot(v1, self.parameters.w))
62 print 64 print
63 print "v[0]:", v0 65 # print "v[0]:", v0
64 print "Q(h[0][i] = 1 | v[0]):", q0 66 # print "Q(h[0][i] = 1 | v[0]):", q0
65 print "h[0]:", h0 67 # print "h[0]:", h0
66 print "P(v[1][j] = 1 | h[0]):", p0 68 # print "P(v[1][j] = 1 | h[0]):", p0
67 print "XENT(P(v[1][j] = 1 | h[0]) | v0):", numpy.sum(crossentropy(p0, v0)) 69 print "XENT(P(v[1][j] = 1 | h[0]) | v0):", numpy.sum(crossentropy(p0, v0))
68 print "v[1]:", v1 70 # print "v[1]:", v1
69 print "Q(h[1][i] = 1 | v[1]):", q1 71 # print "Q(h[1][i] = 1 | v[1]):", q1
70 72
71 self.parameters.w += LR * (dot(v0.T, h0) - dot(v1.T, q1)) 73 # print
72 self.parameters.b += LR * (h0 - q1) 74 # print v0.T.shape
73 self.parameters.c += LR * (v0 - v1) 75 # print h0.shape
76 # print dot(v0.T, h0).shape
77 # print self.parameters.w.shape
78 self.parameters.w += LR * (dot(v0.T, h0) - dot(v1.T, q1)) / minibatch
79 # print
80 # print h0.shape
81 # print q1.shape
82 # print self.parameters.b.shape
83 self.parameters.b += LR * numpy.sum(h0 - q1, axis=0) / minibatch
84 # print v0.shape, v1.shape
85 # print
86 # print self.parameters.c.shape
87 self.parameters.c += LR * numpy.sum(v0 - v1, axis=0) / minibatch
74 # print self.parameters 88 # print self.parameters