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comparison algorithms/stacker.py @ 516:2b0e10ac6929

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misc
author Olivier Breuleux <breuleuo@iro.umontreal.ca>
date Mon, 03 Nov 2008 00:10:18 -0500
parents c7ce66b4e8f4
children
comparison
equal deleted inserted replaced
515:dc2d93590da0 516:2b0e10ac6929
1
2 # for example in examples:
3 # repr = example
4 # for layer in stacked.layers:
5 # layer.update(repr)
6 # repr = layer.representation(repr)
1 7
2 import theano 8 import theano
3 from theano import tensor as T 9 from theano import tensor as T
4 import sys 10 import sys
5 import numpy as N 11 import numpy as N
22 self.layers = layers 28 self.layers = layers
23 29
24 self.input = self.layers[0].input 30 self.input = self.layers[0].input
25 self.output = current 31 self.output = current
26 32
33 representation = []
27 local_update = [] 34 local_update = []
28 global_update = [] 35 global_update = []
29 to_update = [] 36 to_update = []
30 all_kits = [] 37 all_kits = []
31 for layer in layers: 38 for layer, (submodule, outname) in zip(layers, submodules):
32 u = layer.update 39 u = layer.update
33 u.resolve_all() 40 u.resolve_all()
34 to_update += u.updates.keys() 41 to_update += u.updates.keys()
35 all_kits += u.kits 42 all_kits += u.kits
36 # the input is the whole deep model's input instead of the layer's own 43 # the input is the whole deep model's input instead of the layer's own
44 # we update the params of the previous layers too but wrt 51 # we update the params of the previous layers too but wrt
45 # this layer's cost 52 # this layer's cost
46 dict((param, param - layer.lr * T.grad(layer.cost, param)) 53 dict((param, param - layer.lr * T.grad(layer.cost, param))
47 for param in to_update), 54 for param in to_update),
48 list(all_kits))) 55 list(all_kits)))
56 representation.append(theano.Method(self.input, layer[outname]))
49 57
50 # @todo: Add diagnostics 58 # @todo: Add diagnostics
51 # self.diagnose_from_input = Method([self.input], self.layers[0].diagnose.outputs + self.layers[1].diagnose.outputs ... 59 # self.diagnose_from_input = Method([self.input], self.layers[0].diagnose.outputs + self.layers[1].diagnose.outputs ...
52 60
53 self.local_update = local_update 61 self.local_update = local_update
54 self.global_update = global_update 62 self.global_update = global_update
63 self.representation = representation
55 self.update = self.global_update[-1] 64 self.update = self.global_update[-1]
56 self.compute = theano.Method(self.input, self.output) 65 self.compute = theano.Method(self.input, self.output)
57 ll = self.layers[-1] 66 ll = self.layers[-1]
58 for name, method in ll.components_map(): 67 for name, method in ll.components_map():
59 if isinstance(method, theano.Method) and not hasattr(self, name): 68 if isinstance(method, theano.Method) and not hasattr(self, name):