Mercurial > pylearn

annotate algorithms/stacker.py @ 494:02a331ba833b

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merge
author Joseph Turian <turian@gmail.com>
date Tue, 28 Oct 2008 11:40:56 -0400
parents bb6bdd3b7ff3
children 2be795cc5c3a
rev   line source
476
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1
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2 import theano
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3 from theano import tensor as T
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4 import sys
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5 import numpy as N
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6
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7 class Stacker(T.RModule):
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8 """
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9 @todo: Maybe compile functions on demand, rather than immediately.
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10 """
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11
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12 def __init__(self, submodules, input = None, regularize = False):
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13 super(Stacker, self).__init__()
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14
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15 current = input
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16 layers = []
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17 for i, (submodule, outname) in enumerate(submodules):
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18 layer = submodule(current, regularize = regularize)
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19 layers.append(layer)
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20 current = layer[outname]
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21 self.layers = layers
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22
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23 self.input = self.layers[0].input
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24 self.output = current
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25
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26 local_update = []
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27 global_update = []
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28 to_update = []
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29 all_kits = []
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30 for layer in layers:
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31 u = layer.update
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32 u.resolve_all()
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33 to_update += u.updates.keys()
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34 all_kits += u.kits
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35 # the input is the whole deep model's input instead of the layer's own
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36 # input (which is previous_layer[outname])
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37 inputs = [self.input] + u.inputs[1:]
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38 method = theano.Method(inputs, u.outputs, u.updates, u.kits)
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39 local_update.append(method)
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40 global_update.append(
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41 theano.Method(inputs,
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42 u.outputs,
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43 # we update the params of the previous layers too but wrt
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44 # this layer's cost
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45 dict((param, param - layer.lr * T.grad(layer.cost, param))
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46 for param in to_update),
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47 list(all_kits)))
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48
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49 self.local_update = local_update
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50 self.global_update = global_update
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51 self.update = self.global_update[-1]
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52 self.compute = theano.Method(self.input, self.output)
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53 ll = self.layers[-1]
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54 for name, method in ll.components_map():
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55 if isinstance(method, theano.Method) and not hasattr(self, name):
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56 m = method.dup()
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57 m.resolve_all()
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58 m.inputs = [self.input if x is ll.input else x for x in m.inputs]
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59 setattr(self, name, m)
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60
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61 def _instance_initialize(self, obj, nunits = None, lr = 0.01, seed = None, **kwargs):
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62 super(Stacker, self)._instance_initialize(obj, **kwargs)
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63 if seed is not None:
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64 R = N.random.RandomState(seed)
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65 else:
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66 R = N.random
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67 for layer in obj.layers:
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68 if layer.lr is None:
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69 layer.lr = lr
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70 if nunits:
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71 if len(nunits) != len(obj.layers) + 1:
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72 raise ValueError('You should give exactly one more unit numbers as there are layers.')
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73 for ni, no, layer in zip(nunits[:-1], nunits[1:], obj.layers):
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74 if seed is not None:
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75 layer.initialize(ni, no, seed = R.random_integers(sys.maxint - 1))
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76 else:
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77 layer.initialize(ni, no)
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78 if seed is not None:
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79 obj.seed(seed)
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80
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81 def _instance_flops_approx(self, obj):
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82 rval = 0
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83 for layer in obj.layers:
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84 rval += layer.flops_approx()
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85 return rval
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86