Mercurial > pylearn

annotate algorithms/stacker.py @ 480:1babf35fcef5

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merged
author James Bergstra <bergstrj@iro.umontreal.ca>
date Mon, 27 Oct 2008 17:29:03 -0400
parents 8fcd0f3d9a17
children bb6bdd3b7ff3
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 def __init__(self, submodules, input = None, regularize = False):
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10 super(Stacker, self).__init__()
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11
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12 current = input
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13 layers = []
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14 for i, (submodule, outname) in enumerate(submodules):
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15 layer = submodule(current, regularize = regularize)
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16 layers.append(layer)
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17 current = layer[outname]
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18 self.layers = layers
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19
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20 self.input = self.layers[0].input
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21 self.output = current
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22
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23 local_update = []
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24 global_update = []
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25 to_update = []
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26 all_kits = []
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27 for layer in layers:
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28 u = layer.update
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29 u.resolve_all()
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30 to_update += u.updates.keys()
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31 all_kits += u.kits
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32 # the input is the whole deep model's input instead of the layer's own
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33 # input (which is previous_layer[outname])
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34 inputs = [self.input] + u.inputs[1:]
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35 method = theano.Method(inputs, u.outputs, u.updates, u.kits)
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36 local_update.append(method)
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37 global_update.append(
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38 theano.Method(inputs,
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39 u.outputs,
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40 # we update the params of the previous layers too but wrt
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41 # this layer's cost
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42 dict((param, param - layer.lr * T.grad(layer.cost, param))
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43 for param in to_update),
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44 list(all_kits)))
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45
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46 self.local_update = local_update
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47 self.global_update = global_update
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48 self.update = self.global_update[-1]
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49 self.compute = theano.Method(self.input, self.output)
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50 ll = self.layers[-1]
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51 for name, method in ll.components_map():
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52 if isinstance(method, theano.Method) and not hasattr(self, name):
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53 m = method.dup()
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54 m.resolve_all()
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55 m.inputs = [self.input if x is ll.input else x for x in m.inputs]
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56 setattr(self, name, m)
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57
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58 def _instance_initialize(self, obj, nunits = None, lr = 0.01, seed = None, **kwargs):
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59 super(Stacker, self)._instance_initialize(obj, **kwargs)
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60 if seed is not None:
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61 R = N.random.RandomState(seed)
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62 else:
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63 R = N.random
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64 for layer in obj.layers:
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65 if layer.lr is None:
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66 layer.lr = lr
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67 if nunits:
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68 if len(nunits) != len(obj.layers) + 1:
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69 raise ValueError('You should give exactly one more unit numbers as there are layers.')
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70 for ni, no, layer in zip(nunits[:-1], nunits[1:], obj.layers):
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71 if seed is not None:
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72 layer.initialize(ni, no, seed = R.random_integers(sys.maxint - 1))
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73 else:
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74 layer.initialize(ni, no)
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75 if seed is not None:
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76 obj.seed(seed)
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77
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78 def _instance_flops_approx(self, obj):
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79 rval = 0
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80 for layer in obj.layers:
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81 rval += layer.flops_approx()
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82 return rval
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83