Mercurial > pylearn
annotate algorithms/stacker.py @ 476:8fcd0f3d9a17
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author | Olivier Breuleux <breuleuo@iro.umontreal.ca> |
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date | Mon, 27 Oct 2008 17:26:00 -0400 |
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children | bb6bdd3b7ff3 |
rev | line source |
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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 |