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annotate algorithms/stacker.py @ 672:27b1344a57b1

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Added preprocessing back in
author Joseph Turian <turian@gmail.com>
date Thu, 20 Nov 2008 06:38:06 -0500
parents 2b0e10ac6929
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rev   line source
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1
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2 # for example in examples:
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3 # repr = example
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4 # for layer in stacked.layers:
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5 # layer.update(repr)
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6 # repr = layer.representation(repr)
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7
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8 import theano
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9 from theano import tensor as T
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10 import sys
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11 import numpy as N
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12
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13 class Stacker(T.RModule):
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14 """
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15 @note: Assumes some names in the layers: input, cost, lr, and update
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16 @todo: Maybe compile functions on demand, rather than immediately.
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17 """
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18
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19 def __init__(self, submodules, input = None, regularize = False):
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20 super(Stacker, self).__init__()
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21
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22 current = input
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23 layers = []
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24 for i, (submodule, outname) in enumerate(submodules):
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25 layer = submodule(current, regularize = regularize)
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26 layers.append(layer)
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27 current = layer[outname]
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28 self.layers = layers
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29
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30 self.input = self.layers[0].input
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31 self.output = current
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32
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33 representation = []
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34 local_update = []
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35 global_update = []
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36 to_update = []
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37 all_kits = []
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38 for layer, (submodule, outname) in zip(layers, submodules):
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39 u = layer.update
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40 u.resolve_all()
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41 to_update += u.updates.keys()
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42 all_kits += u.kits
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43 # the input is the whole deep model's input instead of the layer's own
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44 # input (which is previous_layer[outname])
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45 inputs = [self.input] + u.inputs[1:]
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46 method = theano.Method(inputs, u.outputs, u.updates, u.kits)
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47 local_update.append(method)
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48 global_update.append(
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49 theano.Method(inputs,
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50 u.outputs,
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51 # we update the params of the previous layers too but wrt
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52 # this layer's cost
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53 dict((param, param - layer.lr * T.grad(layer.cost, param))
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54 for param in to_update),
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55 list(all_kits)))
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56 representation.append(theano.Method(self.input, layer[outname]))
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57
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58 # @todo: Add diagnostics
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59 # self.diagnose_from_input = Method([self.input], self.layers[0].diagnose.outputs + self.layers[1].diagnose.outputs ...
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60
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61 self.local_update = local_update
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62 self.global_update = global_update
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63 self.representation = representation
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64 self.update = self.global_update[-1]
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65 self.compute = theano.Method(self.input, self.output)
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66 ll = self.layers[-1]
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67 for name, method in ll.components_map():
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68 if isinstance(method, theano.Method) and not hasattr(self, name):
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69 m = method.dup()
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70 m.resolve_all()
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71 m.inputs = [self.input if x is ll.input else x for x in m.inputs]
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72 setattr(self, name, m)
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73
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74 def _instance_initialize(self, obj, nunits = None, lr = 0.01, seed = None, **kwargs):
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75 super(Stacker, self)._instance_initialize(obj, **kwargs)
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76 if seed is not None:
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77 R = N.random.RandomState(seed)
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78 else:
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79 R = N.random
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80 for layer in obj.layers:
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81 if layer.lr is None:
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82 layer.lr = lr
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83 if nunits:
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84 obj.input_dimension = nunits[0]
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85 obj.output_dimension = nunits[-1]
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86 if len(nunits) != len(obj.layers) + 1:
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87 raise ValueError('You should give exactly one more unit numbers as there are layers.')
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88 for ni, no, layer in zip(nunits[:-1], nunits[1:], obj.layers):
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89 if seed is not None:
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90 layer.initialize(ni, no, seed = R.random_integers(sys.maxint - 1))
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91 else:
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92 layer.initialize(ni, no)
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93 if seed is not None:
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94 obj.seed(seed)
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95
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96 def _instance_flops_approx(self, obj):
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97 rval = 0
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98 for layer in obj.layers:
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99 rval += layer.flops_approx()
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100 return rval
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101