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annotate mlp_factory_approach.py @ 207:c5a7105fa40b

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author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Fri, 16 May 2008 16:38:15 -0400
parents f2ddc795ec49 e816821c1e50
children bf320808919f
rev   line source
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1 import dataset
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2 import theano
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3 import theano.tensor as t
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4 import numpy
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5 import nnet_ops
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6
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7 def _randshape(*shape):
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8 return (numpy.random.rand(*shape) -0.5) * 0.001
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9 def _function(inputs, outputs, linker='c&py'):
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10 return theano.function(inputs, outputs, unpack_single=False,linker=linker)
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11
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12 class NeuralNet(object):
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13
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14 class Model(object):
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15 def __init__(self, nnet, params):
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16 self.nnet = nnet
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17 self.params = params
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18
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19 def update(self, trainset, stopper=None):
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20 """Update this model from more training data."""
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21 v = self.nnet.v
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22 params = self.params
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23 update_fn = _function([v.input, v.target] + v.params, [v.nll] + v.new_params)
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24 if stopper is not None:
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25 raise NotImplementedError()
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26 else:
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27 for i in xrange(100):
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28 for input, target in trainset.minibatches(['input', 'target'],
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29 minibatch_size=min(32, len(trainset))):
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30 results = update_fn(input, target[:,0], *params)
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31 if 0: print results[0]
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32 # print params['b']
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33
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34 def __call__(self, testset,
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35 output_fieldnames=['output_class'],
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36 test_stats_collector=None,
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37 copy_inputs=False,
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38 put_stats_in_output_dataset=True,
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39 output_attributes=[]):
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40 """Apply this model (as a function) to new data"""
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41 inputs = [self.nnet.v.input, self.nnet.v.target] + self.nnet.v.params
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42 fn = _function(inputs, [getattr(self.nnet.v, name) for name in output_fieldnames])
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43 if 'target' in testset.fieldNames():
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44 return dataset.ApplyFunctionDataSet(testset,
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45 lambda input, target: fn(input, target[:,0], *self.params),
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46 output_fieldnames)
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47 else:
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48 return dataset.ApplyFunctionDataSet(testset,
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49 lambda input: fn(input, numpy.zeros(1,dtype='int64'), *self.params),
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50 output_fieldnames)
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51
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52 def __init__(self, ninputs, nhid, nclass, lr, nepochs,
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53 l2coef=0.0,
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54 linker='c&yp',
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55 hidden_layer=None):
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56 if not hidden_layer:
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57 hidden_layer = AffineSigmoidLayer("hidden",ninputs,nhid,l2coef)
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58 class Vars:
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59 def __init__(self, lr, l2coef):
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60 lr = t.constant(lr)
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61 l2coef = t.constant(l2coef)
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62 input = t.matrix('input') # n_examples x n_inputs
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63 target = t.ivector('target') # n_examples x 1
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64 W2 = t.matrix('W2')
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65 b2 = t.vector('b2')
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66
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67 hid = hidden_layer(input)
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68 hid_params = hidden_layer.params()
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69 hid_params_init_vals = hidden_layer.params_ivals()
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70 hid_regularization = hidden_layer.regularization()
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71
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72 params = [W2, b2] + hid_params
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73 nll, predictions = nnet_ops.crossentropy_softmax_1hot( b2 + t.dot(hid, W2), target)
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74 regularization = l2coef * t.sum(W2*W2) + hid_regularization
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75 output_class = t.argmax(predictions,1)
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76 loss_01 = t.neq(output_class, target)
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77 g_params = t.grad(nll + regularization, params)
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78 new_params = [t.sub_inplace(p, lr * gp) for p,gp in zip(params, g_params)]
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79 setattr_and_name(self, locals())
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80 self.nhid = nhid
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81 self.nclass = nclass
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82 self.nepochs = nepochs
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83 self.v = Vars(lr, l2coef)
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84 self.params = None
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85
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86 def __call__(self, trainset=None, iparams=None):
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87 if iparams is None:
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88 iparams = LookupList(["W","b"],[_randshape(self.nhid, self.nclass), _randshape(self.nclass)])
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89 + self.v.hid_params_init_vals()
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90 rval = NeuralNet.Model(self, iparams)
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91 if trainset:
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92 rval.update(trainset)
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93 return rval
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94
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95
206
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96 def setattr_and_name(self, dict):
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97 """This will do a self.__setattr__ for all elements in the dict
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98 (except for element self). In addition it will make sure that
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99 each element's .name (if it exists) is set to the element's key
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100 in the dicitonary.
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101 Typical usage: setattr_and_name(self, locals()) """
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102 for varname,var in locals.items():
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103 if var is not self:
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104 if hasattr(var,"name") and not var.name:
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105 var.name=varname
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106 self.__setattr__(varname,var)
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107
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108
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109 if __name__ == '__main__':
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110 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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111 [0, 1, 1],
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112 [1, 0, 1],
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113 [1, 1, 1]]),
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114 {'input':slice(2),'target':2})
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115 training_set2 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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116 [0, 1, 1],
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117 [1, 0, 0],
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118 [1, 1, 1]]),
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119 {'input':slice(2),'target':2})
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120 test_data = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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121 [0, 1, 1],
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122 [1, 0, 0],
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123 [1, 1, 1]]),
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124 {'input':slice(2)})
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125
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126
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127 learn_algo = NeuralNet(2, 10, 3, .1, 1000)
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128
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129 model = learn_algo()
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130
187
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131 model1 = learn_algo(training_set1)
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132
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133 model2 = learn_algo(training_set2)
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134
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135 n_match = 0
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136 for o1, o2 in zip(model1(test_data), model2(test_data)):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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137 n_match += (o1 == o2)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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138
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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139 print n_match, numpy.sum(training_set1.fields()['target'] ==
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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140 training_set2.fields()['target'])
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
141