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annotate mlp_factory_approach.py @ 190:aa7a3ecbcc90

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progress toward early stopping
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 14 May 2008 16:24:10 -0400
parents 8f58abb943d4
children e816821c1e50
rev   line source
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1 import copy
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2 import numpy
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3
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4 import theano
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5 import theano.tensor as t
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6
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7 import dataset
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8 import nnet_ops
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9
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10 def _randshape(*shape):
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11 return (numpy.random.rand(*shape) -0.5) * 0.001
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12
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13 class NeuralNet(object):
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14
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15 class _Model(object):
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16 def __init__(self, nnet, params):
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17 self.nnet = nnet
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18 self.params = params
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19
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20 def __copy__(self):
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21 return _Model(self.nnet, [copy.copy(p) for p in params])
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22
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23 def update(self, trainset, stopper=None):
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24 """Update this model from more training data."""
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25 v = self.nnet.v
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26 params = self.params
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27 update_fn = self.nnet._fn([v.input, v.target] + v.params, [v.nll] + v.new_params)
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28 if stopper is not None:
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29 raise NotImplementedError()
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30 else:
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31 for i in xrange(100):
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32 for input, target in trainset.minibatches(['input', 'target'],
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33 minibatch_size=min(32, len(trainset))):
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34 dummy = update_fn(input, target[:,0], *params)
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35 if 0: print dummy[0] #the nll
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36
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37 def __call__(self, testset,
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38 output_fieldnames=['output_class'],
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39 test_stats_collector=None,
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40 copy_inputs=False,
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41 put_stats_in_output_dataset=True,
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42 output_attributes=[]):
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43 """Apply this model (as a function) to new data"""
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44 v = self.nnet.v
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45 outputs = [getattr(self.nnet.v, name) for name in output_fieldnames]
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46 if 'target' in testset:
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47 fn = self.nnet._fn([v.input, v.target] + v.params, outputs)
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48 return dataset.ApplyFunctionDataSet(testset,
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49 lambda input, target: fn(input, target[:,0], *self.params),
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50 output_fieldnames)
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51 else:
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52 fn = self.nnet._fn([v.input] + v.params, outputs)
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53 return dataset.ApplyFunctionDataSet(testset,
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54 lambda input: fn(input, *self.params),
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55 output_fieldnames)
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56 def _fn(self, inputs, outputs):
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57 #it is possible for this function to implement function caching
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58 #... but not necessarily desirable.
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59 #- caching ruins the possibility of multi-threaded learning
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60 #- caching demands more efficiency in the face of resizing inputs
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61 #- caching makes it really hard to borrow references to function outputs
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62 return theano.function(inputs, outputs, unpack_single=False, linker=self.linker)
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63
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64 def __init__(self, ninputs, nhid, nclass, lr, nepochs,
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65 l2coef=0.0,
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66 linker='c&py',
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67 hidden_layer=None):
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68 class Vars:
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69 def __init__(self, lr, l2coef):
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70 lr = t.constant(lr)
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71 l2coef = t.constant(l2coef)
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72 input = t.matrix('input') # n_examples x n_inputs
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73 target = t.ivector('target') # n_examples x 1
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74 W2 = t.matrix('W2')
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75 b2 = t.vector('b2')
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76
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77 if hidden_layer:
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78 hid, hid_params, hid_ivals, hid_regularization = hidden_layer(input)
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79 else:
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80 W1 = t.matrix('W1')
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81 b1 = t.vector('b1')
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82 hid = t.tanh(b1 + t.dot(input, W1))
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83 hid_params = [W1, b1]
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84 hid_regularization = l2coef * t.sum(W1*W1)
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85 hid_ivals = lambda : [_randshape(ninputs, nhid), _randshape(nhid)]
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86
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87 params = [W2, b2] + hid_params
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88 activations = b2 + t.dot(hid, W2)
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89 nll, predictions = nnet_ops.crossentropy_softmax_1hot(activations, target)
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90 regularization = l2coef * t.sum(W2*W2) + hid_regularization
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91 output_class = t.argmax(activations,1)
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92 loss_01 = t.neq(output_class, target)
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93 g_params = t.grad(nll + regularization, params)
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94 new_params = [t.sub_inplace(p, lr * gp) for p,gp in zip(params, g_params)]
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95 self.__dict__.update(locals()); del self.self
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96 self.nhid = nhid
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97 self.nclass = nclass
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98 self.nepochs = nepochs
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99 self.v = Vars(lr, l2coef)
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100 self.params = None
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101 self.linker = linker
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102
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103 def __call__(self, trainset=None, iparams=None):
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104 if iparams is None:
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105 iparams = [_randshape(self.nhid, self.nclass), _randshape(self.nclass)]\
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106 + self.v.hid_ivals()
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107 rval = NeuralNet._Model(self, iparams)
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108 if trainset:
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109 rval.update(trainset)
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110 return rval
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111
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112
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113 if __name__ == '__main__':
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114 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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115 [0, 1, 1],
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116 [1, 0, 1],
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117 [1, 1, 1]]),
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118 {'input':slice(2),'target':2})
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119 training_set2 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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120 [0, 1, 1],
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121 [1, 0, 0],
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122 [1, 1, 1]]),
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123 {'input':slice(2),'target':2})
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124 test_data = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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125 [0, 1, 1],
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126 [1, 0, 0],
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127 [1, 1, 1]]),
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128 {'input':slice(2)})
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129
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130 learn_algo = NeuralNet(2, 10, 3, .1, 1000)
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131
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132 model1 = learn_algo(training_set1)
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133
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134 model2 = learn_algo(training_set2)
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135
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136 n_match = 0
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137 for o1, o2 in zip(model1(test_data), model2(test_data)):
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138 n_match += (o1 == o2)
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139
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140 print n_match, numpy.sum(training_set1.fields()['target'] ==
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
141 training_set2.fields()['target'])
ebbb0e749565 added mlp_factory_approach
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
142