Mercurial > pylearn

annotate mlp_factory_approach.py @ 224:2be51c13b55f

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added LookupList to pylearn namespace
author James Bergstra <bergstrj@iro.umontreal.ca>
date Fri, 23 May 2008 17:10:03 -0400
parents 6fa8fbb0c3f6
children df3fae88ab46 8bc16220b29a
rev   line source
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1 import copy, sys
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2 import numpy
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3
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4 import theano
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5 from theano import tensor as t
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6
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7 import dataset, nnet_ops, stopper
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8
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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 def _cache(d, key, valfn):
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14 #valfn() is only evaluated if key isn't in dictionary d
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15 if key not in d:
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16 d[key] = valfn()
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17 return d[key]
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18
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19 class _Model(object):
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20 def __init__(self, algo, params):
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21 self.algo = algo
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22 self.params = params
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23 v = algo.v
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24 self.update_fn = algo._fn([v.input, v.target] + v.params, [v.nll] + v.new_params)
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25 self._fn_cache = {}
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26
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27 def __copy__(self):
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28 return _Model(self.algo, [copy.copy(p) for p in params])
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29
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30 def update(self, input_target):
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31 """Update this model from more training data."""
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32 params = self.params
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33 #TODO: why should we have to unpack target like this?
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34 for input, target in input_target:
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35 rval= self.update_fn(input, target[:,0], *params)
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36 #print rval[0]
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37
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38 def __call__(self, testset, fieldnames=['output_class']):
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39 """Apply this model (as a function) to new data"""
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40 #TODO: cache fn between calls
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41 assert 'input' == testset.fieldNames()[0]
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42 assert len(testset.fieldNames()) <= 2
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43 v = self.algo.v
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44 outputs = [getattr(v, name) for name in fieldnames]
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45 inputs = [v.input] + ([v.target] if 'target' in testset else [])
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46 inputs.extend(v.params)
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47 theano_fn = _cache(self._fn_cache, (tuple(inputs), tuple(outputs)),
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48 lambda: self.algo._fn(inputs, outputs))
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49 lambda_fn = lambda *args: theano_fn(*(list(args) + self.params))
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50 return dataset.ApplyFunctionDataSet(testset, lambda_fn, fieldnames)
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51
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52 class AutonameVars(object):
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53 def __init__(self, dct):
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54 for key, val in dct.items():
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55 if type(key) is str and hasattr(val, 'name'):
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56 val.name = key
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57 self.__dict__.update(dct)
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58
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59 class MultiLayerPerceptron(object):
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60
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61 def __init__(self, ninputs, nhid, nclass, lr,
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62 l2coef=0.0,
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63 linker='c&py',
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64 hidden_layer=None,
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65 early_stopper=None,
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66 validation_portion=0.2,
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67 V_extern=None):
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68 class V_intern(AutonameVars):
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69 def __init__(v_self, lr, l2coef, **kwargs):
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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() # n_examples x n_inputs
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73 target = t.ivector() # len: n_examples
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74 W2, b2 = t.matrix(), t.vector()
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75
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76 if hidden_layer:
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77 hid, hid_params, hid_ivals, hid_regularization = hidden_layer(input)
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78 else:
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79 W1, b1 = t.matrix(), t.vector()
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80 hid = t.tanh(b1 + t.dot(input, W1))
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81 hid_params = [W1, b1]
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82 hid_regularization = l2coef * t.sum(W1*W1)
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83 hid_ivals = lambda : [_randshape(ninputs, nhid), _randshape(nhid)]
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84
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85 params = [W2, b2] + hid_params
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86 activations = b2 + t.dot(hid, W2)
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87 nll, predictions = nnet_ops.crossentropy_softmax_1hot(activations, target)
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88 regularization = l2coef * t.sum(W2*W2) + hid_regularization
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89 output_class = t.argmax(activations,1)
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90 loss_01 = t.neq(output_class, target)
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91 g_params = t.grad(nll + regularization, params)
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92 new_params = [t.sub_inplace(p, lr * gp) for p,gp in zip(params, g_params)]
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93 self.__dict__.update(locals()); del self.self
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94 AutonameVars.__init__(v_self, locals())
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95 self.nhid = nhid
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96 self.nclass = nclass
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97 self.v = V_intern(**locals()) if V_extern is None else V_extern(**locals())
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98 self.linker = linker
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99 self.early_stopper = early_stopper if early_stopper is not None else lambda: stopper.NStages(10,1)
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100 self.validation_portion = validation_portion
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101
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102 def _fn(self, inputs, outputs):
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103 # Caching here would hamper multi-threaded apps
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104 # prefer caching in _Model.__call__
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105 return theano.function(inputs, outputs, unpack_single=False, linker=self.linker)
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106
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107 def __call__(self, trainset=None, iparams=None, input='input', target='target'):
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108 """Allocate and optionally train a model"""
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109 if iparams is None:
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110 iparams = [_randshape(self.nhid, self.nclass), _randshape(self.nclass)]\
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111 + self.v.hid_ivals()
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112 rval = _Model(self, iparams)
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113 if trainset:
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114 if len(trainset) == sys.maxint:
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115 raise NotImplementedError('Learning from infinite streams is not supported')
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116 nval = int(self.validation_portion * len(trainset))
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117 nmin = len(trainset) - nval
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118 assert nmin >= 0
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119 minset = trainset[:nmin] #real training set for minimizing loss
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120 valset = trainset[nmin:] #validation set for early stopping
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121 best = rval
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122 for stp in self.early_stopper():
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123 rval.update(
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124 minset.minibatches([input, target], minibatch_size=min(32,
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125 len(trainset))))
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126 #print 'mlp.__call__(), we did an update'
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127 if stp.set_score:
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128 stp.score = rval(valset, ['loss_01'])
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129 if (stp.score < stp.best_score):
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130 best = copy.copy(rval)
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131 rval = best
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132 return rval
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133
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134
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135 import unittest
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136
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137 class TestMLP(unittest.TestCase):
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138 def test0(self):
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139
208
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140 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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141 [0, 1, 1],
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142 [1, 0, 1],
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143 [1, 1, 1]]),
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144 {'input':slice(2),'target':2})
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145 training_set2 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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146 [0, 1, 1],
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147 [1, 0, 0],
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148 [1, 1, 1]]),
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149 {'input':slice(2),'target':2})
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150 test_data = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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151 [0, 1, 1],
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152 [1, 0, 0],
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153 [1, 1, 1]]),
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154 {'input':slice(2)})
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155
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156 learn_algo = MultiLayerPerceptron(2, 10, 2, .1
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157 , linker='c&py'
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158 , early_stopper = lambda:stopper.NStages(100,1))
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159
211
bd728c83faff in __get__, problem if the i.stop was None, i being the slice, added one line replacing None by the len(self)
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160 model1 = learn_algo(training_set1,input='input',target='target')
208
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161
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162 model2 = learn_algo(training_set2)
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163
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164 n_match = 0
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165 for o1, o2 in zip(model1(test_data), model2(test_data)):
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166 #print o1
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167 #print o2
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168 n_match += (o1 == o2)
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169
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170 assert n_match == (numpy.sum(training_set1.fields()['target'] ==
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171 training_set2.fields()['target']))
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172
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173 if __name__ == '__main__':
208
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174 unittest.main()
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175