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annotate mlp_factory_approach.py @ 228:6f55e301c687

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optimisation of ArrayDataSet
author Frederic Bastien <bastienf@iro.umontreal.ca>
date Fri, 16 May 2008 16:38:07 -0400
parents e816821c1e50
children c5a7105fa40b
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 from tlearn import dataset, nnet_ops, stopper
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8
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9 def _randshape(*shape):
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10 return (numpy.random.rand(*shape) -0.5) * 0.001
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11
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12 def _cache(d, key, valfn):
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13 #valfn() is only evaluated if key isn't in dictionary d
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14 if key not in d:
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15 d[key] = valfn()
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16 return d[key]
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17
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18 class _Model(object):
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19 def __init__(self, algo, params):
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20 self.algo = algo
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21 self.params = params
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22 v = algo.v
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23 self.update_fn = algo._fn([v.input, v.target] + v.params, [v.nll] + v.new_params)
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24 self._fn_cache = {}
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25
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26 def __copy__(self):
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27 return _Model(self.algo, [copy.copy(p) for p in params])
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28
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29 def update(self, input_target):
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30 """Update this model from more training data."""
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31 params = self.params
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32 #TODO: why should we have to unpack target like this?
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33 for input, target in input_target:
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34 self.update_fn(input, target[:,0], *params)
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35
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36 def __call__(self, testset, fieldnames=['output_class']):
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37 """Apply this model (as a function) to new data"""
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38 #TODO: cache fn between calls
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39 assert 'input' == testset.fieldNames()[0]
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40 assert len(testset.fieldNames()) <= 2
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41 v = self.algo.v
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42 outputs = [getattr(v, name) for name in fieldnames]
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43 inputs = [v.input] + ([v.target] if 'target' in testset else [])
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44 inputs.extend(v.params)
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45 theano_fn = _cache(self._fn_cache, (tuple(inputs), tuple(outputs)),
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46 lambda: self.algo._fn(inputs, outputs))
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47 lambda_fn = lambda *args: theano_fn(*(list(args) + self.params))
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48 return dataset.ApplyFunctionDataSet(testset, lambda_fn, fieldnames)
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49
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50 class AutonameVars(object):
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51 def __init__(self, dct):
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52 for key, val in dct.items():
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53 if type(key) is str and hasattr(val, 'name'):
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54 val.name = key
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55 self.__dict__.update(dct)
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56
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57 class MultiLayerPerceptron(object):
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58
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59 def __init__(self, ninputs, nhid, nclass, lr,
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60 l2coef=0.0,
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61 linker='c&py',
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62 hidden_layer=None,
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63 early_stopper=None,
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64 validation_portion=0.2,
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65 V_extern=None):
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66 class V_intern(AutonameVars):
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67 def __init__(v_self, lr, l2coef, **kwargs):
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68 lr = t.constant(lr)
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69 l2coef = t.constant(l2coef)
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70 input = t.matrix() # n_examples x n_inputs
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71 target = t.ivector() # len: n_examples
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72 W2, b2 = t.matrix(), t.vector()
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73
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74 if hidden_layer:
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75 hid, hid_params, hid_ivals, hid_regularization = hidden_layer(input)
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76 else:
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77 W1, b1 = t.matrix(), t.vector()
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78 hid = t.tanh(b1 + t.dot(input, W1))
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79 hid_params = [W1, b1]
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80 hid_regularization = l2coef * t.sum(W1*W1)
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81 hid_ivals = lambda : [_randshape(ninputs, nhid), _randshape(nhid)]
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82
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83 params = [W2, b2] + hid_params
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84 activations = b2 + t.dot(hid, W2)
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85 nll, predictions = nnet_ops.crossentropy_softmax_1hot(activations, target)
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86 regularization = l2coef * t.sum(W2*W2) + hid_regularization
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87 output_class = t.argmax(activations,1)
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88 loss_01 = t.neq(output_class, target)
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89 g_params = t.grad(nll + regularization, params)
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90 new_params = [t.sub_inplace(p, lr * gp) for p,gp in zip(params, g_params)]
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91 self.__dict__.update(locals()); del self.self
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92 AutonameVars.__init__(v_self, locals())
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93 self.nhid = nhid
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94 self.nclass = nclass
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95 self.v = V_intern(**locals()) if V_extern is None else V_extern(**locals())
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96 self.linker = linker
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97 self.early_stopper = early_stopper if early_stopper is not None else lambda: stopper.NStages(10,1)
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98 self.validation_portion = validation_portion
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99
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100 def _fn(self, inputs, outputs):
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101 # Caching here would hamper multi-threaded apps
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102 # prefer caching in _Model.__call__
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103 return theano.function(inputs, outputs, unpack_single=False, linker=self.linker)
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104
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105 def __call__(self, trainset=None, iparams=None):
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106 """Allocate and optionally train a model"""
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107 if iparams is None:
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108 iparams = [_randshape(self.nhid, self.nclass), _randshape(self.nclass)]\
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109 + self.v.hid_ivals()
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110 rval = _Model(self, iparams)
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111 if trainset:
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112 if len(trainset) == sys.maxint:
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113 raise NotImplementedError('Learning from infinite streams is not supported')
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114 nval = int(self.validation_portion * len(trainset))
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115 nmin = len(trainset) - nval
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116 assert nmin >= 0
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117 minset = trainset[:nmin] #real training set for minimizing loss
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118 valset = trainset[nmin:] #validation set for early stopping
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119 best = rval
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120 for stp in self.early_stopper():
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121 rval.update(
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122 trainset.minibatches(['input', 'target'], minibatch_size=min(32,
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123 len(trainset))))
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124 if stp.set_score:
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125 stp.score = rval(valset, ['loss_01'])
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126 if (stp.score < stp.best_score):
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127 best = copy.copy(rval)
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128 rval = best
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129 return rval
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130
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131
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132 import unittest
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133
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134 class TestMLP(unittest.TestCase):
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135 def test0(self):
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136
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137 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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138 [0, 1, 1],
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139 [1, 0, 1],
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140 [1, 1, 1]]),
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141 {'input':slice(2),'target':2})
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142 training_set2 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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143 [0, 1, 1],
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144 [1, 0, 0],
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145 [1, 1, 1]]),
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146 {'input':slice(2),'target':2})
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147 test_data = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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148 [0, 1, 1],
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149 [1, 0, 0],
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150 [1, 1, 1]]),
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151 {'input':slice(2)})
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152
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153 learn_algo = MultiLayerPerceptron(2, 10, 2, .1
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154 , linker='c&py'
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155 , early_stopper = lambda:stopper.NStages(100,1))
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156
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157 model1 = learn_algo(training_set1)
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158
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159 model2 = learn_algo(training_set2)
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160
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161 n_match = 0
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162 for o1, o2 in zip(model1(test_data), model2(test_data)):
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163 #print o1
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164 #print o2
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165 n_match += (o1 == o2)
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166
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167 assert n_match == (numpy.sum(training_set1.fields()['target'] ==
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168 training_set2.fields()['target']))
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169
187
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170 if __name__ == '__main__':
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171 unittest.main()
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172