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annotate mlp_factory_approach.py @ 251:7e6edee187e3

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optimization of CachedDataSet__getitem__
author Frederic Bastien <bastienf@iro.umontreal.ca>
date Tue, 03 Jun 2008 12:25:53 -0400
parents 3156a9976183
children a1793a5e9523
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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7 from pylearn import dataset, nnet_ops, stopper, LookupList
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8
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9 class AbstractFunction (Exception): pass
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11 class AutoName(object):
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12 """
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13 By inheriting from this class, class variables which have a name attribute
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14 will have that name attribute set to the class variable name.
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15 """
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16 class __metaclass__(type):
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17 def __init__(cls, name, bases, dct):
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18 type.__init__(name, bases, dct)
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19 for key, val in dct.items():
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20 assert type(key) is str
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21 if hasattr(val, 'name'):
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22 val.name = key
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23
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24 class GraphLearner(object):
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25 class Model(object):
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26 def __init__(self, algo, params):
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27 self.algo = algo
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28 self.params = params
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29 graph = self.algo.graph
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30 self.update_fn = algo._fn([graph.input, graph.target] + graph.params,
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31 [graph.nll] + graph.new_params)
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32 self._fn_cache = {}
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34 def __copy__(self):
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35 raise Exception('why not called?')
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36 return GraphLearner.Model(self.algo, [copy.copy(p) for p in params])
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38 def _cache(self, key, valfn):
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39 d = self._fn_cache
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40 if key not in d:
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41 d[key] = valfn()
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42 return d[key]
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43
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44 def update_minibatch(self, minibatch):
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45 assert isinstance(minibatch, LookupList)
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46 self.update_fn(minibatch['input'], minibatch['target'], *self.params)
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47
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48 def update(self, dataset,
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49 default_minibatch_size=32):
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50 """Update this model from more training data."""
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51 params = self.params
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52 minibatch_size = min(default_minibatch_size, len(dataset))
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53 for mb in dataset.minibatches(['input', 'target'], minibatch_size=minibatch_size):
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54 self.update_minibatch(mb)
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55
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56 def __call__(self, testset, fieldnames=['output_class']):
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57 """Apply this model (as a function) to new data.
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59 @param testset: DataSet, whose fields feed Result terms in self.algo.g
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60 @type testset: DataSet
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62 @param fieldnames: names of results in self.algo.g to compute.
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63 @type fieldnames: list of strings
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65 @return: DataSet with fields from fieldnames, computed from testset by
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66 this model.
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67 @rtype: ApplyFunctionDataSet instance
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68
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69 """
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70 graph = self.algo.graph
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71 def getresult(name):
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72 r = getattr(graph, name)
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73 if not isinstance(r, theano.Result):
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74 raise TypeError('string does not name a theano.Result', (name, r))
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75 return r
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76
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77 provided = [getresult(name) for name in testset.fieldNames()]
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78 wanted = [getresult(name) for name in fieldnames]
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79 inputs = provided + graph.params
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80
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81 theano_fn = self._cache((tuple(inputs), tuple(wanted)),
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82 lambda: self.algo._fn(inputs, wanted))
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83 lambda_fn = lambda *args: theano_fn(*(list(args) + self.params))
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84 return dataset.ApplyFunctionDataSet(testset, lambda_fn, fieldnames)
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85
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86 class Graph(object):
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87 class Opt(object):
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88 merge = theano.gof.MergeOptimizer()
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89 gemm_opt_1 = theano.gof.TopoOptimizer(theano.tensor_opt.gemm_pattern_1)
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90 sqr_opt_0 = theano.gof.TopoOptimizer(theano.gof.PatternSub(
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91 (T.mul,'x', 'x'),
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92 (T.sqr, 'x')))
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93
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94 def __init__(self, do_sqr=True):
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95 self.do_sqr = do_sqr
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96
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97 def __call__(self, env):
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98 self.merge(env)
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99 self.gemm_opt_1(env)
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100 if self.do_sqr:
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101 self.sqr_opt_0(env)
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102 self.merge(env)
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103
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104 def linker(self):
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105 return theano.gof.PerformLinker()
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106
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107 def early_stopper(self):
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108 stopper.NStages(10,1)
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109
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110 def train_iter(self, trainset):
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111 raise AbstractFunction
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112 optimizer = Opt()
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113
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114 def __init__(self, graph):
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115 self.graph = graph
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116
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117 def _fn(self, inputs, outputs):
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118 # Caching here would hamper multi-threaded apps
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119 # prefer caching in Model.__call__
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120 return theano.function(inputs, outputs,
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121 unpack_single=False,
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122 optimizer=self.graph.optimizer,
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123 linker=self.graph.linker() if hasattr(self.graph, 'linker')
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124 else 'c&py')
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125
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126 def __call__(self,
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127 trainset=None,
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128 validset=None,
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129 iparams=None):
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130 """Allocate and optionally train a model
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131
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132 @param trainset: Data for minimizing the cost function
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133 @type trainset: None or Dataset
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134
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135 @param validset: Data for early stopping
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136 @type validset: None or Dataset
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137
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138 @param input: name of field to use as input
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139 @type input: string
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140
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141 @param target: name of field to use as target
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142 @type target: string
187
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143
244
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144 @return: model
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145 @rtype: GraphLearner.Model instance
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146
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147 """
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148 iparams = self.graph.iparams() if iparams is None else iparams
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149 curmodel = GraphLearner.Model(self, iparams)
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150 best = curmodel
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151
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152 if trainset is not None:
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153 #do some training by calling Model.update_minibatch()
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154 stp = self.graph.early_stopper()
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155 for mb in self.graph.train_iter(trainset):
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156 curmodel.update_minibatch(mb)
208
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157 if stp.set_score:
244
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158 if validset:
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159 stp.score = curmodel(validset, ['validset_score'])
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160 if (stp.score < stp.best_score):
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161 best = copy.copy(curmodel)
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162 else:
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163 stp.score = 0.0
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164 stp.next()
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165 if validset:
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166 curmodel = best
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167 return curmodel
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168
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169 def graphMLP(ninputs, nhid, nclass, lr_val, l2coef_val=0.0):
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170 def wrapper(i, node, thunk):
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171 if 0:
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172 print i, node
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173 print thunk.inputs
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174 print thunk.outputs
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175 if node.op == nnet_ops.crossentropy_softmax_1hot_with_bias:
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176 print 'here is the nll op'
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177 thunk() #actually compute this piece of the graph
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178
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179 class G(GraphLearner.Graph, AutoName):
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180
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181 lr = T.constant(lr_val)
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182 assert l2coef_val == 0.0
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183 l2coef = T.constant(l2coef_val)
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184 input = T.matrix() # n_examples x n_inputs
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185 target = T.ivector() # len: n_examples
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186 W2, b2 = T.matrix(), T.vector()
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187
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188 W1, b1 = T.matrix(), T.vector()
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189 hid = T.tanh(b1 + T.dot(input, W1))
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190 hid_regularization = l2coef * T.sum(W1*W1)
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191
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192 params = [W1, b1, W2, b2]
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193 activations = b2 + T.dot(hid, W2)
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194 nll, predictions = nnet_ops.crossentropy_softmax_1hot(activations, target)
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195 regularization = l2coef * T.sum(W2*W2) + hid_regularization
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196 output_class = T.argmax(activations,1)
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197 loss_01 = T.neq(output_class, target)
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198 #g_params = T.grad(nll + regularization, params)
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199 g_params = T.grad(nll, params)
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200 new_params = [T.sub_inplace(p, lr * gp) for p,gp in zip(params, g_params)]
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201
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202 def iparams(self):
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203 def randsmall(*shape):
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204 return (numpy.random.rand(*shape) -0.5) * 0.001
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205 return [randsmall(ninputs, nhid)
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206 , randsmall(nhid)
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207 , randsmall(nhid, nclass)
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208 , randsmall(nclass)]
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209
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210 def train_iter(self, trainset):
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211 return trainset.minibatches(['input', 'target'],
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212 minibatch_size=min(len(trainset), 32), n_batches=300)
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213 def early_stopper(self):
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214 return stopper.NStages(300,1)
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215
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216 return G()
187
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217
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218
208
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219 import unittest
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220
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221 class TestMLP(unittest.TestCase):
244
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222 def blah(self, g):
208
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223 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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224 [0, 1, 1],
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225 [1, 0, 1],
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226 [1, 1, 1]]),
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227 {'input':slice(2),'target':2})
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228 training_set2 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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229 [0, 1, 1],
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230 [1, 0, 0],
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231 [1, 1, 1]]),
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232 {'input':slice(2),'target':2})
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233 test_data = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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234 [0, 1, 1],
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235 [1, 0, 0],
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236 [1, 1, 1]]),
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237 {'input':slice(2)})
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238
244
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239 learn_algo = GraphLearner(g)
208
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240
232
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241 model1 = learn_algo(training_set1)
208
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242
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243 model2 = learn_algo(training_set2)
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244
244
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245 omatch = [o1 == o2 for o1, o2 in zip(model1(test_data),
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246 model2(test_data))]
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247
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248 n_match = sum(omatch)
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249
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250 self.failUnless(n_match == (numpy.sum(training_set1.fields()['target'] ==
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251 training_set2.fields()['target'])), omatch)
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252
244
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253 def equiv(self, g0, g1):
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254 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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255 [0, 1, 1],
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256 [1, 0, 1],
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257 [1, 1, 1]]),
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258 {'input':slice(2),'target':2})
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259 learn_algo_0 = GraphLearner(g0)
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260 learn_algo_1 = GraphLearner(g1)
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261
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262 model_0 = learn_algo_0(training_set1)
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263 model_1 = learn_algo_1(training_set1)
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264
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265 print '----'
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266 for p in zip(model_0.params, model_1.params):
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267 abs_rel_err = theano.gradient.numeric_grad.abs_rel_err(p[0], p[1])
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268 max_abs_rel_err = numpy.max(abs_rel_err)
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269 if max_abs_rel_err > 1.0e-7:
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270 print 'p0', p[0]
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271 print 'p1', p[1]
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272 #self.failUnless(max_abs_rel_err < 1.0e-7, max_abs_rel_err)
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273
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274
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275 def test0(self): self.blah(graphMLP(2, 10, 2, .1))
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276 def test1(self): self.blah(graphMLP(2, 3, 2, .1))
191
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277
187
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278 if __name__ == '__main__':
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279 unittest.main()
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280
244
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281