Mercurial > pylearn
annotate mlp_factory_approach.py @ 529:4e3629a894fa
the function compile.eval_outputs was retired. Now use function instead.
| author | Frederic Bastien <bastienf@iro.umontreal.ca> |
|---|---|
| date | Mon, 17 Nov 2008 14:15:19 -0500 |
| parents | 93280a0c151a |
| children |
| rev | line source |
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1 import copy, sys, os |
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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, filetensor |
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8 from pylearn.lookup_list import LookupList |
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9 |
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10 |
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11 class AbstractFunction (Exception): pass |
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12 |
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13 class AutoName(object): |
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14 """ |
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15 By inheriting from this class, class variables which have a name attribute |
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16 will have that name attribute set to the class variable name. |
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17 """ |
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18 class __metaclass__(type): |
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19 def __init__(cls, name, bases, dct): |
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20 type.__init__(name, bases, dct) |
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21 for key, val in dct.items(): |
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22 assert type(key) is str |
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23 if hasattr(val, 'name'): |
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24 val.name = key |
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25 |
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26 class GraphLearner(object): |
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27 class Model(object): |
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28 def __init__(self, algo, params): |
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29 self.algo = algo |
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30 self.params = params |
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31 graph = self.algo.graph |
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32 self.update_fn = algo._fn([graph.input, graph.target] + graph.params, |
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33 [graph.nll] + graph.new_params) |
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34 self._fn_cache = {} |
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35 |
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36 def __copy__(self): |
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37 raise Exception('why not called?') |
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38 return GraphLearner.Model(self.algo, [copy.copy(p) for p in params]) |
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39 |
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40 def __eq__(self,other,tolerance=0.) : |
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41 """ Only compares weights of matrices and bias vector. """ |
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42 if not isinstance(other,GraphLearner.Model) : |
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43 return False |
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44 for p in range(4) : |
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45 if self.params[p].shape != other.params[p].shape : |
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46 return False |
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47 if not numpy.all( numpy.abs(self.params[p] - other.params[p]) <= tolerance ) : |
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48 return False |
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49 return True |
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50 |
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51 def _cache(self, key, valfn): |
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52 d = self._fn_cache |
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53 if key not in d: |
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54 d[key] = valfn() |
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55 return d[key] |
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56 |
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57 def update_minibatch(self, minibatch): |
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58 if not isinstance(minibatch, LookupList): |
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59 print type(minibatch) |
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60 assert isinstance(minibatch, LookupList) |
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61 self.update_fn(minibatch['input'], minibatch['target'], *self.params) |
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62 |
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63 def update(self, dataset, |
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64 default_minibatch_size=32): |
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65 """ |
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66 Update this model from more training data.Uses all the data once, cut |
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67 into minibatches. No early stopper here. |
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68 """ |
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69 params = self.params |
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70 minibatch_size = min(default_minibatch_size, len(dataset)) |
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71 for mb in dataset.minibatches(['input', 'target'], minibatch_size=minibatch_size): |
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72 self.update_minibatch(mb) |
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73 |
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74 def save(self, f): |
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75 self.algo.graph.save(f, self) |
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76 |
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77 def __call__(self, testset, fieldnames=['output_class']): |
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78 """Apply this model (as a function) to new data. |
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79 |
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80 @param testset: DataSet, whose fields feed Result terms in self.algo.g |
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81 @type testset: DataSet |
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82 |
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83 @param fieldnames: names of results in self.algo.g to compute. |
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84 @type fieldnames: list of strings |
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85 |
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86 @return: DataSet with fields from fieldnames, computed from testset by |
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87 this model. |
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88 @rtype: ApplyFunctionDataSet instance |
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89 |
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90 """ |
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91 graph = self.algo.graph |
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92 def getresult(name): |
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93 r = getattr(graph, name) |
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94 if not isinstance(r, theano.Result): |
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95 raise TypeError('string does not name a theano.Result', (name, r)) |
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96 return r |
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97 |
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98 provided = [getresult(name) for name in testset.fieldNames()] |
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99 wanted = [getresult(name) for name in fieldnames] |
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100 inputs = provided + graph.params |
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101 |
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102 theano_fn = self._cache((tuple(inputs), tuple(wanted)), |
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103 lambda: self.algo._fn(inputs, wanted)) |
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104 lambda_fn = lambda *args: theano_fn(*(list(args) + self.params)) |
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105 return dataset.ApplyFunctionDataSet(testset, lambda_fn, fieldnames) |
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106 |
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107 class Graph(object): |
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108 class Opt(object): |
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109 merge = theano.gof.MergeOptimizer() |
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110 gemm_opt_1 = theano.gof.TopoOptimizer(theano.tensor_opt.gemm_pattern_1) |
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111 sqr_opt_0 = theano.gof.TopoOptimizer(theano.gof.PatternSub( |
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112 (T.mul,'x', 'x'), |
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113 (T.sqr, 'x'))) |
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114 |
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115 def __init__(self, do_sqr=True): |
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116 self.do_sqr = do_sqr |
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117 |
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118 def __call__(self, env): |
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119 self.merge(env) |
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120 self.gemm_opt_1(env) |
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121 if self.do_sqr: |
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122 self.sqr_opt_0(env) |
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123 self.merge(env) |
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124 |
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125 def linker(self): |
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126 return theano.gof.PerformLinker() |
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127 |
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128 def early_stopper(self): |
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129 stopper.NStages(300,1) |
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130 |
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131 def train_iter(self, trainset): |
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132 raise AbstractFunction |
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133 optimizer = Opt() |
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134 |
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135 def load(self,f) : |
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136 raise AbstractFunction |
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137 |
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138 def save(self,f,model) : |
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139 raise AbstractFunction |
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140 |
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141 |
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142 def __init__(self, graph): |
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143 self.graph = graph |
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144 |
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145 def _fn(self, inputs, outputs): |
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146 # Caching here would hamper multi-threaded apps |
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147 # prefer caching in Model.__call__ |
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148 return theano.function(inputs, outputs, |
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149 unpack_single=False, |
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150 optimizer=self.graph.optimizer, |
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151 linker=self.graph.linker() if hasattr(self.graph, 'linker') |
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152 else 'c|py') |
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153 |
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154 def __call__(self, |
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155 trainset=None, |
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156 validset=None, |
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157 iparams=None, |
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158 stp=None): |
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159 """Allocate and optionally train a model |
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160 |
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161 @param trainset: Data for minimizing the cost function |
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162 @type trainset: None or Dataset |
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163 |
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164 @param validset: Data for early stopping |
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165 @type validset: None or Dataset |
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166 |
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167 @param input: name of field to use as input |
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168 @type input: string |
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169 |
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170 @param target: name of field to use as target |
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171 @type target: string |
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187
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172 |
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173 @param stp: early stopper, if None use default in graphMLP.G |
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174 @type stp: None or early stopper |
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175 |
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176 @return: model |
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177 @rtype: GraphLearner.Model instance |
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178 |
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179 """ |
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180 |
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181 iparams = self.graph.iparams() if iparams is None else iparams |
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182 |
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183 # if we load, type(trainset) == 'str' |
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184 if isinstance(trainset,str) or isinstance(trainset,file): |
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185 #loadmodel = GraphLearner.Model(self, iparams) |
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186 loadmodel = self.graph.load(self,trainset) |
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187 return loadmodel |
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188 |
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189 curmodel = GraphLearner.Model(self, iparams) |
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190 best = curmodel |
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191 |
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192 if trainset is not None: |
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193 #do some training by calling Model.update_minibatch() |
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194 if stp == None : |
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195 stp = self.graph.early_stopper() |
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196 try : |
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197 countiter = 0 |
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198 for mb in self.graph.train_iter(trainset): |
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199 curmodel.update_minibatch(mb) |
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200 if stp.set_score: |
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201 if validset: |
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202 stp.score = curmodel(validset, ['validset_score']) |
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203 if (stp.score < stp.best_score): |
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204 best = copy.copy(curmodel) |
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205 else: |
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206 stp.score = 0.0 |
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207 countiter +=1 |
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208 stp.next() |
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209 except StopIteration : |
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210 print 'Iterations stopped after ', countiter,' iterations' |
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244
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211 if validset: |
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212 curmodel = best |
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213 return curmodel |
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214 |
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264
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215 |
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216 def graphMLP(ninputs, nhid, nclass, lr_val, l2coef_val=0.0): |
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217 |
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218 |
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219 def wrapper(i, node, thunk): |
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220 if 0: |
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221 print i, node |
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222 print thunk.inputs |
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223 print thunk.outputs |
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224 if node.op == nnet_ops.crossentropy_softmax_1hot_with_bias: |
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225 print 'here is the nll op' |
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226 thunk() #actually compute this piece of the graph |
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227 |
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228 class G(GraphLearner.Graph, AutoName): |
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229 |
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230 lr = T.constant(lr_val) |
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231 assert l2coef_val == 0.0 |
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232 l2coef = T.constant(l2coef_val) |
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233 input = T.matrix() # n_examples x n_inputs |
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234 target = T.ivector() # len: n_examples |
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299
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235 #target = T.matrix() |
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236 W2, b2 = T.matrix(), T.vector() |
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237 |
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238 W1, b1 = T.matrix(), T.vector() |
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239 hid = T.tanh(b1 + T.dot(input, W1)) |
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240 hid_regularization = l2coef * T.sum(W1*W1) |
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241 |
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242 params = [W1, b1, W2, b2] |
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243 activations = b2 + T.dot(hid, W2) |
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299
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244 nll, predictions = nnet_ops.crossentropy_softmax_1hot(activations, target ) |
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245 regularization = l2coef * T.sum(W2*W2) + hid_regularization |
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246 output_class = T.argmax(activations,1) |
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247 loss_01 = T.neq(output_class, target) |
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248 #g_params = T.grad(nll + regularization, params) |
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249 g_params = T.grad(nll, params) |
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250 new_params = [T.sub_inplace(p, lr * gp) for p,gp in zip(params, g_params)] |
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251 |
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264
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252 |
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253 def __eq__(self,other) : |
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254 print 'G.__eq__ from graphMLP(), not implemented yet' |
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255 return NotImplemented |
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256 |
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257 |
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258 def load(self, algo, f): |
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259 """ Load from file the 2 matrices and bias vectors """ |
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260 cloase_at_end = False |
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261 if isinstance(f,str) : |
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262 f = open(f,'r') |
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263 close_at_end = True |
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264 params = [] |
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265 for i in xrange(4): |
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266 params.append(filetensor.read(f)) |
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267 if close_at_end : |
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268 f.close() |
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269 return GraphLearner.Model(algo, params) |
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270 |
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271 def save(self, f, model): |
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272 """ Save params to file, so 2 matrices and 2 bias vectors. Same order as iparams. """ |
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273 cloase_at_end = False |
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274 if isinstance(f,str) : |
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275 f = open(f,'w') |
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276 close_at_end = True |
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277 for p in model.params: |
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278 filetensor.write(f,p) |
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279 if close_at_end : |
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280 f.close() |
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281 |
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282 |
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283 def iparams(self): |
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264
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284 """ init params. """ |
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244
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285 def randsmall(*shape): |
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286 return (numpy.random.rand(*shape) -0.5) * 0.001 |
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287 return [randsmall(ninputs, nhid) |
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288 , randsmall(nhid) |
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289 , randsmall(nhid, nclass) |
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290 , randsmall(nclass)] |
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291 |
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292 def train_iter(self, trainset): |
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293 return trainset.minibatches(['input', 'target'], |
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304
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294 minibatch_size=min(len(trainset), 32), n_batches=2000) |
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295 def early_stopper(self): |
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304
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296 """ overwrites GraphLearner.graph function """ |
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297 return stopper.NStages(300,1) |
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298 |
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299 return G() |
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300 |
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301 |
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302 import unittest |
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303 |
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304 class TestMLP(unittest.TestCase): |
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305 def blah(self, g): |
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306 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0], |
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307 [0, 1, 1], |
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308 [1, 0, 1], |
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309 [1, 1, 1]]), |
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310 {'input':slice(2),'target':2}) |
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311 training_set2 = dataset.ArrayDataSet(numpy.array([[0, 0, 0], |
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312 [0, 1, 1], |
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313 [1, 0, 0], |
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314 [1, 1, 1]]), |
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315 {'input':slice(2),'target':2}) |
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316 test_data = dataset.ArrayDataSet(numpy.array([[0, 0, 0], |
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317 [0, 1, 1], |
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318 [1, 0, 0], |
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319 [1, 1, 1]]), |
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320 {'input':slice(2)}) |
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321 |
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322 learn_algo = GraphLearner(g) |
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323 |
| 232 | 324 model1 = learn_algo(training_set1) |
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325 |
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326 model2 = learn_algo(training_set2) |
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327 |
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328 omatch = [o1 == o2 for o1, o2 in zip(model1(test_data), |
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329 model2(test_data))] |
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330 |
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331 n_match = sum(omatch) |
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332 |
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333 self.failUnless(n_match == (numpy.sum(training_set1.fields()['target'] == |
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334 training_set2.fields()['target'])), omatch) |
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335 |
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264
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336 model1.save('/tmp/model1') |
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337 |
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265
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338 #denoising_aa = GraphLearner(denoising_g) |
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339 #model1 = denoising_aa(trainset) |
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340 #hidset = model(trainset, fieldnames=['hidden']) |
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341 #model2 = denoising_aa(hidset) |
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342 |
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343 #f = open('blah', 'w') |
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344 #for m in model: |
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345 # m.save(f) |
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346 #filetensor.write(f, initial_classification_weights) |
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347 #f.flush() |
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348 |
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349 #deep_sigmoid_net = GraphLearner(deepnetwork_g) |
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350 #deep_model = deep_sigmoid_net.load('blah') |
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351 #deep_model.update(trainset) #do some fine tuning |
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352 |
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353 model1_dup = learn_algo('/tmp/model1') |
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354 |
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355 |
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356 def equiv(self, g0, g1): |
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357 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0], |
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358 [0, 1, 1], |
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359 [1, 0, 1], |
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360 [1, 1, 1]]), |
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361 {'input':slice(2),'target':2}) |
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362 learn_algo_0 = GraphLearner(g0) |
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363 learn_algo_1 = GraphLearner(g1) |
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364 |
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365 model_0 = learn_algo_0(training_set1) |
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366 model_1 = learn_algo_1(training_set1) |
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367 |
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368 print '----' |
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369 for p in zip(model_0.params, model_1.params): |
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370 abs_rel_err = theano.gradient.numeric_grad.abs_rel_err(p[0], p[1]) |
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371 max_abs_rel_err = numpy.max(abs_rel_err) |
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372 if max_abs_rel_err > 1.0e-7: |
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373 print 'p0', p[0] |
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374 print 'p1', p[1] |
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375 #self.failUnless(max_abs_rel_err < 1.0e-7, max_abs_rel_err) |
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376 |
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377 |
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378 def test0(self): self.blah(graphMLP(2, 10, 2, .1)) |
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379 def test1(self): self.blah(graphMLP(2, 3, 2, .1)) |
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191
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380 |
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381 if __name__ == '__main__': |
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382 unittest.main() |
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383 |
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384 |