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annotate mlp_factory_approach.py @ 329:9ce791fb2cbf

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little hack in MiniBatchToSingleExampleIterator, there was a problem which I think was not a bug, we were receiving [array(3)] and everything was crashing. Hack is kinda slow
author Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
date Mon, 16 Jun 2008 16:03:16 -0400
parents 93280a0c151a
children
rev   line source
264
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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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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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11 class AbstractFunction (Exception): pass
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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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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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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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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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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
208
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117
244
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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
264
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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
244
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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
244
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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')
304
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152 else 'c|py')
244
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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):
244
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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
187
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172
304
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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
244
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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 """
264
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180
244
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181 iparams = self.graph.iparams() if iparams is None else iparams
264
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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
244
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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'
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
264
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215
244
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216 def graphMLP(ninputs, nhid, nclass, lr_val, l2coef_val=0.0):
264
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217
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218
244
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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
299
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235 #target = T.matrix()
244
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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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244 nll, predictions = nnet_ops.crossentropy_softmax_1hot(activations, target )
244
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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
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
a1793a5e9523 we can now load and save in a file, see test class in the file for an example, but basically it's model1.save(filename) or learn_algo(filename) to load
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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. """
a1793a5e9523 we can now load and save in a file, see test class in the file for an example, but basically it's model1.save(filename) or learn_algo(filename) to load
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273 cloase_at_end = False
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parents: 244
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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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parents: 244
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279 if close_at_end :
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parents: 244
diff changeset
280 f.close()
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281
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282
244
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283 def iparams(self):
264
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284 """ init params. """
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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diff changeset
291
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diff changeset
292 def train_iter(self, trainset):
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293 return trainset.minibatches(['input', 'target'],
304
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parents: 299
diff changeset
294 minibatch_size=min(len(trainset), 32), n_batches=2000)
244
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295 def early_stopper(self):
304
6ead65d30f1e while learning using __call__, we can now set the early stopper
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parents: 299
diff changeset
296 """ overwrites GraphLearner.graph function """
244
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parents: 232
diff changeset
297 return stopper.NStages(300,1)
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diff changeset
298
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diff changeset
299 return G()
187
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parents:
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300
ebbb0e749565 added mlp_factory_approach
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parents:
diff changeset
301
208
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diff changeset
302 import unittest
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diff changeset
303
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diff changeset
304 class TestMLP(unittest.TestCase):
244
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diff changeset
305 def blah(self, g):
208
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diff changeset
306 training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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diff changeset
307 [0, 1, 1],
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diff changeset
308 [1, 0, 1],
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diff changeset
309 [1, 1, 1]]),
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diff changeset
310 {'input':slice(2),'target':2})
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diff changeset
311 training_set2 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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diff changeset
312 [0, 1, 1],
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diff changeset
313 [1, 0, 0],
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diff changeset
314 [1, 1, 1]]),
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diff changeset
315 {'input':slice(2),'target':2})
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diff changeset
316 test_data = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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parents: 207
diff changeset
317 [0, 1, 1],
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diff changeset
318 [1, 0, 0],
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diff changeset
319 [1, 1, 1]]),
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diff changeset
320 {'input':slice(2)})
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diff changeset
321
244
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diff changeset
322 learn_algo = GraphLearner(g)
208
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diff changeset
323
232
c047238e5b3f Fixed by James
delallea@opale.iro.umontreal.ca
parents: 226
diff changeset
324 model1 = learn_algo(training_set1)
208
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diff changeset
325
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diff changeset
326 model2 = learn_algo(training_set2)
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diff changeset
327
244
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diff changeset
328 omatch = [o1 == o2 for o1, o2 in zip(model1(test_data),
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parents: 232
diff changeset
329 model2(test_data))]
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diff changeset
330
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parents: 232
diff changeset
331 n_match = sum(omatch)
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diff changeset
332
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diff changeset
333 self.failUnless(n_match == (numpy.sum(training_set1.fields()['target'] ==
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parents: 232
diff changeset
334 training_set2.fields()['target'])), omatch)
208
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diff changeset
335
264
a1793a5e9523 we can now load and save in a file, see test class in the file for an example, but basically it's model1.save(filename) or learn_algo(filename) to load
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parents: 244
diff changeset
336 model1.save('/tmp/model1')
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parents: 244
diff changeset
337
265
ae0a8345869b commented junk in the default test (main function) of mlp_factory_approach so the test still works
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents: 264
diff changeset
338 #denoising_aa = GraphLearner(denoising_g)
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parents: 264
diff changeset
339 #model1 = denoising_aa(trainset)
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parents: 264
diff changeset
340 #hidset = model(trainset, fieldnames=['hidden'])
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parents: 264
diff changeset
341 #model2 = denoising_aa(hidset)
264
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342
265
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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()
264
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348
265
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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
264
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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
244
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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))
191
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380
187
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381 if __name__ == '__main__':
208
bf320808919f back to James' version
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382 unittest.main()
187
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383
244
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384