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annotate mlp.py @ 121:2ca8dccba270

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debugging mlp.py
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Wed, 07 May 2008 16:08:18 -0400
parents d0a1bd0378c6
children 4efe6d36c061
rev   line source
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1
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2 from learner import *
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3 from theano import tensor as t
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4 from nnet_ops import *
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5
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6 # this is one of the simplest example of learner, and illustrates
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7 # the use of theano
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8
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9
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10 class OneHiddenLayerNNetClassifier(MinibatchUpdatesTLearner):
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11 """
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12 Implement a straightforward classicial feedforward
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13 one-hidden-layer neural net, with L2 regularization.
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14
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15 The predictor parameters are obtained by minibatch/online gradient descent.
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16 Training can proceed sequentially (with multiple calls to update with
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17 different disjoint subsets of the training sets).
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18
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19 Hyper-parameters:
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20 - L2_regularizer
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21 - learning_rate
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22 - n_hidden
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23
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24 For each (input_t,output_t) pair in a minibatch,::
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25
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26 output_activations_t = b2+W2*tanh(b1+W1*input_t)
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27 output_t = softmax(output_activations_t)
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28 output_class_t = argmax(output_activations_t)
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29 class_error_t = 1_{output_class_t != target_t}
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30 nll_t = -log(output_t[target_t])
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31
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32 and the training criterion is::
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33
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34 loss = L2_regularizer*(||W1||^2 + ||W2||^2) + sum_t nll_t
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35
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36 The parameters are [b1,W1,b2,W2] and are obtained by minimizing the loss by
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37 stochastic minibatch gradient descent::
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38
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39 parameters[i] -= learning_rate * dloss/dparameters[i]
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40
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41 The fields and attributes expected and produced by use and update are the following:
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42
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43 - Input and output fields (example-wise quantities):
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44
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45 - 'input' (always expected by use and update)
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46 - 'target' (optionally expected by use and always by update)
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47 - 'output' (optionally produced by use)
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48 - 'output_class' (optionally produced by use)
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49 - 'class_error' (optionally produced by use)
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50 - 'nll' (optionally produced by use)
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51
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52 - optional attributes (optionally expected as input_dataset attributes)
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53 (warning, this may be dangerous, the 'use' method will use those provided in the
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54 input_dataset rather than those learned during 'update'; currently no support
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55 for providing these to update):
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56
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57 - 'L2_regularizer'
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58 - 'b1'
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59 - 'W1'
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60 - 'b2'
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61 - 'W2'
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62 - 'parameters' = [b1, W1, b2, W2]
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63 - 'regularization_term'
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64
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65 """
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66
121
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67 def __init__(self,n_hidden,n_classes,learning_rate,init_range=1.):
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68 self._n_outputs = n_classes
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69 self._n_hidden = n_hidden
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70 self._init_range = init_range
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71 self.learning_rate = learning_rate # this is the float
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72 self._learning_rate = t.scalar('learning_rate') # this is the symbol
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73 self._input = t.matrix('input') # n_examples x n_inputs
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74 self._target = t.matrix('target','int32') # n_examples x n_outputs
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75 self._L2_regularizer = t.scalar('L2_regularizer')
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76 self._W1 = t.matrix('W1')
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77 self._W2 = t.matrix('W2')
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78 self._b1 = t.row('b1')
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79 self._b2 = t.row('b2')
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80 self._regularization_term = self._L2_regularizer * (t.dot(self._W1,self._W1) + t.dot(self._W2,self._W2))
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81 self._output_activations =self._b2+t.dot(t.tanh(self._b1+t.dot(self._input,self._W1.T)),self._W2.T)
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82 self._nll,self._output = crossentropy_softmax_1hot(self._output_activations,self._target)
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83 self._output_class = t.argmax(self._output,1)
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84 self._class_error = self._output_class != self._target
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85 self._minibatch_criterion = self._nll + self._regularization_term / t.shape(self._input)[0]
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86 MinibatchUpdatesTLearner.__init__(self)
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87
111
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88 def attributeNames(self):
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89 return ["parameters","b1","W2","b2","W2", "L2_regularizer","regularization_term"]
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90
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91 def parameterAttributes(self):
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92 return ["b1","W1", "b2", "W2"]
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93
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94 def useInputAttributes(self):
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95 return self.parameterAttributes()
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96
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97 def useOutputAttributes(self):
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98 return []
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99
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100 def updateInputAttributes(self):
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101 return self.parameterAttributes() + ["L2_regularizer"]
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102
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103 def updateMinibatchInputFields(self):
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104 return ["input","target"]
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105
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106 def updateEndOutputAttributes(self):
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107 return ["regularization_term"]
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108
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109 def lossAttribute(self):
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110 return "minibatch_criterion"
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111
111
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112 def defaultOutputFields(self, input_fields):
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113 output_fields = ["output", "output_class",]
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114 if "target" in input_fields:
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115 output_fields += ["class_error", "nll"]
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116 return output_fields
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117
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118 def allocate(self,minibatch):
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119 minibatch_n_inputs = minibatch["input"].shape[1]
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120 if not self._n_inputs:
118
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121 self._n_inputs = minibatch_n_inputs
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122 self.b1 = numpy.zeros(self._n_hidden)
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123 self.b2 = numpy.zeros(self._n_outputs)
111
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124 self.forget()
118
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125 elif self._n_inputs!=minibatch_n_inputs:
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126 # if the input changes dimension on the fly, we resize and forget everything
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127 self.forget()
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128
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129 def forget(self):
118
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130 if self._n_inputs:
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131 r = self._init_range/math.sqrt(self._n_inputs)
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132 self.W1 = numpy.random.uniform(low=-r,high=r,
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133 size=(self._n_hidden,self._n_inputs))
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134 r = self._init_range/math.sqrt(self._n_hidden)
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135 self.W2 = numpy.random.uniform(low=-r,high=r,
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136 size=(self._n_outputs,self._n_hidden))
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137 self.b1[:]=0
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138 self.b2[:]=0
111
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139
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140
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141 class MLP(MinibatchUpdatesTLearner):
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142 """
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143 Implement a feedforward multi-layer perceptron, with or without L1 and/or L2 regularization.
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144
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145 The predictor parameters are obtained by minibatch/online gradient descent.
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146 Training can proceed sequentially (with multiple calls to update with
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147 different disjoint subsets of the training sets).
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148
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149 Hyper-parameters:
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150 - L1_regularizer
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151 - L2_regularizer
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152 - neuron_sparsity_regularizer
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153 - initial_learning_rate
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154 - learning_rate_decrease_rate
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155 - n_hidden_per_layer (a list of integers)
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156 - activation_function ("sigmoid","tanh", or "ratio")
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157
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158 The output/task type (classification, regression, etc.) is obtained by specializing MLP.
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159
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160 For each (input[t],output[t]) pair in a minibatch,::
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161
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162 activation[0] = input_t
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163 for k=1 to n_hidden_layers:
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164 activation[k]=activation_function(b[k]+ W[k]*activation[k-1])
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165 output_t = output_activation_function(b[n_hidden_layers+1]+W[n_hidden_layers+1]*activation[n_hidden_layers])
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166
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167 and the b and W are obtained by minimizing the following by stochastic minibatch gradient descent::
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168
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169 L2_regularizer sum_{ijk} W_{kij}^2 + L1_regularizer sum_{kij} |W_{kij}|
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170 + neuron_sparsity_regularizer sum_{ki} |b_{ki} + infinity|
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171 - sum_t log P_{output_model}(target_t | output_t)
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172
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173 The fields and attributes expected and produced by use and update are the following:
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174
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175 - Input and output fields (example-wise quantities):
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176
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177 - 'input' (always expected by use and update)
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178 - 'target' (optionally expected by use and always by update)
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179 - 'output' (optionally produced by use)
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180 - error fields produced by sub-class of MLP
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181
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182 - optional attributes (optionally expected as input_dataset attributes)
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183 (warning, this may be dangerous, the 'use' method will use those provided in the
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184 input_dataset rather than those learned during 'update'; currently no support
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185 for providing these to update):
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186
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187 - 'L1_regularizer'
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188 - 'L2_regularizer'
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189 - 'b'
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190 - 'W'
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191 - 'parameters' = [b[1], W[1], b[2], W[2], ...]
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192 - 'regularization_term'
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193
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194 """
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195
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196 def attributeNames(self):
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197 return ["parameters","b","W","L1_regularizer","L2_regularizer","neuron_sparsity_regularizer","regularization_term"]
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198
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199 def useInputAttributes(self):
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200 return ["b","W"]
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201
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202 def useOutputAttributes(self):
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203 return []
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204
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205 def updateInputAttributes(self):
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206 return ["b","W","L1_regularizer","L2_regularizer","neuron_sparsity_regularizer"]
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207
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208 def updateMinibatchInputFields(self):
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209 return ["input","target"]
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210
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211 def updateMinibatchInputAttributes(self):
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212 return ["b","W"]
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213
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214 def updateMinibatchOutputAttributes(self):
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215 return ["new_XtX","new_XtY"]
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216
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217 def updateEndInputAttributes(self):
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218 return ["theta","XtX","XtY"]
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219
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220 def updateEndOutputAttributes(self):
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221 return ["new_theta","b","W","regularization_term"] # CHECK: WILL b AND W CONTAIN OLD OR NEW THETA? @todo i.e. order of computation = ?
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222
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223 def parameterAttributes(self):
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224 return ["b","W"]
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225
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226 def defaultOutputFields(self, input_fields):
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227 output_fields = ["output"]
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228 if "target" in input_fields:
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229 output_fields.append("squared_error")
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230 return output_fields
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231
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232 def __init__(self):
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233 self._input = t.matrix('input') # n_examples x n_inputs
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234 self._target = t.matrix('target') # n_examples x n_outputs
121
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235 self._L2_regularizer = t.scalar('L2_regularizer')
111
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236 self._theta = t.matrix('theta')
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237 self._W = self._theta[:,1:]
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238 self._b = self._theta[:,0]
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239 self._XtX = t.matrix('XtX')
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240 self._XtY = t.matrix('XtY')
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241 self._extended_input = t.prepend_one_to_each_row(self._input)
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242 self._output = t.dot(self._input,self._W.T) + self._b # (n_examples , n_outputs) matrix
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243 self._squared_error = t.sum_within_rows(t.sqr(self._output-self._target)) # (n_examples ) vector
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244 self._regularizer = self._L2_regularizer * t.dot(self._W,self._W)
111
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245 self._new_XtX = add_inplace(self._XtX,t.dot(self._extended_input.T,self._extended_input))
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246 self._new_XtY = add_inplace(self._XtY,t.dot(self._extended_input.T,self._target))
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247 self._new_theta = t.solve_inplace(self._theta,self._XtX,self._XtY)
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248
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249 OneShotTLearner.__init__(self)
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250
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251 def allocate(self,minibatch):
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252 minibatch_n_inputs = minibatch["input"].shape[1]
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253 minibatch_n_outputs = minibatch["target"].shape[1]
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254 if not self._n_inputs:
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255 self._n_inputs = minibatch_n_inputs
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256 self._n_outputs = minibatch_n_outputs
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257 self.XtX = numpy.zeros((1+self._n_inputs,1+self._n_inputs))
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258 self.XtY = numpy.zeros((1+self._n_inputs,self._n_outputs))
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259 self.theta = numpy.zeros((self._n_outputs,1+self._n_inputs))
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260 self.forget()
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261 elif self._n_inputs!=minibatch_n_inputs or self._n_outputs!=minibatch_n_outputs:
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262 # if the input or target changes dimension on the fly, we resize and forget everything
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263 self.forget()
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264
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265 def forget(self):
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266 if self._n_inputs and self._n_outputs:
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267 self.XtX.resize((1+self.n_inputs,1+self.n_inputs))
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268 self.XtY.resize((1+self.n_inputs,self.n_outputs))
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269 self.XtX.data[:,:]=0
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270 self.XtY.data[:,:]=0
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diff changeset
271 numpy.diag(self.XtX.data)[1:]=self.L2_regularizer
111
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272