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annotate mlp.py @ 155:ae5651a3696b

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new argmax calling convention
author James Bergstra <bergstrj@iro.umontreal.ca>
date Mon, 12 May 2008 16:16:32 -0400
parents 3f4e5c9bdc5e
children e9a95e19e6f8 4090779e39a9
rev   line source
132
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Joseph Turian <turian@gmail.com>
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1 """
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2 A straightforward classicial feedforward
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3 one-hidden-layer neural net, with L2 regularization.
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4 This is one of the simplest example of L{Learner}, and illustrates
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5 the use of theano.
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6 """
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7
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8 from learner import *
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9 from theano import tensor as t
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10 from nnet_ops import *
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11 import math
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13
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14 class OneHiddenLayerNNetClassifier(OnlineGradientTLearner):
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15 """
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16 Implement a straightforward classicial feedforward
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17 one-hidden-layer neural net, with L2 regularization.
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18
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19 The predictor parameters are obtained by minibatch/online gradient descent.
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20 Training can proceed sequentially (with multiple calls to update with
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21 different disjoint subsets of the training sets).
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22
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23 Hyper-parameters:
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24 - L2_regularizer
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25 - learning_rate
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26 - n_hidden
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27
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28 For each (input_t,output_t) pair in a minibatch,::
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29
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30 output_activations_t = b2+W2*tanh(b1+W1*input_t)
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31 output_t = softmax(output_activations_t)
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32 output_class_t = argmax(output_activations_t)
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33 class_error_t = 1_{output_class_t != target_t}
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34 nll_t = -log(output_t[target_t])
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35
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36 and the training criterion is::
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37
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38 loss = L2_regularizer*(||W1||^2 + ||W2||^2) + sum_t nll_t
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39
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40 The parameters are [b1,W1,b2,W2] and are obtained by minimizing the loss by
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41 stochastic minibatch gradient descent::
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42
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43 parameters[i] -= learning_rate * dloss/dparameters[i]
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44
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45 The fields and attributes expected and produced by use and update are the following:
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46
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47 - Input and output fields (example-wise quantities):
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48
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49 - 'input' (always expected by use and update)
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50 - 'target' (optionally expected by use and always by update)
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51 - 'output' (optionally produced by use)
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52 - 'output_class' (optionally produced by use)
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53 - 'class_error' (optionally produced by use)
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54 - 'nll' (optionally produced by use)
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55
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56 - optional attributes (optionally expected as input_dataset attributes)
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57 (warning, this may be dangerous, the 'use' method will use those provided in the
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58 input_dataset rather than those learned during 'update'; currently no support
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59 for providing these to update):
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60
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61 - 'L2_regularizer'
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62 - 'b1'
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63 - 'W1'
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64 - 'b2'
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65 - 'W2'
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66 - 'parameters' = [b1, W1, b2, W2]
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67 - 'regularization_term'
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68
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69 """
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70
134
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71 def __init__(self,n_hidden,n_classes,learning_rate,max_n_epochs,L2_regularizer=0,init_range=1.,n_inputs=None,minibatch_size=None):
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72 self._n_inputs = n_inputs
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73 self._n_outputs = n_classes
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74 self._n_hidden = n_hidden
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75 self._init_range = init_range
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76 self._max_n_epochs = max_n_epochs
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77 self._minibatch_size = minibatch_size
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78 self.learning_rate = learning_rate # this is the float
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79 self.L2_regularizer = L2_regularizer
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80 self._learning_rate = t.scalar('learning_rate') # this is the symbol
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81 self._input = t.matrix('input') # n_examples x n_inputs
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82 self._target = t.imatrix('target') # n_examples x 1
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83 self._target_vector = self._target[:,0]
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84 self._L2_regularizer = t.scalar('L2_regularizer')
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85 self._W1 = t.matrix('W1')
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86 self._W2 = t.matrix('W2')
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87 self._b1 = t.row('b1')
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88 self._b2 = t.row('b2')
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89 self._regularization_term = self._L2_regularizer * (t.sum(self._W1*self._W1) + t.sum(self._W2*self._W2))
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90 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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91 self._nll,self._output = crossentropy_softmax_1hot(self._output_activations,self._target_vector)
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92 self._output_class = t.argmax(self._output,1)
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93 self._class_error = t.neq(self._output_class,self._target_vector)
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94 self._minibatch_criterion = self._nll + self._regularization_term / t.shape(self._input)[0]
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95 OnlineGradientTLearner.__init__(self)
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96
111
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97 def attributeNames(self):
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98 return ["parameters","b1","W2","b2","W2", "L2_regularizer","regularization_term"]
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99
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100 def parameterAttributes(self):
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101 return ["b1","W1", "b2", "W2"]
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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((1,self._n_hidden))
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123 self.b2 = numpy.zeros((1,self._n_outputs))
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124 self.forget()
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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):
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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
133
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139 self._n_epochs=0
111
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140
133
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141 def isLastEpoch(self):
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142 self._n_epochs +=1
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143 return self._n_epochs>=self._max_n_epochs
111
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144
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145 class MLP(MinibatchUpdatesTLearner):
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146 """
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147 Implement a feedforward multi-layer perceptron, with or without L1 and/or L2 regularization.
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148
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149 The predictor parameters are obtained by minibatch/online gradient descent.
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150 Training can proceed sequentially (with multiple calls to update with
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151 different disjoint subsets of the training sets).
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152
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153 Hyper-parameters:
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154 - L1_regularizer
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155 - L2_regularizer
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156 - neuron_sparsity_regularizer
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157 - initial_learning_rate
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158 - learning_rate_decrease_rate
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159 - n_hidden_per_layer (a list of integers)
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160 - activation_function ("sigmoid","tanh", or "ratio")
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161
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162 The output/task type (classification, regression, etc.) is obtained by specializing MLP.
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163
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164 For each (input[t],output[t]) pair in a minibatch,::
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165
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166 activation[0] = input_t
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167 for k=1 to n_hidden_layers:
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168 activation[k]=activation_function(b[k]+ W[k]*activation[k-1])
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169 output_t = output_activation_function(b[n_hidden_layers+1]+W[n_hidden_layers+1]*activation[n_hidden_layers])
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170
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171 and the b and W are obtained by minimizing the following by stochastic minibatch gradient descent::
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172
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173 L2_regularizer sum_{ijk} W_{kij}^2 + L1_regularizer sum_{kij} |W_{kij}|
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174 + neuron_sparsity_regularizer sum_{ki} |b_{ki} + infinity|
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175 - sum_t log P_{output_model}(target_t | output_t)
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176
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177 The fields and attributes expected and produced by use and update are the following:
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178
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179 - Input and output fields (example-wise quantities):
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180
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181 - 'input' (always expected by use and update)
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182 - 'target' (optionally expected by use and always by update)
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183 - 'output' (optionally produced by use)
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184 - error fields produced by sub-class of MLP
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185
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186 - optional attributes (optionally expected as input_dataset attributes)
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187 (warning, this may be dangerous, the 'use' method will use those provided in the
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188 input_dataset rather than those learned during 'update'; currently no support
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189 for providing these to update):
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190
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191 - 'L1_regularizer'
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192 - 'L2_regularizer'
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193 - 'b'
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194 - 'W'
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195 - 'parameters' = [b[1], W[1], b[2], W[2], ...]
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196 - 'regularization_term'
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197
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198 """
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199
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200 def attributeNames(self):
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201 return ["parameters","b","W","L1_regularizer","L2_regularizer","neuron_sparsity_regularizer","regularization_term"]
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202
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203 def useInputAttributes(self):
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204 return ["b","W"]
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205
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206 def useOutputAttributes(self):
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207 return []
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208
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209 def updateInputAttributes(self):
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210 return ["b","W","L1_regularizer","L2_regularizer","neuron_sparsity_regularizer"]
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211
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212 def updateMinibatchInputFields(self):
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213 return ["input","target"]
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214
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215 def updateMinibatchInputAttributes(self):
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216 return ["b","W"]
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217
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218 def updateMinibatchOutputAttributes(self):
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219 return ["new_XtX","new_XtY"]
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220
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221 def updateEndInputAttributes(self):
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222 return ["theta","XtX","XtY"]
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223
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224 def updateEndOutputAttributes(self):
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225 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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226
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227 def parameterAttributes(self):
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228 return ["b","W"]
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229
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230 def defaultOutputFields(self, input_fields):
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231 output_fields = ["output"]
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232 if "target" in input_fields:
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233 output_fields.append("squared_error")
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234 return output_fields
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235
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236 def __init__(self):
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237 self._input = t.matrix('input') # n_examples x n_inputs
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238 self._target = t.matrix('target') # n_examples x n_outputs
121
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239 self._L2_regularizer = t.scalar('L2_regularizer')
111
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240 self._theta = t.matrix('theta')
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241 self._W = self._theta[:,1:]
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242 self._b = self._theta[:,0]
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243 self._XtX = t.matrix('XtX')
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244 self._XtY = t.matrix('XtY')
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245 self._extended_input = t.prepend_one_to_each_row(self._input)
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246 self._output = t.dot(self._input,self._W.T) + self._b # (n_examples , n_outputs) matrix
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247 self._squared_error = t.sum_within_rows(t.sqr(self._output-self._target)) # (n_examples ) vector
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248 self._regularizer = self._L2_regularizer * t.dot(self._W,self._W)
111
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249 self._new_XtX = add_inplace(self._XtX,t.dot(self._extended_input.T,self._extended_input))
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250 self._new_XtY = add_inplace(self._XtY,t.dot(self._extended_input.T,self._target))
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251 self._new_theta = t.solve_inplace(self._theta,self._XtX,self._XtY)
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252
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253 OneShotTLearner.__init__(self)
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254
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255 def allocate(self,minibatch):
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256 minibatch_n_inputs = minibatch["input"].shape[1]
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257 minibatch_n_outputs = minibatch["target"].shape[1]
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258 if not self._n_inputs:
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259 self._n_inputs = minibatch_n_inputs
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260 self._n_outputs = minibatch_n_outputs
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261 self.XtX = numpy.zeros((1+self._n_inputs,1+self._n_inputs))
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262 self.XtY = numpy.zeros((1+self._n_inputs,self._n_outputs))
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263 self.theta = numpy.zeros((self._n_outputs,1+self._n_inputs))
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264 self.forget()
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265 elif self._n_inputs!=minibatch_n_inputs or self._n_outputs!=minibatch_n_outputs:
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266 # if the input or target changes dimension on the fly, we resize and forget everything
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267 self.forget()
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268
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269 def forget(self):
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270 if self._n_inputs and self._n_outputs:
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271 self.XtX.resize((1+self.n_inputs,1+self.n_inputs))
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272 self.XtY.resize((1+self.n_inputs,self.n_outputs))
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273 self.XtX.data[:,:]=0
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274 self.XtY.data[:,:]=0
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diff changeset
275 numpy.diag(self.XtX.data)[1:]=self.L2_regularizer
111
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276