Mercurial > pylearn
annotate doc/v2_planning/plugin_RP.py @ 1371:98d4232df1d8
comment on OD idea
author | Razvan Pascanu <r.pascanu@gmail.com> |
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date | Mon, 15 Nov 2010 16:28:02 -0500 |
parents | 681b5e7e3b81 |
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1 ''' |
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2 |
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3 !!! Incomplete file .. many of the things I've set up to do are not done |
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4 yet !!! |
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5 |
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6 ============ |
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7 Introduction |
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8 ============ |
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9 |
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10 What this file talks about |
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11 ========================== |
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12 * Proposal for the layer committee |
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13 * Proposal of how to deal with plug-ins ( STEP 2) |
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14 * Description of how to glue the two parts |
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15 * Some personal beliefs and argumentation |
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16 |
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17 The file will point out how : |
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18 * to use the API's other committee proposed or why and how they should |
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19 change |
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20 * it satisfies the listed requirements ( or why it doesn't) |
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21 * this approach might be better then others ( or worse) to the best of |
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22 my knowledge |
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23 |
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24 |
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25 Motivation for writing this file |
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26 ================================ |
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27 |
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28 I wrote this file because: |
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29 * It will probably answer most of the questions regarding my view, |
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30 minimizing the time wasted on talks |
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31 * When prezenting the entire interface helps see holes in the approach |
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32 * Is here for everybody to read ( easier disimination of information) |
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33 |
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34 |
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35 ======= |
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36 Concept |
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37 ======= |
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38 |
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39 I think any experiment that we ( or anybody else ) would want to run with |
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40 our library will be composed of two steps : |
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41 |
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42 * Step 1. Constructing (or choosing or initializing) the model, the |
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43 datasets, error measures, optimizers and so on ( everything up to the |
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44 iterative loop). I think this step has been covered by different |
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45 committies but possibly glued together by the layer committee. |
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46 |
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47 * Step 2. Compose the iterative loops and perform them ( this is what the |
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48 architecture committee dealt with) |
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49 |
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50 I believe there is a natural way of going from *Step 1* to *Step 2* |
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51 which would be presented as Step 1.5 |
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52 |
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53 Step 2 |
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54 ====== |
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55 |
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56 I will start with step 2 ( because I think that is more of a hot subject |
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57 right now). I will assume you have the right plugins at hand. |
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58 This is a DBN with early stopping and .. |
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59 |
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60 .. code-block:: python |
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61 ''' |
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62 data = load_mnist() |
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63 train_xy valid_xy test_xy = split(data, split = |
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64 [(0,40000),(40000,50000),[50000,60000]]) |
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65 train_x, train_y = train_xy |
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66 valid_x, valid_y = valid_xy |
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67 test_x, test_y = test_xy |
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68 |
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69 ################# CONSTRUCTING THE MODEL ################################### |
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70 ############################################################################ |
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71 |
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72 x0 = pca(train_x) |
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73 |
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74 ## Layer 1: |
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75 h1 = sigmoid(dotW_b(x0,units = 200), constraint = L1( coeff = 0.1)) |
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76 x1 = recurrent_layer() |
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77 x1.t0 = x0 |
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78 x1.value = binomial_sample(sigmoid( reconstruct( binomial_sample(h1), x0))) |
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79 cost = free_energy(train_x) - free_energy(x1.t(5)) |
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80 grads = [ (g.var, T.grad(cost.var, g.var)) for g in cost.params ] |
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81 pseudo_cost = sum([ pl.sum(pl.abs(g)) for g in cost.params]) |
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82 rbm1 = SGD( cost = pseudo_cost, grads = grads) |
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83 |
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84 # Layer 2: |
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85 rbm2,h2 = rbm(h1, units = 200, k = 5, use= 'CD') |
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86 # Logreg |
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87 logreg,out = logreg(h2, units = 10) |
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88 train_err = mean_over(missclassification(argmax(out), train_y)) |
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89 valid_err = train_err.replace({train_x:valid_x, train_y:valid_y}) |
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90 test_err = train_err.replace({train_x: test_x, train_y: test_y}) |
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91 |
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92 ########################################################################## |
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93 ############### Constructing the training loop ########################### |
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94 |
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95 ca = Schedular() |
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96 |
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97 |
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98 ### Constructing Modes ### |
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99 class pretrain_layer1 () |
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100 |
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101 def register() |
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102 { |
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103 } |
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104 pretrain_layer2 = ca.mode('pretrain1') |
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105 early_stopping = ca.mode('early') |
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106 code_block = ca.mode('code_block') |
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107 kfolds = ca.mode('kfolds') |
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108 |
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109 # Construct modes dependency graph |
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110 code_block.include([ pretrian_layer1, pretrain_layer2, early_stopper]) |
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111 kfolds.include( code_block ) |
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112 |
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113 pretrain_layer1.act( on = code_block.begin(), when = always()) |
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114 pretrain_layer2.act( on = pretrain_layer1.end(), when = always()) |
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115 early_stopping.act ( on = pretrain_layer2.end(), when = always()) |
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116 |
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117 |
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118 # Construct counter plugin that keeps track of number of epochs |
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119 @FnPlugin |
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120 def counter(self, msg): |
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121 # a bit of a hack.. it will look more classic if you would |
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122 # start with a class instead |
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123 if not hasattr(self, 'val'): |
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124 self.val = 0 |
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125 |
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126 if msg = Message('eod'): |
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127 self.val += 1 |
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128 if self.val < 10: |
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129 self.fire(Message('continue')) |
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130 else: |
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131 self.fire(Message('terminate')) |
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132 |
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133 |
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134 # Construct pre-training plugins |
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135 rbm1_plugin = pretrain_layer1.include(plugin_wrapper(rbm1)) |
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136 rbm2_plugin = pretrain_layer2.include(plugin_wrapper(rbm2)) |
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137 rbm1_counter = pretrain_layer1.include(counter) |
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138 rbm2_counter = pretrain_layer2.include(counter) |
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139 |
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140 rbm1_plugin.listen(Message('init'), update_hyperparameters) |
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141 rbm1_plugin.listen(Message('continue'), dataset_restart) |
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142 rbm2_plugin.listen(Message('init'), update_hyperparameters) |
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143 rbm2_plugin.listen(Message('continue'), dataset_restart) |
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144 |
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145 |
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146 # Dependency graph for pre-training layer 0 |
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147 rbm1_plugin.act( on = [ pretrain_layer1.begin() , |
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148 rbm1_plugin.value() ] , |
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149 when = always()) |
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150 rbm1_counter.act( on = rbm1_plugin.eod(), when = always() ) |
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151 |
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152 |
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153 # Dependency graph for pre-training layer 1 |
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154 rbm2_plugin.act( on = [ pretrain_layer2.begin() , |
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155 rbm2_plugin.value() ] , |
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156 when = always()) |
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157 pretrain_layer2.stop( on = rbm2_plugin.eod(), when = always()) |
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158 |
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159 |
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160 # Constructing fine-tunning plugins |
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161 learner = early_stopper.include(plugin_wrapper(logreg)) |
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162 validation = early_stopper.include( plugin_wrapper(valid_err))) |
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163 clock = early_stopper.include( ca.generate_clock()) |
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164 early_stopper_plugin = early_stopper.include( early_stopper_plugin) |
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165 |
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166 |
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167 def save_model(plugin): |
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168 cPickle.dump(plugin.object, 'just_the_model.pkl') |
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169 |
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170 learner.listen(Message('init'), update_hyperparameters) |
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171 validation.listen(Message('init'), update_hyperparameters) |
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172 validation.listen(early_stopper_plugin.new_best_score(), save_model) |
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173 |
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174 learner.act( on = early_stopper.begin(), when = always()) |
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175 learner.act( on = learner.value(), when = always()) |
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176 validation.act( on = clock.hour(), when = every(n = 1)) |
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177 early_stopper.act( on = validation.value(), when = always()) |
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178 |
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179 @FnPlugin |
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180 def kfolds_plugin(self,event): |
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181 if not hasattr(self, 'n'): |
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182 self.n = -1 |
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183 self.splits = [ [ ( 0,40000),(40000,50000),(50000,60000) ], |
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184 [ (10000,50000),(50000,60000),( 0,10000) ], |
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185 [ (20000,60000),( 0,10000),(10000,20000) ] ] |
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186 if self.n < 3: |
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187 self.n += 1 |
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188 msg = Message('new split') |
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189 msg.data = (data.get_hyperparam('split'),self.splits[self.n]) |
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190 self.fire(msg) |
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191 else: |
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192 self.fire(Message('terminate')) |
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193 |
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194 |
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195 kfolds.include(kfolds_plugin) |
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196 kfolds_plugin.act([kfolds.begin(), Message('new split')], when = always()) |
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197 kfolds_plugin.act(code_block.end(), always() ) |
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198 code_block.act(Message('new split'), always() ) |
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199 |
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200 sched.include(kfolds) |
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201 |
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202 sched.run() |
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203 |
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204 ''' |
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205 |
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206 |
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207 |
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208 Notes: |
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209 when a mode is regstered to begin with a certain message, it will |
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210 rebroadcast that message when it starts, with only switching the |
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211 type from whatever it was to 'init'. It will also send all 'init' messages |
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212 of the mode in which is included ( or of the schedular). |
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213 |
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214 one might be able to shorten this by having Macros that creates modes |
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215 and automatically register certain plugins to it; you can always |
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216 afterwards add plugins to any mode |
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217 |
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218 |
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219 |
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220 Step 1 |
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221 ====== |
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222 |
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223 |
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224 You start with the dataset that you construct as the dataset committee |
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225 proposed to. You continue constructing your model by applying |
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226 transformation, more or less like you would in Theano. When constructing |
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227 your model you also get a graph "behind the scene". Note though that |
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228 this graph is totally different then the one Theano would create! |
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229 Let start with an example: |
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230 |
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231 .. code-block:: python |
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232 |
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233 ''' |
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234 data_x, data_y = GPU_transform(load_mnist()) |
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235 output = sigmoid(dotW_b(data_x,10)) |
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236 err = cross_entropy(output, data_y) |
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237 learner = SGD(err) |
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238 ''' |
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239 |
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240 This shows how to create the learner behind the logistic regression, |
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241 but not the function that will compute the validation error or the test |
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242 error ( or any other statistics). Before going into the detail of what |
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243 all those transforms ( or the results after applying one) means, here |
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244 is another partial example for a SdA : |
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245 |
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246 .. code-block:: python |
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247 |
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248 ''' |
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249 ## Layer 1: |
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250 |
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251 data_x,data_y = GPU_transform(load_mnist()) |
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252 noisy_data_x = gaussian_noise(data_x, amount = 0.1) |
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253 hidden1 = tanh(dotW_b(data_x, n_units = 200)) |
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254 reconstruct1 = reconstruct(hidden1.replace(data_x, noisy_data_x), |
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255 noisy_data_x) |
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256 err1 = cross_entropy(reconstruct1, data_x) |
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257 learner1 = SGD(err1) |
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258 |
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259 # Layer 2 : |
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260 noisy_hidden1 = gaussian_noise(hidden1, amount = 0.1) |
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261 hidden2 = tanh(dotW_b(hidden1, n_units = 200)) |
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262 reconstruct2 = reconstruct(hidden2.replace(hidden1,noisy_hidden1), |
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263 noisy_hidden1) |
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264 err2 = cross_entropy(reconstruct2, hidden) |
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265 learner2 = SGD(err2) |
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266 |
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267 # Top layer: |
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269 output = sigmoid(dotW_b(hidden2, n_units = 10)) |
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270 err = cross_entropy(output, data_y) |
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271 learner = SGD(err) |
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272 |
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273 ''' |
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274 |
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275 What's going on here? |
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276 --------------------- |
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277 |
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278 By calling different "transforms" (we could call them ops or functions) |
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279 you decide what the architecture does. What you get back from applying |
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280 any of these transforms, are nodes. You have different types of nodes |
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281 (which I will enumerate a bit later) but they all offer a basic interface. |
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282 That interface is the dataset API + a few more methods and/or attributes. |
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283 There are also a few transform that work on the graph that I think will |
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284 be pretty useful : |
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285 |
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286 * .replace(dict) -> method; replaces the subgraphs given as keys with |
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287 the ones given as values; throws an exception if it |
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288 is impossible |
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289 |
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290 * replace(nodes, dict) -> function; call replace on all nodes given that dictionary |
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291 |
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292 * reconstruct(dict) -> transform; tries to reconstruct the nodes given as |
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293 keys starting from the nodes given as values by |
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294 going through the inverse of all transforms that |
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295 are in between |
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297 * .tm, .tp -> methods; returns nodes that correspond to the value |
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298 at t-k or t+k |
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299 * recurrent_layer -> function; creates a special type of node that is |
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300 recurrent; the node has two important attributes that |
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301 need to be specified before calling the node iterator; |
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302 those attributes are .t0 which represents the initial |
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303 value and .value which should describe the recurrent |
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304 relation |
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305 * add_constraints -> transform; adds a constraint to a given node |
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306 * data_listener -> function; creates a special node that listens for |
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307 messages to get data; it should be used to decompose |
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308 the architecture in modules that can run on different |
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309 machines |
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310 |
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311 * switch(hyperparam, dict) -> transform; a lazy switch that allows you |
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312 do construct by hyper-parameters |
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313 |
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314 * get_hyperparameter(name) -> method; given a name it will return the first node |
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315 starting from top that is a hyper parameter and has |
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316 that name |
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317 * get_parameter(name) -> method; given a name it will return the first node |
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318 starting from top that is a parameter and has that |
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319 name |
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320 * get_hyperparameters() |
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321 * get_parameters() |
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322 |
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323 |
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324 |
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325 |
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326 Because every node provides the dataset API it means you can iterate over |
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327 any of the nodes. They will produce the original dataset transformed up |
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328 to that point. |
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329 |
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330 ** NOTES ** |
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331 1. This is not like a symbolic graph. When adding a transform |
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332 you can get a warning straight forward. This is because you start from |
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333 the dataset and you always have access to some data. Though sometime |
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334 you would want to have the nodes lazy, i.e. not try to compute everything |
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335 until the graph is done. |
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336 |
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337 2. You can still have complex Theano expressions. Each node has a |
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338 theano variable describing the graph up to that point + optionally |
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339 a compiled function over which you can iterate. We can use some |
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340 on_demand mechanism to compile when needed. |
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341 |
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342 What types of nodes do you have |
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343 -------------------------------- |
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344 |
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345 Note that this differentiation is more or less semantical and not |
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346 mandatory syntactical. Is just to help understanding the graph. |
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347 |
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348 |
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349 * Data Nodes -- datasets are such nodes; the result of any |
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350 simple transform is also a data node ( like the result |
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351 of a sigmoid, or dotW_b) |
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352 * Learner Nodes -- they are the same as data nodes, with the |
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353 difference that they have side effects on the model; |
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354 they update the weights |
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355 * Apply Nodes -- they are used to connect input variables to |
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356 the transformation/op node and output nodes |
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357 * Dependency Nodes -- very similar to apply nodes just that they connect |
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358 constraints subgraphs to a model graph |
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359 * Parameter Nodes -- when iterating over them they will only output |
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360 the values of the parameters; |
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361 * Hyper-parameter Nodes -- very similar to parameter nodes; this is a |
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362 semantical difference ( they are not updated by the |
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363 any learner nodes) |
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364 * Transform Nodes -- this nodes describe the mathematical function |
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365 and if there is one the inverse of that transform; there |
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366 would usually be two types of transforms; ones that use |
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367 theano and those that do not -- this is because those that |
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368 do can be composed |
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369 |
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370 Each node is lazy, in the sense that unless you try to iterate on it, it |
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371 will not try to compute the next value. |
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372 |
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374 Isn't this too low level ? |
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375 -------------------------- |
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376 |
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377 I think that way of writing and decomposing your neural network is |
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378 efficient and useful when writing such networks. Of course when you |
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379 just want to run a classical SdA you shouldn't need to go through the |
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380 trouble of writing all that. I think we should have Macors for this. |
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381 |
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382 * Macro -- syntactically it looks just like a transform (i.e. a python |
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383 function) only that it actually applies multiple transforms to the input |
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384 and might return several nodes (not just one). |
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385 Example: |
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386 |
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387 |
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388 learner, prediction, pretraining-learners = SdA( |
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389 input = data_x, |
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390 target = data_y, |
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391 hiddens = [200,200], |
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392 noises = [0.1,0.1]) |
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393 |
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394 |
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395 How do you deal with loops ? |
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396 ---------------------------- |
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398 When implementing architectures you some time need to loop like for |
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399 RNN or CD, PCD etc. Adding loops in such a scheme is always hard. |
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400 I borrowed the idea in the code below from PyBrain. You first construct |
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401 a shell layer that you call recurrent layer. Then you define the |
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402 functionality by giving the initial value and the recurrent step. |
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403 For example: |
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404 |
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405 .. code-block:: python |
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406 |
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407 ''' |
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408 # sketch of writing a RNN |
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409 x = load_mnist() |
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410 y = recurrent_layer() |
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411 y.value = tanh(dotW(x, n=50).t(0) + dotW(y.t(-1),50)) |
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412 y.t0 = zeros( (50,)) |
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413 out = dotW(y,10) |
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414 |
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415 |
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416 # sketch of writing CDk starting from x |
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417 x = recurrent_layer() |
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418 x.t0 = input_values |
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419 h = binomial_sample( sigmoid( dotW_b(x.tm(1)))) |
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420 x.value = binomial_sample( sigmoid( reconstruct(h, x.tm(1)))) |
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421 ## the assumption is that the inverse of sigmoid is the identity fn |
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422 pseudo_cost = free_energy(x.tp(k)) - free_energy(x.t0) |
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423 |
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424 |
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425 ''' |
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426 |
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427 How do I deal with constraints ? |
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428 -------------------------------- |
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429 |
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430 Use the add constrain. You are required to pass a transform with its |
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431 hyper-parameters initial values ? |
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432 |
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433 |
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434 How do I deal with other type of networs ? |
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435 ------------------------------------------ |
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436 |
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437 (opaque transforms) |
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438 |
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439 new_data = PCA(data_x) |
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440 |
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441 |
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442 svn_predictions = SVN(data_x) |
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443 svn_learner = SVN_learner(svn_predictions) |
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444 # Note that for the SVN this might be just syntactic sugar; we have the two |
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445 # steps because we expect different interfaces for this nodes |
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446 |
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447 |
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448 |
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449 Step 1.5 |
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450 ======== |
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451 |
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452 There is a wrapper function called plugin. Once you call plugin over |
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453 any of the previous nodes you will get a plugin that has a certain |
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454 set of conventions |
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456 '''' |