Mercurial > pylearn
annotate doc/v2_planning/plugin_PL.py @ 1326:1b97fae7ea0d
added the parameter noise_value to binomial_noise formula.
author | Frederic Bastien <nouiz@nouiz.org> |
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date | Wed, 13 Oct 2010 15:04:42 -0400 |
parents | 826d78f0135f |
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rev | line source |
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1 class RBM(Model): |
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2 ''' |
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3 Restricted Boltzmann Machine. |
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4 ''' |
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5 def __init__(self, n_visible, n_hidden, visible = None, name = None): |
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6 |
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7 if name is None: |
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8 self.__name__ = self.__class__.__name__ |
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9 else: |
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10 self.__name__ = name |
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11 self.n_visible = n_visible |
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12 self.n_hidden = n_hidden |
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13 |
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14 if self.visible is None: |
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15 self.visible = theano.tensor.matrix([name='.'.join(self.__name__, 'visible'])) |
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16 else: |
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17 self.visible = visible |
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18 |
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19 self.W = theano.shared( |
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20 numpy.zeros((n_visible, n_hidden), dtype=theano.config.floatX), |
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21 name=self.__name__ + '.W') |
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22 self.b_hid = theano.shared( |
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23 numpy.zeros((n_hidden,), dtype=theano.config.floatX), |
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24 name=self.__name__ + '.b_hid') |
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25 self.b_vis = theano.shared( |
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26 numpy.zeros((n_hidden,), dtype=theano.config.floatX), |
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27 name=self.__name__ + '.b_vis') |
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28 |
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29 self.inputs = [self.visible] |
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30 self.targets = [] |
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31 self.parameters = [self.W, self.b_hid, self.b_vis] |
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32 self.outputs = ... |
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33 self.cost = None |
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34 self.gradients = [...] |
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35 |
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36 class LogisticRegression(Model): |
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37 pass |
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38 |
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39 class GradientBasedLearner(Learner): |
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40 ''' |
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41 Learner that uses a gradient-base Optimizer to train a Model. |
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42 ''' |
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43 def __init__(self, model, optimizer, name = None): |
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44 self.model = model |
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45 self.optimizer = optimizer |
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46 ... |
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47 |
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48 self.updates = optimizer.iterative_optimizer( |
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49 parameters = model.parameters, |
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50 cost = model.cost) |
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51 |
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52 # TODO: not sure of how to interface data set with the function |
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53 self.train_fn = theano.function(model.inputs+model.targets, |
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54 model.cost, updates=self.updates) |
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55 |
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56 def use_dataset(self, dataset): |
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57 self.train_set = dataset |
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58 |
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59 # The decorator indicates that this function will declare some hooks. |
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60 # More hooks could be automatically declared, for instance |
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61 # 'begin_function' and 'end_function' |
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62 @declare_hooks(['begin_train_iter', 'end_train_iter']) |
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63 def adapt(self, n_steps=1): |
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64 for i in xrange(n_steps): |
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65 self.adapt.hooks.execute( |
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66 'begin_train_iter', |
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67 context = dict(iter=i, total=n_steps, locals=locals())) |
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68 |
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69 data = self.train_set.next() |
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70 self.train_fn(data) |
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71 |
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72 self.adapt.hooks.execute( |
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73 'end_train_iter', |
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74 context = dict(iter = i, locals=locals())) |
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75 |
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76 |
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77 class SGD(Optimizer): |
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78 ''' |
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79 Stochastic gradient descent with fixed learning rate. |
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80 ''' |
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81 def __init__(self, step_size): |
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82 self.step_size = step_size |
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83 |
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84 def iterative_optimizer( |
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85 parameters, |
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86 cost=None, |
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87 gradients=None, |
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88 stop=None, |
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89 updates=None, |
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90 ): |
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91 |
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92 if updates is not None: |
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93 ret = updates |
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94 else: |
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95 ret = {} |
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96 |
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97 if gradients is None: |
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98 if cost is None: |
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99 raise SomeError('SGD needs to be provided either a cost or a gradients list') |
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100 gradients = theano.tensor.grad(cost, parameters) |
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101 |
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102 for p, g in izip(parameters, gradients): |
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103 if p in updates: |
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104 raise KeyError('Parameter %s already has an update value (%s)' % (p, g)) |
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105 ret[p] = p - self.step_size * g |
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106 |
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107 # never stop |
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108 if stop is not None: |
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109 ret[stop] = False |
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110 |
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111 return ret |
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112 |
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113 |
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114 |
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115 class DBN(Learner): |
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116 ''' |
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117 Deep Belief Network. |
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118 ''' |
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119 def __init__(self, n_layers, layer_config, n_ft_steps, ft_step_size): |
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120 # Layers are GradientBasedLearners, with DBN as Model |
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121 self.layers = [] |
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122 # Pretraining cumulative schedule |
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123 self.pt_cumul_schedule = [0] |
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124 # Build the layers and the fully-connected model |
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125 self.input = theano.tensor.matrix(name='.'.join([self.__name__, 'input'])) |
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126 self.output = self.input |
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127 self.ft_params = [] |
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128 for i,lconf in enumerate(layer_config): |
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129 rbm = RBM(visible = self.output, ...) |
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130 self.output = rbm.hidden_expectation |
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131 layer = GradientBasedLearner(...) |
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132 self.layers.append(layer) |
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133 |
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134 self.pt_cumul_schedule.append( |
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135 self.pt_cumul_schedule[-1]+lc.n_pretrain_steps) |
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136 |
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137 self.ft_params.extend([rbm.W, rbm.b_hid]) |
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138 |
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139 # Build the fine-tunable model |
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140 self.target = theano.tensor.ivector(name='.'.join([self.__name__, 'target'])) |
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141 logreg = LogisticRegression(...) |
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142 self.output = logreg.output |
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143 self.cost = logreg.nll |
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144 self.ft_params.extend([logreg.W, logreg.b]) |
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145 |
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146 ft_optimizer = SGD(ft_step_size) |
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147 self.ft_updates = ft_optimizer.iterative_optimizer( |
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148 parameters = self.ft_params, |
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149 cost = self.cost) |
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150 self.ft_fn = theano.function( |
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151 [self.input, self.target], |
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152 self.cost, |
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153 updates = self.ft_updates, |
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154 name='.'.join([self.__name__, 'ft_fn'])) |
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155 |
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156 self.stage = 0 |
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157 |
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158 @declare_hooks([ |
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159 'begin_pretrain_layer','end_pretrain_layer', |
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160 'begin_finetune_iter', 'end_finetune_iter']) |
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161 |
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162 def adapt(self, n_steps=1): |
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163 ''' |
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164 Each "step" is accomplished by the corresponding Learner (either an RBM, |
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165 or the global NNet). |
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166 ''' |
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167 train_x, train_y = self.dataset |
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168 n_remaining_steps = n_steps |
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169 # Unsupervised pre-training |
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170 for i, layer in ienumerate(self.layers): |
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171 if (self.pt_cumul_schedule[i] <= self.stage |
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172 and self.stage < self.pt_cumul_schedule[i+1]): |
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173 |
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174 self.adapt.hooks.execute( |
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175 'begin_pretrain_layer', |
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176 context = dict(iter=i, total=len(self.layers), locals=locals())) |
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177 |
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178 n_pt_steps = min(n_remaining_steps, self.pt_cumul_schedule[i+1] - self.stage) |
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179 layer.use_dataset(train_x) |
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180 layer.adapt(n_steps = n_pt_steps) |
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181 self.stage += self.n_pt_steps |
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182 n_remaining_steps -= n_pt_steps |
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183 |
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184 self.adapt.hooks.execute( |
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185 'end_pretrain_layer', |
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186 context = dict(iter=i, total=len(self.layers), locals=locals())) |
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187 |
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188 # For the next layer, the data needs to be preprocessed |
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189 train_x = layer.compute_Eh_given_v(train_x) # or just compute_output? |
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190 |
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191 # Supervised fine-tuning |
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192 if n_remaining_steps > 0: |
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193 sup_data = train_x, train_y |
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194 for i in xrange(n_remaining_steps): |
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195 self.adapt.hooks.execute( |
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196 'begin_train_iter', |
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197 context = dict(iter=i, total=n_steps, locals=locals())) |
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198 |
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199 data = self.train_set.next() |
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200 self.ft_fn(data) |
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201 |
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202 self.adapt.hooks.execute( |
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203 'end_train_iter', |
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204 context = dict(iter = i, locals=locals())) |
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205 |
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206 |
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207 ## TODO: implement k-fold cross-validation |
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208 |
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209 class Hooks: |
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210 def __init__(self): |
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211 # The DB consists in a dictionary, |
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212 # the keys are the hooks' names (as strings), |
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213 # the values are lists of (function, exec_condition) pairs of functions |
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214 self.db = {} |
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215 |
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216 def declare(self, name): |
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217 if name in self.db: |
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218 raise KeyError('Hook "%s" is already declared' % name) |
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219 self.db[name] = [] |
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220 |
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221 def execute(self, name, context): |
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222 if name not in self.db: |
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223 raise KeyError('Hook "%s" does not exist', % name) |
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224 #TODO: add contextual information to context, like current time, time of last call,... |
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225 for fn, cond in self.db[name]: |
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226 if cond(**context): |
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227 fn(**context) |
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228 |
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229 def register(self, name, function, exec_condition): |
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230 if name not in self.db: |
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231 raise KeyError('Hook "%s" does not exist', % name) |
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232 self.db[name].append((function, exec_condition)) |
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233 |
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234 #TODO: add __getattr__ to have more intuitive access to the hooks |
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235 |
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236 # Hook declaration mechanism |
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237 def declare_hooks(hooks_list): |
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238 def deco(f): |
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239 f.hooks = Hooks() |
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240 for hook_name in hooks_list: |
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241 f.hooks.declare(hook_name) |
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242 |
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243 return deco |
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244 |
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245 # Conditions |
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246 def always(): |
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247 return lambda *args, **kwargs: True |
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248 |
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249 |
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250 def main(): |
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251 train_data = MNIST.gettrain() |
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252 test_data = MNIST.gettest() |
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253 |
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254 train_x, train_y = train_data |
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255 |
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256 preprocessor = PCA(ndim = 80) |
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257 preprocessor.train(train_x) |
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258 |
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259 preprocessed_x = preprocessor.compute_output(train_x) |
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260 ## for more robustess, we can have something like: |
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261 # peprocessed_x = ProcessDataSet(orig_data = train_x, function = preprocessor.compute_output) |
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262 |
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263 |
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264 x = matrix() |
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265 dbn = DBN( |
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266 input = x, |
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267 n_layers = 3, |
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268 layer_config = [dict(n_hidden = 500, n_unsup_steps=1000)] * 3 |
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269 ) |
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270 |
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271 dbn.layers[0].adapt.hooks.register( |
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272 'begin_train_iter', |
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273 function = ..., |
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274 exec_cond = always() |
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275 ) |
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276 |
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277 dbn.layers[0].adapt.hooks.register( |
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278 'end_train_iter', |
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279 function = ..., |
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280 exec_cond = lambda iter, **kwargs: iter%20==0 |
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281 ) |
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282 |
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283 |
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284 if __name__ == '__main__': |
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285 main() |
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286 |
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287 |