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annotate doc/v2_planning/plugin_PL.py @ 1276:822c7691a759

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pca - added pca_whiten_inverse function
author James Bergstra <bergstrj@iro.umontreal.ca>
date Tue, 14 Sep 2010 22:35:51 -0400
parents 826d78f0135f
children
rev   line source
1253
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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