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annotate doc/v2_planning/dataset.txt @ 1170:53340a8df1fa

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coding_style: Started to write full code sample
author Olivier Delalleau <delallea@iro>
date Fri, 17 Sep 2010 14:37:00 -0400
parents d9550c27a192
children 9ff2242a817b
rev   line source
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f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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1 Discussion of Function Specification for Dataset Types
f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
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2 ======================================================
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3
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4 Some talking points from the September 2 meeting:
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5
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6 * Datasets as views/tasks (Pascal Vincent's idea): our dataset specification
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7 needs to be flexible enough to accommodate different (sub)tasks and views of
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8 the same underlying data.
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9 * Datasets as probability distributions from which one can sample.
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10 * That's not something I would consider to be a dataset-related problem to
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11 tackle now: a probability distribution in Pylearn would probably be a
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
Olivier Delalleau <delallea@iro>
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12 different kind of beast, and it should be easy enough to have a
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
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13 DatasetToDistribution class for instance, that would take care of viewing a
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14 dataset as a probability distribution. -- OD
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15 * Our specification should allow transparent handling of infinite datasets (or
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16 simply datasets which cannot fit in memory)
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17 * GPU/buffering issues.
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18
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19 Commiteee: DE, OB, OD, AB, PV
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20 Leader: DE
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21
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22 Some ideas from existing ML libraries:
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23
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24 - PyML: notion of dataset containers: VectorDataSet, SparseDataSet, KernelData,
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25 PairDataSet, Aggregate. Ultimately, the learner decides
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26 - mlpy: very primitive notions of data (simple 2D matrices)
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27 - PyBrain: Datasets are geared towards specific tasks: ClassificationDataSet,
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28 SequentialDataSet, ReinforcementDataSet, ... Each class is quite
20a1af112a75 dataset: Looked into datasets from some other ML libraries
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29 constrained and may have a different interface.
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30 - MDP: Seems to have restrictions on the type of data being passed around, as
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31 well as its dimensionality ("Input array data is typically assumed to be
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32 two-dimensional and ordered such that observations of the same variable are
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33 stored on rows and different variables are stored on columns.")
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34 - Orange: Data matrices, with names and types associated to each column.
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35 Basically there seems to be only one base dataset class that contains the
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36 data. Data points are lists (of values corresponding to each column).
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37 - APGL: Hard to say how they deal with data from the documentation alone.
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38 - Monte: Data is simply numpy arrays.
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39 - scikits.learn: Dataset is a simple container with e.g. dataset.data being
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40 a 2D numpy array of input features, and dataset.target the target vector.
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41 - Shogun: Vade Retro C++! (may be worth looking into their feature concept
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42 though).
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43 - Any more worth looking at?
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44
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45 A few things that our dataset containers should support at a minimum:
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46
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47 - streams, possibly infinite
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48 - task/views of the data for different problems
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49 - indexing & slicing
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50 - pairs or triples or etc of examples
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51 - a 'distance/gram matrix' container (imagine that the data is given to you
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52 as a distance matrix)
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53 - multi-dimensional time-series (again, maybe with pairs/triples, maybe
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54 given to you as a distance matrix over time)
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55
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56 Another question to consider is the following: how tight should it integrate
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57 with Theano? Do we want to be able to store data as shared variables or just
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58 have an option for that? Theano + GPU constrains things that we can do (in terms
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59 of sizes, buffering, etc): these are things we need to think about, but it's not
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60 clear whether we should aim for building them into the interface.
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61
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62 Task views of the data for different problems: How can we achieve this? Should
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63 we simply have a set of standard dataset descriptors ('classification',
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64 'regression', 'multi-label', 'density_estimation') and have a set_view method
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65 that changes the current dataset view type?
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66
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67 There is then the question of how to approach the design of a Dataset class from
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68 an OOP perspective. So far, my (Dumi's) idea is to have an almost 'abstract class'
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69 Dataset that doesn't implement any methods except a few setters/getters. The reason
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70 to have the methods listed that way is to have a common 'specification', but classes
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71 that inherit from Dataset need not implement every single method (only the ones
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72 that are relevant) and can obviously implement other methods as appropriate. The
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73 reason to have a common specification (as abstract as it might be) is to, well,
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74 have a common specification that would make our code clearer and cleaner.
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75
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76 An example of what I (Dumi) am thinking in terms of concrete API:
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77
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78 class Dataset:
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79 def __init__(self):
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80 self.type = None
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81 self.in_memory = None
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82 self.inputs = None # list of filepaths, or objects in memory, or...
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83 self.outputs = None
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84
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85 def get_example(self,example_index):
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86 raise NotImplementedError()
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87
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88 def get_next_example(self):
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89 raise NotImplementedError()
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90
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91 def get_batch(self,batch_index):
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92 raise NotImplementedError()
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93
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94 def get_next_batch(self):
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95 raise NotImplementedError()
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96
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97 def get_slice(self,slice_object):
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98 raise NotImplementedError()
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99
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100 def set_view(self,view_type):
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101 self.view_type = view_type
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102 self.n_classes = None
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103
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104 def set_n_classes(self,n_classes):
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105 self.n_classes = n_classes
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106
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107 def set_batch_size(self,batch_size):
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108 self.batch_size = batch_size
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109
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110 You will note that there is no notion of train/valid/test in this class: I think we should
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111 just have a train dataset, a valid one and a test one instead or (if it's in one
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112 big file or infinite stream) just handle the split ourselves (via slicing, for
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113 instance). I (Dumi) am of the opinion that it keeps things cleaner, but the
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114 specification does not preclude more fine-grained 'splitting' of the data.
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115
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116 A concrete implementation would look like this (we would have one class per
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117 dataset that we use, and the class declaration contains essentially everything
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118 there is to know about the dataset):
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119
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120 class MNIST(Dataset):
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121 def __init__(self,inputs=['train_x.npy'],outputs=['train_y.npy']):
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122 self.type='standard_xy'
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123 self.in_memory = True
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124 self.inputs = inputs # load them or create
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125 self.outputs = outputs
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126 self.set_view('classification')
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127 self.set_n_classes(10)
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128 self.set_batch_size(20)
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129 self.n_batches = self._compute_n_batches()
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130
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131 def get_batch(self,batch_index):
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132 x,y = self._fetch_batch(batch_index)
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133 if self.view_type == 'classification':
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134 return x,numpy.int32(y)
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135 elif self.view_type == 'density_estimation':
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136 return x
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137 else:
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138 raise NotImplementedError()
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139
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140 def shared_data(self):
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141 shared_x = theano.shared(numpy.asarray(self.inputs, dtype=theano.config.floatX))
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142 shared_y = theano.shared(numpy.asarray(self.outputs, dtype=theano.config.floatX))
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143 return shared_x, T.cast(shared_y, 'int32')
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144
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145 def _compute_n_batches(self):
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146 pass
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147
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148 def _fetch_batch(self,batch_index):
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149 pass
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150
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151 But nothing stops you from defining get_train_batch, get_valid_batch and stuff
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152 like that!
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153
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154 So we'd use it as:
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155
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156 train_mnist = MNIST(inputs = ['train_x.npy'], outputs = ['train_y.npy'])
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157 valid_mnist = MNIST(inputs = ['valid_x.npy'], outputs = ['valid_y.npy'])
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158
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159 x,y = train_mnist.get_batch(0)
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160 train_mnist.set_view('density_estimation')
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161 x = train_mnist.get_batch(0)
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162
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163 or
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164
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165 mnist_data = MNIST(inputs = ['x.npy'], outputs = ['y.npy'])
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166 batches_train = range(int(mnist_data.n_batches*0.8))
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167 batches_valid = range(int(mnist_data.n_batches*0.8),mnist_data.n_batches)
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168
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169 xt,yt = mnist_data.get_batch(batches_train[0])
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170 xv,yv = mnist_data.get_batch(batches_valid[0])
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171
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172
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173
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174
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175 COMMENTS
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176 ~~~~~~~~
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177
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178 JB asks: How about asking datasets to also provide a visualization mechanism
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179 for showing / playing individual examples from the dataset, but also other
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180 external objects that are similar to dataset examples (e.g. filters from a
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181 weight matrix that filters images). This doesn't have to be complicated, and it
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182 can be shared between datasets that exist in one modality (e.g. image datasets
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183 can all use an image-rending method)
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184
1131
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185 OD replies: Besides being able to display data without prior knowledge of the
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186 kind of data inside a dataset, is there any reason to put this within the
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187 dataset class? If not, it seems to me it may be more appropriate to have a way
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188 for the dataset to describe the kind of data it holds, and keep the
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189 visualization code separate from the dataset itself. It would make it easier
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190 in particular to try different visualization systems, and description of the
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191 data may turn out to be useful for other reasons (however, it also means we'd
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192 need to come up with a good way to describe data, which could prove
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193 difficult).
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194
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195 JB asks: What may be passed as argument to the functions in Dataset, and what
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196 can be expected in return? Are there side effects (e.g. on the state of the
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197 Dataset) associated with any of the functions?
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198
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199 JB asks: What properties are part of the Dataset API? What possible types can
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200 they have, are they expected to be read-only or writeable? What do they mean?
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201
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202
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203 JB asks: What is a view? Does set_view change the Dataset or return a new
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204 Dataset with a certain view of the original (in which case call it get_view)?
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205 Does the view imply the types of the return-value of functions like
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206 get_batch? What is the difference between the view and the subclasses of
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207 Dataset in PyML?
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208
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209 JB asks: Do container formats (I'm thinking of HDF5) offer features for fast
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210 retrieval that we would like to expose via this interface?
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211
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212 JB asks: How would you recommend using this sort of dataset in a boosting
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213 algorithm where points need to be re-weighted.
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214
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215
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216 JB asks: Do we want to provide for the possibility of feedback that modifies the
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217 dataset? For example, curriculum learning might be adaptive in this sense, or
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218 if we wanted to provide a virtual world for an agent as a dataset then we need
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219 to provide 'actions' to get the next batch. Could this be done in the current
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220 API?
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221
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222
1082
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223 Field names and attributes
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224 ~~~~~~~~~~~~~~~~~~~~~~~~~~
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225
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226 OD: One important question is how to handle fields' names and characteristics.
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227 For instance, it can be useful to know that the 3rd input field represents a
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228 number of fingers, and is a non-negative discrete field whose numeric value is
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229 meaningful (compared, to, say, an integer index that would correspond to an
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230 animal's category). We mentioned metadata during the meeting, but we did not
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231 get into its details: that may be a place where to put this kind of things.
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232
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233
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Olivier Delalleau <delallea@iro>
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234 Freeing memory
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235 ~~~~~~~~~~~~~~
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236
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237 OD: It is sometimes useful to be able to free memory used by previous
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238 computations. A typical example is when you load in memory the original
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239 dataset, then perform various processing steps, ending with a new dataset that
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240 you also store in memory before feeding it to the learner. Unless you very
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241 carefully design your code to avoid it, your original dataset will still
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242 remain in memory (as well as maybe the results of some computations performed
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243 along the way). So there may be a use for a `clear()` method that would be
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244 called by the topmost dataset (the one doing the final memory caching), and
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245 would be forwarded iteratively to previous datasets so as to get back all this
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246 wasted memory space.
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247
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248 What is a mini-batch?
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249 ~~~~~~~~~~~~~~~~~~~~~
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250
4c00af69c164 dataset: Asking what we want from mini-batches
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251 This is a follow-up to the meeting's discussion about whether a mini-batch
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252 returned by a dataset should be itself a dataset.
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253
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254 OD: During the meeting I was voting in favor of a 'yes', mostly because it
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255 made sense to me (a mini-batch is a subset of a dataset and thus should be a
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256 dataset), but now I tend towards 'no'. The main reason is it is not clear yet
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257 what the dataset interface will be, so that it is hard to judge whether this
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258 is good idea (my main concern is how much additional work would be required by
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259 the writer of a new dataset subclass). Anyway, maybe a first thing we could
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260 think about is what we want a mini-batch to be. I think we can agree that we
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261 would like to be able to do something like:
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262 for mb in dataset.mini_batches(size=10):
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263 learner.update(mb.input, mb.target)
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264 so that it should be ok for a mini-batch to be an object whose fields
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265 (that should have the same name as those of the dataset) are numpy arrays.
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266 More generally, we would like to be able to iterate on samples in a
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267 mini-batch, or do random access on them, so a mini-batch should implement
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268 __iter__ and __getitem__.
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269 Besides this, is there any other typical use-case of a mini-batch? In
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270 particular, is there any reason to want an infinite mini-batch, or a very big
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271 mini-batch that may not fit in memory? (in which case we may need to revise
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272 our idea of what 'mini' means) Hopefully the answer to that last question is
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273 no, as I think it would definitely keep things simpler, since we could simply
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274 use numpy arrays (for numeric data) or lists (for anything else) to store
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275 mini-batches' data. So I vote for 'no'.
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276
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277 YB: I agree that a mini-batch should definitely be safely assumed
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278 to fit in memory. That makes it at least in principle semantically
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279 different from a dataset. But barring that restriction, it might
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280 share of the properties of a dataset.
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281
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282 A dataset is a learner
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283 ~~~~~~~~~~~~~~~~~~~~~~
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284
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285 OD: (this is hopefully a clearer re-write of the original version from
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286 r7e6e77d50eeb, which I was not happy with).
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287 There are typically three kinds of objects that spit out data:
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288 1. Datasets that are loaded from disk or are able to generate data all by
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289 themselves (i.e. without any other dataset as input)
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290 2. Datasets that transform their input dataset in a way that only depends on
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291 the input dataset (e.g. filtering samples or features, normalizing data, etc.)
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292 3. Datasets that transform their input dataset in a way that is learned on a
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293 potentially different dataset (e.g. PCA when you want to learn the projection
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294 space on the training set in order to transform both the training and test
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295 sets).
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296 My impression currently is that we would use dataset subclasses to handle 1
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297 and 2. However, 3 requires a learner framework, so you would need to have
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298 something like a LearnerOutputDataset(trained_learner, dataset).
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299
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300 Note however that 2 is a special case of 3 (where training does nothing), and
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301 1 is a special case of 2 (where we do not care about being given an input
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302 dataset). Thus you could decide to also implement 1 and 2 as learners wrapped
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303 by LearnerOutputDataset.
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304
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305 The main advantages I find in this approach (that I have been using at
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306 Ubisoft) are:
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307 - You only need to learn how to subclass the learner class. The only dataset
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308 class is LearnerOutputDataset, which you could just name Dataset.
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309 - You do not have different ways to achieve the same result (having to figure
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310 out which one is most appropriate).
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311 - Upgrading code from 2 to 3 is more straighforward. Such a situation can
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312 happen e.g. if you write some code that normalizes your input dataset
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313 (situation 2), then realize later you would like to be able to normalize new
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314 datasets using the same parameters (e.g. same shift & rescaling), which
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315 requires situation 3.
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316 - It can make your life easier when thinking about how to plug things together
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317 (something that has not been discussed yet), because the interfaces of the
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318 various components are less varied.
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319
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320 I am not saying that we should necessarily do it this way, but I think it is
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321 worth at least keeping in mind this close relationship between simple
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322 processing and learning, and thinking about what are the benefits / drawbacks
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323 in keeping them separate in the class hierarchy.
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324
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325 RP: I actually like this idea of having the dataset implement the same
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326 interface as the learner ( or actually a subset of the interface .. ).
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327 I hope people decide to do this.
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328
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329 Support for shared variables
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330 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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331
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332 RP asks: What is the status of having the dataset support copying data
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333 on the GPU ( by storing data in shared variables) ? Have you decided to
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334 include this feature or not ? I think that the strongest selling point of
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335 Theano is that it runs on GPU transperently, and I see this as a good
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336 selling point for the library as well. Plus we intend to move more and
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337 more towards running things on GPU. If the dataset object does not support
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338 this feature we will need to find hacks around it ..
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339
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340 OD: I have like zero experience with GPU so hopefully someone else can answer
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341 this. But the way I see it, hopefully it could work by having some dataset
319de699fb67 dataset: Reply to GPU question
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342 object that would take care of storing its input data into a shared variable.
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343 OD (continued): After thinking a bit more about it, I am not sure that would
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344 work. I definitely need to look at some code doing it to get a better
75175e2e697d dataset: Continued comment about GPU and shared variables
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345 understanding of it, but my feeling is that you need your learner to be
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346 written in a specific way to achieve this, in which case it may be up to the
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347 learner to take its input data and store it into a shared variable.
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348
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349 RP comment: Yes, the dataset object alone can not handle this, the issue is somewhere
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350 between the dataset and the learner. Or in other words, everytime you change
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351 the data you need to recompile your theano function. So the learner can not
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352 only get data from the dataset, it needs to get a shared variable. The learner
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353 should also be aware when the dataset is changed, to recompile its internal
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354 functions. I'm not sure which is the best wa to do this. My personal feeling
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355 is that the dataset should be part of the learner. The lerner should provide
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356 a function use_dataset ( or replace_dataset). When this function is called,
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357 all the theano functions in the learner get recompiled based on shared
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358 variables that the dataset object provides. It sort of fits very well in the
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359 framework that I have in mind, which was spattered around in the learner.txt
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360 and some of my previous emails. I think it shares a lot with James concepts,
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361 since it follows quite closely the concepts behind Theano.
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362
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363 OD asks: Ok, so why would the dataset have to be responsible for providing a
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364 shared variable? Why wouldn't the learner just create this shared variable
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365 internally and copy into it the data provided by the dataset?
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366
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367 RP replies: Sure, the learner could take care of all this. Note though that the
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368 learner should take care to divide the dataset into chunks that fit in the
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369 GPU memory ( in case of a large dataset) and then take care of updating the
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370 shared variables acording to the current chunk. Personally I feel like all
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371 this data division, management and so on should be done by the dataset.
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372 It feels more natural that way. For example assume you have a dataset that
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373 is composed of a time series and some static data ( carre-tech heart beat
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374 data is a good example). The static data is small enough so that you could
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375 always store on the GPU, and you would only need to split the time series.
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parents: 1105
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376 For the learner to do this ( since it gets the same interface from any
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
377 dataset object) would be like and if <this case> then, while for the
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
378 dataset is just a different class. But I'm happy to have all this GPU stuff
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
379 send to the learner as well if everybody else believe that is better.
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
380
1110
4797a4cb73e1 added comment to dataset.
Frederic Bastien <nouiz@nouiz.org>
parents: 1109
diff changeset
381 FB comment: I don't understand why you would need to recompile the theano function.
4797a4cb73e1 added comment to dataset.
Frederic Bastien <nouiz@nouiz.org>
parents: 1109
diff changeset
382 Their is 2 cases, the data is in a shared variable. You can directly change the data
4797a4cb73e1 added comment to dataset.
Frederic Bastien <nouiz@nouiz.org>
parents: 1109
diff changeset
383 in the shared variable without recompiling the theano fct. The second case is when
4797a4cb73e1 added comment to dataset.
Frederic Bastien <nouiz@nouiz.org>
parents: 1109
diff changeset
384 the dataset is in an ordinary theano variable. In that case, the first step in the
4797a4cb73e1 added comment to dataset.
Frederic Bastien <nouiz@nouiz.org>
parents: 1109
diff changeset
385 theano fct will be to transfer the dataset to the gpu before computation. If the data
4797a4cb73e1 added comment to dataset.
Frederic Bastien <nouiz@nouiz.org>
parents: 1109
diff changeset
386 change at each call, that will be as efficient as changing the data manually every time
4797a4cb73e1 added comment to dataset.
Frederic Bastien <nouiz@nouiz.org>
parents: 1109
diff changeset
387 in the shared variable.
1116
18a092001752 An idea about Datasets and GPU.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1110
diff changeset
388
18a092001752 An idea about Datasets and GPU.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1110
diff changeset
389 AB: I have an idea about this which kind of fits in the "building a
18a092001752 An idea about Datasets and GPU.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1110
diff changeset
390 theano op" thing that we talked about at the last meeting.
18a092001752 An idea about Datasets and GPU.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1110
diff changeset
391
1117
c1943feada10 Proposal for theano dataset wrapper. The details still have to be worked out.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1116
diff changeset
392 We can just build a theano Op that wraps dataset objects and takes
c1943feada10 Proposal for theano dataset wrapper. The details still have to be worked out.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1116
diff changeset
393 care of the details of tranferring data to the GPU or otherwise.
c1943feada10 Proposal for theano dataset wrapper. The details still have to be worked out.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1116
diff changeset
394
c1943feada10 Proposal for theano dataset wrapper. The details still have to be worked out.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1116
diff changeset
395 I have a prototype interface/implemantation in the shared_dataset.py
c1943feada10 Proposal for theano dataset wrapper. The details still have to be worked out.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1116
diff changeset
396 file in this directory.
1127
7207f86a661f dataset: Comment on AB's idea to handle the GPU/shared variable issue
Olivier Delalleau <delallea@iro>
parents: 1124
diff changeset
397
7207f86a661f dataset: Comment on AB's idea to handle the GPU/shared variable issue
Olivier Delalleau <delallea@iro>
parents: 1124
diff changeset
398 OD: I like AB's approach.
7207f86a661f dataset: Comment on AB's idea to handle the GPU/shared variable issue
Olivier Delalleau <delallea@iro>
parents: 1124
diff changeset
399