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annotate doc/v2_planning/dataset.txt @ 1199:98954d8cb92d

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v2planning - modifs to plugin_JB
author James Bergstra <bergstrj@iro.umontreal.ca>
date Mon, 20 Sep 2010 02:56:11 -0400
parents 9ff2242a817b
children 7dfc3d3052ea
rev   line source
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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>
parents:
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2 ======================================================
f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
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3
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Dumitru Erhan <dumitru.erhan@gmail.com>
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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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Frederic Bastien <nouiz@nouiz.org>
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7 needs to be flexible enough to accommodate different (sub)tasks and views of
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Frederic Bastien <nouiz@nouiz.org>
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8 the same underlying data.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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9 * Datasets as probability distributions from which one can sample.
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Olivier Delalleau <delallea@iro>
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10 * That's not something I would consider to be a dataset-related problem to
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
Olivier Delalleau <delallea@iro>
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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
Olivier Delalleau <delallea@iro>
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13 DatasetToDistribution class for instance, that would take care of viewing a
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
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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,
20a1af112a75 dataset: Looked into datasets from some other ML libraries
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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.
20a1af112a75 dataset: Looked into datasets from some other ML libraries
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30 - MDP: Seems to have restrictions on the type of data being passed around, as
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
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31 well as its dimensionality ("Input array data is typically assumed to be
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
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32 two-dimensional and ordered such that observations of the same variable are
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
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33 stored on rows and different variables are stored on columns.")
20a1af112a75 dataset: Looked into datasets from some other ML libraries
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34 - Orange: Data matrices, with names and types associated to each column.
20a1af112a75 dataset: Looked into datasets from some other ML libraries
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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.
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
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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.
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
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41 - Shogun: Vade Retro C++! (may be worth looking into their feature concept
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
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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 .. code-block:: python
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121
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122 class MNIST(Dataset):
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123 def __init__(self,inputs=['train_x.npy'],outputs=['train_y.npy']):
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124 self.type='standard_xy'
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125 self.in_memory = True
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126 self.inputs = inputs # load them or create
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127 self.outputs = outputs
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128 self.set_view('classification')
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129 self.set_n_classes(10)
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130 self.set_batch_size(20)
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131 self.n_batches = self._compute_n_batches()
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132
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133 def get_batch(self,batch_index):
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134 x,y = self._fetch_batch(batch_index)
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135 if self.view_type == 'classification':
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136 return x,numpy.int32(y)
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137 elif self.view_type == 'density_estimation':
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138 return x
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139 else:
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140 raise NotImplementedError()
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141
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142 def shared_data(self):
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143 shared_x = theano.shared(numpy.asarray(self.inputs, dtype=theano.config.floatX))
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144 shared_y = theano.shared(numpy.asarray(self.outputs, dtype=theano.config.floatX))
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145 return shared_x, T.cast(shared_y, 'int32')
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146
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147 def _compute_n_batches(self):
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148 pass
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149
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150 def _fetch_batch(self,batch_index):
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151 pass
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152
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153 But nothing stops you from defining get_train_batch, get_valid_batch and stuff
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154 like that!
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155
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156 So we'd use it as:
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157
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158 train_mnist = MNIST(inputs = ['train_x.npy'], outputs = ['train_y.npy'])
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159 valid_mnist = MNIST(inputs = ['valid_x.npy'], outputs = ['valid_y.npy'])
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160
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161 x,y = train_mnist.get_batch(0)
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162 train_mnist.set_view('density_estimation')
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163 x = train_mnist.get_batch(0)
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164
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165 or
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166
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167 mnist_data = MNIST(inputs = ['x.npy'], outputs = ['y.npy'])
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168 batches_train = range(int(mnist_data.n_batches*0.8))
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169 batches_valid = range(int(mnist_data.n_batches*0.8),mnist_data.n_batches)
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170
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171 xt,yt = mnist_data.get_batch(batches_train[0])
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172 xv,yv = mnist_data.get_batch(batches_valid[0])
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173
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174
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175
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176
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177 COMMENTS
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178 ~~~~~~~~
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179
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180 JB asks: How about asking datasets to also provide a visualization mechanism
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181 for showing / playing individual examples from the dataset, but also other
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182 external objects that are similar to dataset examples (e.g. filters from a
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183 weight matrix that filters images). This doesn't have to be complicated, and it
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184 can be shared between datasets that exist in one modality (e.g. image datasets
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185 can all use an image-rending method)
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186
1131
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187 OD replies: Besides being able to display data without prior knowledge of the
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188 kind of data inside a dataset, is there any reason to put this within the
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189 dataset class? If not, it seems to me it may be more appropriate to have a way
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190 for the dataset to describe the kind of data it holds, and keep the
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191 visualization code separate from the dataset itself. It would make it easier
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192 in particular to try different visualization systems, and description of the
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193 data may turn out to be useful for other reasons (however, it also means we'd
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194 need to come up with a good way to describe data, which could prove
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195 difficult).
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196
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197 JB asks: What may be passed as argument to the functions in Dataset, and what
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198 can be expected in return? Are there side effects (e.g. on the state of the
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199 Dataset) associated with any of the functions?
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200
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201 JB asks: What properties are part of the Dataset API? What possible types can
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202 they have, are they expected to be read-only or writeable? What do they mean?
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203
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204
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205 JB asks: What is a view? Does set_view change the Dataset or return a new
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206 Dataset with a certain view of the original (in which case call it get_view)?
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207 Does the view imply the types of the return-value of functions like
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208 get_batch? What is the difference between the view and the subclasses of
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209 Dataset in PyML?
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210
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211 JB asks: Do container formats (I'm thinking of HDF5) offer features for fast
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212 retrieval that we would like to expose via this interface?
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213
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214 JB asks: How would you recommend using this sort of dataset in a boosting
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215 algorithm where points need to be re-weighted.
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216
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217
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218 JB asks: Do we want to provide for the possibility of feedback that modifies the
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219 dataset? For example, curriculum learning might be adaptive in this sense, or
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220 if we wanted to provide a virtual world for an agent as a dataset then we need
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221 to provide 'actions' to get the next batch. Could this be done in the current
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222 API?
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223
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224
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225 Field names and attributes
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226 ~~~~~~~~~~~~~~~~~~~~~~~~~~
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227
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228 OD: One important question is how to handle fields' names and characteristics.
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229 For instance, it can be useful to know that the 3rd input field represents a
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230 number of fingers, and is a non-negative discrete field whose numeric value is
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231 meaningful (compared, to, say, an integer index that would correspond to an
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232 animal's category). We mentioned metadata during the meeting, but we did not
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233 get into its details: that may be a place where to put this kind of things.
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234
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235
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236 Freeing memory
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237 ~~~~~~~~~~~~~~
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238
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239 OD: It is sometimes useful to be able to free memory used by previous
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240 computations. A typical example is when you load in memory the original
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241 dataset, then perform various processing steps, ending with a new dataset that
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242 you also store in memory before feeding it to the learner. Unless you very
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243 carefully design your code to avoid it, your original dataset will still
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244 remain in memory (as well as maybe the results of some computations performed
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245 along the way). So there may be a use for a `clear()` method that would be
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246 called by the topmost dataset (the one doing the final memory caching), and
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247 would be forwarded iteratively to previous datasets so as to get back all this
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248 wasted memory space.
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249
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250 What is a mini-batch?
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251 ~~~~~~~~~~~~~~~~~~~~~
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252
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253 This is a follow-up to the meeting's discussion about whether a mini-batch
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254 returned by a dataset should be itself a dataset.
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255
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256 OD: During the meeting I was voting in favor of a 'yes', mostly because it
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257 made sense to me (a mini-batch is a subset of a dataset and thus should be a
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258 dataset), but now I tend towards 'no'. The main reason is it is not clear yet
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259 what the dataset interface will be, so that it is hard to judge whether this
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260 is good idea (my main concern is how much additional work would be required by
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261 the writer of a new dataset subclass). Anyway, maybe a first thing we could
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262 think about is what we want a mini-batch to be. I think we can agree that we
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263 would like to be able to do something like:
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264
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265 .. code-block:: python
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266
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267 for mb in dataset.mini_batches(size=10):
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268 learner.update(mb.input, mb.target)
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269
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270 so that it should be ok for a mini-batch to be an object whose fields
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271 (that should have the same name as those of the dataset) are numpy arrays.
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272 More generally, we would like to be able to iterate on samples in a
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273 mini-batch, or do random access on them, so a mini-batch should implement
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274 __iter__ and __getitem__.
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275 Besides this, is there any other typical use-case of a mini-batch? In
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276 particular, is there any reason to want an infinite mini-batch, or a very big
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277 mini-batch that may not fit in memory? (in which case we may need to revise
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278 our idea of what 'mini' means) Hopefully the answer to that last question is
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279 no, as I think it would definitely keep things simpler, since we could simply
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280 use numpy arrays (for numeric data) or lists (for anything else) to store
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281 mini-batches' data. So I vote for 'no'.
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282
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283 YB: I agree that a mini-batch should definitely be safely assumed
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284 to fit in memory. That makes it at least in principle semantically
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285 different from a dataset. But barring that restriction, it might
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286 share of the properties of a dataset.
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287
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288 A dataset is a learner
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289 ~~~~~~~~~~~~~~~~~~~~~~
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290
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291 OD: (this is hopefully a clearer re-write of the original version from
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292 r7e6e77d50eeb, which I was not happy with).
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293 There are typically three kinds of objects that spit out data:
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294
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295 1. Datasets that are loaded from disk or are able to generate data all by
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296 themselves (i.e. without any other dataset as input)
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297 2. Datasets that transform their input dataset in a way that only depends on
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298 the input dataset (e.g. filtering samples or features, normalizing data, etc.)
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299 3. Datasets that transform their input dataset in a way that is learned on a
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300 potentially different dataset (e.g. PCA when you want to learn the projection
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301 space on the training set in order to transform both the training and test
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302 sets).
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303
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304 My impression currently is that we would use dataset subclasses to handle 1
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305 and 2. However, 3 requires a learner framework, so you would need to have
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306 something like a LearnerOutputDataset(trained_learner, dataset).
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307
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308 Note however that 2 is a special case of 3 (where training does nothing), and
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309 1 is a special case of 2 (where we do not care about being given an input
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310 dataset). Thus you could decide to also implement 1 and 2 as learners wrapped
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311 by LearnerOutputDataset.
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312
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313 The main advantages I find in this approach (that I have been using at
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314 Ubisoft) are:
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315
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316 - You only need to learn how to subclass the learner class. The only dataset
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317 class is LearnerOutputDataset, which you could just name Dataset.
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318 - You do not have different ways to achieve the same result (having to figure
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319 out which one is most appropriate).
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320 - Upgrading code from 2 to 3 is more straighforward. Such a situation can
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321 happen e.g. if you write some code that normalizes your input dataset
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322 (situation 2), then realize later you would like to be able to normalize new
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323 datasets using the same parameters (e.g. same shift & rescaling), which
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324 requires situation 3.
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325 - It can make your life easier when thinking about how to plug things together
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326 (something that has not been discussed yet), because the interfaces of the
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327 various components are less varied.
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328
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329 I am not saying that we should necessarily do it this way, but I think it is
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330 worth at least keeping in mind this close relationship between simple
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331 processing and learning, and thinking about what are the benefits / drawbacks
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332 in keeping them separate in the class hierarchy.
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333
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334 RP: I actually like this idea of having the dataset implement the same
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335 interface as the learner ( or actually a subset of the interface .. ).
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336 I hope people decide to do this.
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337
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338 Support for shared variables
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339 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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340
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341 RP asks: What is the status of having the dataset support copying data
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342 on the GPU ( by storing data in shared variables) ? Have you decided to
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343 include this feature or not ? I think that the strongest selling point of
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344 Theano is that it runs on GPU transperently, and I see this as a good
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345 selling point for the library as well. Plus we intend to move more and
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346 more towards running things on GPU. If the dataset object does not support
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347 this feature we will need to find hacks around it ..
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348
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349 OD: I have like zero experience with GPU so hopefully someone else can answer
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350 this. But the way I see it, hopefully it could work by having some dataset
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351 object that would take care of storing its input data into a shared variable.
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352 OD (continued): After thinking a bit more about it, I am not sure that would
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353 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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354 understanding of it, but my feeling is that you need your learner to be
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355 written in a specific way to achieve this, in which case it may be up to the
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356 learner to take its input data and store it into a shared variable.
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357
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358 RP comment: Yes, the dataset object alone can not handle this, the issue is somewhere
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359 between the dataset and the learner. Or in other words, everytime you change
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360 the data you need to recompile your theano function. So the learner can not
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361 only get data from the dataset, it needs to get a shared variable. The learner
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362 should also be aware when the dataset is changed, to recompile its internal
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363 functions. I'm not sure which is the best wa to do this. My personal feeling
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364 is that the dataset should be part of the learner. The lerner should provide
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365 a function use_dataset ( or replace_dataset). When this function is called,
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366 all the theano functions in the learner get recompiled based on shared
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367 variables that the dataset object provides. It sort of fits very well in the
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368 framework that I have in mind, which was spattered around in the learner.txt
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369 and some of my previous emails. I think it shares a lot with James concepts,
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370 since it follows quite closely the concepts behind Theano.
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371
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372 OD asks: Ok, so why would the dataset have to be responsible for providing a
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373 shared variable? Why wouldn't the learner just create this shared variable
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374 internally and copy into it the data provided by the dataset?
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375
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376 RP replies: Sure, the learner could take care of all this. Note though that the
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377 learner should take care to divide the dataset into chunks that fit in the
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
378 GPU memory ( in case of a large dataset) and then take care of updating the
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
379 shared variables acording to the current chunk. Personally I feel like all
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
380 this data division, management and so on should be done by the dataset.
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
381 It feels more natural that way. For example assume you have a dataset that
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
382 is composed of a time series and some static data ( carre-tech heart beat
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
383 data is a good example). The static data is small enough so that you could
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
384 always store on the GPU, and you would only need to split the time series.
29b48deb6a84 reply/comment regarding the GPU and datasets
Razvan Pascanu <r.pascanu@gmail.com>
parents: 1105
diff changeset
385 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
386 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
387 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
388 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
389
1110
4797a4cb73e1 added comment to dataset.
Frederic Bastien <nouiz@nouiz.org>
parents: 1109
diff changeset
390 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
391 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
392 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
393 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
394 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
395 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
396 in the shared variable.
1116
18a092001752 An idea about Datasets and GPU.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1110
diff changeset
397
18a092001752 An idea about Datasets and GPU.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1110
diff changeset
398 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
399 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
400
1117
c1943feada10 Proposal for theano dataset wrapper. The details still have to be worked out.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1116
diff changeset
401 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
402 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
403
c1943feada10 Proposal for theano dataset wrapper. The details still have to be worked out.
Arnaud Bergeron <abergeron@gmail.com>
parents: 1116
diff changeset
404 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
405 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
406
7207f86a661f dataset: Comment on AB's idea to handle the GPU/shared variable issue
Olivier Delalleau <delallea@iro>
parents: 1124
diff changeset
407 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
408