Mercurial > pylearn
annotate doc/v2_planning/main_plan.txt @ 1214:681b5e7e3b81
a few comments on James version
| author | Razvan Pascanu <r.pascanu@gmail.com> |
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| date | Wed, 22 Sep 2010 10:39:39 -0400 |
| parents | 0e12ea6ba661 |
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| rev | line source |
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| 941 | 1 |
| 2 Motivation | |
| 3 ========== | |
| 4 | |
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5 Yoshua (points discussed Thursday Sept 2, 2010 at LISA tea-talk) |
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6 ---------------------------------------------------------------- |
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8 ****** Why we need to get better organized in our code-writing ****** |
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9 |
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10 - current state of affairs on top of Theano is anarchic and does not lend itself to easy code re-use |
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11 - the lab is growing and will continue to grow significantly, and more people outside the lab are using Theano |
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12 - we have new industrial partners and funding sources that demand deliverables, and more/better collectively organized efforts |
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13 |
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14 *** Who can take advantage of this *** |
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15 |
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16 - us, directly, taking advantage of the different advances made by different researchers in the lab to yield better models |
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17 - us, easier to compare different models and different datasets with different metrics on different computing platforms available to us |
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18 - future us, new students, able to quickly move into 'production' mode without having to reinvent the wheel |
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19 - students in the two ML classes, able to play with the library to explore new ML variants |
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20 - other ML researchers in academia, able to play with our algorithms, try new variants, cite our papers |
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21 - non-ML users in or out of academia, and our user-partners |
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22 |
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23 |
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24 *** Move with care *** |
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25 |
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26 - Write down use-cases, examples for each type of module, do not try to be TOO general |
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27 - Want to keep ease of exploring and flexibility, not create a prison |
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28 - Too many constraints can lead to paralysis, especially in C++ object-oriented model |
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29 - Too few guidelines lead to code components that are not interchangeable |
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30 - Poor code practice leads to buggy, spaguetti code |
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31 |
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32 *** What *** |
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33 |
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34 - define standards |
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35 - write-up a few instances of each basic type (dataset, learner, optimizer, hyper-parameter exploration boilerplate, etc.) enough to implement some of the basic algorithms we use often (e.g. like those in the tutorials) |
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36 - let the library grow according to our needs |
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37 - keep tight reins on it to control quality |
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38 |
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39 *** Content and Form *** |
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40 |
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41 We need to establish guidelines and conventions for |
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42 |
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43 * Content: what are the re-usable components? define conventions or API for each, make sure they fit with each other |
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44 * Form: social engineering, coding practices and conventions, code review, incentives |
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45 |
| 941 | 46 Yoshua: |
| 47 ------- | |
| 48 | |
| 49 We are missing a *Theano Machine Learning library*. | |
| 50 | |
| 51 The deep learning tutorials do a good job but they lack the following features, which I would like to see in a ML library: | |
| 52 | |
| 53 - a well-organized collection of Theano symbolic expressions (formulas) for handling most of | |
| 54 what is needed either in implementing existing well-known ML and deep learning algorithms or | |
| 55 for creating new variants (without having to start from scratch each time), that is the | |
| 56 mathematical core, | |
| 57 | |
| 58 - a well-organized collection of python modules to help with the following: | |
| 59 - several data-access models that wrap around learning algorithms for interfacing with various types of data (static vectors, images, sound, video, generic time-series, etc.) | |
| 60 - generic utility code for optimization | |
| 61 - stochastic gradient descent variants | |
| 62 - early stopping variants | |
| 63 - interfacing to generic 2nd order optimization methods | |
| 64 - 2nd order methods tailored to work on minibatches | |
| 65 - optimizers for sparse coefficients / parameters | |
| 66 - generic code for model selection and hyper-parameter optimization (including the use and coordination of multiple jobs running on different machines, e.g. using jobman) | |
| 67 - generic code for performance estimation and experimental statistics | |
| 68 - visualization tools (using existing python libraries) and examples for all of the above | |
| 69 - learning algorithm conventions and meta-learning algorithms (bagging, boosting, mixtures of experts, etc.) which use them | |
| 70 | |
| 71 [Note that many of us already use some instance of all the above, but each one tends to reinvent the wheel and newbies don't benefit from a knowledge base.] | |
| 72 | |
| 73 - a well-documented set of python scripts using the above library to show how to run the most | |
| 74 common ML algorithms (possibly with examples showing how to run multiple experiments with | |
| 75 many different models and collect statistical comparative results). This is particularly | |
| 76 important for pure users to adopt Theano in the ML application work. | |
| 77 | |
| 78 Ideally, there would be one person in charge of this project, making sure a coherent and | |
| 79 easy-to-read design is developed, along with many helping hands (to implement the various | |
| 80 helper modules, formulae, and learning algorithms). | |
| 81 | |
| 82 | |
| 83 James: | |
| 84 ------- | |
| 85 | |
| 86 I am interested in the design and implementation of the "well-organized collection of Theano | |
| 87 symbolic expressions..." | |
| 88 | |
| 89 I would like to explore algorithms for hyper-parameter optimization, following up on some | |
| 90 "high-throughput" work. I'm most interested in the "generic code for model selection and | |
| 91 hyper-parameter optimization..." and "generic code for performance estimation...". | |
| 92 | |
| 93 I have some experiences with the data-access requirements, and some lessons I'd like to share | |
| 94 on that, but no time to work on that aspect of things. | |
| 95 | |
| 96 I will continue to contribute to the "well-documented set of python scripts using the above to | |
| 97 showcase common ML algorithms...". I have an Olshausen&Field-style sparse coding script that | |
| 98 could be polished up. I am also implementing the mcRBM and I'll be able to add that when it's | |
| 99 done. | |
| 100 | |
| 101 | |
| 102 | |
| 103 Suggestions for how to tackle various desiderata | |
| 104 ================================================ | |
| 105 | |
| 106 | |
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107 Theano Symbolic Expressions for ML |
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108 ---------------------------------- |
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109 |
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110 We could make this a submodule of pylearn: ``pylearn.nnet``. |
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111 |
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112 Yoshua: I would use a different name, e.g., "pylearn.formulas" to emphasize that it is not just |
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113 about neural nets, and that this is a collection of formulas (expressions), rather than |
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114 completely self-contained classes for learners. We could have a "nnet.py" file for |
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115 neural nets, though. |
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116 |
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117 There are a number of ideas floating around for how to handle classes / |
| 947 | 118 modules (LeDeepNet, pylearn.shared.layers, pynnet, DeepAnn) so lets implement as much |
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119 math as possible in global functions with no classes. There are no models in |
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120 the wish list that require than a few vectors and matrices to parametrize. |
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121 Global functions are more reusable than classes. |
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122 |
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123 |
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124 Data access |
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125 ----------- |
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126 |
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127 A general interface to datasets from the perspective of an experiment driver |
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128 (e.g. kfold) is to see them as a function that maps index (typically integer) |
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129 to example (whose type and nature depends on the dataset, it could for |
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130 instance be an (image, label) pair). This interface permits iterating over |
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131 the dataset, shuffling the dataset, and splitting it into folds. For |
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132 efficiency, it is nice if the dataset interface supports looking up several |
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133 index values at once, because looking up many examples at once can sometimes |
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134 be faster than looking each one up in turn. In particular, looking up |
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135 a consecutive block of indices, or a slice, should be well supported. |
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136 |
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137 Some datasets may not support random access (e.g. a random number stream) and |
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138 that's fine if an exception is raised. The user will see a NotImplementedError |
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139 or similar, and try something else. We might want to have a way to test |
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140 that a dataset is random-access or not without having to load an example. |
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141 |
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142 |
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143 A more intuitive interface for many datasets (or subsets) is to load them as |
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144 matrices or lists of examples. This format is more convenient to work with at |
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145 an ipython shell, for example. It is not good to provide only the "dataset |
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146 as a function" view of a dataset. Even if a dataset is very large, it is nice |
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147 to have a standard way to get some representative examples in a convenient |
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148 structure, to be able to play with them in ipython. |
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149 |
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150 |
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151 Another thing to consider related to datasets is that there are a number of |
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152 other efforts to have standard ML datasets, and we should be aware of them, |
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153 and compatible with them when it's easy: |
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154 |
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155 - mldata.org (they have a file format, not sure how many use it) |
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156 - weka (ARFF file format) |
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157 - scikits.learn |
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158 - hdf5 / pytables |
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159 |
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160 |
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161 pylearn.datasets uses a DATA_ROOT environment variable to locate a filesystem |
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162 folder that is assumed to have a standard form across different installations. |
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163 That's where the data files are. The correct format of this folder is currently |
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164 defined implicitly by the contents of /data/lisa/data at DIRO, but it would be |
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165 better to document in pylearn what the contents of this folder should be as |
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166 much as possible. It should be possible to rebuild this tree from information |
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167 found in pylearn. |
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168 |
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169 Yoshua (about ideas proposed by Pascal Vincent a while ago): |
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170 |
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171 - we may want to distinguish between datasets and tasks: a task defines |
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172 not just the data but also things like what is the input and what is the |
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173 target (for supervised learning), and *importantly* a set of performance metrics |
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174 that make sense for this task (e.g. those used by papers solving a particular |
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175 task, or reported for a particular benchmark) |
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176 |
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177 - we should discuss about a few "standards" that datasets and tasks may comply to, such as |
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178 - "input" and "target" fields inside each example, for supervised or semi-supervised learning tasks |
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179 (with a convention for the semi-supervised case when only the input or only the target is observed) |
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180 - "input" for unsupervised learning |
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181 - conventions for missing-valued components inside input or target |
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182 - how examples that are sequences are treated (e.g. the input or the target is a sequence) |
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183 - how time-stamps are specified when appropriate (e.g., the sequences are asynchronous) |
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184 - how error metrics are specified |
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185 * example-level statistics (e.g. classification error) |
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186 * dataset-level statistics (e.g. ROC curve, mean and standard error of error) |
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187 |
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188 |
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189 Model Selection & Hyper-Parameter Optimization |
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190 ---------------------------------------------- |
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191 |
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192 Driving a distributed computing job for a long time to optimize |
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193 hyper-parameters using one or more clusters is the goal here. |
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194 Although there might be some library-type code to write here, I think of this |
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195 more as an application template. The user would use python code to describe |
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196 the experiment to run and the hyper-parameter space to search. Then this |
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197 application-driver would take control of scheduling jobs and running them on |
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198 various computers... I'm imagining a potentially ugly brute of a hack that's |
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199 not necessarily something we will want to expose at a low-level for reuse. |
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200 |
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201 Yoshua: We want both the library-defined driver that takes instructions about how to generate |
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202 new hyper-parameter combinations (e.g. implicitly providing a prior distribution from which |
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203 to sample them), and examples showing how to use it in typical cases. |
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204 Note that sometimes we just want to find the best configuration of hyper-parameters, |
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205 but sometimes we want to do more subtle analysis. Often a combination of both. |
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206 In this respect it could be useful for the user to define hyper-parameters over |
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207 which scientific questions are sought (e.g. depth of an architecture) vs |
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208 hyper-parameters that we would like to marginalize/maximize over (e.g. learning rate). |
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209 This can influence both the sampling of configurations (we want to make sure that all |
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210 combinations of question-driving hyper-parameters are covered) and the analysis |
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211 of results (we may be willing to estimate ANOVAs or averaging or quantiles over |
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212 the non-question-driving hyper-parameters). |
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213 |
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214 Python scripts for common ML algorithms |
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215 --------------------------------------- |
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216 |
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217 The script aspect of this feature request makes me think that what would be |
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218 good here is more tutorial-type scripts. And the existing tutorials could |
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219 potentially be rewritten to use some of the pylearn.nnet expressions. More |
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220 tutorials / demos would be great. |
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221 |
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222 Yoshua: agreed that we could write them as tutorials, but note how the |
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223 spirit would be different from the current deep learning tutorials: we would |
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224 not mind using library code as much as possible instead of trying to flatten |
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225 out everything in the interest of pedagogical simplicity. Instead, these |
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226 tutorials should be meant to illustrate not the algorithms but *how to take |
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227 advantage of the library*. They could also be used as *BLACK BOX* implementations |
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228 by people who don't want to dig lower and just want to run experiments. |
| 941 | 229 |
| 230 Functional Specifications | |
| 231 ========================= | |
| 232 | |
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233 TODO: |
| 941 | 234 Put these into different text files so that this one does not become a monster. |
| 235 For each thing with a functional spec (e.g. datasets library, optimization library) make a | |
| 236 separate file. | |
| 237 | |
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238 Indexing Convention |
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239 =================== |
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240 |
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241 Something to decide on - Fortran-style or C-style indexing. Although we have |
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242 often used c-style indexing in the past (for efficiency in c!) this is no |
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243 longer an issue with numpy because the physical layout is independent of the |
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244 indexing order. The fact remains that Fortran-style indexing follows linear |
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245 algebra conventions, while c-style indexing does not. If a global function |
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246 includes a lot of math derivations, it would be *really* nice if the code used |
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247 the same convention for the orientation of matrices, and endlessly annoying to |
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248 have to be always transposing everything. |
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249 |