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[Structuring Machine Learning Projects] week2. ML Strategy (2)
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Part 9 of «Andrew Ng Deep Learning MOOC»
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目录
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<div id="toc"><ul><li><a class="toc-href" href="#i-error-analysis" title="I-Error Analysis">I-Error Analysis</a><ul><li><a class="toc-href" href="#carrying-out-error-analysis" title="Carrying out error analysis">Carrying out error analysis</a></li><li><a class="toc-href" href="#cleaning-up-incorrectly-labeled-data" title="Cleaning up incorrectly labeled data">Cleaning up incorrectly labeled data</a></li><li><a class="toc-href" href="#build-your-first-system-quickly-then-iterate" title="Build your first system quickly, then iterate">Build your first system quickly, then iterate</a></li></ul></li><li><a class="toc-href" href="#ii-mistmatched-training-and-devtest-set_1" title="II-Mistmatched training and dev/test set">II-Mistmatched training and dev/test set</a><ul><li><a class="toc-href" href="#training-and-testing-on-different-distributions" title="Training and testing on different distributions">Training and testing on different distributions</a></li><li><a class="toc-href" href="#bias-and-variance-with-mismatched-data-distributions" title="Bias and Variance with mismatched data distributions">Bias and Variance with mismatched data distributions</a></li><li><a class="toc-href" href="#addressing-data-mismatch" title="Addressing data mismatch">Addressing data mismatch</a></li></ul></li><li><a class="toc-href" href="#iii-learning-from-multiple-tasks_1" title="III-Learning from multiple tasks">III-Learning from multiple tasks</a><ul><li><a class="toc-href" href="#transfer-learning" title="Transfer learning">Transfer learning</a></li><li><a class="toc-href" href="#multi-task-learning" title="Multi-task learning">Multi-task learning</a></li></ul></li><li><a class="toc-href" href="#iv-end-to-end-deep-learning_1" title="IV-End-to-end deep learning">IV-End-to-end deep learning</a><ul><li><a class="toc-href" href="#what-is-end-to-end-deep-learning" title="What is end-to-end deep learning?">What is end-to-end deep learning?</a></li><li><a class="toc-href" href="#whether-to-use-end-to-end-deep-learning" title="Whether to use end-to-end deep learning">Whether to use end-to-end deep learning</a></li></ul></li></ul></div>
</div>
</div>
<h2 id="i-error-analysis">I-Error Analysis</h2>
<h3 id="carrying-out-error-analysis">Carrying out error analysis</h3>
<p>"<strong>Error analysis</strong>": manually examine the mistakes → get insight of what's next. </p>
<p>"<em>ceiling on performance</em>" </p>
<p>example:<br/>
cat classification, found some false-positives of dog pictures. → should you try to make ML system better on dog or not ?<br/>
→ error analysis: </p>
<ul>
<li>get ~100 false positive examples </li>
<li>count how many are dogs </li>
</ul>
<p>→ if only 5% of errors are dogs → performance can improve by <=5% even if totaly solved dog problem.<br/>
→ if 50% are dos → might need to improve on dogs. </p>
<p>example2 (evaluate multiple ideas in parallel):<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image.png"/><br/>
Pick one idea to iterate on: <em>use a spreadsheet</em><br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image001.png"/> </p>
<h3 id="cleaning-up-incorrectly-labeled-data">Cleaning up incorrectly labeled data</h3>
<p>What to do if there are <em>incorrect labels</em> in data ? </p>
<ul>
<li>In training set: </li>
</ul>
<p><em>DL algos are quite robust to random errors in training set.</em><br/>
→ if incorrect labels is close to random errors (percentage not too high), it's OK to train.<br/>
caveat: Robust to <em>random</em> errors, not <em>systematic</em> errors. E.g. all white dogs are labeled as cats. </p>
<ul>
<li>In dev/test set: </li>
</ul>
<p>In error analysis, count cases of incorrect labels.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image002.png"/><br/>
If #incorrect labels makes a significent different for evaluating, then fix it.<br/>
example:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image004.png"/><br/>
Remember: goal of dev set is to help selecting between two models. </p>
<p>Correcting labels in dev/test sets: </p>
<ul>
<li>apply the same process to test set — dev/test sets have the same distribution. </li>
<li>consider both false positive and false negatives. → to make estimate of performance unbiased. (might take longer time) </li>
<li>less important to correct training set: training set can come from slight different distribution, but important dev/test come from the same distribution. </li>
</ul>
<h3 id="build-your-first-system-quickly-then-iterate">Build your first system quickly, then iterate</h3>
<p>example: speech recognition<br/>
many directions to go → which direction to pick ? </p>
<p>Build system quickly and iterate. </p>
<ul>
<li>set up dev/test set, set metric </li>
<li>build intitial system <em>quickly</em>: build something quick & dirty that works. </li>
<li>Bias/Variance analysis & Error analysis → prioritize next steps </li>
</ul>
<h2 id="ii-mistmatched-training-and-devtest-set_1">II-Mistmatched training and dev/test set</h2>
<h3 id="training-and-testing-on-different-distributions">Training and testing on different distributions</h3>
<p>When distribution of train and dev/test sets are different. </p>
<p>example: cat app<br/>
Two sources: </p>
<ul>
<li>webpages (high resolution, a lot of data) </li>
<li>user uploaded (blury, relatively small amount). </li>
</ul>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image005.png"/><br/>
<strong>option1</strong>. put both data together, randomly shuffle & split train/dev/test<br/>
<em>advantage</em>: train/dev/test come from distribution<br/>
<em>disadvantage</em>: many examples in dev/test set come from webpages — but cares more about performance on user-uploaded examples → not recommended, target is not really what we care about </p>
<p><strong>option2</strong>. training set mostly from web, for dev/test all from user-uploaded.<br/>
<em>advantage</em>: Err_dev/Err_test really reflects what the target is. </p>
<p>example2: speech recognition (speech activated rearview mirror) </p>
<ul>
<li>training data: many data coming from different sources of speech recognition. </li>
<li>dev/test: small amount, coming from speech activated rearview mirror. </li>
</ul>
<p>takeaway: </p>
<ul>
<li>use large training set, even if distribution is different from dev/test set </li>
<li>dev/test data should reflect what to expect from the system. </li>
</ul>
<h3 id="bias-and-variance-with-mismatched-data-distributions">Bias and Variance with mismatched data distributions</h3>
<p>B&V analysis changes when training set distribution is different from dev/test set. </p>
<p>When distr(train)!=distr(dev/test):<br/>
<em>No longer can say system has large variance problem when seeing Err_train < Err_dev</em>. (Poor performance on dev set may not come from overfitting, but may also from change of distrubtion in data).<br/>
⇒ introduce <strong>training-dev set</strong>: <em>same distrubution as training set, but not used in training</em>.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image007.png"/><br/>
Now can look at Err_traindev and see if model has variance/bias problem or data-mismatch problem:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image010.png"/> </p>
<p><strong>General principles</strong>:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image011.png"/><br/>
(also possible to have Err_dev/Err_test < Err_train/Err_traindev, because of data mismatch) </p>
<p><strong>More general formulation</strong> (example: rearview mirror):<br/>
include Err_human <em>on dev/test data</em>.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image012.png"/> </p>
<h3 id="addressing-data-mismatch">Addressing data mismatch</h3>
<p>How to address data-mismatch problem? → no systematic solution. </p>
<ul>
<li>manual error analysis: understand difference between train and dev sets. </li>
</ul>
<p>e.g. noise in car </p>
<ul>
<li>make training data more similar / collect more data similar to dev/test set. </li>
</ul>
<p>e.g. simulate noisy in-car data (<em>artificial data synthesis</em>) </p>
<p><strong>Artificial data synthesis</strong><br/>
caution: avoid synthesise only a small part of all possible examples.<br/>
car noise example:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image014.png"/><br/>
car recognition example:<br/>
synthesis car pictures from a video game<br/>
problem: if there're only 20 different cars in video cars → overfit </p>
<h2 id="iii-learning-from-multiple-tasks_1">III-Learning from multiple tasks</h2>
<h3 id="transfer-learning">Transfer learning</h3>
<p>Learned knowledge from one task applied to a second task.<br/>
reason: some low-level features can be shared for different tasks. </p>
<p>example 1. cat classifier applied to X-ray scans diagnosis.<br/>
<em>change last output layer of original model</em>, initial w[L]/b[L] of last layer and retrain the params.<br/>
if dataset small: only retrain last layer params (<em>pre-training</em>)<br/>
else: retrain all params (<em>fine-tuning</em>)<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image017.png"/><br/>
example 2. speech recognition transfer to trigger word detection<br/>
also possilbe to create more layers to NN<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image018.png"/><br/>
<strong>When to use transfer learning:</strong> </p>
<ul>
<li>task A ans B have the same input </li>
<li>a lot of data for task A, relatively small amount of data for task B </li>
<li>low level feature of task A could be helpful for task B </li>
</ul>
<h3 id="multi-task-learning">Multi-task learning</h3>
<p>transfer learning: task A and B are sequential<br/>
multi-task learning: <em>in parallel</em> </p>
<p>example: self-driving car<br/>
multiple kind of objects to detect<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image019.png"/><br/>
multi-label problem (<em>each example can have multiple labels</em>):<br/>
→ output layer should no longer be softmax<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image020.png"/> </p>
<p>Training on NN for 4 tasks instead of 4 separate NNs: early-layer features can be shared. </p>
<p>With missing entries are in labels:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image021.png"/><br/>
⇒ in loss function, sum only on labeled entries.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image022.png"/> </p>
<p><strong>When to use multi-task learning</strong>: </p>
<ul>
<li>lower-level features can be shared </li>
<li>similar amount of data for each task — data for other tasks could help learning of main task </li>
</ul>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image023.png"/> </p>
<ul>
<li>can train a <em>big enough</em> NN to do well on all tasks. </li>
</ul>
<p>in practice: multi-task learning is <em>much less common</em> than transfer learning. </p>
<h2 id="iv-end-to-end-deep-learning_1">IV-End-to-end deep learning</h2>
<h3 id="what-is-end-to-end-deep-learning">What is end-to-end deep learning?</h3>
<p>E2E: omit multiple stages in pipeline by a single NN. </p>
<p>example: speech recognition.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image024.png"/> </p>
<p><strong>note</strong>: E2E can work well only when have <em>really large dataset</em>. </p>
<p>example2: face recognition from camera.<br/>
2-stage works better than E2E:<br/>
image → face detection → face recognition.<br/>
reason: a lot of data for each of the 2 tasks, but much less data for E2E. </p>
<p>exapmle3: machine translation.<br/>
E2E works well because of large amount of training data. </p>
<p>example4: estimating child's age from X-ray img.<br/>
separate stages works better.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image026.png"/> </p>
<h3 id="whether-to-use-end-to-end-deep-learning">Whether to use end-to-end deep learning</h3>
<p>Pros and cons of E2E learning.<br/>
<strong>Pros</strong>: </p>
<ul>
<li>let the data speak, avoid intermediate values (e.g. phonemes in speech recognition) </li>
<li>less hand-designing of components needed </li>
</ul>
<p><strong>Cons</strong>: </p>
<ul>
<li>Need large amount of data (X, Y) </li>
<li>Excludes potentially useful hand-designed components </li>
</ul>
<p>Key question for applying E2E learning: <em>sufficient</em> data available to learn a function of the <em>complexity</em> needed to map from x to y? </p>
<p>example: self-driving cars<br/>
in practice: multi-stage system<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk2/pasted_image027.png"/> </p>
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<div id="toc"><ul><li><a class="toc-href" href="#i-error-analysis" title="I-Error Analysis">I-Error Analysis</a><ul><li><a class="toc-href" href="#carrying-out-error-analysis" title="Carrying out error analysis">Carrying out error analysis</a></li><li><a class="toc-href" href="#cleaning-up-incorrectly-labeled-data" title="Cleaning up incorrectly labeled data">Cleaning up incorrectly labeled data</a></li><li><a class="toc-href" href="#build-your-first-system-quickly-then-iterate" title="Build your first system quickly, then iterate">Build your first system quickly, then iterate</a></li></ul></li><li><a class="toc-href" href="#ii-mistmatched-training-and-devtest-set_1" title="II-Mistmatched training and dev/test set">II-Mistmatched training and dev/test set</a><ul><li><a class="toc-href" href="#training-and-testing-on-different-distributions" title="Training and testing on different distributions">Training and testing on different distributions</a></li><li><a class="toc-href" href="#bias-and-variance-with-mismatched-data-distributions" title="Bias and Variance with mismatched data distributions">Bias and Variance with mismatched data distributions</a></li><li><a class="toc-href" href="#addressing-data-mismatch" title="Addressing data mismatch">Addressing data mismatch</a></li></ul></li><li><a class="toc-href" href="#iii-learning-from-multiple-tasks_1" title="III-Learning from multiple tasks">III-Learning from multiple tasks</a><ul><li><a class="toc-href" href="#transfer-learning" title="Transfer learning">Transfer learning</a></li><li><a class="toc-href" href="#multi-task-learning" title="Multi-task learning">Multi-task learning</a></li></ul></li><li><a class="toc-href" href="#iv-end-to-end-deep-learning_1" title="IV-End-to-end deep learning">IV-End-to-end deep learning</a><ul><li><a class="toc-href" href="#what-is-end-to-end-deep-learning" title="What is end-to-end deep learning?">What is end-to-end deep learning?</a></li><li><a class="toc-href" href="#whether-to-use-end-to-end-deep-learning" title="Whether to use end-to-end deep learning">Whether to use end-to-end deep learning</a></li></ul></li></ul></div>
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