Deep-Learning-with-PyTorch

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324 CHAPTER 12 Improving training with metrics and augmentationevents in the middle of our graph. They must pick a vertical bark-worthiness threshold,which means it’s impossible for either one of them to do so perfectly. Sometimesthe person hauling your appliances to their van is the repair person you hired to fixyour washing machine, and sometimes burglars show up in a van that says “WashingMachine Repair” on the side. Expecting a dog to pick up on those nuances is boundto fail.The actual input data we’re going to use has high dimensionality—we need to considera ton of CT voxel values, along with more abstract things like candidate size,overall location in the lungs, and so on. The job of our model is to map each of theseevents and respective properties into this rectangle in such a way that we can separatethose positive and negative events cleanly using a single vertical line (our classificationthreshold). This is done by the nn.Linear layers at the end of our model. The positionof the vertical line corresponds exactly to the classificationThreshold_floatwe saw in section 11.6.1. There, we chose the hardcoded value 0.5 as our threshold.Note that in reality, the data presented is not two-dimensional; it goes from very-highdimensionalafter the second-to-last layer, to one-dimensional (here, our X-axis) at theoutput—just a single scalar per sample (which is then bisected by the classificationthreshold). Here, we use the second dimension (the Y-axis) to represent per-samplefeatures that our model cannot see or use: things like age or gender of the patient,location of the nodule candidate in the lung, or even local aspects of the candidate thatthe model hasn’t utilized. It also gives us a convenient way to represent confusionbetween non-nodule and nodule samples.The quadrant areas in figure 12.5 and the count of samples contained in each willbe the values we use to discuss model performance, since we can use the ratios betweenthese values to construct increasingly complex metrics that we can use to objectivelymeasure how well we are doing. As they say, “the proof is in the proportions.” 1 Next,we’ll use ratios between these event subsets to start defining better metrics.12.3.1 Recall is Roxie’s strengthRecall is basically “Make sure you never miss any interesting events!” Formally, recall isthe ratio of the true positives to the union of true positives and false negatives. We cansee this depicted in figure 12.6.NOTE In some contexts, recall is referred to as sensitivity.To improve recall, minimize false negatives. In guard dog terms, that means if you’reunsure, bark at it, just in case. Don’t let any rodent thieves sneak by on your watch!Roxie accomplishes having an incredibly high recall by pushing her classificationthreshold all the way to the left, such that it encompasses nearly all of the positiveevents in figure 12.7. Note how doing so means her recall value is near 1.0, whichmeans 99% of robbers are barked at. Since that’s how Roxie defines success, in hermind, she’s doing a great job. Never mind the huge expanse of false positives!1No one actually says this.
Graphing the positives and negatives325RecaLlis the ratio determinedby false negativesVS.Figure 12.6 Recall is the ratio of the true positives to the union of true positivesand false negatives. High recall minimizes false negatives.Roxie Barks at everythingLow bark threshold, High RecaLlFigure 12.7 Roxie’s choice of threshold prioritizes minimizing falsenegatives. Every last rat is barked at . . . and cats, and most birds.
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Eli StevensLuca AntigaThomas Viehma
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Deep Learningwith PyTorchELI STEVEN
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To my wife (this book would not hav
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viCONTENTS23Pretrained networks 162
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viiiCONTENTS675.4 Down along the gr
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xCONTENTS10119.5 Conclusion 2529.6
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xiiCONTENTS1413.3 Semantic segmenta
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forewordWhen we started the PyTorch
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prefaceAs kids in the 1980s, taking
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acknowledgmentsWe are deeply indebt
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about this bookThis book has the ai
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ABOUT THIS BOOKxxiiiPART 2In part 2
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ABOUT THIS BOOKxxvMany of the code
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about the authorsEli Stevens has sp
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Part 1Core PyTorchWelcome to the fi
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4 CHAPTER 1 Introducing deep learni
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10 CHAPTER 1 Introducing deep learn
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Pretrained networksThis chapter cov
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18 CHAPTER 2 Pretrained networksThe
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20 CHAPTER 2 Pretrained networkscom
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22 CHAPTER 2 Pretrained networksale
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24 CHAPTER 2 Pretrained networkswid
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26 CHAPTER 2 Pretrained networksA s
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28 CHAPTER 2 Pretrained networksWhi
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30 CHAPTER 2 Pretrained networksAs
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32 CHAPTER 2 Pretrained networksFig
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34 CHAPTER 2 Pretrained networks“
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36 CHAPTER 2 Pretrained networksOpt
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38 CHAPTER 2 Pretrained networks2.6
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40 CHAPTER 3 It starts with a tenso
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Real-world datarepresentationusing
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72 CHAPTER 4 Real-world data repres
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100 CHAPTER 4 Real-world data repre
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102 CHAPTER 4 Real-world data repre
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104 CHAPTER 5 The mechanics of lear
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142 CHAPTER 6 Using a neural networ
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Telling birdsfrom airplanes:Learnin
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166 CHAPTER 7 Telling birds from ai
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194 CHAPTER 8 Using convolutions to
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Part 2Learning from imagesin the re
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236 CHAPTER 9 Using PyTorch to figh
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Combining data sourcesinto a unifie
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256 CHAPTER 10 Combining data sourc
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End-to-endnodule analysis,and where
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406 CHAPTER 14 End-to-end nodule an
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Deploying to productionThis chapter
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Serving PyTorch models447The API ca
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Serving PyTorch models449When using
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Serving PyTorch models451IMPLEMENTA
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Serving PyTorch models453Listing 15
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Exporting models45515.2 Exporting m
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Exporting models457But then all we
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Interacting with the PyTorch JIT459
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LibTorch: PyTorch in C++465Listing
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LibTorch: PyTorch in C++467Our tens
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LibTorch: PyTorch in C++469We’ll
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LibTorch: PyTorch in C++471...model
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Going mobile473Then, we need to inc
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Going mobile475and full of copying
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Summary47715.7 ConclusionThis concl
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480INDEXbool tensors 51Boolean inde
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482INDEXdata loading (continued)con
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484INDEXimage recognition (continue
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486INDEXneural networks (continued)
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488INDEXResNetGenerator module470-4
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490INDEXVval_loss tensor 138val_neg
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PYTHON/DATA SCIENCEDeep Learning wi
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