Deep-Learning-with-PyTorch

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334 CHAPTER 12 Improving training with metrics and augmentation12.4 What does an ideal dataset look like?Before we start crying into our cups over the current sorry state of affairs, let’s insteadthink about what we actually want our model to do. Figure 12.14 says that first we needto balance our data so that our model can train properly. Let’s build up the logicalsteps needed to get us there.1. Guard dogs2. Birds andburglars5. BalancingPOSNEG6.Augmentationn3. Ratios recaLland precision4. new metric:f1 score7. Workin’ great!Figure 12.14 The set of topics for this chapter, with a focus on balancing our positive andnegative samplesRecall figure 12.5 earlier, and the following discussion of classification thresholds.Getting better results by moving the threshold has limited effectiveness—there’s justtoo much overlap between the positive and negative classes to work with. 3Instead, we want to see an image like figure 12.15. Here, our label threshold is nearlyvertical. That’s what we want, because it means the label threshold and our classificationthreshold can line up reasonably well. Similarly, most of the samples are concentrated ateither end of the diagram. Both of these things require that our data be easily separableand that our model have the capacity to perform that separation. Our model currentlyhas enough capacity, so that’s not the issue. Instead, let’s take a look at our data.Recall that our data is wildly imbalanced. There’s a 400:1 ratio of positive samplesto negative ones. That’s crushingly imbalanced! Figure 12.16 shows what that lookslike. No wonder our “actually nodule” samples are getting lost in the crowd!3Keep in mind that these images are just a representation of the classification space and do not representground truth.
What does an ideal dataset look like?335Few incoRrectpredictionsMost events inclearly separate claSsesFigure 12.15 A well-trainedmodel can cleanly separatedata, making it easy to pick aclassification threshold withfew trade-offs.NegativeSamplesStiLl tOomany falsepositivespPositiveSamplesFigure 12.16An imbalanced dataset that roughly approximates the imbalance in our LUNA classification data
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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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280 CHAPTER 11 Training a classific
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282 CHAPTER 11 Training a classific
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384 CHAPTER 13 Using segmentation t
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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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