Deep-Learning-with-PyTorch

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228 CHAPTER 8 Using convolutions to generalizefragile in convergence. This is why we used more-detailed initializations and trainedour NetRes with a learning rate of 3e – 3 instead of the 1e – 2 we used for the othernetworks. We trained none of the networks to convergence, but we would not havegotten anywhere without these tweaks.All this shouldn’t encourage us to seek depth on a dataset of 32 × 32 images, but itclearly demonstrates how this can be achieved on more challenging datasets like Image-Net. It also provides the key elements for understanding existing implementations formodels like ResNet, for instance, in torchvision.INITIALIZATIONLet’s briefly comment about the earlier initialization. Initialization is one of theimportant tricks in training neural networks. Unfortunately, for historical reasons,PyTorch has default weight initializations that are not ideal. People are looking at fixingthe situation; if progress is made, it can be tracked on GitHub (https://github.com/pytorch/pytorch/issues/18182). In the meantime, we need to fix theweight initialization ourselves. We found that our model did not converge and lookedat what people commonly choose as initialization (a smaller variance in weights; andzero mean and unit variance outputs for batch norm), and then we halved the outputvariance in the batch norm when the network would not converge.Weight initialization could fill an entire chapter on its own, but we think thatwould be excessive. In chapter 11, we’ll bump into initialization again and use whatarguably could be PyTorch defaults without much explanation. Once you’ve progressedto the point where the details of weight initialization are of specific interest toyou—probably not before finishing this book—you might revisit this topic. 108.5.4 Comparing the designs from this sectionWe summarize the effect of each of our design modifications in isolation in figure8.13. We should not overinterpret any of the specific numbers—our problem setupand experiments are simplistic, and repeating the experiment with different randomseeds will probably generate variation at least as large as the differences in validationaccuracy. For this demonstration, we left all other things equal, from learning rate tonumber of epochs to train; in practice, we would try to get the best results by varyingthose. Also, we would likely want to combine some of the additional design elements.But a qualitative observation may be in order: as we saw in section 5.5.3, when discussingvalidatioin and overfitting, The weight decay and dropout regularizations,which have a more rigorous statistical estimation interpretation as regularization thanbatch norm, have a much narrower gap between the two accuracies. Batch norm, which10 The seminal paper on the topic is by X. Glorot and Y. Bengio: “Understanding the Difficulty of Training DeepFeedforward Neural Networks” (2010), which introduces PyTorch’s Xavier initializations (http://mng.bz/vxz7). The ResNet paper we mentioned expands on the topic, too, giving us the Kaiming initializationsused earlier. More recently, H. Zhang et al. have tweaked initialization to the point that they do not needbatch norm in their experiments with very deep residual networks (https://arxiv.org/abs/1901.09321).
Conclusion2291.000.95ACcuracy0.900.850.800.750.70baselinewidth12 regdropoutbatch_normdepthrestrainvalres dEepFigure 8.13The modified networks all perform similarly.serves more as a convergence helper, lets us train the network to nearly 100% trainingaccuracy, so we interpret the first two as regularization.8.5.5 It’s already outdatedThe curse and blessing of a deep learning practitioner is that neural network architecturesevolve at a very rapid pace. This is not to say that what we’ve seen in this chapteris necessarily old school, but a thorough illustration of the latest and greatest architecturesis a matter for another book (and they would cease to be the latest and the greatestpretty quickly anyway). The take-home message is that we should make every effortto proficiently translate the math behind a paper into actual PyTorch code, or at leastunderstand the code that others have written with the same intention. In the last fewchapters, you have hopefully gathered quite a few of the fundamental skills to translateideas into implemented models in PyTorch.8.6 ConclusionAfter quite a lot of work, we now have a model that our fictional friend Jane can use tofilter images for her blog. All we have to do is take an incoming image, crop and resizeit to 32 × 32, and see what the model has to say about it. Admittedly, we have solvedonly part of the problem, but it was a journey in itself.We have solved just part of the problem because there are a few interestingunknowns we would still have to face. One is picking out a bird or airplane from a
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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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278 CHAPTER 10 Combining data sourc
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280 CHAPTER 11 Training a classific
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Improving trainingwith metrics anda
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320 CHAPTER 12 Improving training w
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358 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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