Deep-Learning-with-PyTorch

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68 CHAPTER 3 It starts with a tensorOwing to this fact, we can pass the returned object to the torch.from_numpy functionto obtain a tensor directly. Note that in this case, the data is copied over to thetensor’s storage:# In[63]:last_points = torch.from_numpy(dset[-2:])f.close()Once we’re finished loading data, we close the file. Closing the HDFS file invalidatesthe datasets, and trying to access dset afterward will give an exception. As long as westick to the order shown here, we are fine and can now work with the last_pointstensor.3.13 ConclusionNow we have covered everything we need to get started with representing everything infloats. We’ll cover other aspects of tensors—such as creating views of tensors; indexingtensors with other tensors; and broadcasting, which simplifies performing element-wiseoperations between tensors of different sizes or shapes—as needed along the way.In chapter 4, we will learn how to represent real-world data in PyTorch. We willstart with simple tabular data and move on to something more elaborate. In the process,we will get to know more about tensors.3.14 Exercises3.15 Summary1 Create a tensor a from list(range(9)). Predict and then check the size, offset,and stride.a Create a new tensor using b = a.view(3, 3). What does view do? Checkthat a and b share the same storage.b Create a tensor c = b[1:,1:]. Predict and then check the size, offset, andstride.2 Pick a mathematical operation like cosine or square root. Can you find a correspondingfunction in the torch library?a Apply the function element-wise to a. Why does it return an error?b What operation is required to make the function work?c Is there a version of your function that operates in place?• Neural networks transform floating-point representations into other floatingpointrepresentations. The starting and ending representations are typicallyhuman interpretable, but the intermediate representations are less so.• These floating-point representations are stored in tensors.• Tensors are multidimensional arrays; they are the basic data structure inPyTorch.
Summary69• PyTorch has a comprehensive standard library for tensor creation, manipulation,and mathematical operations.• Tensors can be serialized to disk and loaded back.• All tensor operations in PyTorch can execute on the CPU as well as on the GPU,with no change in the code.• PyTorch uses a trailing underscore to indicate that a function operates in placeon a tensor (for example, Tensor.sqrt_).
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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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118 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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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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