Deep-Learning-with-PyTorch

480
INDEX
bool tensors 51
Boolean indexing 302
Bottleneck modules 23
bounding boxes 372–375
boundingBox_a 373
boxed numeric values 43
boxing 50
broadcasting 47, 111, 155
buffer protocol 64
_build2dTransformMatrix
function 386
byte pair encoding method 97
C
C++
C++ API 468–472
LibTorch 465–472
running JITed models from
C++ 465–468
__call__ method 152–153
cancer detector project
classification model training
disconnect 315–316
evaluating the model
308–309
first-pass neural network
design 289–295
foundational model and
training loop 280–282
graphing training
metrics 309–314
main entry point for
application 282–284
outputting performance
metrics 300–304
pretraining setup and
initialization 284–289
running training
script 304–307
training and validating the
model 295–300
CT scans 238–241
data augmentation 346–354
improvement from
352–354
techniques 347–352
data loading
loading individual CT
scans 262–265
locating nodules 265–271
parsing LUNA's annotation
data 256–262
raw CT data files 256
straightforward dataset
implementation
271–277
deployment
enterprise serving of
PyTorch models 476
exporting models 455–458
interacting with PyTorch
JIT 458–465
LibTorch 465–472
mobile 472–476
serving PyTorch
models 446–454
difficulty of 245–247
end-to-end analysis 405–407
bridging CT segmentation
and nodule candidate
classification 408–416
diagnosis script 432–434
independence of validation
set 407–408
predicting
malignancy 417–431
quantitative validation
416–417
training, validation, and test
sets 433–434
false positives and false
negatives 320–322
high-level plan for
improvement 319–320
LUNA Grand Challenge data
source
downloading 251–252
overview 251
metrics
graphing positives and
negatives 322–333
ideal dataset 334–344
nodules 249–250
overview 236–237
preparing for large-scale
projects 237–238
second model 358–360
segmentation
semantic
segmentation 361–366
types of 360–361
updating dataset for
369–386
updating model for
366–369
updating training script
for 386–399
structure of 241–252
candidate_count 412
candidateInfo_list 381, 414
candidateInfo_tup 373
CandidateInfoTuple data
structure 377
candidateLabel_a array 411
categorical values 80
center_crop 464
center_index - index_radius 373
center_index +
index_radius 373
chain rule 123
ChainDataset 174
channel dimension 76
channels 197
CIFAR-10 dataset 165–173
data transforms 168–170
Dataset class 166–167
downloading 166
normalizing data 170–172
CIFAR-100 166
cifar2 object 174
CImg library 465–468
class balancing 339–341
class_index 180
classification
classifying by diameter
419–422
to reduce false positives
412–416
classification model training
disconnect 315–316
evaluating the model
308–309
first-pass neural network
design 289–295
converting from convolution
to linear 294–295
core convolutions 290–292
full model 293–295
initialization 295
foundational model and training
loop 280–282
graphing training
metrics 309–314
adding TensorBoard support
to the metrics logging
function 313–314
running TensorBoard
309–313
writing scalars to
TensorBoard 314
main entry point for
application 282–284