Deep-Learning-with-PyTorch

482
INDEX
data loading (continued)
constructing dataset in
LunaDataset.__init__
275
rendering data 277
segregation between
training and validation
sets 275–276
data representation using
tensors
images 71–75
3D images 75–76
adding color channels 72
changing layout 73–74
loading image files 72–73
normalizing data 74–75
tabular data 77–87
categorization 83–84
loading a data tensor 78–80
one-hot encoding 81–83
real-world dataset 77–78
representing scores 81
thresholds 84–87
text 93–101
converting text to
numbers 94
one-hot encoding
characters 94–95
one-hot encoding whole
words 96–98
text embeddings 98–100
text embeddings as
blueprint 100–101
time series 87–93
adding time
dimensions 88–89
shaping data by time
period 89–90
training 90–93
Data Science Bowl 2017 438
data tensor 85
data.CIFAR10 dataset 167
DataLoader class 11, 280, 284,
288, 381, 414
DataParallel 286, 387
dataset argument 418
Dataset class 11, 166–167
Dataset subclass 173, 256,
271–273, 275, 279, 284,
339, 378, 414
dataset.CIFAR10 169
datasets module 166
deep learning
exercises 15
hardware and software
requirements 13–15
paradigm shift from 4–6
PyTorch for 6–9
how supports deep learning
projects 10–13
reasons for using 7–9
def __len__ method 272
dense tensors 65
DenseNet 226
deployment
enterprise serving of PyTorch
models 476
exporting models 455–458
ONNX 455–456
tracing 456–458
interacting with PyTorch
JIT 458–465
dual nature of PyTorch as
interface and
backend 460
expectations 458–460
scripting gaps of
traceability 464–465
TorchScript 461–464
LibTorch 465–472
C++ API 468–472
running JITed models from
C++ 465–468
mobile 472–476
serving PyTorch models
446–454
Flask server 446–448
goals of deployment
448–449
request batching 449–454
depth of network 223–228
building very deep
models 226–228
initialization 228
skip connections 223–226
device argument 64
device attribute 63–64
device variable 215
diameter_mm 258
Dice loss 389–392
collecting metrics 392
loss weighting 391
diceLoss_g 391
DICOM (Digital Imaging and
Communications in
Medicine) 76, 256, 267
DICOM UID 264
Dirac distribution 187
discrete convolution 195
discrete cross-correlations 195
discrimination 337
discriminator network 28
diskcache library 274, 384
dispatching mechanism 65
DistilBERT 475
distillation 475
DistributedDataParallel 286
doTraining function
296, 301, 314
doValidation function
301, 393, 397
downsampling 203–204
dropout 25, 220–222
Dropout module 221
DSB (Data Science Bowl) 438
dsets.py:32 260
dtype argument 64
managing 51–52
precision levels 51
specifying numeric types
with 50–51
dtype torch.float 65
dull batches 367
E
edge detection kernel 201
einsum function 48
embedding text
as blueprint 100–101
data representation with
tensors 98–100
embeddings 96, 99
end-to-end analysis 405–407
bridging CT segmentation
and nodule candidate
classification 408–416
classification to reduce false
positives 412–416
grouping voxels into
nodule candidates
411–412
segmentation 410–411
diagnosis script 432–434
independence of validation
set 407–408
predicting malignancy 417–431
classifying by
diameter 419–422
getting malignancy
information 417–418
reusing preexisting
weights 422–427
TensorBoard 428–431