Deep-Learning-with-PyTorch

INDEX 485
machine learning: gradient
descent (continued)
computing derivatives 115
data visualization 122
decreasing loss 113–114
defining gradient
function 116
Iterating to fit model
116–119
normalizing inputs
119–121
loss function 109–112
modeling 104–106
parameter estimation
106–109
choosing linear
model 108–109
data gathering 107–108
data visualization 108
example problem 107
switching to PyTorch 110–112
main method 283, 471
malignancy classification 407
malignancy model 407
--malignancy-path argument 432
MalignancyLunaDataset
class 418
malignant classification 413
map_location keyword
argument 217
Mask R-CNN 246
Mask R-CNN models 465
masked arrays 302
masks
caching chunks of mask in
addition to CT 376
calling mask creation during
CT initialization 375
constructing 302–304
math ops 53
Matplotlib 172, 247, 431
max function 26
max pooling 203
mean square loss 111
memory bandwidth 384
Mercator projection map 267
metadata, tensor 55–62
contiguous tensors 60–62
transposing in higher
dimensions 60
transposing without
copying 58–59
views of another tensor’s
storage 56–58
MetaIO format 263
metrics
graphing positives and
negatives 322–333
F1 score 328–332
performance 332–333
precision 326–328, 332
recall 324, 327–328, 332
ideal dataset 334–344
class balancing 339–341
contrasting training with
balanced LUNA
Dataset to previous
runs 341–343
making data look less like
the actual and more
like the ideal 336–341
samplers 338–339
symptoms of
overfitting 343–344
metrics_dict 303, 314
METRICS_PRED_NDX
values 302
metrics_t parameter 298, 301
metrics_t tensor 428
millimeter-based coordinate
system 265
minibatches 129, 184–185
mirroring 348–349
MIT license 367
mixed-precision training 475
mixup 435
MLflow 476
MNIST dataset 165–166
mobile deployment 472–476
mode_str argument 301
model design 217–229
comparing designs 228–229
depth of network 223–228
building very deep
models 226–228
initialization 228
skip connections 223–226
outdated 229
regularization 219–223
batch normalization
222–223
dropout 220–222
weight penalties 219–220
width of network 218–219
model function 131, 142
Model Runner function
450–451
model_runner function
453–454
model.backward() method 159
Model.load method 474
model.parameters()
method 159
model.state_dict() function 397
model.train() method 223
ModelRunner class 452
models module 22
modules 151
MS COCO dataset 35
MSE (Mean Square Error)
157, 180, 182
MSELoss 175
multichannel images 197
multidimensional arrays,
tensors as 42
multitask learning 436
mutating ops 53
N
N dimension 89
named tensors 46, 48–49
named_parameters method 159
names argument 48
NDET (Nodule detection) 251
ndx integer 272
needs_processing event
452, 454
needs_processing.
ModelRunner 452
neg_list 418
neg_ndx 340
negLabel_mask 303
negPred_mask 302
netG model 30
neural networks
__call__ method 152–153
activation functions 145–149
capping output range 146
choosing 148–149
compressing output
range 146–147
composing multilayer
networks 144
error function 144–145
first-pass, for cancer
detector 289–295
converting from convolution
to linear 294–295
core convolutions 290–292
full model 293–295
initialization 295
inspecting parameters
159–161