Advanced Deep Learning with Keras

More documents

Recommendations

Info

Chapter 2This concludes our look at the Functional API. We should take this time to rememberthat the focus of this chapter is building deep neural networks, specifically ResNetand DenseNet. Therefore, we're only covering the Functional API materials neededto build them, as to cover the entire API would be beyond the scope of this book.The reader is referred to visit https://keras.io/ for additionalinformation on functional API.Deep residual networks (ResNet)One key advantage of deep networks is that they have a great ability to learndifferent levels of representations from both inputs and feature maps. In bothclassification, segmentation, detection and a number of other computer visionproblems, learning different levels of features generally leads to better performance.However, you'll find that it's not easy to train deep networks as a result ofthe gradient vanishes (or explodes) with depth in the shallow layers duringbackpropagation. Figure 2.2.1 illustrates the problem of vanishing gradient. Thenetwork parameters are updated by backpropagation from the output layerto all previous layers. Since backpropagation is based on the chain rule, there isa tendency for gradients to diminish as they reach the shallow layers. This is dueto the multiplication of small numbers, especially for the small absolute value oferrors and parameters.The number of multiplication operations will be proportional to the depth of thenetwork. It's also worth noting that if the gradient degrades, the parameters will notbe updated appropriately.[ 49 ]
Deep Neural NetworksHence, the network will fail to improve its performance:Figure 2.2.1: A common problem in deep networks is that the gradientvanishes as it reaches the shallow layers during backpropagation.[ 50 ]
Page 2 and 3:
Advanced Deep Learningwith KerasApp
Page 4 and 5:
mapt.ioMapt is an online digital li
Page 6 and 7:
I would like to thank my family, Ch
Page 8 and 9:
Table of ContentsPrefaceVChapter 1:
Page 10 and 11:
[ iii ]Table of ContentsChapter 7:
Page 12 and 13:
[ v ]PrefaceIn recent years, deep l
Page 14 and 15:
Chapter 5, Improved GANs, covers al
Page 16 and 17: def encoder_layer(inputs,filters=16
Page 18 and 19: Introducing Advanced DeepLearning w
Page 20 and 21: Chapter 1Installing Keras and Tenso
Page 22 and 23: Chapter 1• RNNs: Recurrent neural
Page 24 and 25: [ 7 ]Chapter 1In the preceding figu
Page 26 and 27: Chapter 1Figure 1.3.3: MLP MNIST di
Page 28 and 29: Chapter 1model.add(Activation('soft
Page 30 and 31: Chapter 1model.add(Activation('relu
Page 32 and 33: Chapter 1As an example, l2 weight r
Page 34 and 35: [ 17 ]Chapter 1How far the predicte
Page 36 and 37: Chapter 1Figure 1.3.8: Plot of a fu
Page 38 and 39: Chapter 1The highest test accuracy
Page 40 and 41: Chapter 1Figure 1.3.9: The graphica
Page 42 and 43: Chapter 1# image is processed as is
Page 44 and 45: Chapter 1The computation involved i
Page 46 and 47: Chapter 1Listing 1.4.2 shows a summ
Page 48 and 49: Chapter 164-64-64 RMSprop Dropout(0
Page 50 and 51: Chapter 1There are the two main dif
Page 52 and 53: Chapter 1Layers Optimizer Regulariz
Page 54: ConclusionThis chapter provided an
Page 57 and 58: Deep Neural NetworksWhile this chap
Page 59 and 60: Deep Neural Networks# reshape and n
Page 61 and 62: Deep Neural NetworksEverything else
Page 63 and 64: Deep Neural Networksfrom keras.util
Page 65: Deep Neural NetworksFigure 2.1.3: T
Page 69 and 70: Deep Neural NetworksGenerally speak
Page 71 and 72: Deep Neural NetworksIn the dataset,
Page 73 and 74: Deep Neural NetworksTransition Laye
Page 75 and 76: Deep Neural NetworksThere are some
Page 77 and 78: Deep Neural NetworksResNet v2 is al
Page 79 and 80: Deep Neural Networks…if version =
Page 81 and 82: Deep Neural NetworksTo prevent the
Page 83 and 84: Deep Neural NetworksAverage Pooling
Page 85 and 86: Deep Neural Networks# orig paper us
Page 88 and 89: AutoencodersIn the previous chapter
Page 90 and 91: Chapter 3The autoencoder has the te
Page 92 and 93: Chapter 3Firstly, we're going to im
Page 94 and 95: Chapter 3# reconstruct the inputout
Page 96 and 97: Chapter 3Figure 3.2.2: The decoder
Page 98 and 99: batch_size=32,model_name="autoencod
Page 100 and 101: Chapter 3Figure 3.2.6: Digits gener
Page 102 and 103: Chapter 3As shown in Figure 3.3.2,
Page 104 and 105: Chapter 3image_size = x_train.shape
Page 106 and 107: Chapter 3# Mean Square Error (MSE)
Page 108 and 109: Chapter 3from keras.layers import R
Page 110 and 111: Chapter 3# build the autoencoder mo
Page 112 and 113: Chapter 3x_train,validation_data=(x
Page 114: Chapter 3ConclusionIn this chapter,
Page 117 and 118:
Generative Adversarial Networks (GA
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Improved GANsIn summary, the goal o
Page 145 and 146:
Improved GANsThe intuition behind E
Page 147 and 148:
Improved GANsThis makes sense since
Page 149 and 150:
Improved GANsIn the context of GANs
Page 151 and 152:
Improved GANsFigure 5.1.3: Top: Tra
Page 153 and 154:
Improved GANsThe functions include:
Page 155 and 156:
Improved GANsmodels = (generator, d
Page 157 and 158:
Improved GANsfor layer in discrimin
Page 159 and 160:
Improved GANsFollowing figure shows
Page 161 and 162:
Improved GANsThe preceding table sh
Page 163 and 164:
Improved GANsFollowing figure shows
Page 165 and 166:
Improved GANsEssentially, in CGAN w
Page 167 and 168:
Improved GANslayer = Dense(layer_fi
Page 169 and 170:
Improved GANsx = BatchNormalization
Page 171 and 172:
Improved GANsdiscriminator.compile(
Page 173 and 174:
Improved GANssize=batch_size)real_i
Page 175 and 176:
Improved GANsUnlike CGAN, the sampl
Page 177 and 178:
Improved GANsConclusionIn this chap
Page 179 and 180:
Disentangled Representation GANsIn
Page 181 and 182:
Disentangled Representation GANsInf
Page 183 and 184:
Disentangled Representation GANsFol
Page 185 and 186:
Disentangled Representation GANs# A
Page 187 and 188:
Disentangled Representation GANsif
Page 189 and 190:
Disentangled Representation GANsLis
Page 191 and 192:
Disentangled Representation GANsdat
Page 193 and 194:
Disentangled Representation GANsy[b
Page 195 and 196:
Disentangled Representation GANspyt
Page 197 and 198:
Disentangled Representation GANsThe
Page 199 and 200:
Disentangled Representation GANsSta
Page 201 and 202:
Disentangled Representation GANs( )
Page 203 and 204:
Disentangled Representation GANsThe
Page 205 and 206:
Disentangled Representation GANsfea
Page 207 and 208:
Disentangled Representation GANs# f
Page 209 and 210:
Disentangled Representation GANslat
Page 211 and 212:
Disentangled Representation GANsDis
Page 213 and 214:
Disentangled Representation GANsz_d
Page 215 and 216:
Disentangled Representation GANs2.
Page 217 and 218:
Disentangled Representation GANsFig
Page 220 and 221:
Cross-Domain GANsIn computer vision
Page 222 and 223:
Chapter 7There are many more exampl
Page 224 and 225:
The CycleGAN ModelFigure 7.1.3 show
Page 226 and 227:
Chapter 7Repeat for n training step
Page 228 and 229:
Chapter 7Implementing CycleGAN usin
Page 230 and 231:
filters=16,kernel_size=3,strides=2,
Page 232 and 233:
Chapter 7kernel_size=kernel_size)e3
Page 234 and 235:
Listing 7.1.3, cyclegan-7.1.1.py sh
Page 236 and 237:
Chapter 71) Build target and source
Page 238 and 239:
Chapter 7preal_target,reco_source,r
Page 240 and 241:
size=batch_size)real_source = sourc
Page 242 and 243:
Chapter 7returndirs=dirs,show=True)
Page 244 and 245:
Chapter 7Figure 7.1.10: Color (from
Page 246 and 247:
[ 229 ]Chapter 7titles = ('MNIST pr
Page 248 and 249:
Chapter 7Figure 7.1.13: Style trans
Page 250 and 251:
Chapter 7Figure 7.1.15: The backwar
Page 252:
Chapter 7References1. Yuval Netzer
Page 255 and 256:
Variational Autoencoders (VAEs)In t
Page 257 and 258:
Variational Autoencoders (VAEs)Typi
Page 259 and 260:
Variational Autoencoders (VAEs)For
Page 261 and 262:
Variational Autoencoders (VAEs)VAEs
Page 263 and 264:
Variational Autoencoders (VAEs)outp
Page 265 and 266:
Variational Autoencoders (VAEs)Figu
Page 267 and 268:
Variational Autoencoders (VAEs)The
Page 269 and 270:
Page 271 and 272:
Variational Autoencoders (VAEs)Prec
Page 273 and 274:
Variational Autoencoders (VAEs)shap
Page 275 and 276:
Variational Autoencoders (VAEs)cvae
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
Variational Autoencoders (VAEs)In F
Page 283 and 284:
Page 285 and 286:
Variational Autoencoders (VAEs)The
Page 288 and 289:
Deep ReinforcementLearningReinforce
Page 290 and 291:
[ 273 ]Chapter 9Formally, the RL pr
Page 292 and 293:
Chapter 9Where:( ) ( , )∗V s maxQ
Page 294 and 295:
Chapter 9Initially, the agent assum
Page 296 and 297:
Chapter 9Figure 9.3.6: Assuming the
Page 298 and 299:
Q-Learning in PythonThe environment
Page 300 and 301:
Chapter 9----------------"""self.re
Page 302 and 303:
Chapter 9# UI to dump Q Table conte
Page 304 and 305:
Chapter 9Figure 9.3.10: The value f
Page 306 and 307:
Chapter 9Figure 9.5.1: Frozen lake
Page 308 and 309:
Chapter 9# discount factorself.gamm
Page 310 and 311:
Chapter 9# training of Q Tableif do
Page 312 and 313:
Chapter 9Where all terms are famili
Page 314 and 315:
Listing 9.6.1 shows us the DQN impl
Page 316 and 317:
Chapter 9if self.ddqn:print("------
Page 318 and 319:
Chapter 9updates# correction on the
Page 320 and 321:
QmaxChapter 9⎧rj+1if episodetermi
Page 322:
References1. Sutton and Barto. Rein
Page 325 and 326:
Policy Gradient MethodsPolicy gradi
Page 327 and 328:
Policy Gradient MethodsGiven a cont
Page 329 and 330:
Policy Gradient MethodsRequire: Dis
Page 331 and 332:
Page 333 and 334:
Page 335 and 336:
Policy Gradient MethodsRequire: θ
Page 337 and 338:
Policy Gradient MethodsThe state is
Page 339 and 340:
Policy Gradient Methodsself.encoder
Page 341 and 342:
Policy Gradient MethodsThe policy n
Page 343 and 344:
Policy Gradient MethodsFigure 10.6.
Page 345 and 346:
Policy Gradient MethodsAfter buildi
Page 347 and 348:
Policy Gradient Methods[_, _, _, re
Page 349 and 350:
Policy Gradient MethodsEach algorit
Page 351 and 352:
Policy Gradient Methodswhile not do
Page 353 and 354:
Page 355 and 356:
Page 357 and 358:
Policy Gradient MethodsTrain REINFO
Page 360 and 361:
Other Books YouMay EnjoyIf you enjo
Page 362:
Other Books You May EnjoyLeave a re
Page 365 and 366:
DenseNet 39DenseNet-BC (Bottleneck-
Page 367:
VVariational Autoencoder (VAE)about
show all

Advanced Deep Learning with Keras

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?