Advanced Deep Learning with Keras

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Chapter 6Reference1. Xi Chen and others. InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial Nets. Advances in NeuralInformation Processing Systems, 2016(http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-byinformation-maximizing-generative-adversarial-nets.pdf).2. Xun Huang and others. Stacked Generative Adversarial Networks. IEEEConference on Computer Vision and Pattern Recognition (CVPR). Vol. 2,2017(http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Stacked_Generative_Adversarial_CVPR_2017_paper.pdf).[ 201 ]
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Advanced Deep Learningwith KerasApp
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mapt.ioMapt is an online digital li
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I would like to thank my family, Ch
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Table of ContentsPrefaceVChapter 1:
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[ iii ]Table of ContentsChapter 7:
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[ v ]PrefaceIn recent years, deep l
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Chapter 5, Improved GANs, covers al
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def encoder_layer(inputs,filters=16
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Introducing Advanced DeepLearning w
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Chapter 1Installing Keras and Tenso
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Chapter 1• RNNs: Recurrent neural
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[ 7 ]Chapter 1In the preceding figu
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Chapter 1Figure 1.3.3: MLP MNIST di
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Chapter 1model.add(Activation('soft
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Chapter 1model.add(Activation('relu
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Chapter 1As an example, l2 weight r
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[ 17 ]Chapter 1How far the predicte
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Chapter 1Figure 1.3.8: Plot of a fu
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Chapter 1The highest test accuracy
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Chapter 1Figure 1.3.9: The graphica
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Chapter 1# image is processed as is
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Chapter 1The computation involved i
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Chapter 1Listing 1.4.2 shows a summ
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Chapter 164-64-64 RMSprop Dropout(0
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Chapter 1There are the two main dif
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Chapter 1Layers Optimizer Regulariz
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ConclusionThis chapter provided an
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Deep Neural NetworksWhile this chap
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Deep Neural Networks# reshape and n
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Deep Neural NetworksEverything else
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Deep Neural Networksfrom keras.util
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Deep Neural NetworksFigure 2.1.3: T
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Deep Neural NetworksHence, the netw
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Deep Neural NetworksGenerally speak
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Deep Neural NetworksIn the dataset,
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Deep Neural NetworksTransition Laye
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Deep Neural NetworksThere are some
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Deep Neural NetworksResNet v2 is al
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Deep Neural Networks…if version =
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Deep Neural NetworksTo prevent the
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Deep Neural NetworksAverage Pooling
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Deep Neural Networks# orig paper us
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AutoencodersIn the previous chapter
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Chapter 3The autoencoder has the te
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Chapter 3Firstly, we're going to im
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Chapter 3# reconstruct the inputout
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Chapter 3Figure 3.2.2: The decoder
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batch_size=32,model_name="autoencod
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Chapter 3Figure 3.2.6: Digits gener
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Chapter 3As shown in Figure 3.3.2,
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Chapter 3image_size = x_train.shape
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Chapter 3# Mean Square Error (MSE)
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Chapter 3from keras.layers import R
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Chapter 3# build the autoencoder mo
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Chapter 3x_train,validation_data=(x
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Chapter 3ConclusionIn this chapter,
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Generative Adversarial Networks (GA
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Improved GANsIn summary, the goal o
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Improved GANsThe intuition behind E
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Improved GANsThis makes sense since
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Improved GANsIn the context of GANs
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Improved GANsFigure 5.1.3: Top: Tra
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Improved GANsThe functions include:
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Improved GANsmodels = (generator, d
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Improved GANsfor layer in discrimin
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Improved GANsFollowing figure shows
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Improved GANsThe preceding table sh
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Improved GANsFollowing figure shows
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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
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Page 221 and 222: Cross-Domain GANsIn this chapter, w
Page 223 and 224: Cross-Domain GANsThe main disadvant
Page 225 and 226: Cross-Domain GANsSimilar to other G
Page 227 and 228: Cross-Domain GANsFigure 7.1.5: The
Page 229 and 230: Cross-Domain GANsAs discussed in th
Page 231 and 232: Cross-Domain GANskernel_size=kernel
Page 233 and 234: Cross-Domain GANsWe should note tha
Page 235 and 236: Cross-Domain GANs# else use 1-dim o
Page 237 and 238: Cross-Domain GANskernel_size=kernel
Page 239 and 240: Cross-Domain GANsmodels (Models): S
Page 241 and 242: Cross-Domain GANsFinally, before we
Page 243 and 244: Cross-Domain GANsSince CycleGAN is
Page 245 and 246: Cross-Domain GANsWe introduce modul
Page 247 and 248: Cross-Domain GANsIn the case of Pat
Page 249 and 250: Cross-Domain GANsFigure 7.1.14: For
Page 251 and 252: Cross-Domain GANsFigure 7.1.14 show
Page 254 and 255: Variational Autoencoders(VAEs)Simil
Page 256 and 257: Chapter 8In other words, considerin
Page 258 and 259: Chapter 8Equation 8.1.10 is the cor
Page 260 and 261: Chapter 8The solution to this probl
Page 262 and 263: Chapter 8epsilon = K.random_normal(
Page 264 and 265: Chapter 8Figure 8.1.3: The encoder
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Chapter 8# instantiate encoder mode
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Chapter 8Figure 8.1.10: The VAE mod
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Chapter 8log Pθ ( x | c) − DKL (
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Chapter 8# instantiate vae modelout
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Chapter 8Figure 8.2.1: The encoder
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Chapter 8Implementing CVAE requires
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Chapter 8Figure 8.2.6: Digits 6 to
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It is straightforward to implement
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Chapter 8Figure 8.3.3: Digits 0 to
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5. I. Higgins, L. Matthey, A. Pal,
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Deep Reinforcement LearningIn summa
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Deep Reinforcement LearningReturn c
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Deep Reinforcement LearningQ-Learni
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Deep Reinforcement LearningFigure 9
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Deep Reinforcement LearningFigure 9
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Deep Reinforcement Learningself.col
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Deep Reinforcement Learning# termin
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Deep Reinforcement LearningThe perc
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Deep Reinforcement LearningPrevious
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Deep Reinforcement LearningThis wil
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Deep Reinforcement Learningq_value
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Deep Reinforcement LearningThe most
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Deep Reinforcement LearningA high c
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Deep Reinforcement Learningfrom ker
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Deep Reinforcement Learning# comput
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Deep Reinforcement Learning# store
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Deep Reinforcement LearningConclusi
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Policy Gradient MethodsIn the final
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Chapter 10( | , ) ( )π a s θ = so
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Chapter 10The gradient updates are
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REINFORCE with baseline methodChapt
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Chapter 10Actor-Critic methodIn REI
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Chapter 10Advantage Actor-Critic (A
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Chapter 10Figure 10.6.1 MountainCar
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Chapter 10Figure 10.6.4 Decoder mod
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Chapter 10Figure 10.6.5: Policy mod
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logp = Lambda(self.logp,output_shap
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mean, stddev = argsdist = tf.distri
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Chapter 10Similarly, the value loss
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Chapter 10next_value = self.value(n
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[ 333 ]Chapter 10The training strat
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Chapter 10Performance evaluation of
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Chapter 10Figure 10.7.4: The number
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Chapter 10Figure 10.7.8: The total
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Chapter 10ConclusionIn this chapter
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Other Books You May EnjoyDeep Learn
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IndexAaccuracy 17Actor-Critic (A2C)
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Actor-Critic method, advantages 317
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Advanced Deep Learning with Keras

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