Advanced Deep Learning with Keras

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Chapter 8Figure 8.2.1: The encoder in CVAE CNN. The input is now made of theconcatenation of the VAE input and a conditioning label.[ 259 ]
Variational Autoencoders (VAEs)Figure 8.2.2: The decoder in CVAE CNN. The input is now made of the concatenationof the z sampling and a conditioning label.Figure 8.2.3: The CVAE model using a CNN. The input is now made of a VAE input and a conditioning label.[ 260 ]
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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
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Improved GANslayer = Dense(layer_fi
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Improved GANsx = BatchNormalization
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Improved GANsdiscriminator.compile(
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Improved GANssize=batch_size)real_i
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Improved GANsUnlike CGAN, the sampl
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Improved GANsConclusionIn this chap
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Disentangled Representation GANsIn
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Disentangled Representation GANsInf
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Disentangled Representation GANsFol
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Disentangled Representation GANs# A
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Disentangled Representation GANsif
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Disentangled Representation GANsLis
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Disentangled Representation GANsdat
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Disentangled Representation GANsy[b
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Disentangled Representation GANspyt
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Disentangled Representation GANsThe
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Disentangled Representation GANsSta
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Disentangled Representation GANs( )
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Disentangled Representation GANsThe
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Disentangled Representation GANsfea
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Disentangled Representation GANs# f
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Disentangled Representation GANslat
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Disentangled Representation GANsDis
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Disentangled Representation GANsz_d
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Disentangled Representation GANs2.
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Disentangled Representation GANsFig
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Cross-Domain GANsIn computer vision
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Chapter 7There are many more exampl
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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
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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
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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
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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
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Page 298 and 299: Q-Learning in PythonThe environment
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Page 302 and 303: Chapter 9# UI to dump Q Table conte
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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
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Policy Gradient MethodsGiven a cont
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Policy Gradient MethodsRequire: Dis
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Policy Gradient MethodsRequire: θ
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Policy Gradient MethodsThe state is
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Policy Gradient Methodsself.encoder
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Policy Gradient MethodsThe policy n
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Policy Gradient MethodsFigure 10.6.
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Policy Gradient MethodsAfter buildi
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Policy Gradient Methods[_, _, _, re
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Policy Gradient MethodsEach algorit
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Policy Gradient Methodswhile not do
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Policy Gradient MethodsTrain REINFO
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Other Books YouMay EnjoyIf you enjo
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Other Books You May EnjoyLeave a re
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DenseNet 39DenseNet-BC (Bottleneck-
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VVariational Autoencoder (VAE)about
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