Advanced Deep Learning with Keras

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Chapter 6Figure 6.2.10: Images generated by using a StackedGAN as the first noise code, z 0, varies fromconstant vector -4.0 to 4.0 for digits 0 to 9. z 0appears to control the thickness of each digit.Figure 6.2.11: The images generated by StackedGAN as the second noise code, z 1, varies from constant vector-1.0 to 1.0 for digits 0 to 9. z 1appears to control the rotation (tilt) and the thickness of stroke of each digit.[ 199 ]
Disentangled Representation GANsFigures 6.2.9 to 6.2.11 demonstrate that the StackedGAN has provided additionalcontrol on the attributes of the generator outputs. The control and attributes are(label, which digit), (z0, digit thickness), and (z1, digit tilt). From this example,there are other possible experiments that we can control such as:• Increasing the number of elements of the stack from the current 2• Decreasing the dimension of codes z0 and z1, like in InfoGANFollowing figure shows the differences between the latent codes of InfoGAN andStackedGAN. The basic idea of disentangling codes is to put a constraint on theloss functions such that only specific attributes are affected by a code. Structure-wise,InfoGAN are easier to implement when compared to StackedGAN. InfoGAN is alsofaster to train:Figure 6.2.12: Latent representations for different GANsConclusionIn this chapter, we've discussed how to disentangle the latent representationsof GANs. Earlier on in the chapter, we discussed how InfoGAN maximizes themutual information in order to force the generator to learn disentangled latentvectors. In the MNIST dataset example, InfoGAN uses three representations anda noise code as inputs. The noise represents the rest of the attributes in the formof an entangled representation. StackedGAN approaches the problem in a differentway. It uses a stack of encoder-GANs to learn how to synthesize fake featuresand images. The encoder is first trained to provide a dataset of features. Then,the encoder-GANs are trained jointly to learn how to use the noise code to controlattributes of the generator output.In the next chapter, we will embark on a new type of GAN that is able to generatenew data in another domain. For example, given an image of a horse, the GANcan perform an automatic transformation to an image of a zebra. The interestingfeature of this type of GAN is that it can be trained without supervision.[ 200 ]
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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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Page 167 and 168: Improved GANslayer = Dense(layer_fi
Page 169 and 170: Improved GANsx = BatchNormalization
Page 171 and 172: Improved GANsdiscriminator.compile(
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Page 175 and 176: Improved GANsUnlike CGAN, the sampl
Page 177 and 178: Improved GANsConclusionIn this chap
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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
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Page 238 and 239: Chapter 7preal_target,reco_source,r
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Page 242 and 243: Chapter 7returndirs=dirs,show=True)
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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
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Variational Autoencoders (VAEs)The
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Variational Autoencoders (VAEs)Figu
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Variational Autoencoders (VAEs)Prec
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Variational Autoencoders (VAEs)shap
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Variational Autoencoders (VAEs)cvae
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Variational Autoencoders (VAEs)In F
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Variational Autoencoders (VAEs)The
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Deep ReinforcementLearningReinforce
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[ 273 ]Chapter 9Formally, the RL pr
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Chapter 9Where:( ) ( , )∗V s maxQ
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Chapter 9Initially, the agent assum
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Chapter 9Figure 9.3.6: Assuming the
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Q-Learning in PythonThe environment
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Chapter 9----------------"""self.re
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Chapter 9# UI to dump Q Table conte
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Chapter 9Figure 9.3.10: The value f
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Chapter 9Figure 9.5.1: Frozen lake
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Chapter 9# discount factorself.gamm
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Chapter 9# training of Q Tableif do
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Chapter 9Where all terms are famili
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Listing 9.6.1 shows us the DQN impl
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Chapter 9if self.ddqn:print("------
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Chapter 9updates# correction on the
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QmaxChapter 9⎧rj+1if episodetermi
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References1. Sutton and Barto. Rein
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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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Advanced Deep Learning with Keras

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