Advanced Deep Learning with Keras

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Chapter 8Figure 8.1.10: The VAE model using CNNsFigure 8.1.11: The latent vector mean values for the test dataset (VAE CNN). The colorbar shows thecorresponding MNIST digit as a function of z. Color images can be found on the book GitHub repository:https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/tree/master/chapter8-vae.[ 253 ]
Variational Autoencoders (VAEs)Preceding figure shows the continuous latent space of a VAE using the CNNimplementation after 30 epochs. The region where each digit is assigned maybe different, but the distribution is roughly the same. Following figure showsus the output of the generative model. Qualitatively, there are fewer digits thatare ambiguous as compared to Figure 8.1.7 with the MLP implementation:Figure 8.1.12: The digits generated as a function of latent vector mean values (VAE CNN).For ease of interpretation, the range of values for the mean is similar to Figure 8.1.11.Conditional VAE (CVAE)Conditional VAE [2] is similar to the idea of CGAN. In the context of the MNISTdataset, if the latent space is randomly sampled, VAE has no control over whichdigit will be generated. CVAE is able to address this problem by includinga condition (a one-hot label) of the digit to produce. The condition is imposedon both the encoder and decoder inputs.Formally, the core equation of VAE in Equation 8.1.10 is modified to include thecondition c:[ 254 ]
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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
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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Page 290 and 291: [ 273 ]Chapter 9Formally, the RL pr
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Page 298 and 299: Q-Learning in PythonThe environment
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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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