Advanced Deep Learning with Keras

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Chapter 8Implementing CVAE requires a few modifications in the code of the VAE. For theCVAE, the VAE CNN implementation is used. Listing 8.2.1 highlights the changesmade to the original code of VAE for MNIST digits. The encoder input is now aconcatenation of original input image and its one-hot label. The decoder input isnow a combination of the latent space sampling and the one-hot label of the image itshould generate. The total number of parameters is 174, 437. The codes related to β-VAE will be discussed in the next section of this chapter.There are no changes in the loss function. However, the one-hot labels are suppliedduring training, testing, and plotting of results. Figures 8.2.1 to 8.2.3 show us theencoder, decoder, and CVAE models. The role of the conditioning label in the formof a one-hot vector is indicated.Figure 8.2.4: The latent vector mean values for the test dataset (CVAE 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.[ 261 ]
Variational Autoencoders (VAEs)Figure 8.2.5: Digits 0 to 5 generated as a function of latent vector mean values and one-hot label(CVAE CNN). For ease of interpretation, the range of values for the mean is similar to Figure 8.2.4.[ 262 ]
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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
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Chapter 7Repeat for n training step
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Page 234 and 235: Listing 7.1.3, cyclegan-7.1.1.py sh
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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 288 and 289: Deep ReinforcementLearningReinforce
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Page 298 and 299: Q-Learning in PythonThe environment
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Page 318 and 319: Chapter 9updates# correction on the
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Page 325 and 326: Policy Gradient MethodsPolicy gradi
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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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