Advanced Deep Learning with Keras

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Chapter 4CGAN is similar to DCGAN except for the additional one-hot vector input. For thegenerator, the one-hot label is concatenated with the latent vector before the Denselayer. For the discriminator, a new Dense layer is added. The new layer is used toprocess the one-hot vector and reshape it so that it is suitable for concatenation tothe other input of the succeeding CNN layer:Figure 4.3.1: The CGAN model is similar to DCGAN except for the one-hot vector, which is used to conditionthe generator and discriminator outputs[ 115 ]
Generative Adversarial Networks (GANs)The generator learns to generate fake images from a 100-dim input vector anda specified digit. The discriminator classifies real from fake images based on realand fake images and their corresponding labels.The basis of CGAN is still the same as the original GAN principle except thatthe discriminator and generator inputs are conditioned on one-hot labels, y.By incorporating this condition in Equations 4.1.1 and 4.1.5, the loss functions forthe discriminator and generator are shown in Equations 4.3.1 and 4.3.2 respectively.Given Figure 4.3.2, it may be more appropriate to write the loss functions as:and( D) ( G) ( D)( θ , θ ) =−Ex~plog ( x | y) −Ezlog( 1 − ( ( z | y′ ) | y′))( D) ( G) ( D)L D D GLdata( θ , θ ) =−Ezlog D( G( z | y′ ) | y′)( G) ( G) ( D)( θ , θ ) =−Ex~plog ( x | y) −Ezlog( 1 − ( ( z | y′)))L D D G (Equation 4.3.1)data.( θ , θ ) =−Ezlog D( ( z | y′))( G) ( G) ( D)L G (Equation 4.3.2)The new loss function of the discriminator aims to minimize the error of predictingreal images coming from the dataset and fake images coming from the generatorgiven their one-hot labels. Figure 4.3.2 shows how to train the discriminator.Figure 4.3.2: Training the CGAN discriminator is similar to training the GAN discriminator.The only difference is both the generated fake and the dataset's real images are conditionedwith their corresponding one-hot labels.[ 116 ]
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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 =
Page 81 and 82: Deep Neural NetworksTo prevent the
Page 83 and 84: Deep Neural NetworksAverage Pooling
Page 85 and 86: Deep Neural Networks# orig paper us
Page 88 and 89: AutoencodersIn the previous chapter
Page 90 and 91: Chapter 3The autoencoder has the te
Page 92 and 93: Chapter 3Firstly, we're going to im
Page 94 and 95: Chapter 3# reconstruct the inputout
Page 96 and 97: Chapter 3Figure 3.2.2: The decoder
Page 98 and 99: batch_size=32,model_name="autoencod
Page 100 and 101: Chapter 3Figure 3.2.6: Digits gener
Page 102 and 103: Chapter 3As shown in Figure 3.3.2,
Page 104 and 105: Chapter 3image_size = x_train.shape
Page 106 and 107: Chapter 3# Mean Square Error (MSE)
Page 108 and 109: Chapter 3from keras.layers import R
Page 110 and 111: Chapter 3# build the autoencoder mo
Page 112 and 113: Chapter 3x_train,validation_data=(x
Page 114: Chapter 3ConclusionIn this chapter,
Page 117 and 118: Generative Adversarial Networks (GA
Page 131: Generative Adversarial Networks (GA
Page 143 and 144: Improved GANsIn summary, the goal o
Page 145 and 146: Improved GANsThe intuition behind E
Page 147 and 148: Improved GANsThis makes sense since
Page 149 and 150: Improved GANsIn the context of GANs
Page 151 and 152: Improved GANsFigure 5.1.3: Top: Tra
Page 153 and 154: Improved GANsThe functions include:
Page 155 and 156: Improved GANsmodels = (generator, d
Page 157 and 158: Improved GANsfor layer in discrimin
Page 159 and 160: Improved GANsFollowing figure shows
Page 161 and 162: Improved GANsThe preceding table sh
Page 163 and 164: Improved GANsFollowing figure shows
Page 165 and 166: 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
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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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Chapter 7Implementing CycleGAN usin
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filters=16,kernel_size=3,strides=2,
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Chapter 7kernel_size=kernel_size)e3
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Listing 7.1.3, cyclegan-7.1.1.py sh
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Chapter 71) Build target and source
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Chapter 7preal_target,reco_source,r
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size=batch_size)real_source = sourc
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Chapter 7returndirs=dirs,show=True)
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Chapter 7Figure 7.1.10: Color (from
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[ 229 ]Chapter 7titles = ('MNIST pr
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Chapter 7Figure 7.1.13: Style trans
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Chapter 7Figure 7.1.15: The backwar
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Chapter 7References1. Yuval Netzer
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Variational Autoencoders (VAEs)In t
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Variational Autoencoders (VAEs)Typi
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Variational Autoencoders (VAEs)For
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Variational Autoencoders (VAEs)VAEs
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Variational Autoencoders (VAEs)outp
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Variational Autoencoders (VAEs)Figu
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Variational Autoencoders (VAEs)The
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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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