Advanced Deep Learning with Keras

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Chapter 6# plot generator images on a periodic basisgan.plot_images(generator,noise_input=noise_input,noise_label=noise_label,noise_codes=[noise_code1, noise_code2],show=show,step=(i + 1),model_name=model_name)# save the model after training the generator# the trained generator can be reloaded for# future MNIST digit generationgenerator.save(model_name + ".h5")Generator outputs of InfoGANSimilar to all previous GANs that have been presented to us, we've trained InfoGANfor 40,000 steps. After the training is completed, we're able to run the InfoGANgenerator to generate new outputs using the model saved on the infogan_mnist.h5file. The following validations are conducted:1. Generate digits 0 to 9 by varying the discrete labels from 0 to 9. Bothcontinuous codes are set to zero. The results are shown in Figure 6.1.5. Wecan see that the InfoGAN discrete code can control the digits produced bythe generator:python3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5--digit=0 --code1=0 --code2=0topython3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5--digit=9 --code1=0 --code2=02. Examine the effect of the first continuous code to understand which attributehas been affected. We vary the first continuous code from -2.0 to 2.0 for digits0 to 9. The second continuous code is set to 0.0. Figure 6.1.6 shows that thefirst continuous code controls the thickness of the digit:python3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5--digit=0 --code1=0 --code2=0 --p13. Similar to the previous step, but instead focusing more on the secondcontinuous code. Figure 6.1.7 shows that the second continuous codecontrols the rotation angle (tilt) of the writing style:[ 177 ]
Disentangled Representation GANspython3 infogan-mnist-6.1.1.py --generator=infogan_mnist.h5--digit=0 --code1=0 --code2=0 --p2Figure 6.1.5: The images generated by the InfoGAN as the discrete codeis varied from 0 to 9. Both continuous codes are set to zero.Figure 6.1.6: The images generated by InfoGAN as the first continuous code is varied from -2.0 to 2.0 for digits0 to 9. The second continuous code is set to zero. The first continuous code controls the thickness of the digit.[ 178 ]
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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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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
Page 183 and 184: Disentangled Representation GANsFol
Page 185 and 186: Disentangled Representation GANs# A
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Page 217 and 218: 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 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)
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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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