Advanced Deep Learning with Keras

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Chapter 5input_shape = (image_size, image_size, 1)batch_size = 64lr = 2e-4decay = 6e-8train_steps = 40000# build discriminator modelinputs = Input(shape=input_shape, name='discriminator_input')discriminator = gan.discriminator(inputs, activation=None)# [1] uses Adam, but discriminator converges# easily with RMSpropoptimizer = RMSprop(lr=lr, decay=decay)# LSGAN uses MSE loss [2]discriminator.compile(loss='mse',optimizer=optimizer,metrics=['accuracy'])discriminator.summary()# build generator modelinput_shape = (latent_size, )inputs = Input(shape=input_shape, name='z_input')generator = gan.generator(inputs, image_size)generator.summary()# build adversarial model = generator + discriminatoroptimizer = RMSprop(lr=lr*0.5, decay=decay*0.5)# freeze the weights of discriminator# during adversarial trainingdiscriminator.trainable = Falseadversarial = Model(inputs,discriminator(generator(inputs)),name=model_name)# LSGAN uses MSE loss [2]adversarial.compile(loss='mse',optimizer=optimizer,metrics=['accuracy'])adversarial.summary()# train discriminator and adversarial networksmodels = (generator, discriminator, adversarial)params = (batch_size, latent_size, train_steps, model_name)gan.train(models, x_train, params)[ 145 ]
Improved GANsFollowing figure shows generated samples after training LSGAN using the MNISTdataset for 40,000 training steps. The output images have better perceptual qualitycompared to Figure 4.2.1 in DCGAN:Figure 5.2.2: Sample outputs of LSGAN vs. training stepsI encourage you to run the trained generator model to see the new synthesizedMNIST digits images:python3 lsgan-mnist-5.2.1.py --generator=lsgan_mnist.h5[ 146 ]
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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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Page 114: Chapter 3ConclusionIn this chapter,
Page 117 and 118: 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: Improved GANsThe preceding table sh
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
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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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