Advanced Deep Learning with Keras
Chapter 8# instantiate encoder modelencoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')encoder.summary()plot_model(encoder, to_file='vae_cnn_encoder.png', show_shapes=True)# build decoder modellatent_inputs = Input(shape=(latent_dim,), name='z_sampling')x = Dense(shape[1]*shape[2]*shape[3], activation='relu')(latent_inputs)x = Reshape((shape[1], shape[2], shape[3]))(x)for i in range(2):x = Conv2DTranspose(filters=filters,kernel_size=kernel_size,activation='relu',strides=2,padding='same')(x)filters //= 2outputs = Conv2DTranspose(filters=1,kernel_size=kernel_size,activation='sigmoid',padding='same',name='decoder_output')(x)# instantiate decoder modeldecoder = Model(latent_inputs, outputs, name='decoder')decoder.summary()plot_model(decoder, to_file='vae_cnn_decoder.png', show_shapes=True)# instantiate vae modeloutputs = decoder(encoder(inputs)[2])vae = Model(inputs, outputs, name='vae')[ 251 ]
Variational Autoencoders (VAEs)Figure 8.1.8: The encoder of VAE CNNFigure 8.1.9: The decoder of VAE CNN[ 252 ]
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- Page 224 and 225: The CycleGAN ModelFigure 7.1.3 show
- Page 226 and 227: Chapter 7Repeat for n training step
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- Page 236 and 237: Chapter 71) Build target and source
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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
- Page 292 and 293: Chapter 9Where:( ) ( , )∗V s maxQ
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- Page 314 and 315: Listing 9.6.1 shows us the DQN impl
- Page 316 and 317: Chapter 9if self.ddqn:print("------
Variational Autoencoders (VAEs)
Figure 8.1.8: The encoder of VAE CNN
Figure 8.1.9: The decoder of VAE CNN
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