Advanced Deep Learning with Keras
Chapter 8Figure 8.2.1: The encoder in CVAE CNN. The input is now made of theconcatenation of the VAE input and a conditioning label.[ 259 ]
Variational Autoencoders (VAEs)Figure 8.2.2: The decoder in CVAE CNN. The input is now made of the concatenationof the z sampling and a conditioning label.Figure 8.2.3: The CVAE model using a CNN. The input is now made of a VAE input and a conditioning label.[ 260 ]
- Page 226 and 227: Chapter 7Repeat for n training step
- Page 228 and 229: Chapter 7Implementing CycleGAN usin
- Page 230 and 231: filters=16,kernel_size=3,strides=2,
- 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
- Page 240 and 241: size=batch_size)real_source = sourc
- Page 242 and 243: Chapter 7returndirs=dirs,show=True)
- Page 244 and 245: Chapter 7Figure 7.1.10: Color (from
- 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
- Page 255 and 256: Variational Autoencoders (VAEs)In t
- Page 257 and 258: Variational Autoencoders (VAEs)Typi
- Page 259 and 260: Variational Autoencoders (VAEs)For
- Page 261 and 262: Variational Autoencoders (VAEs)VAEs
- Page 263 and 264: Variational Autoencoders (VAEs)outp
- Page 265 and 266: Variational Autoencoders (VAEs)Figu
- Page 267 and 268: Variational Autoencoders (VAEs)The
- Page 269 and 270: Variational Autoencoders (VAEs)Figu
- Page 271 and 272: Variational Autoencoders (VAEs)Prec
- Page 273 and 274: Variational Autoencoders (VAEs)shap
- Page 275: Variational Autoencoders (VAEs)cvae
- Page 279 and 280: Variational Autoencoders (VAEs)Figu
- Page 281 and 282: Variational Autoencoders (VAEs)In F
- Page 283 and 284: Variational Autoencoders (VAEs)Figu
- Page 285 and 286: Variational Autoencoders (VAEs)The
- Page 288 and 289: Deep ReinforcementLearningReinforce
- Page 290 and 291: [ 273 ]Chapter 9Formally, the RL pr
- Page 292 and 293: Chapter 9Where:( ) ( , )∗V s maxQ
- Page 294 and 295: Chapter 9Initially, the agent assum
- Page 296 and 297: Chapter 9Figure 9.3.6: Assuming the
- Page 298 and 299: Q-Learning in PythonThe environment
- Page 300 and 301: Chapter 9----------------"""self.re
- Page 302 and 303: Chapter 9# UI to dump Q Table conte
- Page 304 and 305: Chapter 9Figure 9.3.10: The value f
- Page 306 and 307: Chapter 9Figure 9.5.1: Frozen lake
- Page 308 and 309: Chapter 9# discount factorself.gamm
- Page 310 and 311: Chapter 9# training of Q Tableif do
- Page 312 and 313: Chapter 9Where all terms are famili
- Page 314 and 315: Listing 9.6.1 shows us the DQN impl
- Page 316 and 317: Chapter 9if self.ddqn:print("------
- Page 318 and 319: Chapter 9updates# correction on the
- Page 320 and 321: QmaxChapter 9⎧rj+1if episodetermi
- Page 322: References1. Sutton and Barto. Rein
- Page 325 and 326: Policy Gradient MethodsPolicy gradi
Chapter 8
Figure 8.2.1: The encoder in CVAE CNN. The input is now made of the
concatenation of the VAE input and a conditioning label.
[ 259 ]
Change language