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Chapter 6generator_AB.save_weights(checkpoint_prefixg_AB.format(epoch=epoch))generator_BA.save_weights(checkpoint_prefixg_BA.format(epoch=epoch))You can see some of the images generated by our CycleGAN. Generator A takesin summer photos and converts them to winter, while generator B takes in winterphotos and converts them to summer:And following are some after the network is trained:[ 227 ]
Generative Adversarial NetworksWe suggest you experiment with other datasets in the TensorFlow CycleGANdatasets. Some will be easy like apples and oranges, but some will require muchmore training. The authors also maintain a GitHub repo where they have sharedtheir own implementation in PyTorch along with the links to implementations inother frameworks including TensorFlow: https://github.com/junyanz/CycleGAN.SummaryThis chapter explored one of the most exciting deep neural networks of our times:GANs. Unlike discriminative networks, GANs have an ability to generate imagesbased on the probability distribution of the input space. We started with the firstGAN model proposed by Ian Goodfellow and used it to generate handwritten digits.We next moved to DCGANs where convolutional neural networks were used togenerate images and we saw the remarkable pictures of celebrities, bedrooms, andeven album artwork generated by DCGANs. Finally, the chapter delved into someawesome GAN architectures: the SRGAN, CycleGAN, and InfoGAN. The chapteralso included an implementation of the CycleGAN in TensorFlow 2.0.In this chapter and the ones before it we have been largely concerned with images;the next chapter will move into textual data. You will learn about word embeddingsand learn to use some of the recent pretrained language models for embeddings.References1. Goodfellow, Ian J. On Distinguishability Criteria for Estimating GenerativeModels. arXiv preprint arXiv:1412.6515 (2014). (https://arxiv.org/pdf/1412.6515.pdf)2. Dumoulin, Vincent, and Francesco Visin. A guide to convolution arithmetic fordeep learning. arXiv preprint arXiv:1603.07285 (2016). (https://arxiv.org/abs/1603.07285)3. Salimans, Tim, et al. Improved Techniques for Training GANs. Advances inneural information processing systems. 2016. (http://papers.nips.cc/paper/6125-improved-techniques-for-training-gans.pdf)4. Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. European conference on computervision. Springer, Cham, 2016. (https://arxiv.org/abs/1603.08155)5. Radford, Alec, Luke Metz, and Soumith Chintala. Unsupervised RepresentationLearning with Deep Convolutional Generative Adversarial Networks. arXivpreprint arXiv:1511.06434 (2015). (https://arxiv.org/abs/1511.06434)[ 228 ]
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Deep Learning withTensorFlow 2 and
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packt.comSubscribe to our online di
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I want to thank my kids, Aurora, Le
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Sujit Pal is a Technology Research
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Table of ContentsPrefacexiChapter 1
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[ iii ]Table of ContentsConverting
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Table of ContentsSo what is the pro
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[ vii ]Table of ContentsChapter 10:
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Table of ContentsPretrained models
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PrefaceDeep Learning with TensorFlo
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• Supervised learning, in which t
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PrefaceThe complexity of deep learn
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PrefaceFigure 5: Adoption of deep l
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Chapter 1, Neural Network Foundatio
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PrefaceChapter 13, TensorFlow for M
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ConventionsThere are a number of te
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PrefaceReferences1. Deep Learning w
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Neural Network Foundations with Ten
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TensorFlow 1.x and 2.xThe intent of
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An example to start withWe'll consi
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Chapter 23. Placeholders: Placehold
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• To create random values from a
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To know the value, we need to creat
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Chapter 2Both PyTorch and TensorFlo
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Chapter 2state = [tf.zeros([100, 10
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Chapter 2For now, there's no need t
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Chapter 2Let's see an example of a
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Chapter 2If you want to save a mode
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Chapter 2supervised=True)train_data
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Chapter 2There, tf.feature_column.n
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Chapter 2print (dz_dx)print (dy_dx)
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Chapter 2In our toy example we use
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Chapter 2For multi-machine training
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Chapter 25. Use tf.layers modules t
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Chapter 2Keras or tf.keras?Another
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• tf.data can be used to load mod
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RegressionLet us imagine a simpler
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RegressionTake a look at the last t
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Regression3. Now, we calculate the
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RegressionIn the next section we wi
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Regression2. Now, we define the fea
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Regression2. Download the dataset:(
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RegressionThe following is the Tens
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RegressionIn regression the aim is
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RegressionThe Estimator outputs the
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RegressionThe following is the grap
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RegressionReferencesHere are some g
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Convolutional Neural NetworksIn thi
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Convolutional Neural NetworksIn thi
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Convolutional Neural NetworksIn oth
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Convolutional Neural NetworksThen w
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Convolutional Neural NetworksHoweve
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Convolutional Neural NetworksPlotti
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Convolutional Neural NetworksIn gen
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Convolutional Neural NetworksOur ne
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Convolutional Neural NetworksThese
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Convolutional Neural NetworksSo, we
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Convolutional Neural NetworksEach i
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Convolutional Neural NetworksVery d
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Convolutional Neural NetworksRecogn
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Convolutional Neural NetworksIf we
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Convolutional Neural NetworksRefere
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Advanced Convolutional Neural Netwo
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Page 211 and 212: Advanced Convolutional Neural Netwo
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Page 228 and 229: [ 193 ]Chapter 6Eventually, we reac
Page 230 and 231: [ 195 ]Chapter 6Next, we combine th
Page 232 and 233: Chapter 6And handwritten digits gen
Page 234 and 235: Chapter 6Figure 1: Visualizing the
Page 236 and 237: Chapter 6The resultant generator mo
Page 238 and 239: Chapter 6Figure 4: A summary of res
Page 240 and 241: Chapter 6def train(self, epochs, ba
Page 242 and 243: Chapter 6The preceding images were
Page 244 and 245: Chapter 6Another interesting paper
Page 246 and 247: Chapter 6To elaborate, let us say t
Page 248 and 249: Chapter 6Figure 7: The architecture
Page 250 and 251: Chapter 6Figure 11: Illegible initi
Page 252 and 253: Chapter 6Bedrooms: Generated bedroo
Page 254 and 255: Chapter 6The images need to be norm
Page 256 and 257: Chapter 6initializer = tf.random_no
Page 258 and 259: Cool, right? Now we can define the
Page 260 and 261: Chapter 6d_loss = (dA_loss + dB_los
Page 264: 6. Ledig, Christian, et al. Photo-R
Page 267 and 268: Word EmbeddingsDeep learning models
Page 269 and 270: Word EmbeddingsFor example, "crucia
Page 271 and 272: Word EmbeddingsAssuming a window si
Page 273 and 274: Word EmbeddingsGloVeThe Global vect
Page 275 and 276: Word Embeddingsgensim is an open so
Page 277 and 278: Word Embeddingsgensim also provides
Page 279 and 280: Word EmbeddingsSpecifically, we wil
Page 281 and 282: Word EmbeddingsWe will also convert
Page 283 and 284: Word EmbeddingsE = np.zeros((vocab_
Page 285 and 286: Word Embeddingsx = self.embedding(x
Page 287 and 288: Word EmbeddingsThe change in valida
Page 289 and 290: Word EmbeddingsThe dataset is a 114
Page 291 and 292: Word Embeddingsprint("random walks
Page 293 and 294: Word Embeddingssize=128, # size of
Page 295 and 296: Word EmbeddingsfastText computes em
Page 297 and 298: Word EmbeddingsIn the future, once
Page 299 and 300: Word EmbeddingsA much earlier relat
Page 301 and 302: Word EmbeddingsOnce you have the fi
Page 303 and 304: Word EmbeddingsThis will create the
Page 305 and 306: Word EmbeddingsClassifying with BER
Page 307 and 308: Word Embeddings2. Each Transformer
Page 309 and 310: Word EmbeddingsOnce trained, we sav
Page 311 and 312: Word Embeddings4. Pennington, J., S
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Word Embeddings34. Google Research,
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Recurrent Neural NetworksWe will th
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Recurrent Neural NetworksFor notati
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Recurrent Neural NetworksThis probl
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Recurrent Neural NetworksThe line a
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Recurrent Neural NetworksGated recu
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Recurrent Neural NetworksThis probl
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Recurrent Neural NetworksThe topolo
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Recurrent Neural Networkstexts = do
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Recurrent Neural Networksdef call(s
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Recurrent Neural Networks# callback
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Recurrent Neural NetworksExample
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Recurrent Neural NetworksAs can be
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Recurrent Neural Networksdata_dir =
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Recurrent Neural NetworksWe can als
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Recurrent Neural NetworksIn order t
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Recurrent Neural Networkssource_voc
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Recurrent Neural NetworksFinally, w
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Recurrent Neural Networks38 - val_l
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Recurrent Neural NetworksIf you wou
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Recurrent Neural NetworksExample
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Recurrent Neural NetworksNext we ha
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Recurrent Neural Networksself.embed
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Recurrent Neural NetworksThis is a
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Recurrent Neural Networksreturn np.
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Recurrent Neural NetworksAttention
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Recurrent Neural NetworksFinally, V
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Recurrent Neural Networks# query.sh
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Recurrent Neural Networksself.atten
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Recurrent Neural Networks30 try to
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Recurrent Neural Networks3. Because
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Recurrent Neural NetworksSummaryIn
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Recurrent Neural Networks18. Shi, X
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AutoencodersAutoencoders are feed-f
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Depending upon the actual dimension
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• __init__(): Here, you define al
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Chapter 9And then we reshape the te
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Chapter 9plt.imshow(x_test[index].r
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Chapter 9Keeping the rest of the co
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noise = np.random.normal(loc=0.5, s
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Chapter 9x_train,validation_data=(x
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Chapter 9import matplotlib.pyplot a
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Chapter 9self.conv4 = Conv2D(1, 3,
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Chapter 9You can see that the image
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[ 367 ]Chapter 9Let us use the prec
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Chapter 9Our autoencoder model take
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We train the autoencoder for 20 epo
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Chapter 90.97905576229095460.989323
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Unsupervised LearningThis chapter d
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Chapter 10Next we load the MNIST da
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Chapter 10TensorFlow Embedding APIT
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3. Recompute the centroids using cu
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Chapter 10Figure 4: Plot of the fin
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Chapter 10In SOMs, neurons are usua
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[ 387 ]Chapter 10Colour mapping usi
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Chapter 10# Calculating Neighbourho
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We will also need to normalize the
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Chapter 10ρρ(vv oo |h oo ) = σσ
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# Generate the sample probabilityde
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Chapter 10And the reconstructed ima
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Chapter 10inpX = rbm.rbm_output(inp
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Chapter 10(60000, 28, 28) (60000,)(
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Chapter 10Figure 11: Summary of the
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Chapter 10This chapter, along with
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Reinforcement LearningThis chapter
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Chapter 11And unlike unsupervised l
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Chapter 11Normally, the value is de
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Chapter 11• The next question tha
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Chapter 11This neural network takes
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Chapter 11The MuJoCo environment re
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Chapter 11We will first import the
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Chapter 11The αα is the learning
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Chapter 11We set up the global valu
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Chapter 11else:return np.argmax(sel
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Chapter 11DQN to play a game of Ata
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Chapter 11self.model.add( Conv2D(64
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Chapter 11Here the action A was sel
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Chapter 11Image source: https://arx
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Chapter 11A neural network is used
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Chapter 1111. Details regarding ins
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TensorFlow and Cloud• Scalability
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TensorFlow and Cloud• Azure DevOp
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TensorFlow and Cloud• Lambda: The
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TensorFlow and Cloud• Deep Learni
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TensorFlow and CloudEC2 on AmazonTo
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TensorFlow and CloudCompute Instanc
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TensorFlow and CloudYou just share
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TensorFlow and CloudIn case you req
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TensorFlow and CloudIt starts with
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TensorFlow and CloudTFX librariesTF
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TensorFlow and CloudReferences1. To
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TensorFlow for Mobile and IoT and T
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An introduction to AutoMLThat is pr
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An introduction to AutoMLFeature co
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An introduction to AutoMLThis Effic
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An introduction to AutoMLGoogle Clo
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An introduction to AutoMLThen, we c
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An introduction to AutoMLIf your mo
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An introduction to AutoMLYou can al
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An introduction to AutoMLLet's star
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An introduction to AutoMLAt the end
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An introduction to AutoMLUsing Clou
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The Math Behind Deep LearningSome m
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The Math Behind Deep LearningTheref
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Tensor Processing UnitMany people b
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Tensor Processing UnitThe sequentia
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Tensor Processing UnitIf you want t
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Tensor Processing UnitOn the other
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Tensor Processing UnitHow to use TP
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Tensor Processing UnitNote that ful
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Tensor Processing UnitEpoch 10/1060
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Tensor Processing UnitThen the usag
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Other Books YouMay EnjoyIf you enjo
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Other Books You May EnjoyAI Crash C
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Other Books You May EnjoyLeave a re
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AutoML pipelinedata preparation 493
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Deep Deterministic Policy Gradient(
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Google cloud consolereference link
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used, for building GAN 193-198MNIST
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regularizersreference link 38reinfo
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TensorFlow Lite 81TensorFlow Core r
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Xxception networks 160, 162YYOLO ne
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