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Chapter 2Keras or tf.keras?Another legitimate question is whether you should use Keras with TensorFlow as abackend or, instead, use the APIs in tf.keras directly available in TensorFlow. Notethat there is not a 1:1 correspondence between Keras and tf.keras. Many endpointsin tf.keras are not implemented in Keras and tf.Keras does not support multiplebackends as Keras. So, Keras or tf.keras? My suggestion is the second optionrather than the first one. tf.keras has multiple advantages over Keras, consistingof TensorFlow enhancements discussed in this chapter (eager execution; nativesupport for distributed training, including training on TPUs; and support for theTensorFlow SavedModel exchange format). However, the first option is still themost relevant one if you plan to write highly portable code that can run on multiplebackends, including Google TensorFlow, Microsoft CNTK, Amazon MXnet, andTheano. Note that Keras is an independent open source project, and its developmentis not dependent on TensorFlow. Therefore, Keras is going to be developed for theforeseeable future. Note that Keras 2.3.0 (released on September 17, 2019) is thefirst release of multi-backend Keras, which supports TensorFlow 2.0. It maintainscompatibility with TensorFlow 1.14 and 1.13, as well as Theano and CNTK.Let's conclude the chapter with a new comparison: the primary machine learningsoftware tools used by the top-5 teams on Kaggle in each competition. This was asurvey ran by Francois Chollet on Twitter at the beginning of April 2019 (thanks,Francois for agreeing to have it included in this book!):Figure 5: Primary ML software tools used by top-5 teams on Kaggle in 2019[ 83 ]
TensorFlow 1.x and 2.xIn this section, we have seen the main differences between Keras and tf.keras.SummaryTensorFlow 2.0 is a rich development ecosystem composed of two main parts:Training and Serving. Training consists of a set of libraries for dealing with datasets(tf.data), a set of libraries for building models, including high-level libraries (tf.Keras and Estimators), low-level libraries (tf.*), and a collection of pretrainedmodels (tf.Hub), which will be discussed in Chapter 5, Advanced Convolutional NeuralNetworks. Training can happen on CPUs, GPUs, and TPUs via distribution strategiesand the result can be saved using the appropriate libraries. Serving can happenon multiple platforms, including on-prem, cloud, Android, iOS, Raspberry Pi, anybrowser supporting JavaScript, and Node.js. Many language bindings are supported,including Python, C, C#, Java, Swift, R, and others. The following diagramsummarizes the architecture of TensorFlow 2.0 as discussed in this chapter:Figure 6: Summary of TensorFlow 2.0 architecture[ 84 ]
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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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Page 88 and 89: An example to start withWe'll consi
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Page 92 and 93: • To create random values from a
Page 94 and 95: To know the value, we need to creat
Page 96 and 97: Chapter 2Both PyTorch and TensorFlo
Page 98 and 99: Chapter 2state = [tf.zeros([100, 10
Page 100 and 101: Chapter 2For now, there's no need t
Page 102 and 103: Chapter 2Let's see an example of a
Page 104 and 105: Chapter 2If you want to save a mode
Page 106 and 107: Chapter 2supervised=True)train_data
Page 108 and 109: Chapter 2There, tf.feature_column.n
Page 110 and 111: Chapter 2print (dz_dx)print (dy_dx)
Page 112 and 113: Chapter 2In our toy example we use
Page 114 and 115: Chapter 2For multi-machine training
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Page 123 and 124: RegressionLet us imagine a simpler
Page 125 and 126: RegressionTake a look at the last t
Page 127 and 128: Regression3. Now, we calculate the
Page 129 and 130: RegressionIn the next section we wi
Page 131 and 132: Regression2. Now, we define the fea
Page 133 and 134: Regression2. Download the dataset:(
Page 135 and 136: RegressionThe following is the Tens
Page 137 and 138: RegressionIn regression the aim is
Page 139 and 140: RegressionThe Estimator outputs the
Page 141 and 142: RegressionThe following is the grap
Page 143 and 144: RegressionReferencesHere are some g
Page 145 and 146: Convolutional Neural NetworksIn thi
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Page 163 and 164: Convolutional Neural NetworksSo, we
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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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GenerativeAdversarial NetworksIn th
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[ 193 ]Chapter 6Eventually, we reac
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[ 195 ]Chapter 6Next, we combine th
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Chapter 6And handwritten digits gen
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Chapter 6Figure 1: Visualizing the
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Chapter 6The resultant generator mo
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Chapter 6Figure 4: A summary of res
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Chapter 6def train(self, epochs, ba
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Chapter 6The preceding images were
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Chapter 6Another interesting paper
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Chapter 6To elaborate, let us say t
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Chapter 6Figure 7: The architecture
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Chapter 6Figure 11: Illegible initi
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Chapter 6Bedrooms: Generated bedroo
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Chapter 6The images need to be norm
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Chapter 6initializer = tf.random_no
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Cool, right? Now we can define the
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Chapter 6d_loss = (dA_loss + dB_los
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Chapter 6generator_AB.save_weights(
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6. Ledig, Christian, et al. Photo-R
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Word EmbeddingsDeep learning models
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Word EmbeddingsFor example, "crucia
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Word EmbeddingsAssuming a window si
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Word EmbeddingsGloVeThe Global vect
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Word Embeddingsgensim is an open so
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Word Embeddingsgensim also provides
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Word EmbeddingsSpecifically, we wil
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Word EmbeddingsWe will also convert
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Word EmbeddingsE = np.zeros((vocab_
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Word Embeddingsx = self.embedding(x
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Word EmbeddingsThe change in valida
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Word EmbeddingsThe dataset is a 114
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Word Embeddingsprint("random walks
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Word Embeddingssize=128, # size of
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Word EmbeddingsfastText computes em
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Word EmbeddingsIn the future, once
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Word EmbeddingsA much earlier relat
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Word EmbeddingsOnce you have the fi
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Word EmbeddingsThis will create the
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Word EmbeddingsClassifying with BER
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Word Embeddings2. Each Transformer
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Word EmbeddingsOnce trained, we sav
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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 AutoMLClicking o
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An introduction to AutoMLYou can al
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An introduction to AutoMLAt the end
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The Math Behind Deep LearningSome m
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The Math Behind Deep LearningTheref
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The Math Behind Deep LearningIf we
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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 UnitFigure 11: Go
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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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