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Chapter 819/19 [==============================] - 6s 291ms/step - loss: 0.0575 -accuracy: 0.9833 - masked_accuracy_fn: 0.8140 - val_loss: 0.1569 - val_accuracy: 0.9615 - val_masked_accuracy_fn: 0.551111/11 [==============================] - 2s 170ms/step - loss: 0.1436 -accuracy: 0.9637 - masked_accuracy_fn: 0.5786test loss: 0.144, test accuracy: 0.963, masked test accuracy: 0.578Here are some examples of POS tags generated for some random sentences in the testset, shown together with the POS tags in the corresponding ground truth sentences.As you can see, while the metric values are not perfect, it seems to have learned to doPOS tagging fairly well:labeled : among/IN segments/NNS that/WDT t/NONE 1/VBP continue/NONE 2/TO to/VB operate/RB though/DT the/NN company/POS 's/NN steel/NN division/VBD continued/NONE 3/TO to/VB suffer/IN from/JJ soft/NN demand/IN for/PRPits/JJ tubular/NNS goods/VBG serving/DT the/NN oil/NN industry/CC and/JJother/NNSpredicted: among/IN segments/NNS that/WDT t/NONE 1/NONE continue/NONE 2/TO to/VB operate/IN though/DT the/NN company/NN 's/NN steel/NN division/NONE continued/NONE 3/TO to/IN suffer/IN from/IN soft/JJ demand/NN for/INits/JJ tubular/NNS goods/DT serving/DT the/NNP oil/NN industry/CC and/JJother/NNSlabeled : as/IN a/DT result/NN ms/NNP ganes/NNP said/VBD 0/NONE t/NONE2/PRP it/VBZ is/VBN believed/IN that/JJ little/CC or/DT no/NN sugar/INfrom/DT the/CD 1989/NN 90/VBZ crop/VBN has/VBN been/NONE shipped/RB 1/RByet/IN even/DT though/NN the/NN crop/VBZ year/CD is/NNS six/JJpredicted: as/IN a/DT result/NN ms/IN ganes/NNP said/VBD 0/NONE t/NONE 2/PRP it/VBZ is/VBN believed/NONE that/DT little/NN or/DT no/NN sugar/INfrom/DT the/DT 1989/CD 90/NN crop/VBZ has/VBN been/VBN shipped/VBN 1/RByet/RB even/IN though/DT the/NN crop/NN year/NN is/JJlabeled : in/IN the/DT interview/NN at/IN headquarters/NN yesterday/NNafternoon/NN both/DT men/NNS exuded/VBD confidence/NN and/CC seemed/VBD1/NONE to/TO work/VB well/RB together/RBpredicted: in/IN the/DT interview/NN at/IN headquarters/NN yesterday/NNafternoon/NN both/DT men/NNS exuded/NNP confidence/NN and/CC seemed/VBD1/NONE to/TO work/VB well/RB together/RBlabeled : all/DT came/VBD from/IN cray/NNP research/NNPpredicted: all/NNP came/VBD from/IN cray/NNP research/NNPlabeled : primerica/NNP closed/VBD at/IN 28/CD 25/NONE u/RB down/CD 50/NNSpredicted: primerica/NNP closed/VBD at/CD 28/CD 25/CD u/CD down/CD[ 315 ]
Recurrent Neural NetworksIf you would like to run this code yourself, you can find the code in the code folderfor this chapter. In order to run it from the command line, enter the followingcommand. The output is written to the console:$ python gru_pos_tagger.pyNow that we have seen some examples of three common RNN network topologies,let us explore the most popular of them all – the seq2seq model, also known as theRecurrent encoder-decoder architecture.Encoder-Decoder architecture – seq2seqThe example of a many-to-many network we just saw was mostly similar to themany-to-one network. The one important difference was that the RNN returnsoutputs at each time step instead of a single combined output at the end. One othernoticeable feature was that the number of input time steps was equal to the numberof output time steps. As you learn about the encoder-decoder architecture, which isthe "other," and arguably more popular, style of a many-to-many network, you willnotice another difference – the output is in line with the input in a many-to-manynetwork, that is, it is not necessary for the network to wait until all of the input isconsumed before generating the output.The Encoder-Decoder architecture is also called a seq2seq model. As the nameimplies, the network is composed of an encoder and a decoder part, both RNNbased,and capable of consuming and returning sequences of outputs correspondingto multiple time steps. The biggest application of the seq2seq network has been inneural machine translation, although it is equally applicable for problems that can beroughly structured as translation problems. Some examples are sentence parsing[10] and image captioning [24]. The seq2seq model has also been used for time seriesanalysis [25] and question answering.In the seq2seq model, the encoder consumes the source sequence, which is a batchof integer sequences. The length of the sequence is the number of input time steps,which corresponds to the maximum input sequence length (padded or truncatedas necessary). Thus the dimensions of the input tensor is (batch_size, number_of_encoder_timesteps). This is passed into an embedding layer, which will convertthe integer at each time step to an embedding vector. The output of the embeddingis a tensor of shape (batch_size, number_of_encoder_timesteps, encoder_embedding_dim).[ 316 ]
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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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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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Page 305 and 306: Word EmbeddingsClassifying with BER
Page 307 and 308: Word Embeddings2. Each Transformer
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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 11The MuJoCo environment re
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Chapter 11The αα is the learning
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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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The Math Behind Deep LearningSome m
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Tensor Processing UnitMany people b
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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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