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Chapter 34. Use the feature_column module of TensorFlow to define numeric featuresof size 28×28:feature_columns = [tf.feature_column.numeric_column("x",shape=[28, 28])]5. Create the logistic regression estimator. We use a simple LinearClassifier.We encourage you to experiment with DNNClassifier as well:classifier = tf.estimator.LinearClassifier(feature_columns=feature_columns,n_classes=10,model_dir="mnist_model/")6. Let us also build an input_function to feed the estimator:train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(x={"x": train_data},y=train_labels,batch_size=100,num_epochs=None,shuffle=True)7. Let's now train the classifier:classifier.train(input_fn=train_input_fn, steps=10)8. Next, we create the input function for validation data:val_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(x={"x": eval_data},y=eval_labels,num_epochs=1,shuffle=False)9. Let us evaluate the trained Linear Classifier on the validation data:eval_results = classifier.evaluate(input_fn=val_input_fn)print(eval_results)10. We get an accuracy of 89.4% after 130 time steps. Not bad, right? Please notethat since we have specified the time steps, the model trains for the specifiedsteps and logs the value after 10 steps (the number of steps specified). Nowif we run train again, then it will start from the state it had at the 10th timestep. The steps will go on increasing with an increment of the number ofsteps mentioned.[ 105 ]
RegressionThe following is the graph of the preceding model:Figure 3: TensorBoard graph of the generated modelFrom TensorBoard we can also visualize the change in accuracy and average loss asthe linear classifier learned in steps of ten:Figure 4: Accuracy and average loss, visualized[ 106 ]
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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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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
Page 116 and 117: Chapter 25. Use tf.layers modules t
Page 118 and 119: Chapter 2Keras or tf.keras?Another
Page 120: • tf.data can be used to load mod
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: RegressionThe Estimator outputs the
Page 143 and 144: RegressionReferencesHere are some g
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Page 175 and 176: Advanced Convolutional Neural Netwo
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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 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 AutoMLAt the end
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The Math Behind Deep LearningSome m
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The Math Behind Deep LearningIf we
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The Math Behind Deep LearningThis c
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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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