Beginning Anomaly Detection Using



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Beginning Anomaly Detection Using Python-Based Deep Learning

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Figure B-39.  The creation of the training and testing data sets

appendix B   intro to pytorch




397

Figure B-40.  The output of the code in Figure 

B-39

The output should look somewhat like Figure 

B-40

.

appendix B   intro to pytorch




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Figure B-41.  Reshaping the training and testing data sets so you can pass them 

into the model

After defining your data sets, you need to reshape the values so that your neural 

network can accept them (see Figure 

B-41


).

The output should look like Figure 

B-42

.

Figure B-42.  The output of the code in Figure 



B-41

appendix B   intro to pytorch




399

Now you can define your model (Figure 

B-43

 and Figure 



B-44

).


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