Beginning Anomaly Detection Using



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Beginning Anomaly Detection Using Python-Based Deep Learning

Figure 4-34.  Code to evaluate the model

Figure 4-33.  The model being trained

Chapter 4   autoenCoders




148

The next step is to use the model to generate the output images for the testing subset. 

This will show how well the reconstruction phase is going. Figure 

4-35


 shows the code to 

predict based on the model.



Figure 4-35.  Code to predict based on the model

You can also see how the encoder phase is working by displaying the test subset 

images in this phase. Figure 

4-36


 shows the code to display the encoded images.

Chapter 4   autoenCoders




149

Figure 


4-37

 shows the graph of the model as visualized by TensorBoard.



Figure 4-36.  Code to display encoded images

Chapter 4   autoenCoders




150

Figure 


4-38

 shows the plotting of the accuracy during the training process through 

the epochs of training.

Figure 4-37.  A model graph shown in TensorBoard

Chapter 4   autoenCoders




151

Figure 


4-39

 shows the plotting of the loss during the training process through the 

epochs of training.


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