Beginning Anomaly Detection Using



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Beginning Anomaly Detection Using Python-Based Deep Learning

Figure A-35.  The general page that appears when you launch TensorBoard

Figure A-34.  You should see something like this after executing the above line in 

command prompt. It should tell you where to go to access TensorBoard, which is 

http://MSI:6006 in this case

Simply follow that link and you should see the screen shown in Figure 

A-35

.

Appendix A   intro to KerAs




356

Figure A-36.  Graphs for val_acc and val_loss

From here, you can see graphs for the metrics accuracy and loss. You can expand the 

other two metrics, val_acc and val_loss, to view those graphs as well (see Figure 

A-36


).

Appendix A   intro to KerAs




357

As for the individual graphs, you can expand them out by pressing the leftmost 

button below the graph, and you can view data on the graph as you move your mouse 

across it, as seen in Figure 

A-37

.

Figure A-37.  The result of pressing the leftmost button underneath the graph. 



Doing so expands the graph, and regardless of whether the graph is expanded or 

not, you can point your mouse cursor at any point along the graph to get more 

details about that point

You can also view a graph of the entire model by pressing the Graphs tab, as shown 

in Figure 

A-38


.

Figure A-38.  There are two tabs. You started on the tab named SCALARS. Press 

GRAPHS to switch the tab

Appendix A   intro to KerAs




358

Doing so will result in a graph similar to the one shown in Figure 

A-39

.

Figure A-39.  The result of clicking on the GRAPHS tab



There are definitely more features and functionality that TensorBoard offers, but the 

general idea is that you will be able to examine your models in a much better fashion.




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