Scala for Machine Learning - Second Edition by Patrick Nicolas

Author:Patrick Nicolas [Nicolas, Patrick]
Language: eng
Format: azw3, pdf
Publisher: Packt Publishing
Published: 2017-10-10T04:00:00+00:00

Note

The adjustable learning rate:

The computation of the new weights of a connection for each new epoch can be further improved by making the learning adjustable.

Step 3 – exit condition

The convergence criterion consists of evaluating the cumulative error (or loss) relevant to the operating mode (or problem) against a predefined convergence, eps. The cumulative error is computed using either the sum of squares error formula (M5) or the cross-entropy formula (M6 and M7). An alternative approach is to compute the difference of the cumulative error between two consecutive epochs and apply the convergence criteria, eps, as the exit condition.

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