Controls

When a new neural network is created the controls are preset to values that will usually be suitable for the network to learn from the training data in the Grid. The training controls can often be optimized to result in much faster learning.   All controls are set using the Controls dialog.  

 

Setting the Learning Rate

 

The Learning Rate will be preset to 0.6 when a new neural network is created.   It can be changed to any value from 0.1 to 10.   Very low values will result in slow learning and values above 1.5 will often result in erratic learning or oscillations.   Check "Optimize" to allow EasyNN-plus to determine the learning rate automatically by running a few learning cycles with different learning rate values.   Check "Decay" to automatically reduce the learning rate during learning if erratic learning or oscillations occurs.

 

Setting the Momentum

 

The Momentum will be preset to 0.8 when a new neural network is created.   It can be changed to any value from 0 to 0.9.   Check "Optimize" to allow EasyNN-plus to determine the momentum automatically by running a few learning cycles with different momentum values.   Check "Decay" to automatically reduce the momentum during learning if oscillations occur.

 

Setting the Target Error

 

The Target Error will be preset to 0.01 when a new neural network is created.   It can be changed to any value from 0 to 0.9 but values above 0.2 usually result in under trained networks.   Learning is set to stop when the average error drops below the target error.   This can be changed so that all errors must drop below the target or the predicted output is in range of the actual output.

 

Setting the Validating Controls

 

Validating cycles only occur when validating examples are included in the Grid.   A number of training examples can be randomly changed to validating examples using the Select control or they can be entered directly into the Grid.   The cycle values are preset so that 100 learning cycles occur for every validating cycle.   The number of learning cycles before the first validating cycles and the number of learning cycles per validating cycle can be changed to any positive values.   Learning can be set to stop when the validating target is reached.   The validating target can be set to any value up to 100%.   The validating test can be either when the validating results are within a specific range or when the results are correct after rounding to the nearest whole number.   Validating Rules can be used for rounding to the nearest multiple.   The validating range can be set to any value from 0 to 50%.