Step 3: Testing and Utilizing the Neural Network

After training, the performance of the neural network model can be evaluated on a new out-of-sample testing data set.

>> [z_out, performance] = nsTest (mynet, z_in, z_desired);
>> performance
performance =
mse: 0.7316
nmse: 0.1728
correlation: 0.9095
percent_error: 13.1862

where z_in and z_desired represent the testing input and desired data respectively. z_out represents the output that the network actually produced when tested with z_in. The variable ”performance” stores indicators comparing the network output z_out with the desired output z_desired.

Production

Once you have created the network, trained and tested it to your satisfaction, the neural network is ready to be utilized in practice with production data.

>> p_out = nsProduction (mynet, p_in);

where p_in is the production input data and p_out is the network output for the production input data.

What is Production?

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