Now that we have configured the neural network, the next step is to start the training instance, followed by evaluation. The evaluation phase is very important for the training instance. The neural network will try to optimize the gradients for optimal results. An optimal neural network will have good and stable evaluation metrics. So it is important to evaluate the neural network to direct the training process toward the desired results. We will use the test dataset to evaluate the neural network.
In the previous chapter, we explored a use case for time series binary classification. Now we have six labels against which to predict. We have discussed various ways to enhance the network's efficiency. We follow the same approach in the next recipe to evaluate the neural network for optimal results.
