![]() If you specify validation data in trainingOptions, then the figure shows validation metrics each time trainNetwork validates the network. Each iteration is an estimation of the gradient and an update of the network parameters. When you set the Plots training option to "training-progress" in trainingOptions and start network training, trainNetwork creates a figure and displays training metrics at every iteration. For more information, see Monitor Custom Training Loop Progress. ![]() For networks trained using a custom training loop, use a trainingProgressMonitor object to plot metrics during training. This example shows how to monitor training progress for networks trained using the trainNetwork function. For example, you can determine if and how quickly the network accuracy is improving, and whether the network is starting to overfit the training data. By plotting various metrics during training, you can learn how the training is progressing. When you train networks for deep learning, it is often useful to monitor the training progress. This example shows how to monitor the training process of deep learning networks.
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