For example, if the BatchNormalizationStatisics training option is "population", then after training, the software finalizes the batch normalization statistics by passing through the training data once more and uses the resulting mean and variance. This is because the mean and variance statistics used for batch normalization can be different after training completes. If your network contains batch normalization layers, then the final validation metrics can be different to the validation metrics evaluated during training. The iteration from which the final validation metrics are calculated is labeled Final in the plots. If the OutputNetwork training option is "best-validation-loss", the finalized metrics correspond to the iteration with the lowest validation loss. If the OutputNetwork training option is "last-iteration" (default), the finalized metrics correspond to the last training iteration. When training finishes, view the Results showing the finalized validation accuracy and the reason that training finished. Once training is complete, trainNetwork returns the trained network. After you click the stop button, it can take a while for the training to complete. For example, you might want to stop training when the accuracy of the network reaches a plateau and it is clear that the accuracy is no longer improving. An epoch is a full pass through the entire data set.ĭuring training, you can stop training and return the current state of the network by clicking the stop button in the top-right corner. The figure marks each training Epoch using a shaded background. For more information about loss functions for classification and regression problems, see Output Layers.įor regression networks, the figure plots the root mean square error (RMSE) instead of the accuracy. If the final layer of your network is a classificationLayer, then the loss function is the cross entropy loss. Training loss, smoothed training loss, and validation loss - The loss on each mini-batch, its smoothed version, and the loss on the validation set, respectively. Validation accuracy - Classification accuracy on the entire validation set (specified using trainingOptions). It is less noisy than the unsmoothed accuracy, making it easier to spot trends. Smoothed training accuracy - Smoothed training accuracy, obtained by applying a smoothing algorithm to the training accuracy. Training accuracy - Classification accuracy on each individual mini-batch. 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. Different file name for checkpoint networks.ValidationPatience training option default is Inf.trainNetwork pads mini-batches to length of longest sequence before splitting when you specify SequenceLength training option as an integer.Stochastic Gradient Descent with Momentum.Monitor Deep Learning Training Progress.Sequence and Numeric Feature Data Workflows.
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