Читать книгу Machine Learning for Tomographic Imaging - Professor Ge Wang - Страница 66

The aforementioned three types of datasets are used at different stages of the network development for a specific application. This model was initially applied to the training set to fit model parameters. A model is trained on training data using a supervised learning method such as gradient descent or random gradient descent search. In practice, a training set typically consists of input vectors (or scalars) and their corresponding output vectors (or scalars), which are also called targets, labels, or markers. Normally, we run the current model on the training set, generate a result for each input in the set, and then compare it to the target. The parameters of the model are adjusted based on the comparison using a particular learning algorithm. Model fitting can be done from scratch or via transfer learning (to be detailed later).

The fitted model is used to process a second dataset called the validation dataset and produce the outputs accordingly. In general, the validation set is coupled with the training set to arrive at an accurate and robust neural network. In other words, in the training process the training set is used to update the parameters of the model while the validation set is used to sense the convergence of the model. The error rate of the final model on the validation set is usually smaller than the true error rate since the validation set is used to confirm, modify, or select the final model. One needs to stop training the network when the validation error increases, as this is a sign of overfitting to the training set. This simple process, called early stopping, is complicated in practice because errors in the validation data can fluctuate during the training process, depending on details of the training protocol in use, typically yielding multiple local minima. This complexity has led to the development of several special rules to detect signs of overfitting.