Then if we keep training the model, it will overfit, and validation errors begin to increase: Given the complexity of real-world problems, it may take hundreds of epochs to train a neural network.Īs a result, we expect to see the learning curve graphs getting better and better until convergence. A single epoch in training is not enough and leads to underfitting. Neural network weights are updated iteratively, as it is a gradient descent based algorithm. Depending on the architecture and data available, we can treat the number of epochs to train as a hyperparameter. We expect a neural network to converge after training for a number of epochs. These graphs help us diagnose if the model has over-learned, under-learned, or fits the learning set. By doing so, we can plot learning curve graphs for different sets. However, we expect both loss and accuracy to stabilize after some point.Īs usual, it is recommended to divide the data set into training and validation sets. During the training, we expect the loss to decrease and accuracy to increase as the number of epochs increases. Depending on the neural network architecture and data set, we need to decide when the neural network weights are converged.įor neural network models, it is common to examine learning curve graphs to decide on model convergence. However, initially, we can’t know how many epochs is good for the model. When building a neural network model, we set the number of epochs parameter before the training starts. In other words, it does not overfit or underfit. On the other hand, if the model is said to be underfitting (high bias) if it didn’t learn the data well enough:Ī good model is expected to capture the underlying structure of the data. We face overfitting (high variance) when the model fits perfectly to the training examples but has limited capability generalization.
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