What is one potential consequence of a high learning rate in an ANN?
Understand the Problem
The question is asking about the potential consequence of using a high learning rate in an artificial neural network (ANN). It is likely testing knowledge regarding the behavior of ANNs during training, specifically focusing on how the learning rate affects weight adjustment and convergence.
Answer
Instability, convergence to a suboptimal solution.
A high learning rate in an ANN can cause the model to converge too quickly to a suboptimal solution or even lead to instability and divergence of the loss function.
Answer for screen readers
A high learning rate in an ANN can cause the model to converge too quickly to a suboptimal solution or even lead to instability and divergence of the loss function.
More Information
When the learning rate is too high, it might cause the model's weights to be updated too aggressively, causing oscillations around the minimum or even diverging completely. This makes it difficult for the model to settle down into a good set of weights that approximate an optimal solution.
Tips
Avoid setting the learning rate too high to prevent instability and missing the optimal solution. Instead, use techniques like learning rate schedules or adaptive learning rates.
Sources
- Understand the dynamics of learning rate on deep learning neural networks - Machine Learning Mastery - machinelearningmastery.com
- Impact of learning rate on a model - GeeksforGeeks - geeksforgeeks.org
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