How do learning rates affect deep learning models?

Learning rates determine how much a deep learning model adjusts its parameters during training. In simple terms, the learning rate controls the size of the steps the optimizer takes to minimize the loss function. If the learning rate is too high, the model may overshoot optimal parameter values, causing unstable training or failure to converge. If it’s too low, the model might learn too slowly, get stuck in suboptimal solutions, or require excessive computational resources. For example, using a learning rate of 0.1 for a complex task like image classification could cause the loss to fluctuate wildly, while a rate like 0.00001 might take days to train even on simple datasets.

The choice of learning rate also interacts with other training dynamics. For instance, adaptive optimizers like Adam or RMSprop adjust learning rates automatically for each parameter, reducing the need for manual tuning. However, even these methods require an initial learning rate setting. A common practice is to start with a default like 0.001 for Adam and adjust based on experimentation. In contrast, when using vanilla stochastic gradient descent (SGD), developers often combine a higher initial rate (e.g., 0.1) with a schedule that reduces it over time. For example, in training a ResNet model on CIFAR-10, starting with a higher rate and decaying it by a factor of 10 every 30 epochs can help balance speed and stability.

Practical implementation involves trial and error. Developers often test a range of rates (e.g., 0.1, 0.01, 0.001) and monitor loss curves. If the loss plateaus, a smaller rate might help refine the model. If loss oscillates or diverges, reducing the rate is a common fix. Tools like learning rate finders—which incrementally increase the rate during a test run—can identify a suitable starting point. For example, in PyTorch, libraries like torch_lr_finder automate this process. Ultimately, the learning rate is a critical hyperparameter that shapes both training efficiency and final model performance, requiring careful tuning tailored to the dataset, architecture, and optimizer.