Learning rate scheduling is a technique used in deep learning algorithms to improve model convergence by gradually decreasing the learning rate over time. The learning rate is a hyperparameter that controls the step size taken by the optimizer during training to update the weights of the neural network. A smaller learning rate means slower convergence, but is less likely to miss the optimal solution, while a larger learning rate speeds up training, but is more likely to result in overshooting the optimal solution.
Learning rate scheduling is motivated by the observation that a large learning rate at the beginning of training can result in large weight updates, which may cause the model to move in the wrong direction and miss the optimal solution. By gradually decreasing the learning rate over time, we can reduce the step size taken by the optimizer, making it easier for the model to converge to the optimal solution.
In Keras, there are several popular learning rate schedules that can be used, including:
1. Step Decay: This schedule drops the learning rate by a factor after a fixed number of training epochs. For example, we can reduce the learning rate by a factor of 0.1 after every 10 epochs, i.e., setting ‘decay_rate = learning_rate / 10‘, where ‘learning_rate‘ is the initial learning rate, and ‘decay_rate‘ is the new learning rate after every step.
2. Exponential Decay: This schedule drops the learning rate exponentially over time. For example, we can set the learning rate as a function of the epoch number, ‘learning_rate = learning_rate0 * e(̂-kt)‘, where ‘learning_rate0‘ is the initial learning rate, ‘k‘ is the decay rate, and ‘t‘ is the epoch number.
3. Cosine Annealing: This schedule drops the learning rate according to a cosine function over time. The learning rate is given by ‘learning_rate = learning_rate0 * 0.5 * (1 + cos(epoch / T))‘, where ‘T‘ is the number of epochs after which the learning rate restarts.
Applying a learning rate schedule can improve model convergence by reducing the chance of the model overshooting the optimal solution or getting stuck in a local minimum during training. By using a learning rate schedule, the model can still take large steps at the beginning of training when the weights are far from the optimal solution, but gradually decrease the step size as the weights get closer to the optimal solution.