Deep learning neural networks are trained using the stochastic gradient descent algorithm. Stochastic gradient descent is an optimization algorithm that estimates the error gradient for the current state of the model using examples from the training dataset, then updates the weights of the model using the backpropagation of errors algorithm.
The amount that the weights are updated during training is referred to as the step size or the learning rate. Specifically, the learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. During training, the backpropagation of error estimates the amount of error for which the weights of a node in the network are responsible. Instead of updating the weight with the full amount, it is scaled by the learning rate. This means that a learning rate of 0.1, a traditionally common default value, would mean that weights in the network are updated 0.1 * (estimated weight error) or 10% of the estimated weight error each time the weights are updated.
The learning rate hyperparameter controls the rate or speed at which the model learns. Specifically, it controls the amount of apportioned error that the weights of the model are updated with each time they are updated, such as at the end of each batch of training examples. Generally, a large learning rate allows the model to learn faster, at the cost of arriving on a sub-optimal final set of weights. A smaller learning rate may allow the model to learn a more optimal or even globally optimal set of weights but may take significantly longer to train.
How to Configure Learning Rate How to Configure Learning Rate. If there is only time to optimize one hyper-parameter and one uses stochastic gradient descent, then this is the hyper-parameter that is worth tuning. Unfortunately, we cannot analytically calculate the optimal learning rate for a given model on a given dataset. Instead, a good (or good enough) learning rate must be discovered via trial and error. The range of values to consider for the learning rate is less than 1.0 and greater than 10^-6. A traditional default value for the learning rate is 0.1 or 0.01, and this may represent a good starting point on your problem. The grid search approach can help to both highlight an order of magnitude where good learning rates may reside, as well as describe the relationship between learning rate and performance.
Moreover, momentum can accelerate learning on those problems where the high-dimensional weight space that is being navigated by the optimization process has structures that mislead the gradient descent algorithm, such as flat regions or steep curvature. Although no single method works best on all problems, the Adaptive Momentum AdaM has proven to be robust over many types of neural network architectures and problem types.