SGD (Stochastic Gradient Descent) is one of the most basic optimization algorithms in deep learning. It is an iterative optimization method used to train neural networks and other machine learning models. The following is important information about the SGD optimizer:
-
Basic principle : The basic idea of SGD is to minimize the loss function by continuously adjusting the model parameters. It randomly selects a mini-batch of samples from the training data to calculate the gradient at each iteration, and then uses the opposite direction of the gradient to update the model parameters. This process is called stochastic gradient descent because each iteration is based on a gradient calculation based on random samples.
-
Learning rate : SGD uses a hyperparameter called the learning rate to control the step size of each parameter update. The choice of learning rate is important. A learning rate that is too small may cause training to be too slow, while a learning rate that is too large may cause instability and oscillation. Often, the learning rate needs to be adjusted and optimized, and learning rate scheduling strategies can be used to improve the training process.
-
Batch size : Mini-batch size in SGD is an important hyperparameter. The choice of mini-batch size affects training speed and the model's ability to generalize. Smaller batch sizes can result in noisier gradient estimates, but generally lead to faster convergence. Larger batch sizes provide more stable gradient estimates but may require more memory and computing resources.
-
Randomness : The stochasticity of SGD is one of its characteristics. Random samples are used to estimate the gradient in each iteration. This randomness can help jump out of local minima, but it can also lead to instability in the training process. Therefore, some improved variants, such as Mini-Batch SGD, Momentum SGD, Adagrad, RMSProp, and Adam, etc., are usually used to control randomness to a certain extent and accelerate convergence.
-
Convergence : SGD usually requires a large number of iterations to achieve convergence, so it may be necessary to set an appropriate number of training rounds or use an early stopping strategy to determine the timing to stop training.
SGD is one of the most basic optimization algorithms in deep learning. Although it is simple, it still performs well in many deep learning tasks. However, in real-world applications, more complex optimization algorithms are often better suited to handle deep neural networks because they can better handle challenges such as learning rate adjustment, parameter initialization, and gradient stability.