Author: Zen and the Art of Computer Programming
Deep learning models usually have complex structures and an excessive amount of parameters. Therefore, when the amount of model training data is insufficient, the model size is too large, or resource constraints, we need to compress it. There are two main methods of model compression: pruning (Pruning) and quantization (Quantization). Both methods are described in detail below.
Pruning refers to pruning unnecessary connections (ie weights) in the network , saving computing resources by reducing the number of neurons and model size . The basic idea is to detect redundant connections in the network and cut them off to obtain a simplified model . Although this technique can effectively reduce the computing resources of the model, it also brings some losses . We hope to find a set of optimal models by pruning parameters reasonably , so that the model can achieve better results in terms of performance and resource consumption.
Quantization (Quantization) refers to the binarization or integerization of floating-point data, thereby reducing the size of the model, accelerating the reasoning process, reducing computing costs, and increasing the speed of reasoning . It compresses floating-point models into a more compact and efficient form without compromising accuracy. However, the use of quantized models may lead to loss of accuracy, so it is usually used in conjunction with model optimization methods.
Hyper parameter tuning is an important part of machine learning. It involves choosing various parameters used during model training, such as learning rate, batch size, regularization coefficients, activation functions, etc. Different hyperparameter settings can affect the quality and efficiency of model training results. Therefore, we should adjust these parameters according to the actual task to obtain the best model performance.
then