How to perform model fusion and model compression in PHP?

With the rapid development of artificial intelligence, the complexity of models is getting higher and higher, and the use of resources is also increasing. In PHP, how to perform model fusion and model compression has become a hot topic.

Model fusion refers to fusing multiple single models together to improve the overall accuracy and efficiency. Model compression reduces the size and computational complexity of the model to save model storage and computing resources. This article will introduce how to perform model fusion and model compression in PHP.

1. Model Fusion

In PHP, there are two commonly used model fusion methods: bagging and boosting.

1. Bagging

Bagging is the abbreviation of Bootstrap Aggregating. It trains multiple models by sampling the training set and averaging the results of these models. produce the final prediction result. That is, N training samples are resampled M times, with or without replacement in each sampling, so each set of data obtained by resampling may be different. In this way, each model can be trained on different data, thereby reducing the variance of the model and improving the overall accuracy.

In PHP, the commonly used bagging algorithm is random forest. Random forest is a bagging algorithm based on decision trees. It uses multiple decision trees to classify the training set and votes on the results of multiple decision trees to obtain the final prediction result.

2. Boosting

Boosting is a method of weighting the training set, training multiple models, and weighting the average of these models to obtain the final prediction result. Methods. It will focus on the erroneous samples in the training data set to improve the overall accuracy.

In PHP, commonly used Boosting algorithms include Adaboost and Gradient Boosting. Adaboost is an iterative algorithm that increases the weight of misclassified data based on the results of the previous round of training so that the next round of training can better identify these misclassified data. Gradient Boosting is an extension of the Boosting algorithm for decision trees. It iteratively trains different decision trees and weights the results of multiple decision trees to obtain the final prediction result.

2. Model compression

In PHP, there are two commonly used model compression methods: quantization and pruning.

1. Quantization

Quantization is a method of converting floating-point parameters in the model into fixed-point parameters, thereby reducing the storage and computing resources of the model. Quantization is divided into quantization of weights and activation values.

In PHP, commonly used quantization algorithms include weight quantization based on L2 norm and activation value quantization based on KL divergence. Weight quantization based on L2 norm converts floating-point weights into smaller integers, thereby reducing model storage space. Activation value quantification based on KL divergence converts the activation value distribution into a uniform distribution or a deterministic distribution, thereby reducing the amount of model calculations.

2. Pruning

Pruning refers to reducing the computational complexity and storage space of the model by deleting some unnecessary or useless parts of the model. Common pruning includes structural pruning, weight pruning and dynamic pruning.

In PHP, commonly used pruning algorithms include L1 structure pruning based on normalization factors and L2 weight pruning based on weight size. L1 structure pruning normalizes neurons and deletes some useless or redundant neurons, thereby reducing model storage space and computational complexity. L2 weight pruning reduces model storage space and computational complexity by deleting some smaller weights. Dynamic pruning performs pruning based on the actual operating conditions of the model, thereby further reducing the resource usage of the model while maintaining accuracy.

Conclusion

Performing model fusion and model compression in PHP can effectively reduce the storage and computing resources of the model. Through the introduction of this article, we can learn about commonly used model fusion and model compression methods, and try them in practice. I hope this article can provide some help to PHP developers learning about model optimization.

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