Automated Neural Network Structure Generation via Reinforcement Learning
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Solution Overview
Problem
Current methods for generating neural network structures are inefficient as they require manual configuration and are time-consuming, especially when different structures and parameters are needed for various tasks or applications.
Innovation Solution
A method and apparatus that sample neural network structures to generate network blocks, construct sampling neural networks, train them based on sample data, and regenerate blocks until the desired accuracy is met, using reinforcement learning to adjust the structure and parameters automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration methods are used to generate neural network structures, then the structure can be precisely designed according to requirements, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables automated self-service for neural network structure generation through reinforcement learning. The algorithm autonomously explores and generates network structures without manual intervention, automatically training and evaluating candidate structures to find optimal configurations for specific tasks, thereby resolving the contradiction between precision and efficiency
Solution Approach 2:
The invention changes the approach from manual parameter configuration to automated parameter exploration through reinforcement learning. The system dynamically adjusts network structure parameters (layers, nodes, connections) based on task requirements and performance feedback, achieving both precision in structure design and efficiency in generation process
2Adaptability or versatility
If different neural network structures are designed for different tasks manually, then the adaptability to specific tasks is improved, but the time and cost for design increase significantly
Solution Approach 1:
The system introduces dynamics into the structure generation process through reinforcement learning. Instead of static manual design, the algorithm dynamically adapts network structures to different tasks by learning from training data and performance feedback, enabling rapid customization for various applications without proportional increase in design time
Solution Approach 2:
The invention performs preliminary action by pre-training the reinforcement learning agent on a diverse set of tasks and network structures. This preliminary training enables the system to quickly adapt to new tasks with minimal additional time, as the agent has already learned general principles of effective network architecture across multiple domains
Data Source
AI summary
Embodiments of the present application disclose a method and apparatus for generating a neural network structure, an electronic device, and a storage medium. The method comprises: sampling a neural network structure to generate a network block, the network block comprising at least one network layer; constructing a sampling neural network based on the network block; training the sampling neural network based on sample data, and obtaining an accuracy corresponding to the sampling neural network; and in response to that the accuracy does not meet a preset condition, regenerating a new network block according to the accuracy until a sampling neural network constructed by the new network block meets the preset condition, and using the sampling neural network meeting the preset condition as a target neural network.