Understanding the Occurrence of Subnormal Phenomena in Neural Network Training and Inference Processes

Will subnormal occur in neural network training and inference?

Neural network training and inference are critical processes in the field of artificial intelligence. However, during these processes, subnormal values may occur, which can affect the performance and stability of the neural network. This article will discuss the causes and solutions of subnormal values in neural network training and inference, aiming to help us better understand and prevent these issues.

1. Causes of subnormal values in neural network training and inference

1.1 Large-scale numerical computation: Neural network training and inference involve large-scale numerical computations, which are prone to numerical errors. When the scale of the numerical computation is too large, the precision of the floating-point number may be lost, leading to subnormal values.

1.2 Initialization of network parameters: The initialization of network parameters is a crucial step in neural network training. If the initial values are not properly set, it may cause the network to enter a local minimum or saddle point, leading to subnormal values during training and inference.

1.3 Learning rate: The learning rate is a key parameter in neural network training. An inappropriate learning rate may cause the network to diverge or converge slowly, resulting in subnormal values.

1.4 Data distribution: The distribution of the training data is an important factor affecting the performance of the neural network. If the data distribution is unbalanced or contains outliers, it may lead to subnormal values during training and inference.

2. Solutions to subnormal values in neural network training and inference

2.1 Improve numerical precision: To reduce the occurrence of subnormal values, we can improve the numerical precision of the computation. For example, we can use double-precision floating-point numbers instead of single-precision floating-point numbers to ensure higher precision.

2.2 Optimize network parameter initialization: Properly initializing network parameters can help the network converge more quickly and avoid subnormal values. We can use techniques such as Xavier initialization or He initialization to initialize network parameters.

2.3 Adjust the learning rate: Choosing an appropriate learning rate is crucial for neural network training. We can use techniques such as learning rate scheduling or adaptive learning rate algorithms to adjust the learning rate dynamically.

2.4 Preprocess the data: To reduce the impact of data distribution on subnormal values, we can preprocess the data before training. This includes techniques such as data normalization, outlier removal, and data augmentation.

In conclusion, subnormal values may occur in neural network training and inference due to various factors. By understanding the causes and implementing the corresponding solutions, we can minimize the impact of subnormal values on neural network performance and stability.