ReLU6: A Modified Version of Rectified Linear Unit
Machine learning algorithms are rapidly changing the computational landscape of artificial intelligence. The rectified linear unit (ReLU) is one of the most popular activation functions used in deep learning models. ReLU functions have been known to offer better performance compared to other activation functions like sigmoid or hyperbolic tangent. The ReLU6 function is a modification of the original ReLU function designed to improve its robustness when used with low-precision computation.
What Is ReLU6?
ReLU6 is a modification of the original ReLU function. Rectified linear units are defined as f(x) = max(0, x). They cut off the lower end of the range, preserving only the positive values (>0). ReLU6 limits the output of the ReLU function to a maximum size of 6. This means that any input below 0 will be discarded, while any input above 6 is converted to 6. The ReLU6 function is defined as f(x) = min(max(0,x),6).
Why Use ReLU6?
The primary reason to use ReLU6 instead of the original ReLU function is to improve the robustness of low-precision computation. Neural networks are typically designed to use high precision floating point arithmetic. However, in applications where computational resources are limited, the use of low-precision arithmetic (e.g. 8-bit or 16-bit) can lead to faster computation with minimal degradation in accuracy. Unfortunately, lower precision arithmetic can also result in numerical instability and degradation in performance of ReLU activation functions.
ReLU6 is designed to address these issues by limiting the activation output to a maximum value of 6. By restricting the output range, ReLU6 reduces the likelihood of overflow or underflow in low-precision arithmetic. This helps maintain the performance of neural networks trained with ReLU activation functions, even when using lower precision arithmetic.
Applications of ReLU6
ReLU6 has several applications in enhancing the robustness of neural networks. One of the most significant applications is in mobile devices, where computational resources are limited. As mentioned earlier, ReLU6 can help maintain neural network performance even under low-precision arithmetic. This can be especially useful in mobile devices where the computational resources may be limited.
Another application of ReLU6 is in object detection and semantic segmentation. These applications involve identifying and mapping objects in images or videos. In these applications, ReLU6 can help maintain better performance of neural networks working with low-precision arithmetic, making them more robust and reliable.
ReLU6 is a modified version of the popular activation function, ReLU. By limiting the output of the ReLU function to a maximum of 6, ReLU6 can improve the robustness of neural networks when used with low-precision arithmetic. There are several applications of ReLU6, including mobile devices and object detection, where maintaining high performance, even under low-resource environments, is critical. ReLU6 is a valuable tool for anyone interested in enhancing the performance or robustness of neural networks using low-precision arithmetic.
