Leaky ReLU

What is Leaky ReLU? Leaky ReLU (Rectified Linear Unit) is a type of activation function used in neural networks. Unlike the standard ReLU function, which outputs zero for negative inputs, Leaky ReLU allows a small, non-zero gradient when the input is negative, preventing the neuron from dying. Why Leaky ReLU Matters Leaky ReLU addresses the "dying ReLU" problem, where neurons stop learning during training because they consistently output zero. By allowing a small gradient for negative inputs, Leaky ReLU ensures that all neurons continue to learn and contribute to the model's predictions. How Leaky ReLU Works Positive Inputs: For positive input values, Leaky ReLU behaves like a standard ReLU, outputting the input value directly. Negative Inputs: For negative input values, Leaky ReLU outputs a small, non-zero value, usually a fraction of the input (e.g., 0.01 * input). Gradient Flow: This small gradient ensures that the model continues to learn even when inputs are negative. Applications of Leaky ReLU Deep Neural Networks: Used in deep learning models to prevent the dying ReLU problem, particularly in convolutional and fully connected layers. Image Processing: Improves the robustness of models in tasks like object detection and image classification. Speech Recognition: Enhances the performance of models that process sequential data, such as audio signals. Conclusion Leaky ReLU is a practical activation function that improves the learning process in neural networks by ensuring that all neurons remain active. Its ability to prevent the dying ReLU problem makes it a valuable tool in deep learning. Keywords: #LeakyReLU, #ActivationFunction, #NeuralNetworks, #DeepLearning, #DyingReLUProblem