Activation Functions
An activation function is a mathematical equation that determines the output of a neural network node, or "neuron." It adds non-linearity to the network, allowing it to learn complex patterns in the data. Without activation functions, a neural network would simply be a linear regression model, incapable of handling complex tasks like image recognition or language processing.
Types of Activation Functions in Neural Networks
Several activation functions are used in neural networks, each with its characteristics and typical use cases. Some of the most common include:
ReLU (Rectified Linear Unit): It allows only positive values to pass through, introducing non-linearity. ReLU is efficient and widely used in deep learning. It is used by default in Edge Impulse for hidden layers.
Sigmoid: This function maps values into a range between 0 and 1, making it ideal for binary classification problems.
Tanh (Hyperbolic Tangent): Similar to the sigmoid but maps values between -1 and 1. It is useful in hidden layers of a neural network.
Softmax: Often used in the output layer of a neural network for multi-class classification; it turns logits into probabilities that sum to one.
Leaky ReLU: A variation of ReLU, it allows a small, non-zero gradient when the unit is not active.
Implementing Activation Functions in Expert Mode
Import the necessary libraries
Define your neural network architecture
When adding layers to your model, specify the activation function you want to use:
Compile and train your model:
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