The activation function plays a crucial role in neural networks by introducing non-linearity into the output of a neuron. The non-linear activation functions, such as sigmoid, tanh, ReLU, or LeakyReLU, are applied to the weighted sum of inputs and biases of a neuron to produce its output, which is then forwarded to the next layer of neurons in the network.
Without the activation function, the network would simply be performing linear transformations, and it would not be able to learn complex decision boundaries or patterns in the data. By applying an activation function, the output of a neuron can become a non-linear function of its inputs, which allows the network to learn complex and hierarchical representations of the data.
Here are the brief descriptions of a few common activation functions used in Keras:
1. Sigmoid: Sigmoid is a popular activation function used in binary output problems or where the output is a probability value. It squashes the input values within the range of 0 and 1, making it suitable for binary classification problems.
2. Tanh: Tanh is another popular activation function that squashes the input values between -1 and 1, making it suitable for regression problems.
3. ReLU (rectified linear unit): ReLU is currently one of the most widely used activation functions in deep learning, it is computationally efficient and doesn’t suffer from vanishing gradient problems which occur with sigmoid and tanh functions. It sets all negative input values to zero and keeps the positive values unchanged.
4. LeakyReLU: LeakyReLU function is a variant of ReLU which introduces a small slope for negative input values rather than setting them to zero. It can help to avoid dead neurons that can sometimes occur when using the ReLU activation function.
There are other activation functions also available which are used in specific scenarios. Choosing the right activation function is an important design decision to optimize the performance and accuracy of the neural network.