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Activation functions are a crucial component of neural networks, serving as a mathematical operation applied to the output of each neuron or layer. They introduce non-linearity, enabling neural networks to learn and model complex patterns in data. The choice of activation function significantly impacts the network’s performance, convergence, and ability to handle different types of data and tasks.
Activation functions transform the input signals into output signals that determine the neuron’s or layer’s activation level. They add non-linear behaviour to the network, allowing it to approximate complex functions and make sophisticated decisions. The activation function’s output is often used as the input for the subsequent layers or the final output of the network.
Two commonly used activation functions are ReLU (Rectified Linear Unit) and Swish. ReLU sets all negative values to zero and keeps positive values unchanged, making it computationally efficient and easy to implement. Swish, on the other hand, is a smooth, non-monotonic function that outperforms ReLU in some scenarios, providing better optimization and gradient flow.
Will not go to the basic definitions of both of them as I am assuming that you know the basics of both functions and this is just a comparative blog.
First talking about the ReLU:
So as we know that the ReLU activation function (f(x) = max(0, x)) has a consistent problem where its derivative is 0 for half of the input values. This leads to an issue in parameter update algorithms like Stochastic Gradient Descent, where a parameter that is already 0 will never be updated. Consequently, this can result in a high number of “dead neurons” (neurons that do not contribute to the network’s output) reaching around 40% in a neural network environment. Previous attempts to address this problem, such as Leaky ReLU or SELU (Self-Normalizing Neural Networks), have been unsuccessful. However, there seems to be a promising revolution in overcoming this issue.
And one of them is the swish activation function
Swish is a smooth, non-monotonic activation function that performs as well as or better than ReLU in deep neural networks across various domains. It is inspired by the sigmoid function used in LSTM and Highway networks, and it can be self-gated or multi-gated. When combined with BatchNorm, Swish allows for deeper network training compared to ReLU, despite its gradient squishing property. In experiments with the MNIST dataset, Swish and ReLU have similar performance up to 40 layers, but Swish significantly outperforms ReLU when the network depth increases, indicating its advantage in challenging optimization scenarios. Swish also outperforms ReLU across different batch sizes, suggesting consistent performance superiority.
40 layers! Looks like too many hidden layers, but in complex deep learning models, 40 layers are nothing, and they work similarly till 40 layers, making the users choose any of the both for not so much complex neural networks, but problem statements which are more complex forces the user/developer to think twice before choosing any of the function.
Swish helps mitigate the issue of dead neurons compared to ReLU, as its non-monotonic nature prevents a significant portion of neurons from becoming inactive by ensuring a non-zero derivative for a larger range of input values. This leads to improved network performance and a reduced occurrence of dead neurons in the neural network environment.
Thus, we can conclude that Swish outperforms ReLU in deep neural networks across various domains, allowing for deeper network training, achieving higher test accuracy, and maintaining performance superiority across different batch sizes.
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