Comparison between Sigmoid and Softmax Activation Function with Python
An activation function is a function used in artificial neural networks which outputs a small value for small inputs, and a larger value if its inputs exceed a threshold.
If the inputs are large enough, the activation function "fires", otherwise it does nothing.
In other words, an activation function is like a gate that checks that an incoming value is greater than a critical number.
I compared Sigmoid and Softmax activation functions, then demonstrated the differences in Python.
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The Python code is uploaded into https://github.com/AIMLModeling/Softmax
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