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Writing the Algorithm (Softmax and Classification)

Explore how to write the softmax activation function for neural networks, implement classification functions, and understand numerical stability challenges. This lesson guides you through forward propagation, classification, and reporting accuracy in building neural network classifiers.

Writing the softmax function

We need to write the softmax() activation function to complete forward propagation. As it turns out, we can implement softmax() in two lines of code—but those two lines require some attention.

Here is the mathematical formula of the softmax:

softmax(li)=eliel\large{\text{softmax}(l_i) = \frac{e^{l_i}}{\sum{e^l}}} ...