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The function would simply be a chain of multiplications and (typically) non-linear transformations. For classification problems, there is often a final non-lin
by Plough_Jogger 7y ago
The function would simply be a chain of multiplications and (typically) non-linear transformations.
For classification problems, there is often a final non-linear transformation, like a softmax.
Example with ReLu activation:
Output_i = max((input_i * weight_i),0)
- jawarner 7y agoI don't understand. The example you gave is not an algebraic function.
- Plough_Jogger 7y agoYou are correct. It is possible to construct a linear neural network, where an algebraic function could be extracted, but in practice, almost all networks use non-linear activation functions. In the case of a linear network, the function would be a dot product between the input and the weights: 𝑥1𝑤1+𝑥2𝑤2+𝑥3𝑤3 ... for all inputs (xi) and weights (wi)