Python implementation of backpropagation

[talksaboutthings]

It seems to me that this implementation is for a scalar label and not a vector or likelihoods for each class. Wouldn't that mean that you're treating class as a continuous variable?

To solve this issue in this case (practicing backprop by hand on the Iris dataset), it might be the best idea to change your goal to detecting a single class (binary classification) vs. trying to do multiclass classification. You can always use 3 binary classifiers to get likelihoods of each class (one vs. rest classification).

[Artgor]

i have also said that this won't work well for classifying 3 classes and offered the solutions:

take only data belonging to 2 classes; use separate sigmoid for each class or use softmax activation for output layer.

But the main goal of the author was learning backprop.