Softmax-Regression | Softmax Regression exercise in the Stanford UFLDL | Machine Learning library

The tutorial you linked to does multiclass classification. In multilabel classification, label for an example is a one-hot array. For example label [0 0 1 0] means this example belongs to class 2 (assuming classes start with 0). Normalizing this vector does not make sense because the values are already between 0 and 1. Also, in multiclass classification, only one of the label can be true and the other have to be false. Values other than 0 and 1 do not make sense in multi class classification.

When representing a batch of examples, it is common to write the labels as integers instead of on-hot arrays for easier readability. For example label [4 6 1 7] means the first example belongs to class 4, the second example belongs to class 6 and so on. Normalizing this representation also does not make sense because this representation is internally converted to one hot array.

Now, if you normalize the second representation, the behavior is undefined because floating points cannot be array indices. It is possible something weird is happening to give you the 99% accuracy. Maybe you normalized the values to 0 to 1 and the resulting one-hot arrays mostly points to class 0 and rarely class 1. That could give you a 99% accuracy.

I would suggest to not normalize the labels.