Vairagi:

from sklearn.utils import shuffle from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score from sklearn.metrics import classification_report


I’ve gotten similar results once. In my case it was because the learning rate was so high, everything diverged and the model just gave equal probabilities for all outputs. I’d advise you to try to overfit to a small training set with a low learning rate, 10⁻5 perhaps