Adjust at the end

Hi,

I don't know if it was already said, but it is still not fixed in the code.
When you are training your model, you calculate the weights_deltas between hidden layer and output layer.
But you are updating your weights too early, you are using the new weights to calculate deltas for previous weights (for back-propagation).
You have to keep deltas in memory then update at the end after the rest.

That's why your model takes a lot of time to be trained for the XOR problem. It shouldn't be that long. By memory you have took 50000 iterations. That is too big for a problem like this.

Keep going ^^.

P.S.: Sorry if my english is not perfect, it is not my main natural language.

Commencez par localiser dans le repository le code d’entraînement et de back-propagation, puis reproduisez le cas XOR décrit dans l’issue. Vérifiez que les deltas des poids restent disponibles jusqu’à la fin de l’étape complète de back-propagation, puis vérifiez que l’entraînement de XOR converge sans nécessiter 50000 itérations.

Rédigé par le modèle d'indexation à partir du texte de l'issue.