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.

まず、リポジトリ内の training と back-propagation のコードを見つけ、issue に記載されている XOR のケースを再現します。完全な back-propagation ステップが完了するまで重みのデルタが利用可能な状態であることを確認し、その後、XOR の training が 50000 回の反復を必要とせずに収束することを検証します。

索引モデルが issue の本文から書いたものです。