class my_NN(object):
    def train(self, X, y, iteration=33):
        for i in range(iteration):
            y_hat = self._forward_propagation(X)
            loss = self._loss(y_hat, y)
            self._backward_propagation(X,y)
            self._update()
            if i%10==0:
                print("loss: ", loss)

    def predict(self, X):
        y_hat = self._forward_propagation(X)
        y_hat = [1 if i[0]>0.5 else 0 for i in y_hat.T]
        return np.array(y_hat)

    def score(self, predict, y):
        cnt = np.sum(predict==y)
        return (cnt/len(y))*100