diff --git a/4_logistic_regression/2-Logistic_regression.ipynb b/4_logistic_regression/2-Logistic_regression.ipynb
index c6a1a2f..497cdea 100644
--- a/4_logistic_regression/2-Logistic_regression.ipynb
+++ b/4_logistic_regression/2-Logistic_regression.ipynb
@@ -241,7 +241,6 @@
     "        pred_func (callable): 预测函数\n",
     "        data (numpy.ndarray): 训练数据集合\n",
     "        label (numpy.ndarray): 训练数据标签\n",
-    "        散开数据，但是不在原来的数据上做修改\n",
     "    \"\"\"\n",
     "    x_min, x_max = data[:, 0].min() - .5, data[:, 0].max() + .5\n",
     "    y_min, y_max = data[:, 1].min() - .5, data[:, 1].max() + .5\n",
@@ -282,8 +281,6 @@
     "    def train(self, num_iteration=150):\n",
     "        \"\"\"随机梯度上升算法\n",
     "        Args:\n",
-    "            data (numpy.ndarray): 训练数据集\n",
-    "            labels (numpy.ndarray): 训练标签\n",
     "            num_iteration (int): 迭代次数\n",
     "        \"\"\"\n",
     "        # 学习速率\n",
@@ -557,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -621,7 +618,7 @@
     "\n",
     "# calculate train/test accuracy\n",
     "acc_train = accuracy_score(y_train, pred_train)\n",
-    "acc_test = accuracy_score(y_test, pred_test)\n",
+    "acc_test  = accuracy_score(y_test, pred_test)\n",
     "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n",
     "\n",
     "score_train = lr.score(x_train, y_train)\n",
@@ -632,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {