diff --git a/notebooks/ch11/ch11.ipynb b/notebooks/ch11/ch11.ipynb
index c480d121..c425b6bd 100644
--- a/notebooks/ch11/ch11.ipynb
+++ b/notebooks/ch11/ch11.ipynb
@@ -27700,7 +27700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -27714,12 +27714,1080 @@
       "displayName": "张银晗",
       "userId": "12610392256073027643"
      },
-     "user_tz": -480
-    },
-    "id": "0pIfBcMMhBi6",
-    "outputId": "6a2231be-c3c2-4fb7-8d52-a2c55691655b"
-   },
-   "outputs": [],
+     "user_tz": -480
+    },
+    "id": "0pIfBcMMhBi6",
+    "outputId": "6a2231be-c3c2-4fb7-8d52-a2c55691655b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train loss 0.125, train acc 0.952, test acc 0.895\n"
+     ]
+    },
+    {
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+       " </g>\n",
+       " <defs>\n",
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+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 350x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# 在 Jupyter Notebook 中使用 Matplotlib 绘图\n",
     "%matplotlib inline\n",
@@ -27821,18 +28889,6 @@
     "train(net, train_iter, test_iter, num_epochs, loss, trainer, device)\n"
    ]
   },
-  {
-   "attachments": {
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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:image.png)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,