diff --git a/09.ipynb b/09.ipynb
index 2f3ed32..04272bf 100644
--- a/09.ipynb
+++ b/09.ipynb
@@ -20,13 +20,13 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-16T08:20:55.729969Z",
-     "start_time": "2025-06-16T08:20:55.664951Z"
+     "end_time": "2025-06-16T09:46:23.991077Z",
+     "start_time": "2025-06-16T09:46:23.982065Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "input_feat = torch.tensor([[4, 1, 7, 5], [4, 4, 2, 5], [7, 7, 2, 4], [1, 0, 2, 4]], dtype=torch.float32)\n",
+    "input_feat = torch.tensor([[4, 1, 7, 5], [4, 4, 2, 5], [7, 7, 2, 4], [1, 0, 2, 4]], dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n",
     "print(input_feat)\n",
     "print(input_feat.shape)"
    ],
@@ -36,26 +36,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[4., 1., 7., 5.],\n",
-      "        [4., 4., 2., 5.],\n",
-      "        [7., 7., 2., 4.],\n",
-      "        [1., 0., 2., 4.]])\n",
-      "torch.Size([4, 4])\n"
+      "tensor([[[[4., 1., 7., 5.],\n",
+      "          [4., 4., 2., 5.],\n",
+      "          [7., 7., 2., 4.],\n",
+      "          [1., 0., 2., 4.]]]])\n",
+      "torch.Size([1, 1, 4, 4])\n"
      ]
     }
    ],
-   "execution_count": 2
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-16T08:22:26.834622Z",
-     "start_time": "2025-06-16T08:22:26.825132Z"
+     "end_time": "2025-06-16T09:47:40.137555Z",
+     "start_time": "2025-06-16T09:47:40.131166Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "conv2d = nn.Conv2d(1, 1, (2, 2), stride=1, padding='same', bias=True)\n",
+    "conv2d = nn.Conv2d(1, 1, (2, 2), stride=1, padding='same', bias=False)\n",
+    "# 卷积核要有四个维度：输出通道数，输入通道数，卷积核高度，卷积核宽度\n",
+    "kernels = torch.tensor([[[[1, 0], [2, 1]]]], dtype=torch.float32)\n",
+    "conv2d.weight = nn.Parameter(kernels, requires_grad=False)  # 设置卷积核\n",
     "# 默认情况随机初始化参数\n",
     "print(conv2d.weight)\n",
     "print(conv2d.bias)"
@@ -67,14 +70,43 @@
      "output_type": "stream",
      "text": [
       "Parameter containing:\n",
-      "tensor([[[[ 0.4068, -0.3036],\n",
-      "          [ 0.4212,  0.4779]]]], requires_grad=True)\n",
-      "Parameter containing:\n",
-      "tensor([0.0521], requires_grad=True)\n"
+      "tensor([[[[1., 0.],\n",
+      "          [2., 1.]]]])\n",
+      "None\n"
      ]
     }
    ],
-   "execution_count": 5
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-16T09:47:42.159880Z",
+     "start_time": "2025-06-16T09:47:42.153928Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "output = conv2d(input_feat)\n",
+    "output"
+   ],
+   "id": "8ebf518ec7c7bc70",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[[16., 11., 16., 15.],\n",
+       "          [25., 20., 10., 13.],\n",
+       "          [ 9.,  9., 10., 12.],\n",
+       "          [ 1.,  0.,  2.,  4.]]]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 17
   }
  ],
  "metadata": {