diff --git a/Lab1/.ipynb_checkpoints/Pytorch基本操作实验报告-checkpoint.ipynb b/Lab1/.ipynb_checkpoints/Pytorch基本操作实验报告-checkpoint.ipynb
index 9791e16..a21eaff 100644
--- a/Lab1/.ipynb_checkpoints/Pytorch基本操作实验报告-checkpoint.ipynb
+++ b/Lab1/.ipynb_checkpoints/Pytorch基本操作实验报告-checkpoint.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "<p style=\"text-align: center;\"><img alt=\"school-logo\" src=\"../images/school_logo.png\" style=\"zoom: 50%;\" /></p>\n",
     "\n",
-    "<h1><center>本科生《深度学习》课程<br>实验报告</center></h1>\n",
+    "<h1 align=\"center\">本科生《深度学习》课程<br>实验报告</h1>\n",
     "<div style=\"text-align: center;\">\n",
     "    <div><span style=\"display: inline-block; width: 65px; text-align: center;\">课程名称</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">深度学习</span></div>\n",
     "    <div><span style=\"display: inline-block; width: 65px; text-align: center;\">实验题目</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">Pytorch基本操作</span></div>\n",
@@ -25,7 +25,7 @@
    "metadata": {},
    "source": [
     "实验环境：\n",
-    "- OS：Ubuntu 22.04 内核版本 6.2.0-34-generic\n",
+    "- OS：Ubuntu 22.04 (Kernel: 6.2.0-34-generic)\n",
     "- CPU：12th Gen Intel(R) Core(TM) i7-12700H\n",
     "- GPU：NVIDIA GeForce RTX 3070 Ti Laptop\n",
     "- cuda: 12.2\n",
@@ -163,21 +163,21 @@
      "output_type": "stream",
      "text": [
       "矩阵 P:\n",
-      "tensor([[-0.0131,  0.0147],\n",
-      "        [ 0.0248, -0.0028],\n",
-      "        [-0.0172,  0.0178]])\n",
+      "tensor([[ 0.0043,  0.0009],\n",
+      "        [-0.0008,  0.0021],\n",
+      "        [-0.0012, -0.0091]])\n",
       "矩阵 Q:\n",
-      "tensor([[ 0.0015,  0.0015],\n",
-      "        [-0.0121, -0.0074],\n",
-      "        [ 0.0072,  0.0039],\n",
-      "        [-0.0032, -0.0061]])\n",
+      "tensor([[ 0.0016,  0.0073],\n",
+      "        [-0.0092,  0.0024],\n",
+      "        [ 0.0026,  0.0171],\n",
+      "        [ 0.0101, -0.0038]])\n",
       "矩阵 QT:\n",
-      "tensor([[ 0.0015, -0.0121,  0.0072, -0.0032],\n",
-      "        [ 0.0015, -0.0074,  0.0039, -0.0061]])\n",
+      "tensor([[ 0.0016, -0.0092,  0.0026,  0.0101],\n",
+      "        [ 0.0073,  0.0024,  0.0171, -0.0038]])\n",
       "矩阵相乘的结果:\n",
-      "tensor([[ 2.8145e-06,  4.9911e-05, -3.6764e-05, -4.7670e-05],\n",
-      "        [ 3.2685e-05, -2.7908e-04,  1.6724e-04, -6.1334e-05],\n",
-      "        [ 1.4138e-06,  7.6416e-05, -5.3995e-05, -5.3379e-05]])\n"
+      "tensor([[ 1.3472e-05, -3.7060e-05,  2.7148e-05,  3.9682e-05],\n",
+      "        [ 1.3877e-05,  1.2322e-05,  3.3492e-05, -1.6069e-05],\n",
+      "        [-6.8047e-05, -1.1357e-05, -1.5886e-04,  2.3463e-05]])\n"
      ]
     }
    ],
@@ -613,18 +613,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10, Loss: 678.0522713065147, Acc: 0.9949060965876567\n",
-      "Epoch 2/10, Loss: 677.2863736152649, Acc: 0.9980913352860563\n",
-      "Epoch 3/10, Loss: 677.197151362896, Acc: 0.9993721880397808\n",
-      "Epoch 4/10, Loss: 677.1782736182213, Acc: 0.9997903927928914\n",
-      "Epoch 5/10, Loss: 677.1754664182663, Acc: 0.9996946183328581\n",
-      "Epoch 6/10, Loss: 677.1741757392883, Acc: 0.9999630627469878\n",
-      "Epoch 7/10, Loss: 677.1742368340492, Acc: 0.9999474390293509\n",
-      "Epoch 8/10, Loss: 677.1745658516884, Acc: 0.9999775205877912\n",
-      "Epoch 9/10, Loss: 677.1739910840988, Acc: 0.9999218865585965\n",
-      "Epoch 10/10, Loss: 677.1743568778038, Acc: 0.9998403212619357\n",
-      "Model weights: -0.0020640366710722446, bias: 0.019105462357401848\n",
-      "Prediction for test data: 0.504260241985321\n"
+      "Epoch 1/10, Loss: 696.7355516552925, Acc: 0.9442648464635192\n",
+      "Epoch 2/10, Loss: 680.1393249630928, Acc: 0.9911232759674347\n",
+      "Epoch 3/10, Loss: 677.770676612854, Acc: 0.9956893804390976\n",
+      "Epoch 4/10, Loss: 677.294788479805, Acc: 0.9982881159072501\n",
+      "Epoch 5/10, Loss: 677.1979722976685, Acc: 0.9991794744511796\n",
+      "Epoch 6/10, Loss: 677.1792464852333, Acc: 0.999493084950588\n",
+      "Epoch 7/10, Loss: 677.1751466989517, Acc: 0.9998704799602793\n",
+      "Epoch 8/10, Loss: 677.1746656894684, Acc: 0.9999325569195194\n",
+      "Epoch 9/10, Loss: 677.1742008328438, Acc: 0.9999852565480795\n",
+      "Epoch 10/10, Loss: 677.1745100617409, Acc: 0.9999026775350377\n",
+      "Model weights: -0.0018169691320508718, bias: 0.018722545355558395\n",
+      "Prediction for test data: 0.5042262673377991\n"
      ]
     }
    ],
@@ -726,18 +726,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10, Loss: 605.8295383291701, Acc: 0.9582065520342055\n",
-      "Epoch 2/10, Loss: 576.8061408727621, Acc: 0.9847528457476354\n",
-      "Epoch 3/10, Loss: 569.2995615954951, Acc: 0.9918221342823426\n",
-      "Epoch 4/10, Loss: 567.0406008049263, Acc: 0.9953926453617279\n",
-      "Epoch 5/10, Loss: 566.3090309665234, Acc: 0.9973164146429515\n",
-      "Epoch 6/10, Loss: 566.0625152740248, Acc: 0.9984245151624715\n",
-      "Epoch 7/10, Loss: 565.9721677527377, Acc: 0.9990875142056446\n",
-      "Epoch 8/10, Loss: 565.9461858826305, Acc: 0.999437410787883\n",
-      "Epoch 9/10, Loss: 565.9334115060377, Acc: 0.9996791976966314\n",
-      "Epoch 10/10, Loss: 565.9350714258352, Acc: 0.9997795372757307\n",
-      "Model weights: -3.688023801126246, bias: 1.8659015788034263\n",
-      "Prediction for test data: 0.139179294539555\n"
+      "Epoch 1/10, Loss: 638.0160498773107, Acc: 0.9937490349019101\n",
+      "Epoch 2/10, Loss: 583.3716011882699, Acc: 0.9804301403247839\n",
+      "Epoch 3/10, Loss: 571.1401001196623, Acc: 0.9896515985806724\n",
+      "Epoch 4/10, Loss: 567.6159870155185, Acc: 0.9943010522600507\n",
+      "Epoch 5/10, Loss: 566.5014995958526, Acc: 0.9966799384882902\n",
+      "Epoch 6/10, Loss: 566.1252285088149, Acc: 0.998098013624422\n",
+      "Epoch 7/10, Loss: 565.9985610526666, Acc: 0.9988608489236236\n",
+      "Epoch 8/10, Loss: 565.9526960214308, Acc: 0.9993323768578708\n",
+      "Epoch 9/10, Loss: 565.9374750639024, Acc: 0.9995989407216784\n",
+      "Epoch 10/10, Loss: 565.9291789969539, Acc: 0.9997716274081613\n",
+      "Model weights: -3.685411369051284, bias: 1.8638604353928832\n",
+      "Prediction for test data: 0.1392477539282304\n"
      ]
     }
    ],
@@ -899,16 +899,15 @@
      "output_type": "stream",
      "text": [
       "输入：\n",
-      "tensor([[ 3.3495e+00, -1.5645e+00,  1.3911e-01,  8.2237e-03,  8.6507e-01],\n",
-      "        [ 3.2858e-01,  3.3071e-01,  1.1809e+00, -1.5414e+00,  1.2054e+00],\n",
-      "        [ 9.6236e-04,  1.2167e+00, -1.8887e-01, -9.1634e-01,  1.9415e+00]],\n",
-      "       requires_grad=True)\n",
+      "tensor([[ 1.0624,  1.7008, -1.2849,  0.4049, -0.3993],\n",
+      "        [ 0.0757,  1.0636,  0.3586, -0.0252, -1.1431],\n",
+      "        [ 0.4754, -1.9538,  0.6616, -1.0363,  0.6049]], requires_grad=True)\n",
       "标签：\n",
-      "tensor([[0., 0., 0., 1., 0.],\n",
-      "        [1., 0., 0., 0., 0.],\n",
-      "        [1., 0., 0., 0., 0.]])\n",
-      "My_CrossEntropyLoss损失值: 2.652722120285034\n",
-      "nn.CrossEntropyLoss损失值: 2.652722120285034\n"
+      "tensor([[0., 0., 0., 0., 1.],\n",
+      "        [0., 1., 0., 0., 0.],\n",
+      "        [0., 0., 0., 0., 1.]])\n",
+      "My_CrossEntropyLoss损失值: 1.5949448347091675\n",
+      "nn.CrossEntropyLoss损失值: 1.594944953918457\n"
      ]
     }
    ],
@@ -1082,16 +1081,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10, Loss: 100.43975830078125, Acc: 0.4251999855041504\n",
-      "Epoch 2/10, Loss: 45.485450744628906, Acc: 0.5367000102996826\n",
-      "Epoch 3/10, Loss: 34.95743179321289, Acc: 0.5881999731063843\n",
-      "Epoch 4/10, Loss: 30.256790161132812, Acc: 0.6238999962806702\n",
-      "Epoch 5/10, Loss: 27.338891983032227, Acc: 0.6474999785423279\n",
-      "Epoch 6/10, Loss: 25.360095977783203, Acc: 0.6664999723434448\n",
-      "Epoch 7/10, Loss: 23.8934326171875, Acc: 0.6789999604225159\n",
-      "Epoch 8/10, Loss: 22.703121185302734, Acc: 0.6876999735832214\n",
-      "Epoch 9/10, Loss: 21.799795150756836, Acc: 0.6959999799728394\n",
-      "Epoch 10/10, Loss: 21.04413414001465, Acc: 0.7023999691009521\n"
+      "Epoch 1/10, Loss: 84.3017807006836, Acc: 0.5087000131607056\n",
+      "Epoch 2/10, Loss: 37.02857208251953, Acc: 0.5946999788284302\n",
+      "Epoch 3/10, Loss: 30.553579330444336, Acc: 0.6287999749183655\n",
+      "Epoch 4/10, Loss: 27.279203414916992, Acc: 0.6550999879837036\n",
+      "Epoch 5/10, Loss: 25.244386672973633, Acc: 0.6694999933242798\n",
+      "Epoch 6/10, Loss: 23.713878631591797, Acc: 0.6798999905586243\n",
+      "Epoch 7/10, Loss: 22.5694580078125, Acc: 0.6924999952316284\n",
+      "Epoch 8/10, Loss: 21.611900329589844, Acc: 0.6965000033378601\n",
+      "Epoch 9/10, Loss: 20.85039520263672, Acc: 0.7014999985694885\n",
+      "Epoch 10/10, Loss: 20.116191864013672, Acc: 0.7102000117301941\n"
      ]
     }
    ],
@@ -1213,16 +1212,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10, Loss: 18.918968200683594, Acc: 0.7260000109672546\n",
-      "Epoch 2/10, Loss: 12.184525489807129, Acc: 0.7475000023841858\n",
-      "Epoch 3/10, Loss: 10.786707878112793, Acc: 0.7612999677658081\n",
-      "Epoch 4/10, Loss: 10.06576919555664, Acc: 0.7705000042915344\n",
-      "Epoch 5/10, Loss: 9.591888427734375, Acc: 0.7785999774932861\n",
-      "Epoch 6/10, Loss: 9.247062683105469, Acc: 0.7856999635696411\n",
-      "Epoch 7/10, Loss: 8.989615440368652, Acc: 0.7890999913215637\n",
-      "Epoch 8/10, Loss: 8.772100448608398, Acc: 0.792199969291687\n",
-      "Epoch 9/10, Loss: 8.593544006347656, Acc: 0.7978000044822693\n",
-      "Epoch 10/10, Loss: 8.453678131103516, Acc: 0.7997999787330627\n"
+      "Epoch 1/10, Loss: 19.19444465637207, Acc: 0.7229999899864197\n",
+      "Epoch 2/10, Loss: 12.180685043334961, Acc: 0.7491999864578247\n",
+      "Epoch 3/10, Loss: 10.77286148071289, Acc: 0.7608999609947205\n",
+      "Epoch 4/10, Loss: 10.058968544006348, Acc: 0.7716000080108643\n",
+      "Epoch 5/10, Loss: 9.58817195892334, Acc: 0.7815999984741211\n",
+      "Epoch 6/10, Loss: 9.245816230773926, Acc: 0.7861999869346619\n",
+      "Epoch 7/10, Loss: 8.98766040802002, Acc: 0.7924000024795532\n",
+      "Epoch 8/10, Loss: 8.778538703918457, Acc: 0.7949999570846558\n",
+      "Epoch 9/10, Loss: 8.59365177154541, Acc: 0.795699954032898\n",
+      "Epoch 10/10, Loss: 8.442872047424316, Acc: 0.7998999953269958\n"
      ]
     }
    ],
@@ -1292,7 +1291,7 @@
    "id": "f40431f2-e77b-4ead-81a3-ff6451a8e452",
    "metadata": {},
    "source": [
-    "**实验心得体会**\n",
+    "# 实验心得体会\n",
     "\n",
     "通过完成本次Pytorch基本操作实验，让我对Pytorch框架有了更加深入的理解。我接触深度学习主要是在大语言模型领域，比较熟悉微调大模型，但是涉及到底层的深度学习知识，我还有很多短板和不足。这次实验对我这方面的锻炼让我收获良多。\n",
     "\n",
diff --git a/Lab1/Pytorch基本操作实验报告.ipynb b/Lab1/Pytorch基本操作实验报告.ipynb
index 9791e16..a21eaff 100644
--- a/Lab1/Pytorch基本操作实验报告.ipynb
+++ b/Lab1/Pytorch基本操作实验报告.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "<p style=\"text-align: center;\"><img alt=\"school-logo\" src=\"../images/school_logo.png\" style=\"zoom: 50%;\" /></p>\n",
     "\n",
-    "<h1><center>本科生《深度学习》课程<br>实验报告</center></h1>\n",
+    "<h1 align=\"center\">本科生《深度学习》课程<br>实验报告</h1>\n",
     "<div style=\"text-align: center;\">\n",
     "    <div><span style=\"display: inline-block; width: 65px; text-align: center;\">课程名称</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">深度学习</span></div>\n",
     "    <div><span style=\"display: inline-block; width: 65px; text-align: center;\">实验题目</span><span style=\"display: inline-block; width: 25px;\">:</span><span style=\"display: inline-block; width: 210px; font-weight: bold; text-align: left;\">Pytorch基本操作</span></div>\n",
@@ -25,7 +25,7 @@
    "metadata": {},
    "source": [
     "实验环境：\n",
-    "- OS：Ubuntu 22.04 内核版本 6.2.0-34-generic\n",
+    "- OS：Ubuntu 22.04 (Kernel: 6.2.0-34-generic)\n",
     "- CPU：12th Gen Intel(R) Core(TM) i7-12700H\n",
     "- GPU：NVIDIA GeForce RTX 3070 Ti Laptop\n",
     "- cuda: 12.2\n",
@@ -163,21 +163,21 @@
      "output_type": "stream",
      "text": [
       "矩阵 P:\n",
-      "tensor([[-0.0131,  0.0147],\n",
-      "        [ 0.0248, -0.0028],\n",
-      "        [-0.0172,  0.0178]])\n",
+      "tensor([[ 0.0043,  0.0009],\n",
+      "        [-0.0008,  0.0021],\n",
+      "        [-0.0012, -0.0091]])\n",
       "矩阵 Q:\n",
-      "tensor([[ 0.0015,  0.0015],\n",
-      "        [-0.0121, -0.0074],\n",
-      "        [ 0.0072,  0.0039],\n",
-      "        [-0.0032, -0.0061]])\n",
+      "tensor([[ 0.0016,  0.0073],\n",
+      "        [-0.0092,  0.0024],\n",
+      "        [ 0.0026,  0.0171],\n",
+      "        [ 0.0101, -0.0038]])\n",
       "矩阵 QT:\n",
-      "tensor([[ 0.0015, -0.0121,  0.0072, -0.0032],\n",
-      "        [ 0.0015, -0.0074,  0.0039, -0.0061]])\n",
+      "tensor([[ 0.0016, -0.0092,  0.0026,  0.0101],\n",
+      "        [ 0.0073,  0.0024,  0.0171, -0.0038]])\n",
       "矩阵相乘的结果:\n",
-      "tensor([[ 2.8145e-06,  4.9911e-05, -3.6764e-05, -4.7670e-05],\n",
-      "        [ 3.2685e-05, -2.7908e-04,  1.6724e-04, -6.1334e-05],\n",
-      "        [ 1.4138e-06,  7.6416e-05, -5.3995e-05, -5.3379e-05]])\n"
+      "tensor([[ 1.3472e-05, -3.7060e-05,  2.7148e-05,  3.9682e-05],\n",
+      "        [ 1.3877e-05,  1.2322e-05,  3.3492e-05, -1.6069e-05],\n",
+      "        [-6.8047e-05, -1.1357e-05, -1.5886e-04,  2.3463e-05]])\n"
      ]
     }
    ],
@@ -613,18 +613,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10, Loss: 678.0522713065147, Acc: 0.9949060965876567\n",
-      "Epoch 2/10, Loss: 677.2863736152649, Acc: 0.9980913352860563\n",
-      "Epoch 3/10, Loss: 677.197151362896, Acc: 0.9993721880397808\n",
-      "Epoch 4/10, Loss: 677.1782736182213, Acc: 0.9997903927928914\n",
-      "Epoch 5/10, Loss: 677.1754664182663, Acc: 0.9996946183328581\n",
-      "Epoch 6/10, Loss: 677.1741757392883, Acc: 0.9999630627469878\n",
-      "Epoch 7/10, Loss: 677.1742368340492, Acc: 0.9999474390293509\n",
-      "Epoch 8/10, Loss: 677.1745658516884, Acc: 0.9999775205877912\n",
-      "Epoch 9/10, Loss: 677.1739910840988, Acc: 0.9999218865585965\n",
-      "Epoch 10/10, Loss: 677.1743568778038, Acc: 0.9998403212619357\n",
-      "Model weights: -0.0020640366710722446, bias: 0.019105462357401848\n",
-      "Prediction for test data: 0.504260241985321\n"
+      "Epoch 1/10, Loss: 696.7355516552925, Acc: 0.9442648464635192\n",
+      "Epoch 2/10, Loss: 680.1393249630928, Acc: 0.9911232759674347\n",
+      "Epoch 3/10, Loss: 677.770676612854, Acc: 0.9956893804390976\n",
+      "Epoch 4/10, Loss: 677.294788479805, Acc: 0.9982881159072501\n",
+      "Epoch 5/10, Loss: 677.1979722976685, Acc: 0.9991794744511796\n",
+      "Epoch 6/10, Loss: 677.1792464852333, Acc: 0.999493084950588\n",
+      "Epoch 7/10, Loss: 677.1751466989517, Acc: 0.9998704799602793\n",
+      "Epoch 8/10, Loss: 677.1746656894684, Acc: 0.9999325569195194\n",
+      "Epoch 9/10, Loss: 677.1742008328438, Acc: 0.9999852565480795\n",
+      "Epoch 10/10, Loss: 677.1745100617409, Acc: 0.9999026775350377\n",
+      "Model weights: -0.0018169691320508718, bias: 0.018722545355558395\n",
+      "Prediction for test data: 0.5042262673377991\n"
      ]
     }
    ],
@@ -726,18 +726,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10, Loss: 605.8295383291701, Acc: 0.9582065520342055\n",
-      "Epoch 2/10, Loss: 576.8061408727621, Acc: 0.9847528457476354\n",
-      "Epoch 3/10, Loss: 569.2995615954951, Acc: 0.9918221342823426\n",
-      "Epoch 4/10, Loss: 567.0406008049263, Acc: 0.9953926453617279\n",
-      "Epoch 5/10, Loss: 566.3090309665234, Acc: 0.9973164146429515\n",
-      "Epoch 6/10, Loss: 566.0625152740248, Acc: 0.9984245151624715\n",
-      "Epoch 7/10, Loss: 565.9721677527377, Acc: 0.9990875142056446\n",
-      "Epoch 8/10, Loss: 565.9461858826305, Acc: 0.999437410787883\n",
-      "Epoch 9/10, Loss: 565.9334115060377, Acc: 0.9996791976966314\n",
-      "Epoch 10/10, Loss: 565.9350714258352, Acc: 0.9997795372757307\n",
-      "Model weights: -3.688023801126246, bias: 1.8659015788034263\n",
-      "Prediction for test data: 0.139179294539555\n"
+      "Epoch 1/10, Loss: 638.0160498773107, Acc: 0.9937490349019101\n",
+      "Epoch 2/10, Loss: 583.3716011882699, Acc: 0.9804301403247839\n",
+      "Epoch 3/10, Loss: 571.1401001196623, Acc: 0.9896515985806724\n",
+      "Epoch 4/10, Loss: 567.6159870155185, Acc: 0.9943010522600507\n",
+      "Epoch 5/10, Loss: 566.5014995958526, Acc: 0.9966799384882902\n",
+      "Epoch 6/10, Loss: 566.1252285088149, Acc: 0.998098013624422\n",
+      "Epoch 7/10, Loss: 565.9985610526666, Acc: 0.9988608489236236\n",
+      "Epoch 8/10, Loss: 565.9526960214308, Acc: 0.9993323768578708\n",
+      "Epoch 9/10, Loss: 565.9374750639024, Acc: 0.9995989407216784\n",
+      "Epoch 10/10, Loss: 565.9291789969539, Acc: 0.9997716274081613\n",
+      "Model weights: -3.685411369051284, bias: 1.8638604353928832\n",
+      "Prediction for test data: 0.1392477539282304\n"
      ]
     }
    ],
@@ -899,16 +899,15 @@
      "output_type": "stream",
      "text": [
       "输入：\n",
-      "tensor([[ 3.3495e+00, -1.5645e+00,  1.3911e-01,  8.2237e-03,  8.6507e-01],\n",
-      "        [ 3.2858e-01,  3.3071e-01,  1.1809e+00, -1.5414e+00,  1.2054e+00],\n",
-      "        [ 9.6236e-04,  1.2167e+00, -1.8887e-01, -9.1634e-01,  1.9415e+00]],\n",
-      "       requires_grad=True)\n",
+      "tensor([[ 1.0624,  1.7008, -1.2849,  0.4049, -0.3993],\n",
+      "        [ 0.0757,  1.0636,  0.3586, -0.0252, -1.1431],\n",
+      "        [ 0.4754, -1.9538,  0.6616, -1.0363,  0.6049]], requires_grad=True)\n",
       "标签：\n",
-      "tensor([[0., 0., 0., 1., 0.],\n",
-      "        [1., 0., 0., 0., 0.],\n",
-      "        [1., 0., 0., 0., 0.]])\n",
-      "My_CrossEntropyLoss损失值: 2.652722120285034\n",
-      "nn.CrossEntropyLoss损失值: 2.652722120285034\n"
+      "tensor([[0., 0., 0., 0., 1.],\n",
+      "        [0., 1., 0., 0., 0.],\n",
+      "        [0., 0., 0., 0., 1.]])\n",
+      "My_CrossEntropyLoss损失值: 1.5949448347091675\n",
+      "nn.CrossEntropyLoss损失值: 1.594944953918457\n"
      ]
     }
    ],
@@ -1082,16 +1081,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10, Loss: 100.43975830078125, Acc: 0.4251999855041504\n",
-      "Epoch 2/10, Loss: 45.485450744628906, Acc: 0.5367000102996826\n",
-      "Epoch 3/10, Loss: 34.95743179321289, Acc: 0.5881999731063843\n",
-      "Epoch 4/10, Loss: 30.256790161132812, Acc: 0.6238999962806702\n",
-      "Epoch 5/10, Loss: 27.338891983032227, Acc: 0.6474999785423279\n",
-      "Epoch 6/10, Loss: 25.360095977783203, Acc: 0.6664999723434448\n",
-      "Epoch 7/10, Loss: 23.8934326171875, Acc: 0.6789999604225159\n",
-      "Epoch 8/10, Loss: 22.703121185302734, Acc: 0.6876999735832214\n",
-      "Epoch 9/10, Loss: 21.799795150756836, Acc: 0.6959999799728394\n",
-      "Epoch 10/10, Loss: 21.04413414001465, Acc: 0.7023999691009521\n"
+      "Epoch 1/10, Loss: 84.3017807006836, Acc: 0.5087000131607056\n",
+      "Epoch 2/10, Loss: 37.02857208251953, Acc: 0.5946999788284302\n",
+      "Epoch 3/10, Loss: 30.553579330444336, Acc: 0.6287999749183655\n",
+      "Epoch 4/10, Loss: 27.279203414916992, Acc: 0.6550999879837036\n",
+      "Epoch 5/10, Loss: 25.244386672973633, Acc: 0.6694999933242798\n",
+      "Epoch 6/10, Loss: 23.713878631591797, Acc: 0.6798999905586243\n",
+      "Epoch 7/10, Loss: 22.5694580078125, Acc: 0.6924999952316284\n",
+      "Epoch 8/10, Loss: 21.611900329589844, Acc: 0.6965000033378601\n",
+      "Epoch 9/10, Loss: 20.85039520263672, Acc: 0.7014999985694885\n",
+      "Epoch 10/10, Loss: 20.116191864013672, Acc: 0.7102000117301941\n"
      ]
     }
    ],
@@ -1213,16 +1212,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10, Loss: 18.918968200683594, Acc: 0.7260000109672546\n",
-      "Epoch 2/10, Loss: 12.184525489807129, Acc: 0.7475000023841858\n",
-      "Epoch 3/10, Loss: 10.786707878112793, Acc: 0.7612999677658081\n",
-      "Epoch 4/10, Loss: 10.06576919555664, Acc: 0.7705000042915344\n",
-      "Epoch 5/10, Loss: 9.591888427734375, Acc: 0.7785999774932861\n",
-      "Epoch 6/10, Loss: 9.247062683105469, Acc: 0.7856999635696411\n",
-      "Epoch 7/10, Loss: 8.989615440368652, Acc: 0.7890999913215637\n",
-      "Epoch 8/10, Loss: 8.772100448608398, Acc: 0.792199969291687\n",
-      "Epoch 9/10, Loss: 8.593544006347656, Acc: 0.7978000044822693\n",
-      "Epoch 10/10, Loss: 8.453678131103516, Acc: 0.7997999787330627\n"
+      "Epoch 1/10, Loss: 19.19444465637207, Acc: 0.7229999899864197\n",
+      "Epoch 2/10, Loss: 12.180685043334961, Acc: 0.7491999864578247\n",
+      "Epoch 3/10, Loss: 10.77286148071289, Acc: 0.7608999609947205\n",
+      "Epoch 4/10, Loss: 10.058968544006348, Acc: 0.7716000080108643\n",
+      "Epoch 5/10, Loss: 9.58817195892334, Acc: 0.7815999984741211\n",
+      "Epoch 6/10, Loss: 9.245816230773926, Acc: 0.7861999869346619\n",
+      "Epoch 7/10, Loss: 8.98766040802002, Acc: 0.7924000024795532\n",
+      "Epoch 8/10, Loss: 8.778538703918457, Acc: 0.7949999570846558\n",
+      "Epoch 9/10, Loss: 8.59365177154541, Acc: 0.795699954032898\n",
+      "Epoch 10/10, Loss: 8.442872047424316, Acc: 0.7998999953269958\n"
      ]
     }
    ],
@@ -1292,7 +1291,7 @@
    "id": "f40431f2-e77b-4ead-81a3-ff6451a8e452",
    "metadata": {},
    "source": [
-    "**实验心得体会**\n",
+    "# 实验心得体会\n",
     "\n",
     "通过完成本次Pytorch基本操作实验，让我对Pytorch框架有了更加深入的理解。我接触深度学习主要是在大语言模型领域，比较熟悉微调大模型，但是涉及到底层的深度学习知识，我还有很多短板和不足。这次实验对我这方面的锻炼让我收获良多。\n",
     "\n",