diff --git a/Lab2/前馈神经网络实验.ipynb b/Lab2/前馈神经网络实验.ipynb
index 88c9a52..e7db924 100644
--- a/Lab2/前馈神经网络实验.ipynb
+++ b/Lab2/前馈神经网络实验.ipynb
@@ -22,13 +22,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "实验环境：\r\n",
- "- OS：Ubuntu 22.04 (Kernel: 6.2.0-34-generic)\r\n",
- "- CPU：12th Gen Intel(R) Core(TM) i7-12700H\r\n",
- "- GPU：NVIDIA GeForce RTX 3070 Ti Laptop\r\n",
- "- cuda: 12.2\r\n",
- "- conda: miniconda 23.9.0\r\n",
- "- python：3.10.13\r\n",
+ "实验环境：\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",
+ "- conda: miniconda 23.9.0\n",
+ "- python：3.10.13\n",
"- pytorch：2.1.0"
]
},
@@ -36,12 +36,24 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'matplotlib'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m/home/kejingfan/Codedir/School-DeepLearningCourse-Lab/Lab2/前馈神经网络实验.ipynb Cell 3\u001b[0m line \u001b[0;36m8\n\u001b[1;32m 6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorch\u001b[39;00m \u001b[39mimport\u001b[39;00m nn\n\u001b[1;32m 7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorchvision\u001b[39;00m \u001b[39mimport\u001b[39;00m datasets, transforms\n\u001b[0;32m----> 8\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
+ ]
+ }
+ ],
"source": [
"import time\n",
"import numpy as np\n",
"import torch\n",
- "from torch.nn.functional import one_hot, softmax\n",
+ "from torch.nn.functional import *\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from torch import nn\n",
"from torchvision import datasets, transforms\n",
@@ -70,21 +82,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "首先生成数据集。\r\n",
- "\r\n",
- "一共有3个数据集：\r\n",
- "\r\n",
- "1. 回归任务数据集。\r\n",
- " - 生成单个数据集。\r\n",
- " - 数据集的大小为$10000$且训练集大小为$7000$，测试集大小为$3000$。\r\n",
- " - 数据集的样本特征维度$p$为$500$，且服从如下的高维线性函数：$y = 0.028 + \\sum_{p}^{i=1}0.0056 x_i + \\epsilon $。\r\n",
- "2. 二分类任务数据集。\r\n",
- " - 共生成两个数据集。\r\n",
- " - 两个数据集的大小均为$10000$且训练集大小为$7000$，测试集大小为$3000$。\r\n",
- " - 两个数据集的样本特征$x$的维度均为$200$，且分别服从均值互为相反数且方差相同的正态分布。\r\n",
- " - 两个数据集的样本标签分别为$0$和$1$。\r\n",
- "3. MNIST手写体数据集。\r\n",
- " - 该数据集包含$60,000$个用于训练的图像样本和$10,000$个用于测试的图像样本。\r\n",
+ "首先生成数据集。\n",
+ "\n",
+ "一共有3个数据集：\n",
+ "\n",
+ "1. 回归任务数据集。\n",
+ " - 生成单个数据集。\n",
+ " - 数据集的大小为$10000$且训练集大小为$7000$，测试集大小为$3000$。\n",
+ " - 数据集的样本特征维度$p$为$500$，且服从如下的高维线性函数：$y = 0.028 + \\sum_{p}^{i=1}0.0056 x_i + \\epsilon $。\n",
+ "2. 二分类任务数据集。\n",
+ " - 共生成两个数据集。\n",
+ " - 两个数据集的大小均为$10000$且训练集大小为$7000$，测试集大小为$3000$。\n",
+ " - 两个数据集的样本特征$x$的维度均为$200$，且分别服从均值互为相反数且方差相同的正态分布。\n",
+ " - 两个数据集的样本标签分别为$0$和$1$。\n",
+ "3. MNIST手写体数据集。\n",
+ " - 该数据集包含$60,000$个用于训练的图像样本和$10,000$个用于测试的图像样本。\n",
" - 图像是固定大小（$28\\times 28$像素），其值为$0$到$1$。为每个图像都被平展并转换为$784$（$28 \\times 28$）个特征的一维numpy数组。 "
]
},
diff --git a/requirements.txt b/requirements.txt
index c02bc74..9c6529c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,9 @@
-black==23.9.1
-ipdb==0.13.13
-jupyter==1.0.0
-numpy==1.26.0
+black
+ipdb
+jupyter
+numpy
torch==2.1.0
torchaudio==2.1.0
torchvision==0.16.0
-tqdm==4.66.1
\ No newline at end of file
+tqdm
+matplotlib
\ No newline at end of file