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{
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## python基础入门"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello woorld\n"
]
}
],
"source": [
"print(\"hello woorld\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 2 3 4 5\n"
]
}
],
"source": [
"print(1,2,3,4,5)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 2\n"
]
}
],
"source": [
"acc=1\n",
"epoch=2\n",
"print(acc,epoch) # acc为深度学习模型训练的精度指标，epoch为轮数"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 2 3 4 5\n",
"1->2->3->4->5\n"
]
}
],
"source": [
"print(1,2,3,4,5)\n",
"print(1,2,3,4,5,sep=\"->\") # sep指定输出的分隔"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 2 3 4 5\t1 2 3 4 5\n"
]
}
],
"source": [
"print(1,2,3,4,5,end=\"\\t\") # \\t为制表符，默认一个tab\n",
"print(1,2,3,4,5)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 2 3 4 5\n",
"1 2 3 4 5\n"
]
}
],
"source": [
"print(1,2,3,4,5) # \\n为换行\n",
"print(1,2,3,4,5)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"f = open(\"test.txt\",\"a\") # 若没有此txt文件则先创建，然后将内容追加在末尾\n",
"print(\"\\n\",file=f)\n",
"print(\"中文报错，print后面括号为中文（）\",file=f)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.30000000000000004\n"
]
}
],
"source": [
"print(0.1+0.2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1690473024512\n",
"1690473024512\n"
]
}
],
"source": [
"list1 = [1,2,3,5]\n",
"print(id(list1))\n",
"list1[1] = 5\n",
"print(id(list1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 格式化输出"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"我叫qianyu，今年18岁啦\n"
]
}
],
"source": [
"print(\"我叫%s，今年%d岁啦\" % (\"qianyu\",18)) # %s为字符串 %d为整型"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"学校的名称是点头教育，学习的网站是wwww.diantouedu.cn\n"
]
}
],
"source": [
"print(\"学校的名称是{}，学习的网站是{}\".format(\"点头教育\",\"wwww.diantouedu.cn\"))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"我叫做qianyu,今年19岁啦\n",
"我叫做qianyu,今年19岁啦\n",
"我叫做qianyu,今年19岁啦\n",
"我叫做qianyu,今年19岁啦\n"
]
}
],
"source": [
"# f表达式格式化输出\n",
"name = \"qianyu\"\n",
"age = 19\n",
"print(f\"我叫做{name},今年{age}岁啦\")\n",
"print(f\"我叫做{name},今年{age}岁啦\")\n",
"print(f\"我叫做{name},今年{age}岁啦\")\n",
"print(f\"我叫做{name},今年{age}岁啦\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"我叫做qianyu，今年19岁啦\n",
"我叫做qianyu，今年19岁啦\n",
"我叫做qianyu，今年19岁啦\n",
"我叫做qianyu，今年19岁啦\n"
]
}
],
"source": [
"print(\"我叫做qianyu，今年19岁啦\")\n",
"print(\"我叫做qianyu，今年19岁啦\")\n",
"print(\"我叫做qianyu，今年19岁啦\")\n",
"print(\"我叫做qianyu，今年19岁啦\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"训练轮数为1\n"
]
}
],
"source": [
"epoch = 1\n",
"print(f\"训练轮数为{epoch}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['False', 'None', 'True', '__peg_parser__', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']\n"
]
}
],
"source": [
"import keyword\n",
"print(keyword.kwlist)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 变量"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2437147683120\n",
"2437147683152\n"
]
}
],
"source": [
"a = 1\n",
"print(id(a)) \n",
"# print(type(a)) ctrl+/为整体注释\n",
"a = 2\n",
"print(id(a))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 输入函数"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n",
"<class 'str'>\n"
]
}
],
"source": [
"age = input() # 无论输入的是什么 都会被转化为字符串类型\n",
"print(age)\n",
"print(type(age))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "test",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
3-AI工具包/3.1-编程语言/3.1.1-Python基础知识/readme.md 0 → 100644
# Python基础编程
# Python基础编程
这个仓库包含了关于第一和第二节课的Python基础编程代码。
## 第一节课
- 代码文件: `python基础入门.ipynb`
- 文本文件: `test.txt` (包含一些常见的报错，是第一节课中用到的文本文件)
## 第二节课
- 代码文件: `六大数据类型2.ipynb`(六大数据类型.ipynb为往期代码，可做参考)
\ No newline at end of file
3-AI工具包/3.1-编程语言/3.1.1-Python基础知识/test.txt 0 → 100644
# Python基础报错例子
# Python基础报错例子
SyntaxError（语法错误）:
print "Hello, World!"
# 缺少括号，应该是 print("Hello, World!")
IndentationError（缩进错误）:
def my_function():
print("Hello, World!")
# 函数体缩进不正确
NameError（名称错误）:
print(x)
# x未定义
TypeError（类型错误）:
x = "5"
y = 2
z = x + y
# 字符串和整数不能直接相加
IndexError（索引错误）:
my_list = [1, 2, 3]
print(my_list[3])
# 索引超出了列表范围
KeyError（键错误）:
my_dict = {"name": "Alice", "age": 30}
print(my_dict["gender"])
# 字典中没有"gender"这个键
ValueError（数值错误）:
int("abc")
# 无法将非数字字符串转换为整数
FileNotFoundError（文件未找到错误）:
with open("nonexistent_file.txt", "r") as f:
content = f.read()
# 文件不存在
ImportError（导入错误）:
import nonexistent_module
# 未安装或导入的模块不存在
ZeroDivisionError（除零错误）:
result = 10 / 0
# 除数为零
AttributeError（属性错误）:
x = 5
print(x.append(1))
# 整数对象没有append()方法
KeyboardInterrupt（键盘中断）:
while True:
pass
# 执行这段代码后，按下Ctrl+C将会中断程序执行hello world
中文报错，print后面括号为中文（）
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3-AI工具包/3.1-编程语言/3.1.2-Python常用函数/python进阶知识.ipynb 0 → 100644
{
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n"
]
}
],
"source": [
"a = 1\n",
"b = 2\n",
"print(a)\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b的值为1\n"
]
}
],
"source": [
"a = -1\n",
"if a>=0:\n",
" print(\"a的值是非负\")\n",
"\n",
"\n",
"b = 1\n",
"print(f\"b的值为{b}\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a是非负数,a的值为0\n"
]
}
],
"source": [
"a = 0\n",
"if a>=0:\n",
" print(f\"a是非负数,a的值为{a}\")\n",
"else:\n",
" print(f\"a是负数,a的值为{a}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"score = 100\n",
"if score>=90 and score<=100:\n",
" print(\"该学生的成绩很不错，为A\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "test",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
3-AI工具包/3.1-编程语言/3.1.2-Python常用函数/readme.md 0 → 100644
### Python进阶知识
### Python进阶知识
这里是Python部分进阶的知识，主要包括以下内容：
#### 1. 三大程序结构
- **顺序：** 指程序从上到下按照顺序执行的结构。
- **分支：** 涉及条件判断，根据条件不同执行不同的代码块。
- **循环：** 允许代码块多次执行，直到满足退出条件为止。
#### 2. 函数的定义和使用
函数是一段可重复使用的代码块，可以接受参数并返回值。
#### 3. 经典编程案例
- **递归求n阶乘：** 通过递归方式计算给定数的阶乘。
- **求解水仙花数：** 寻找指定范围内的水仙花数，即每位数字的立方和等于该数本身的数。
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3-AI工具包/3.1-编程语言/3.1.3-Python面向对象编程/readme.md 0 → 100644
## 本章节讲解的是 Python 面向对象编程的相关内容，包括类和对象的定义和使用、封装、继承、多态。在学习本章之前需要先看环境配置、Python基础、进阶课程
## 本章节讲解的是 Python 面向对象编程的相关内容，包括类和对象的定义和使用、封装、继承、多态。在学习本章之前需要先看环境配置、Python基础、进阶课程
3-AI工具包/3.2-科学计算库numpy、pandas/a.json 0 → 100644
[
[
{
"id": "001",
"name": "点头教育",
"url": "www.diantouedu.cn",
"age": 10
},
{
"id": "002",
"name": "Google",
"url": "www.google.com",
"age": 100
},
{
"id": "003",
"name": "淘宝",
"url": "www.taobao.com",
"age": 50
}
]
\ No newline at end of file
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3-AI工具包/3.3-可视化绘图matplotlib、seaborn/seaborn.ipynb 0 → 100644
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3-AI工具包/3.4-深度学习框架/3.4.2-PyTorch框架的使用/data_conv.ipynb 0 → 100644
{
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FashionMNIST 数据集介绍\n",
"\n",
"`FashionMNIST` 数据集是由 Zalando 提供的一个包含时尚商品图像的数据集，旨在为机器学习和计算机视觉任务提供一个简单的基准数据集。与经典的 MNIST 数据集类似，FashionMNIST 包含 10 个类别的图像，但这些图像是时尚产品（如衣物、鞋子等），而不是手写数字。每张图像的大小为 28x28 像素，灰度图像，共有 70,000 张样本（其中 60,000 张用于训练，10,000 张用于测试）。\n",
"\n",
"## 类别标签及其中文翻译\n",
"\n",
"以下是 `FashionMNIST` 数据集中的 10 个类别标签及其对应的中文翻译：\n",
"\n",
"1. **T-shirt/top** - T恤/上衣\n",
"2. **Trouser** - 长裤\n",
"3. **Pullover** - 套头衫\n",
"4. **Dress** - 连衣裙\n",
"5. **Coat** - 外套\n",
"6. **Sandal** - 凉鞋\n",
"7. **Shirt** - 衬衫\n",
"8. **Sneaker** - 运动鞋\n",
"9. **Bag** - 包\n",
"10. **Ankle boot** - 短靴\n",
"\n",
"这些标签代表了数据集中包含的各种时尚商品类别，每个类别的图像都是通过计算机视觉方法进行分类的目标。\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 导入必要的库\n",
"import os # 用于操作文件和目录\n",
"import torch # 用于PyTorch相关操作，尽管在本代码中未使用（可能未来有用）\n",
"from torchvision import datasets, transforms # 用于数据集和数据变换\n",
"from PIL import Image # 用于图像操作，特别是将Tensor转换为图像并保存\n",
"from torchvision.datasets import FashionMNIST # 用于加载FashionMNIST数据集\n",
"\n",
"# 定义数据集保存路径\n",
"data_dir = './FashionMNIST_images' # 定义数据集的根目录\n",
"train_dir = os.path.join(data_dir, 'train') # 训练集保存路径\n",
"test_dir = os.path.join(data_dir, 'test') # 测试集保存路径\n",
"\n",
"# 定义分类标签，FashionMNIST数据集有10个类别\n",
"class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
"\n",
"# 创建文件夹结构\n",
"def create_folders(base_dir):\n",
" \"\"\"\n",
" 创建用于保存数据集的文件夹结构。将数据集分为训练集和测试集，并根据类别在每个集下创建子文件夹。\n",
" \"\"\"\n",
" for split in ['train', 'test']: # 对训练集和测试集分别操作\n",
" split_dir = os.path.join(base_dir, split) # 拼接路径\n",
" os.makedirs(split_dir, exist_ok=True) # 如果文件夹不存在则创建，存在则不做任何操作\n",
" for class_name in class_names: # 为每个类别创建一个文件夹\n",
" class_dir = os.path.join(split_dir, class_name) # 拼接类别文件夹路径\n",
" os.makedirs(class_dir, exist_ok=True) # 创建类别文件夹\n",
"\n",
"# 保存图像到文件夹\n",
"def save_images(dataset, split='train'):\n",
" \"\"\"\n",
" 将数据集中的图像按类别保存到相应的文件夹。\n",
" \"\"\"\n",
" for i, (image, label) in enumerate(dataset): # 遍历数据集中的每个图像和标签\n",
" class_name = class_names[label] # 获取类别名称\n",
" folder_path = os.path.join(data_dir, split, class_name) # 拼接类别文件夹路径\n",
" \n",
" # 保存图片\n",
" image = transforms.ToPILImage()(image) # 将Tensor格式的图像转换为PIL图像，以便保存\n",
" image.save(os.path.join(folder_path, f'{split}_{i}.png')) # 保存为PNG格式，文件名为 \"split_i.png\"\n",
"\n",
"# 下载FashionMNIST数据集并应用转换\n",
"transform = transforms.Compose([transforms.ToTensor()]) # 将图片转换为Tensor格式\n",
"train_dataset = FashionMNIST(root='./data', train=True, download=True, transform=transform) # 下载并加载训练集\n",
"test_dataset = FashionMNIST(root='./data', train=False, download=True, transform=transform) # 下载并加载测试集\n",
"\n",
"# 创建用于保存数据集的文件夹结构\n",
"create_folders(data_dir)\n",
"\n",
"# 将训练和测试数据保存到文件夹中\n",
"save_images(train_dataset, 'train') # 保存训练集图像\n",
"save_images(test_dataset, 'test') # 保存测试集图像\n",
"\n",
"# 打印保存成功的信息\n",
"print(\"Images saved successfully.\")\n"
]
}
],
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3-AI工具包/3.4-深度学习框架/3.4.2-PyTorch框架的使用/readme.md 0 → 100644
# 项目名称
# 项目名称
该项目包含FashionMNIST数据集的转换、预处理、模型训练及推理。
## 项目文件说明
- `data_conv.ipynb`: FashionMNIST数据集转换脚本。该脚本用于将从`torchvision`中下载的FashionMNIST数据集转换为图片格式并保存。
- `train.ipynb`: 数据预处理、模型训练及推理的完整流程脚本。该notebook包含了从数据加载到最终推理的所有步骤。
- `train_0.py`: 单独的训练脚本（可选），可独立运行以进行模型训练。
## 数据集
项目中使用的FashionMNIST数据集，可以通过`torchvision`下载。转换后的图片格式数据将用于模型训练和推理。
## 项目使用说明
1. 下载数据集转换脚本并运行`data_conv.ipynb`，将FashionMNIST数据集转换为图片格式。
2. 运行`train.ipynb`，进行数据预处理、模型训练及推理。
3. （可选）运行`train_0.py`，单独进行模型训练。
## 数据集链接
链接: [https://pan.baidu.com/s/1uxkvg3NWyollRVqrfkAXcQ?pwd=8888](https://pan.baidu.com/s/1uxkvg3NWyollRVqrfkAXcQ?pwd=8888)
提取码: 8888
3-AI工具包/3.4-深度学习框架/3.4.2-PyTorch框架的使用/train_0.py 0 → 100644
import torch
import torch
import torch.nn as nn
import torch.optim as optim # 导入优化器
from torchvision import datasets, transforms # 导入数据集和数据预处理库
from torch.utils.data import DataLoader # 数据加载库
# 设置随机种子
torch.manual_seed(21)
# 定义数据预处理
transform = transforms.Compose([
transforms.ToTensor(), # 将图像转换为张量
transforms.Normalize((0.5), (0.5)) # 标准化图像数据 灰度图，只需要一个0.5 -1 - 1
])
# 加载FashionMNIST数据集
train_dataset = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform) # 下载训练集
test_dataset = datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform) # 下载测试集
# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True) # 对训练集进行打包， 一批次64个图像塞入神经网络训练
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False) # 对测试集进行打包
# 定义神经网络模型
class QYNN(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(28*28, 128) # 定义第一个全连接层 隐藏层的神经元个数为128
self.fc2 = nn.Linear(128, 10) # 定义第二个全连接层 输出神经元个数 10 因为我们需要做10分类
def forward(self, x): # 前向传播
x = torch.flatten(x, start_dim=1) # 展平数据，方便进行全连接
x = torch.relu(self.fc1(x)) # 非线性
x = self.fc2(x) # 十分类 [0.1,0.2,0.5,0.2,0,0,0,0,0,0]
return x
# 初始化模型
model = QYNN()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss() # 交叉熵
optimizer = optim.SGD(model.parameters(), lr=0.01) # lr 学习率 用来调整模型收敛速度 0.1
# 训练模型
epochs = 10
for epoch in range(epochs): # 0-9
running_loss = 0.0 # 定义初始loss为0
for inputs, labels in train_loader:
optimizer.zero_grad() # 梯度清零
outputs = model(inputs) # 将图片塞进网络训练获得 输出
loss = criterion(outputs, labels) # 根据输出和标签做对比计算损失
loss.backward() # 反向传播
optimizer.step() # 更新参数
running_loss += loss.item() # loss值累加
print(f"Epoch {epoch+1}/{epochs}, Loss: {running_loss/len(train_loader)}")
# 测试模型
correct = 0 # 正确的数量
total = 0 # 样本总数
with torch.no_grad(): # 不用进行梯度计算
for inputs, labels in test_loader:
# print(labels.shape)
outputs = model(inputs) # [0.1,0.2,0.5,0.2,0,0,0,0,0,0] 2
# print(outputs.shape)
_, predicted = torch.max(outputs, 1) # _取到的最大值，可以不要， 我们需要的是最大值对应的索引 也就是label（predicted）
total += labels.size(0) # 获取当前批次样本数量
correct += (predicted == labels).sum().item() # 对预测对的值进行累加
print(f"Accuracy on test set: {correct/total:.2%}")
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4-数据处理/4.1-结构化数据处理/sweet_report.html 0 → 100644
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4-数据处理/4.2-影像数据处理/.idea/.gitignore 0 → 100644
# Default ignored files
# Default ignored files
/shelf/
/workspace.xml
# Editor-based HTTP Client requests
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml
4-数据处理/4.2-影像数据处理/.idea/ImageData.iml 0 → 100644
<?xml version="1.0" encoding="UTF-8"?>
<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="Python 3.10" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>
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4-数据处理/4.2-影像数据处理/.idea/deployment.xml 0 → 100644
<?xml version="1.0" encoding="UTF-8"?>
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="PublishConfigData" remoteFilesAllowedToDisappearOnAutoupload="false">
<serverData>
<paths name="root@region-45.autodl.pro:28338 password">
<serverdata>
<mappings>
<mapping local="$PROJECT_DIR$" web="/" />
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</serverdata>
</paths>
<paths name="root@region-45.autodl.pro:28338 password (2)">
<serverdata>
<mappings>
<mapping local="$PROJECT_DIR$" web="/" />
</mappings>
</serverdata>
</paths>
</serverData>
</component>
</project>
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4-数据处理/4.2-影像数据处理/.idea/inspectionProfiles/Project_Default.xml 0 → 100644
<component name="InspectionProjectProfileManager">
<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredPackages">
<value>
<list size="11">
<item index="0" class="java.lang.String" itemvalue="tensorboard" />
<item index="1" class="java.lang.String" itemvalue="opencv-python" />
<item index="2" class="java.lang.String" itemvalue="torch" />
<item index="3" class="java.lang.String" itemvalue="numpy" />
<item index="4" class="java.lang.String" itemvalue="torchvision" />
<item index="5" class="java.lang.String" itemvalue="tqdm" />
<item index="6" class="java.lang.String" itemvalue="scipy" />
<item index="7" class="java.lang.String" itemvalue="h5py" />
<item index="8" class="java.lang.String" itemvalue="matplotlib" />
<item index="9" class="java.lang.String" itemvalue="opencv_python" />
<item index="10" class="java.lang.String" itemvalue="Pillow" />
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</value>
</option>
</inspection_tool>
</profile>
</component>
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4-数据处理/4.2-影像数据处理/.idea/inspectionProfiles/profiles_settings.xml 0 → 100644
<component name="InspectionProjectProfileManager">
<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>
\ No newline at end of file
4-数据处理/4.2-影像数据处理/.idea/misc.xml 0 → 100644
<?xml version="1.0" encoding="UTF-8"?>
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10" project-jdk-type="Python SDK" />
</project>
\ No newline at end of file
4-数据处理/4.2-影像数据处理/.idea/modules.xml 0 → 100644
<?xml version="1.0" encoding="UTF-8"?>
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/ImageData.iml" filepath="$PROJECT_DIR$/.idea/ImageData.iml" />
</modules>
</component>
</project>
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4-数据处理/4.2-影像数据处理/main.py 0 → 100644
# This is a sample Python script.
# This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
def print_hi(name):
# Use a breakpoint in the code line below to debug your script.
print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint.
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
print_hi('PyCharm')
# See PyCharm help at https://www.jetbrains.com/help/pycharm/
5-算法选择/5.1-KAN&变体/kan.ipynb 0 → 100644
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5-算法选择/5.8-影像类算法选择/README.md 0 → 100644
下载链接<br>
下载链接<br>
链接：https://pan.baidu.com/s/1Kmue_npXNeHLU1z2XuB68Q?pwd=wgxq <br>
提取码：wgxq<br>
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