feat(AIandML): lab 3.0

AIandML/e3_deep_learning/PyTorch.md

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+# 安装PyTorch
+
+**人工智能与机器学习**课程的深度学习部分代码采用PyTorch框架。以下为安装说明：
+
+### 1. 官网
+
+以下为官方网站，其中给出了安装方式。
+
+```
+https://pytorch.org/
+```
+
+###2. 选择安装方式
+
+需注意，安装时需注意考虑：
+
+* 操作系统（Windows、Linux或MacOs）
+* CPU或GPU，后者需安装cuda（指定版本）
+
+在官网上，选择适当安装方式后，将给出在线安装命令。例如，用conda安装cuda10.2版本的PyTorch：
+
+```shell
+conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
+```
+
+将其拷贝到命令行中执行即可。
+
+### 3. 建议
+
+因为PyTorch可能较大，可以下载whl文件安装。

AIandML/e3_deep_learning/e3.0_tensor.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 实验3-1 Tensors\n",
+    "\n",
+    "实验目标：\n",
+    "\n",
+    "* 初步掌握PyTorch的张量用法\n",
+    "\n",
+    "张量(Tensor)是一种特殊的数据结构（可简单理解为高维数组），在使用方法上与数组或矩阵相似。在PyTorch中，我们使用张量来描述模型的输入、输出，以及模型参数。\n",
+    "\n",
+    "张量类似于NumPy的ndarrays，并且张量可以在GPU上运行（或其他专用硬件），以实现加速计算。如果我们熟悉numpy.ndarray，就很容易掌握PyTorch的Tensor。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Tensor 初始化\n",
+    "\n",
+    "张量可以通过多种方式初始化。请看以下示例：\n",
+    "\n",
+    "#### 1.1 直接来自数据\n",
+    "\n",
+    "张量可以直接从数据中创建。数据类型是自动推断的。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1, 2],\n",
+      "        [3, 4]])\n",
+      "torch.int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = [[1, 2],[3, 4]]\n",
+    "x_data = torch.tensor(data)\n",
+    "print(x_data)\n",
+    "print(x_data.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.2 从NumPy数组创建\n",
+    "\n",
+    "张量可以从NumPy数组创建（反之亦然）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "np_array = np.array(data)\n",
+    "x_np = torch.from_numpy(np_array)\n",
+    "print(x_np.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3 来自另一个张量\n",
+    "\n",
+    "新张量保留参数张量的属性（形状、数据类型），除非显式重写。\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ones Tensor: \n",
+      " tensor([[1, 1],\n",
+      "        [1, 1]]) \n",
+      "\n",
+      "torch.int64\n",
+      "Random Tensor: \n",
+      " tensor([[0.4114, 0.9433],\n",
+      "        [0.6890, 0.5708]]) \n",
+      "\n",
+      "torch.float32\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_ones = torch.ones_like(x_data) # retains the properties of x_data\n",
+    "print(f\"Ones Tensor: \\n {x_ones} \\n\")\n",
+    "print(x_ones.dtype)\n",
+    "x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data\n",
+    "print(f\"Random Tensor: \\n {x_rand} \\n\")\n",
+    "print(x_rand.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.4 使用随机或常量值：\n",
+    "\n",
+    "''shape'' 是张量维数的元组。在下面的函数中，它确定输出张量的维数。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.float32\n",
+      "torch.float32\n",
+      "torch.float32\n",
+      "Random Tensor: \n",
+      " tensor([[0.3535, 0.5414, 0.5149],\n",
+      "        [0.7026, 0.4758, 0.2522]]) \n",
+      "\n",
+      "Ones Tensor: \n",
+      " tensor([[1., 1., 1.],\n",
+      "        [1., 1., 1.]]) \n",
+      "\n",
+      "Zeros Tensor: \n",
+      " tensor([[0., 0., 0.],\n",
+      "        [0., 0., 0.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "shape = (2,3,)\n",
+    "rand_tensor = torch.rand(shape)\n",
+    "ones_tensor = torch.ones(shape)\n",
+    "zeros_tensor = torch.zeros(shape)\n",
+    "print(rand_tensor.dtype)\n",
+    "print(ones_tensor.dtype)\n",
+    "print(zeros_tensor.dtype)\n",
+    "\n",
+    "print(f\"Random Tensor: \\n {rand_tensor} \\n\")\n",
+    "print(f\"Ones Tensor: \\n {ones_tensor} \\n\")\n",
+    "print(f\"Zeros Tensor: \\n {zeros_tensor}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> 请回答：\n",
+    "> 1. 采用以上各种方法创建Tensor时，其数据类型（即`.dtype`属性)是怎样的？"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "数据类型可在上面的输出中发现。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Tensor 属性\n",
+    "\n",
+    "\n",
+    "张量属性描述它们的形状、数据类型以及存储它们的设备。\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of dimension: 2\n",
+      "Shape of tensor: torch.Size([5, 4])\n",
+      "Datatype of tensor: torch.float32\n",
+      "Device tensor is stored on: cpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "tensor = torch.rand(5,4)\n",
+    "\n",
+    "print(f\"Number of dimension: {tensor.ndim}\")\n",
+    "print(f\"Shape of tensor: {tensor.shape}\")\n",
+    "print(f\"Datatype of tensor: {tensor.dtype}\")\n",
+    "print(f\"Device tensor is stored on: {tensor.device}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> 请回答：\n",
+    "> 1. 修改变量 tensor 的尺寸，重新执行，给出结果。\n",
+    "> 2. 并根据结果分析和解释`tensor.ndim`、`tensor.shape`、`tensor.dtype`和`tensor.device`各是什么含义？\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分别为：维度、形状、数据类型、存储位置"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Tensor 操作\n",
+    "\n",
+    "\n",
+    "* 在\"官网文档<https://pytorch.org/docs/stable/torch.html>\"中，详尽的介绍了约百余个张量的运算函数，包括转置、索引、切片、数学运算，线性代数，随机抽样等。\n",
+    "\n",
+    "* 需知晓的是，这些函数都可以在GPU上运行。在批量化运行时，GPU运算速度通常比在CPU上运行的速度更高。\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We move our tensor to the GPU if available\n",
+    "if torch.cuda.is_available():\n",
+    "  tensor = tensor.to('cuda')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> 请回答：\n",
+    "> 1. 使用张量的任意操作函数，并给出代码和效果。\n",
+    "* 如果你熟悉NumPy API，你会发现Tensor API使用起来轻而易举。\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[0.8814, 0.4445, 0.7783, 0.5680],\n",
+      "        [0.8376, 0.7283, 0.5712, 0.6707],\n",
+      "        [0.7175, 0.3082, 0.1837, 0.4545],\n",
+      "        [0.2929, 0.8227, 0.3114, 0.1816],\n",
+      "        [0.9362, 0.5050, 0.8165, 0.6510]], device='cuda:0')\n",
+      "tensor([[0.4919, 1.1102, 0.6788, 0.9667],\n",
+      "        [0.5779, 0.7550, 0.9628, 0.8356],\n",
+      "        [0.7705, 1.2575, 1.3861, 1.0990],\n",
+      "        [1.2735, 0.6047, 1.2541, 1.3882],\n",
+      "        [0.3591, 1.0414, 0.6154, 0.8619]], device='cuda:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tensor)\n",
+    "print(torch.acos(tensor))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. 类似 numpy的索引(indexing)和切片(slicing) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "tensor = torch.ones(4, 4)\n",
+    "tensor[:,1] = 0\n",
+    "print(tensor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> 请回答：\n",
+    "> 1. 请用索引或切片操作，给出`tensor`的第一行、第一列和中间2x2的子矩阵。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([1., 0., 1., 1.])\n",
+      "tensor([1., 1., 1., 1.])\n",
+      "tensor([[0., 1.],\n",
+      "        [0., 1.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tensor[0])\n",
+    "print(tensor[:,0])\n",
+    "print(tensor[1:3,1:3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. 合并Tensor\n",
+    "\n",
+    "合并Tensor有多种方式，例如`torch.cat`和`torch.stack`。\n",
+    "\n",
+    "> 请回答：\n",
+    "> 1. 请查阅文档，并用示例描述`torch.cat`和`torch.stack`各是什么方式合并，有何不同。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "t1 = torch.cat([tensor, tensor, tensor], dim=1)\n",
+    "print(t1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1., 2., 3.],\n",
+      "        [1., 2., 3.]])\n",
+      "tensor([1., 2., 3., 1., 2., 3.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "  #注: .cat 和 .stack的区别在于 cat会增加现有维度的值,，stack会新加增加一个维度\n",
+    "a=torch.Tensor([1,2,3])\n",
+    "print(torch.stack((a,a)))\n",
+    "print(torch.cat((a,a)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Tensors相乘\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor.mul(tensor) \n",
+      " tensor([[1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.]]) \n",
+      "\n",
+      "tensor * tensor \n",
+      " tensor([[1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This computes the element-wise product\n",
+    "print(f\"tensor.mul(tensor) \\n {tensor.mul(tensor)} \\n\")\n",
+    "# Alternative syntax:\n",
+    "print(f\"tensor * tensor \\n {tensor * tensor}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这将计算两个张量之间的矩阵乘法\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor.matmul(tensor.T) \n",
+      " tensor([[3., 3., 3., 3.],\n",
+      "        [3., 3., 3., 3.],\n",
+      "        [3., 3., 3., 3.],\n",
+      "        [3., 3., 3., 3.]]) \n",
+      "\n",
+      "tensor @ tensor.T \n",
+      " tensor([[3., 3., 3., 3.],\n",
+      "        [3., 3., 3., 3.],\n",
+      "        [3., 3., 3., 3.],\n",
+      "        [3., 3., 3., 3.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"tensor.matmul(tensor.T) \\n {tensor.matmul(tensor.T)} \\n\")\n",
+    "# Alternative syntax:\n",
+    "print(f\"tensor @ tensor.T \\n {tensor @ tensor.T}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> 请回答：\n",
+    "> 1. 请查阅文档，并结合示例，说明以上两种乘法的区别"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一种是按元素相乘，一种是矩阵乘法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7. 就地（In-place）操作\n",
+    "\n",
+    "> 就地操作是指，操作的输入和输出都是同一个变量。例如C语言中的`x++`和`y*=5`都属于就地操作。由于就地操作避免了内存拷贝，可以提升运算速度。\n",
+    "\n",
+    "\n",
+    "具有`_`后缀的成员函数即为就地操作。例如：`x.copy_(y)`、`x.t_()`执行后将都将更改变量`x`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.],\n",
+      "        [1., 0., 1., 1.]]) \n",
+      "\n",
+      "tensor([[6., 5., 6., 6.],\n",
+      "        [6., 5., 6., 6.],\n",
+      "        [6., 5., 6., 6.],\n",
+      "        [6., 5., 6., 6.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tensor, \"\\n\")\n",
+    "tensor.add_(5)\n",
+    "print(tensor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意：\n",
+    "\n",
+    "就地操作虽然可节省一些内存，但在计算梯度时可能会出现问题。因为就地操作会丢失计算的历史。因此，不鼓励使用它们。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> 请回答：\n",
+    "> 1. 请尝试`x.copy_(y)`函数，并说明其用法。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1, 2],\n",
+      "        [3, 4]])\n",
+      "tensor([[4, 3],\n",
+      "        [2, 1]])\n",
+      "tensor([[4, 3],\n",
+      "        [2, 1]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "#将y复制到x中\n",
+    "x = torch.tensor([[1, 2],[3, 4]])\n",
+    "print(x)\n",
+    "y = torch.tensor([[4, 3],[2,1]])\n",
+    "print(y)\n",
+    "x.copy_(y)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 8. 与 NumPy 间的转换\n",
+    "\n",
+    "Torch的Tensor可以和NumPy的ndarray互相转换。\n",
+    "\n",
+    "若Tensor是在CPU上，那么其转换所得 NumPy 数组可与之共享其底层内存。也就是，更改其中一个将导致另一个也被更改。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 8.1 Tensor 转为 NumPy array\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t: tensor([1., 1., 1., 1., 1.])\n",
+      "n: [1. 1. 1. 1. 1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "t = torch.ones(5)\n",
+    "print(f\"t: {t}\")\n",
+    "n = t.numpy()\n",
+    "print(f\"n: {n}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "张量的更改亦反映在NumPy数组中。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t: tensor([2., 2., 2., 2., 2.])\n",
+      "n: [2. 2. 2. 2. 2.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "t.add_(1)\n",
+    "print(f\"t: {t}\")\n",
+    "print(f\"n: {n}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 8.2 NumPy array 转为 Tensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = np.ones(5)\n",
+    "t = torch.from_numpy(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更改NumPy数组亦影响张量。\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t: tensor([2., 2., 2., 2., 2.], dtype=torch.float64)\n",
+      "n: [2. 2. 2. 2. 2.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "np.add(n, 1, out=n)\n",
+    "print(f\"t: {t}\")\n",
+    "print(f\"n: {n}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> 请回答：\n",
+    "> 1. 请查阅文档，以了解如何复制Tensor或ndarray，以避免内存共享时的干扰。请给出示例代码。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([2., 2., 2.])\n",
+      "[2. 2. 2.]\n",
+      "tensor([2., 2., 2.])\n",
+      "[1. 1. 1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#使用clone方法\n",
+    "a = torch.ones(3)\n",
+    "b = a.numpy()\n",
+    "a.add_(1)\n",
+    "print(a)\n",
+    "print(b) #a和b共享内存\n",
+    "\n",
+    "a = torch.ones(3)\n",
+    "b = a.clone().numpy()\n",
+    "a.add_(1)\n",
+    "print(a)\n",
+    "print(b) #a和b不共享内存"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.8.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "0733c54d9044ea299f7b7f48049f3576c8ad4e6ff5a97e2c60d8a9e3bff0bc54"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

AIandML/requirements.txt

@@ -1,62 +1,78 @@
+anyio==3.6.2
 appdirs==1.4.4
 argon2-cffi==21.3.0
 argon2-cffi-bindings==21.2.0
-asttokens==2.0.8
+arrow==1.2.3
+asttokens==2.2.1
 attrs==22.1.0
 backcall==0.2.0
 beautifulsoup4==4.11.1
 bleach==5.0.1
-certifi==2022.9.14
+certifi==2022.12.7
 cffi==1.15.1
-contourpy==1.0.5
+charset-normalizer==2.1.1
+comm==0.1.2
+contourpy==1.0.6
 cycler==0.11.0
-debugpy==1.6.3
+debugpy==1.6.4
 decorator==5.1.1
 defusedxml==0.7.1
 entrypoints==0.4
-executing==1.0.0
+executing==1.2.0
 fastjsonschema==2.16.2
-fonttools==4.37.3
+fonttools==4.38.0
-importlib-metadata==4.12.0
+fqdn==1.5.1
-ipykernel==6.15.3
+idna==3.4
-ipython==8.5.0
+importlib-metadata==5.1.0
+importlib-resources==5.10.1
+ipykernel==6.19.2
+ipython==8.7.0
 ipython-genutils==0.2.0
-ipywidgets==8.0.2
+ipywidgets==8.0.3
-jedi==0.18.1
+isoduration==20.11.0
+jedi==0.18.2
 Jinja2==3.1.2
 joblib==1.2.0
-jsonschema==4.16.0
+jsonpointer==2.3
+jsonschema==4.17.3
 jupyter==1.0.0
 jupyter-console==6.4.4
-jupyter-core==4.11.1
+jupyter-events==0.5.0
-jupyter_client==7.3.5
+jupyter_client==7.4.8
+jupyter_core==5.1.0
+jupyter_server==2.0.1
+jupyter_server_terminals==0.4.2
 jupyterlab-pygments==0.2.2
-jupyterlab-widgets==3.0.3
+jupyterlab-widgets==3.0.4
 kiwisolver==1.4.4
-lxml==4.9.1
+lxml==4.9.2
 MarkupSafe==2.1.1
-matplotlib==3.6.0
+matplotlib==3.6.2
 matplotlib-inline==0.1.6
 mistune==2.0.4
-nbclient==0.6.8
+nbclassic==0.4.8
-nbconvert==7.0.0
+nbclient==0.7.2
-nbformat==5.5.0
+nbconvert==7.2.6
-nest-asyncio==1.5.5
+nbformat==5.7.0
-notebook==6.4.12
+nest-asyncio==1.5.6
-numpy==1.23.3
+notebook==6.5.2
-packaging==21.3
+notebook_shim==0.2.2
-pandas==1.5.0
+numpy==1.23.5
-pandoc==2.2
+packaging==22.0
+pandas==1.5.2
+pandoc==2.3
 pandocfilters==1.5.0
 parso==0.8.3
 pexpect==4.8.0
 pickleshare==0.7.5
-Pillow==9.2.0
+Pillow==9.3.0
-plumbum==1.7.2
+pkgutil_resolve_name==1.3.10
+platformdirs==2.6.0
+plumbum==1.8.0
 ply==3.11
-prometheus-client==0.14.1
+prometheus-client==0.15.0
-prompt-toolkit==3.0.31
+prompt-toolkit==3.0.36
-psutil==5.9.2
+psutil==5.9.4
 ptyprocess==0.7.0
 pure-eval==0.2.2
 pycparser==2.21
@@ -64,28 +80,41 @@ pyee==8.2.2
 Pygments==2.13.0
 pyparsing==3.0.9
 pyppeteer==1.0.2
-pyrsistent==0.18.1
+pyrsistent==0.19.2
 python-dateutil==2.8.2
-pytz==2022.2.1
+python-json-logger==2.0.4
-pyzmq==24.0.0
+pytz==2022.6
-qtconsole==5.3.2
+PyYAML==6.0
-QtPy==2.2.0
+pyzmq==24.0.1
-scikit-learn==1.1.3
+qtconsole==5.4.0
+QtPy==2.3.0
+requests==2.28.1
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+scikit-learn==1.2.0
 scipy==1.9.3
-seaborn==0.12.0
+seaborn==0.12.1
 Send2Trash==1.8.0
 six==1.16.0
+sniffio==1.3.0
 soupsieve==2.3.2.post1
-stack-data==0.5.0
+stack-data==0.6.2
-terminado==0.15.0
+terminado==0.17.1
 threadpoolctl==3.1.0
-tinycss2==1.1.1
+tinycss2==1.2.1
+torch==1.13.0+cu116
+torchaudio==0.13.0+cu116
+torchvision==0.14.0+cu116
 tornado==6.2
 tqdm==4.64.1
-traitlets==5.4.0
+traitlets==5.7.1
-urllib3==1.26.12
+typing_extensions==4.4.0
+uri-template==1.2.0
+urllib3==1.26.13
 wcwidth==0.2.5
+webcolors==1.12
 webencodings==0.5.1
-websockets==10.3
+websocket-client==1.4.2
-widgetsnbextension==4.0.3
+websockets==10.4
-zipp==3.8.1
+widgetsnbextension==4.0.4
+zipp==3.11.0