{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f6d471db",
   "metadata": {},
   "source": [
    "## 安装torch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bb2e25",
   "metadata": {},
   "source": [
    "创建虚拟环境 (推荐使用uv工具)\n",
    "\n",
    "- pip install uv\n",
    "\n",
    "- mkdir [项目文件夹名称]\n",
    "\n",
    "- cd [项目文件夹名称]\n",
    "\n",
    "- uv venv [环境名称] -p python[版本]\n",
    "\n",
    "- source [环境名称]/bin/activate\n",
    "\n",
    "使用pip安装torch\n",
    "\n",
    "- uv pip install torch torchvision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1187101",
   "metadata": {},
   "source": [
    "## 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9200d264",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import torch   # torch 主要函数库\n",
    "from torch import nn   # 神经网络相关\n",
    "from torch.utils.data import DataLoader, Dataset, random_split  # 数据加载器\n",
    "import torch.optim as optim  # 参数调节器相关\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec356df",
   "metadata": {},
   "source": [
    "## 一个简单的例子 —— 函数拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff713f4",
   "metadata": {},
   "source": [
    "### 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "188da057",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义我们想要拟合的目标函数\n",
    "def target_function(x):\n",
    "    return np.sin(x / 2) + np.exp(-0.1 * x**2) * np.cos(5 * x)\n",
    "\n",
    "# 我们可以为函数添加一些噪声，让任务更真实\n",
    "def noisy_target_function(x):\n",
    "    return target_function(x) + 0.1 * np.random.randn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e8b28ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x738dc144a8d0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 1000)\n",
    "plt.plot(x, target_function(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d257a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "816d0016",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FunctionDataset(Dataset):\n",
    "    \"\"\"一个动态生成数据的Dataset\"\"\"\n",
    "    def __init__(self, func, x_range=(-10, 10), n_samples=10000):\n",
    "        self.func = func\n",
    "        self.x_range = x_range\n",
    "        self.n_samples = n_samples\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.n_samples\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        x_val = (self.x_range[1] - self.x_range[0]) * np.random.rand() + self.x_range[0]\n",
    "        y_val = self.func(x_val)\n",
    "        \n",
    "        x = torch.tensor([x_val], dtype=torch.float32)\n",
    "        y = torch.tensor([y_val], dtype=torch.float32)\n",
    "        \n",
    "        return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d72c5a",
   "metadata": {},
   "source": [
    "### 网络定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da9459b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Network(nn.Module):\n",
    "    \"\"\"\n",
    "    一个全连接神经网络\n",
    "    \"\"\"\n",
    "    def __init__(self, hidden_dim=128):\n",
    "        super(Network, self).__init__()\n",
    "        self.network = nn.Sequential(\n",
    "            nn.Linear(1, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden_dim*2),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim*2, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.network(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c7490025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.linspace(-10., 10., steps=100)\n",
    "plt.plot(x, nn.ReLU()(x))\n",
    "plt.title('relu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44dfcf19",
   "metadata": {},
   "source": [
    "### 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49311543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda:1\n",
      "Starting training with a more complex function...\n",
      "Epoch 10/200, Average Loss: 0.058662\n",
      "Epoch 20/200, Average Loss: 0.034428\n",
      "Epoch 30/200, Average Loss: 0.022347\n",
      "Epoch 40/200, Average Loss: 0.018028\n",
      "Epoch 50/200, Average Loss: 0.014576\n",
      "Epoch 60/200, Average Loss: 0.013586\n",
      "Epoch 70/200, Average Loss: 0.012801\n",
      "Epoch 80/200, Average Loss: 0.013448\n",
      "Epoch 90/200, Average Loss: 0.011601\n",
      "Epoch 100/200, Average Loss: 0.012230\n",
      "Epoch 110/200, Average Loss: 0.011736\n",
      "Epoch 120/200, Average Loss: 0.011748\n",
      "Epoch 130/200, Average Loss: 0.012542\n",
      "Epoch 140/200, Average Loss: 0.011409\n",
      "Epoch 150/200, Average Loss: 0.012132\n",
      "Epoch 160/200, Average Loss: 0.011858\n",
      "Epoch 170/200, Average Loss: 0.011167\n",
      "Epoch 180/200, Average Loss: 0.011083\n",
      "Epoch 190/200, Average Loss: 0.011320\n",
      "Epoch 200/200, Average Loss: 0.012253\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "# --- 设置超参数和设备 ---\n",
    "LEARNING_RATE = 1e-3\n",
    "BATCH_SIZE = 512\n",
    "EPOCHS = 200  # *** 增加训练周期以更好地拟合复杂函数 ***\n",
    "X_RANGE = (-10, 10)\n",
    "NUM_SAMPLES_PER_EPOCH = 20000\n",
    "\n",
    "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# --- 准备数据 ---\n",
    "train_dataset = FunctionDataset(noisy_target_function, x_range=X_RANGE, n_samples=NUM_SAMPLES_PER_EPOCH)\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "\n",
    "# --- 初始化模型、损失函数和优化器 ---\n",
    "model = Network(hidden_dim=128).to(device) # *** 使用更强大的模型 ***\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
    "\n",
    "# --- 训练模型 ---\n",
    "print(\"Starting training with a more complex function...\")\n",
    "for epoch in range(EPOCHS):\n",
    "    epoch_loss = 0.0\n",
    "    for x_batch, y_batch in train_loader:\n",
    "        x_batch, y_batch = x_batch.to(device), y_batch.to(device)\n",
    "        \n",
    "        y_pred = model(x_batch)\n",
    "        loss = criterion(y_pred, y_batch)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "    \n",
    "    avg_loss = epoch_loss / len(train_loader)\n",
    "    # 每10个epoch打印一次损失，避免信息刷屏\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f\"Epoch {epoch+1}/{EPOCHS}, Average Loss: {avg_loss:.6f}\")\n",
    "\n",
    "print(\"Training finished!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360e97ac",
   "metadata": {},
   "source": [
    "### 检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3fb92b90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizing results...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Visualizing results...\")\n",
    "\n",
    "# 1. 生成用于绘图的x值\n",
    "x_test = torch.linspace(X_RANGE[0], X_RANGE[1], 1000).view(-1, 1).to(device) # 增加点数以显示更多细节\n",
    "\n",
    "# 2. 计算真实函数值\n",
    "y_true = target_function(x_test.cpu().numpy())\n",
    "\n",
    "# 3. 使用训练好的模型进行预测\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    y_pred = model(x_test)\n",
    "\n",
    "y_pred_np = y_pred.cpu().numpy()\n",
    "\n",
    "# 4. 绘制图像\n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.plot(x_test.cpu().numpy(), y_true, label='True Function: $y = \\sin(x/2) + e^{-0.1x^2}\\cos(5x)$', color='blue', linewidth=2)\n",
    "plt.plot(x_test.cpu().numpy(), y_pred_np, label='Model Prediction', color='red', linestyle='--', linewidth=2.5)\n",
    "\n",
    "# 绘制一些训练数据点作为参考\n",
    "sample_x, sample_y = next(iter(train_loader))\n",
    "plt.scatter(sample_x.numpy(), sample_y.numpy(), label='Training Data Sample', color='green', alpha=0.5, s=15)\n",
    "\n",
    "plt.title('Fitting a Complex Function with a Neural Network')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(fontsize=12)\n",
    "plt.grid(True, linestyle=':')\n",
    "plt.ylim(-2, 2) # 设置y轴范围，让图像更清晰\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ebbbdfa",
   "metadata": {},
   "source": [
    "## Tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "014da572",
   "metadata": {},
   "source": [
    "tensor 是 torch 库的主要数据类型，其地位等于 numpy 库中的 array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3da02a",
   "metadata": {},
   "source": [
    "### 定义 tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e125c956",
   "metadata": {},
   "source": [
    "从列表定义 tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34d95f6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1, 2],\n",
      "        [3, 4]])\n"
     ]
    }
   ],
   "source": [
    "data = [[1, 2],[3, 4]]\n",
    "x_data = torch.tensor(data)\n",
    "print(x_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864044a5",
   "metadata": {},
   "source": [
    "从 numpy array 定义 tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e5827a6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1, 2],\n",
      "        [3, 4]])\n"
     ]
    }
   ],
   "source": [
    "np_array = np.array(data)\n",
    "x_np = torch.from_numpy(np_array)\n",
    "print(x_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40b4c8b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_np.numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f6c7c13",
   "metadata": {},
   "source": [
    "常用定义方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0cbf9ecd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机张量: \n",
      " tensor([[0.7296, 0.0204, 0.0455],\n",
      "        [0.3082, 0.2020, 0.5644]]) \n",
      "\n",
      "单位张量: \n",
      " tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.]]) \n",
      "\n",
      "零张量: \n",
      " tensor([[0., 0., 0.],\n",
      "        [0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "shape = (2,3,)\n",
    "rand_tensor = torch.rand(shape)  # 随机张量，[0, 1]之间\n",
    "ones_tensor = torch.ones(shape)  # 全为1\n",
    "zeros_tensor = torch.zeros(shape)   # 全为0\n",
    "\n",
    "print(f\"随机张量: \\n {rand_tensor} \\n\")\n",
    "print(f\"单位张量: \\n {ones_tensor} \\n\")\n",
    "print(f\"零张量: \\n {zeros_tensor}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3816ee",
   "metadata": {},
   "source": [
    "### Tensor 的属性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0f47d6",
   "metadata": {},
   "source": [
    "形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a99eb994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 2],\n",
       "        [3, 4]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98a6bdfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aae018e",
   "metadata": {},
   "source": [
    "元素数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57551b56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d2de43",
   "metadata": {},
   "source": [
    "设备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d287248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data.to(device).device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "882521cd",
   "metadata": {},
   "source": [
    "### 常用操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785b3f26",
   "metadata": {},
   "source": [
    "筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a6bcb6c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始张量: \n",
      "tensor([[0.7296, 0.0204, 0.0455],\n",
      "        [0.3082, 0.2020, 0.5644]])\n",
      "\n",
      "第一行: \n",
      "tensor([0.7296, 0.0204, 0.0455])\n",
      "\n",
      "第二列: \n",
      "tensor([0.0204, 0.2020])\n",
      "\n",
      "对角项: \n",
      "tensor([0.7296, 0.2020])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"原始张量: \\n{rand_tensor}\\n\")\n",
    "print(f\"第一行: \\n{rand_tensor[0]}\\n\")\n",
    "print(f\"第二列: \\n{rand_tensor[:, 1]}\\n\")\n",
    "print(f\"对角项: \\n{rand_tensor.diag()}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a75c04",
   "metadata": {},
   "source": [
    "形状操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "529bf289",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "转置: \n",
      "tensor([[0.6596, 0.4263],\n",
      "        [0.6333, 0.2771],\n",
      "        [0.3032, 0.5949]])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"转置: \\n{rand_tensor.transpose(1, 0)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f01757bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.7296, 0.0204, 0.0455],\n",
       "        [0.3082, 0.2020, 0.5644]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "742ee1dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1., 1.],\n",
       "        [1., 1., 1.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a9f3c881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "拼接: \n",
      "tensor([[0.7296, 0.0204, 0.0455],\n",
      "        [0.3082, 0.2020, 0.5644],\n",
      "        [1.0000, 1.0000, 1.0000],\n",
      "        [1.0000, 1.0000, 1.0000]])\n",
      "拼接: \n",
      "tensor([[0.7296, 0.0204, 0.0455, 1.0000, 1.0000, 1.0000],\n",
      "        [0.3082, 0.2020, 0.5644, 1.0000, 1.0000, 1.0000]])\n"
     ]
    }
   ],
   "source": [
    "print(f\"拼接: \\n{torch.cat((rand_tensor, ones_tensor))}\")\n",
    "print(f\"拼接: \\n{torch.cat((rand_tensor, ones_tensor), dim=1)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e44b4cf",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "only one element tensors can be converted to Python scalars",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m torch\u001b[38;5;241m.\u001b[39mtensor([rand_tensor, ones_tensor])\n",
      "\u001b[0;31mValueError\u001b[0m: only one element tensors can be converted to Python scalars"
     ]
    }
   ],
   "source": [
    "torch.tensor([rand_tensor, ones_tensor])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1bd11f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "堆叠: \n",
      "tensor([[[0.7296, 0.0204, 0.0455],\n",
      "         [0.3082, 0.2020, 0.5644]],\n",
      "\n",
      "        [[1.0000, 1.0000, 1.0000],\n",
      "         [1.0000, 1.0000, 1.0000]]])\n",
      "堆叠: \n",
      "tensor([[[0.7296, 0.0204, 0.0455],\n",
      "         [1.0000, 1.0000, 1.0000]],\n",
      "\n",
      "        [[0.3082, 0.2020, 0.5644],\n",
      "         [1.0000, 1.0000, 1.0000]]])\n"
     ]
    }
   ],
   "source": [
    "print(f\"堆叠: \\n{torch.stack((rand_tensor, ones_tensor))}\")\n",
    "print(f\"堆叠: \\n{torch.stack((rand_tensor, ones_tensor), dim=1)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a9e402e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "扩增维度: \n",
      "tensor([[[0.6596],\n",
      "         [0.6333],\n",
      "         [0.3032]],\n",
      "\n",
      "        [[0.4263],\n",
      "         [0.2771],\n",
      "         [0.5949]]])\n",
      "torch.Size([2, 3, 1])\n",
      "\n",
      "扩增维度: \n",
      "tensor([[[0.6596, 0.6333, 0.3032],\n",
      "         [0.4263, 0.2771, 0.5949]]])\n",
      "torch.Size([1, 2, 3])\n",
      "\n",
      "扩增维度: \n",
      "tensor([[[0.6596, 0.6333, 0.3032]],\n",
      "\n",
      "        [[0.4263, 0.2771, 0.5949]]])\n",
      "torch.Size([2, 1, 3])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"扩增维度: \\n{rand_tensor.unsqueeze(-1)}\\n{rand_tensor.unsqueeze(-1).shape}\\n\")\n",
    "print(f\"扩增维度: \\n{rand_tensor.unsqueeze(0)}\\n{rand_tensor.unsqueeze(0).shape}\\n\")\n",
    "print(f\"扩增维度: \\n{rand_tensor.unsqueeze(1)}\\n{rand_tensor.unsqueeze(1).shape}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f5d1abc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "展平: \n",
      "tensor([0.7296, 0.0204, 0.0455, 0.3082, 0.2020, 0.5644])\n",
      "torch.Size([6])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"展平: \\n{rand_tensor.flatten()}\\n{rand_tensor.flatten().shape}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f22dc5",
   "metadata": {},
   "source": [
    "变更数据类型与设备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d0be7821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.complex32"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.complex32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8061c4be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "浮点数,\n",
      " tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.]])\n",
      "\n",
      "布尔类,\n",
      " tensor([[True, True, True],\n",
      "        [True, True, True]])\n",
      "\n",
      "使用GPU,\n",
      " cuda:0\n",
      "\n",
      "使用CPU,\n",
      " cpu\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"浮点数,\\n {ones_tensor.to(torch.float32)}\\n\")  # 或 ones_tensor.float()\n",
    "print(f\"布尔类,\\n {ones_tensor.to(torch.bool)}\\n\")   # 或 ones_tensor.bool()\n",
    "ones_tensor_gpu = ones_tensor.to(\"cuda:0\")\n",
    "print(f\"使用GPU,\\n {ones_tensor_gpu.device}\\n\")\n",
    "ones_tensor_cpu = ones_tensor.to(\"cpu\")\n",
    "print(f\"使用CPU,\\n {ones_tensor_cpu.device}\\n\")  # 或 ones_tensor.cpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543bf4e3",
   "metadata": {},
   "source": [
    "## 自动微分"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "194a985f",
   "metadata": {},
   "source": [
    "指定可微变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "02762c56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.0000, 0.0833, 0.1667, 0.2500, 0.3333, 0.4167, 0.5000, 0.5833, 0.6667,\n",
      "        0.7500, 0.8333, 0.9167, 1.0000, 1.0833, 1.1667, 1.2500, 1.3333, 1.4167,\n",
      "        1.5000, 1.5833, 1.6667, 1.7500, 1.8333, 1.9167, 2.0000],\n",
      "       requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "x = torch.linspace(0., 2., steps=25, requires_grad=True)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "600ad04a",
   "metadata": {},
   "source": [
    "计算标量函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7be5b7ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = torch.rand_like(x)\n",
    "y = torch.sum(x * w)\n",
    "y.backward()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce7b897a",
   "metadata": {},
   "source": [
    "验证梯度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "55e52962",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(torch.equal(w, x.grad))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da0ba62",
   "metadata": {},
   "source": [
    "## 神经网络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03155fa2",
   "metadata": {},
   "source": [
    "### 层定义"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c5ce57",
   "metadata": {},
   "source": [
    "线性层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c8a173",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = 32\n",
    "output_dim = 256\n",
    "nn.Linear(input_dim, output_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11949d3",
   "metadata": {},
   "source": [
    "非线性层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31c5e5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'relu')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.linspace(-10., 10., steps=100)\n",
    "plt.plot(x, nn.ReLU()(x))\n",
    "plt.title('relu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e43c6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'leaky relu')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.linspace(-10., 10., steps=100)\n",
    "plt.plot(x, nn.LeakyReLU()(x))\n",
    "plt.title('leaky relu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dfe6909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'gelu')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.linspace(-10., 10., steps=100)\n",
    "plt.plot(x, nn.GELU()(x))\n",
    "plt.title('gelu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "eceecb99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.linspace(-10., 10., steps=100)\n",
    "plt.plot(x, nn.CELU()(x))\n",
    "plt.title('celu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1e2d427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'sigmoid')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.linspace(-10., 10., steps=100)\n",
    "plt.plot(x, nn.Sigmoid()(x))\n",
    "plt.title('sigmoid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc93d10",
   "metadata": {},
   "source": [
    "特殊层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87a5f6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.MaxPool2d   # 池化\n",
    "nn.Dropout     # 随机去除参数\n",
    "nn.Parameter   # 单一参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b624e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = nn.Parameter()\n",
    "torch.exp(-alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a23d28a",
   "metadata": {},
   "source": [
    "组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "351f69a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.Sequential(nn.Linear(28*28, 512),\n",
    "              nn.ReLU(),\n",
    "              nn.Linear(512, 512),\n",
    "              nn.ReLU(),\n",
    "              nn.Linear(512, 10),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71d88f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.ModuleList([nn.Linear(10, 10) for _ in range(10)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa657375",
   "metadata": {},
   "source": [
    "### 预定义网络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa528eb",
   "metadata": {},
   "source": [
    "卷积网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b97df576",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.Conv2d\n",
    "nn.Conv3d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee8d6c7",
   "metadata": {},
   "source": [
    "循环网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61ecb2f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.RNN\n",
    "nn.GRU\n",
    "nn.LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48aff3fe",
   "metadata": {},
   "source": [
    "注意力与Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fde9fefc",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.attention\n",
    "nn.Transformer\n",
    "nn.TransformerDecoder\n",
    "nn.TransformerEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c80b1be",
   "metadata": {},
   "source": [
    "### 完整网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "586e818b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Network(nn.Module):\n",
    "    \"\"\"\n",
    "    一个全连接神经网络\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        super(Network, self).__init__()\n",
    "        self.network = nn.Sequential(\n",
    "            nn.Linear(1, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.network(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705f872b",
   "metadata": {},
   "source": [
    "提取网络中的参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "043b9baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12ab2d76",
   "metadata": {},
   "source": [
    "## 数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f760faf",
   "metadata": {},
   "source": [
    "通常使用Dataset的子类，其定义必须包含：\n",
    "1. 初始化方法 __init__\n",
    "2. 长度 __len__\n",
    "3. 抽取一个元素 __getitem__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc853b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FunctionDataset(Dataset):\n",
    "    \"\"\"一个动态生成数据的Dataset\"\"\"\n",
    "    def __init__(self, func, x_range=(-10, 10), n_samples=10000):\n",
    "        self.func = func\n",
    "        self.x_range = x_range\n",
    "        self.n_samples = n_samples\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.n_samples\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        x_val = (self.x_range[1] - self.x_range[0]) * np.random.rand() + self.x_range[0]\n",
    "        y_val = self.func(x_val)\n",
    "        \n",
    "        x = torch.tensor([x_val], dtype=torch.float32)\n",
    "        y = torch.tensor([y_val], dtype=torch.float32)\n",
    "        \n",
    "        return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcde678",
   "metadata": {},
   "source": [
    "将dataset用DataLoader包装，方便抽取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "655b7acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 512\n",
    "X_RANGE = (-10, 10)\n",
    "NUM_SAMPLES_PER_EPOCH = 20000\n",
    "\n",
    "train_dataset = FunctionDataset(noisy_target_function, x_range=X_RANGE, n_samples=NUM_SAMPLES_PER_EPOCH)\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab78fae8",
   "metadata": {},
   "source": [
    "自动分割数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f28478b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset, val_dataset, test_dataset = random_split(train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2570e22",
   "metadata": {},
   "source": [
    "## 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ae288c",
   "metadata": {},
   "source": [
    "超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd3c243f",
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 1e-3  # 学习率\n",
    "EPOCHS = 200   # 循环次数\n",
    "\n",
    "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")  # 设备\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f40d63",
   "metadata": {},
   "source": [
    "初始化模型，损失函数，调参器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9de39049",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Network().to(device)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)   # 参数调节器"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1231f9e",
   "metadata": {},
   "source": [
    "常用损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0dbe08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.MSELoss  # 均方差\n",
    "nn.L1Loss   # 均绝对差\n",
    "nn.CrossEntropyLoss   # 交叉熵\n",
    "nn.KLDivLoss   # K-L散度\n",
    "nn.NLLLoss     # 负对数似然"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f54379b6",
   "metadata": {},
   "source": [
    "常用调参器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb5aa663",
   "metadata": {},
   "outputs": [],
   "source": [
    "optim.SGD    # 标准的随机梯度下降方法\n",
    "optim.Adam   # 最常用的带动量项的方法\n",
    "optim.LBFGS  # 效率最高的二阶下降方法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a2f809",
   "metadata": {},
   "source": [
    "训练过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c49dce58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 训练模型 ---\n",
    "print(\"Starting training with a more complex function...\")\n",
    "for epoch in range(EPOCHS):\n",
    "    epoch_loss = 0.0\n",
    "    for x_batch, y_batch in train_loader:\n",
    "        x_batch, y_batch = x_batch.to(device), y_batch.to(device)\n",
    "        \n",
    "        y_pred = model(x_batch)\n",
    "        loss = criterion(y_pred, y_batch)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "    \n",
    "    avg_loss = epoch_loss / len(train_loader)\n",
    "    # 每10个epoch打印一次损失，避免信息刷屏\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f\"Epoch {epoch+1}/{EPOCHS}, Average Loss: {avg_loss:.6f}\")\n",
    "\n",
    "print(\"Training finished!\")"
   ]
  }
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