Codes/appendix-A/03_main-chapter-code/exercise-solutions.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 练习 A.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "class NeuralNetwork(torch.nn.Module):\n",
    "    def __init__(self, num_inputs, num_outputs):\n",
    "        super().__init__()\n",
    "\n",
    "        self.layers = torch.nn.Sequential(\n",
    "                \n",
    "            # 第一个隐藏层\n",
    "            torch.nn.Linear(num_inputs, 30),\n",
    "            torch.nn.ReLU(),\n",
    "\n",
    "            # 第二个隐藏层\n",
    "            torch.nn.Linear(30, 20),\n",
    "            torch.nn.ReLU(),\n",
    "\n",
    "            # 输出层\n",
    "            torch.nn.Linear(20, num_outputs),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        logits = self.layers(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of trainable model parameters: 752\n"
     ]
    }
   ],
   "source": [
    "model = NeuralNetwork(2, 2)\n",
    "\n",
    "num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(\"Total number of trainable model parameters:\", num_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 练习 A.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "qGgnamiyLJxp"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "# 创建随机向量\n",
    "a = torch.rand(100, 200)\n",
    "b = torch.rand(200, 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CvGvIeVkLzXE",
    "outputId": "44d027be-0787-4348-9c06-4e559d94d0e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63.8 µs ± 8.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# 使用 @ 符号进行矩阵相乘，并计算执行时间\n",
    "# %timeit 是 IPython 提供的魔术命令，用于多次执行代码以获取平均执行时间\n",
    "# 它会自动选择执行次数以确保结果的准确性\n",
    "%timeit a @ b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "OmRtZLa9L2ZG"
   },
   "outputs": [],
   "source": [
    "# 将 a 和 b 移动到 CUDA 设备上以利用 GPU 加速计算\n",
    "a, b = a.to(\"cuda\"), b.to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "duLEhXDPL6k0",
    "outputId": "3486471d-fd62-446f-9855-2d01f41fd101"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.8 µs ± 425 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit a @ b"
   ]
  }
 ],
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