code/10-多层感知机/mlp-scratch.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# 多层感知机的从零开始实现\n",
    ":label:`sec_mlp_scratch`\n",
    "\n",
    "我们已经在 :numref:`sec_mlp`中描述了多层感知机（MLP），\n",
    "现在让我们尝试自己实现一个多层感知机。\n",
    "为了与之前softmax回归（ :numref:`sec_softmax_scratch` ）\n",
    "获得的结果进行比较，\n",
    "我们将继续使用Fashion-MNIST图像分类数据集\n",
    "（ :numref:`sec_fashion_mnist`）。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "origin_pos": 4,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 256\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)#调用下载函数下载数据集\n",
    "#此处的warning为pytorch内部问题，会在之后版本改进，无需担心"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## 初始化模型参数\n",
    "\n",
    "回想一下，Fashion-MNIST中的每个图像由\n",
    "$28 \\times 28 = 784$个灰度像素值组成。\n",
    "所有图像共分为10个类别。\n",
    "忽略像素之间的空间结构，\n",
    "我们可以将每个图像视为具有784个输入特征\n",
    "和10个类的简单分类数据集。\n",
    "首先，我们将[**实现一个具有单隐藏层的多层感知机，\n",
    "它包含256个隐藏单元**]。\n",
    "注意，我们可以将这两个变量都视为超参数。\n",
    "通常，我们选择2的若干次幂作为层的宽度。\n",
    "因为内存在硬件中的分配和寻址方式，这么做往往可以在计算上更高效。\n",
    "\n",
    "我们用几个张量来表示我们的参数。\n",
    "注意，对于每一层我们都要记录一个权重矩阵和一个偏置向量。\n",
    "跟以前一样，我们要为这些参数的损失的梯度分配内存。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "origin_pos": 7,
    "tab": [
     "pytorch"
    ]
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   "outputs": [],
   "source": [
    "num_inputs, num_outputs, num_hiddens = 784, 10, 256\n",
    "\n",
    "#初始化参数，W为正态分布随机变量*0.01，B为0\n",
    "W1 = nn.Parameter(torch.randn(num_inputs, num_hiddens, requires_grad=True) * 0.01)\n",
    "b1 = nn.Parameter(torch.zeros(num_hiddens, requires_grad=True))\n",
    "W2 = nn.Parameter(torch.randn(num_hiddens, num_outputs, requires_grad=True) * 0.01)\n",
    "b2 = nn.Parameter(torch.zeros(num_outputs, requires_grad=True))\n",
    "\n",
    "params = [W1, b1, W2, b2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 9
   },
   "source": [
    "## 激活函数\n",
    "\n",
    "为了确保我们对模型的细节了如指掌，\n",
    "我们将[**实现ReLU激活函数**]，\n",
    "而不是直接调用内置的`relu`函数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "origin_pos": 11,
    "tab": [
     "pytorch"
    ]
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   "outputs": [],
   "source": [
    "def relu(X):\n",
    "    a = torch.zeros_like(X)\n",
    "    return torch.max(X, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 13
   },
   "source": [
    "## 模型\n",
    "\n",
    "因为我们忽略了空间结构，\n",
    "所以我们使用`reshape`将每个二维图像转换为一个长度为`num_inputs`的向量。\n",
    "只需几行代码就可以(**实现我们的模型**)。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "origin_pos": 15,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def net(X):\n",
    "    X = X.reshape((-1, num_inputs))\n",
    "    H = relu(X@W1 + b1)  # 这里“@”代表矩阵乘法\n",
    "    return (H@W2 + b2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 17
   },
   "source": [
    "## 损失函数\n",
    "\n",
    "由于我们已经从零实现过softmax函数（ :numref:`sec_softmax_scratch`），\n",
    "因此在这里我们直接使用高级API中的内置函数来计算softmax和交叉熵损失。\n",
    "回想一下我们之前在 :numref:`subsec_softmax-implementation-revisited`中\n",
    "对这些复杂问题的讨论。\n",
    "我们鼓励感兴趣的读者查看损失函数的源代码，以加深对实现细节的了解。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "origin_pos": 19,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "loss = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 21
   },
   "source": [
    "## 训练\n",
    "\n",
    "幸运的是，[**多层感知机的训练过程与softmax回归的训练过程完全相同**]。\n",
    "可以直接调用`d2l`包的`train_ch3`函数（参见 :numref:`sec_softmax_scratch` ），\n",
    "将迭代周期数设置为10，并将学习率设置为0.1.\n"
   ]
  },
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   "execution_count": 7,
   "metadata": {
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   "source": [
    "num_epochs, lr = 10, 0.1\n",
    "updater = torch.optim.SGD(params, lr=lr)\n",
    "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)#调用训练函数进行训练"
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  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 25
   },
   "source": [
    "为了对学习到的模型进行评估，我们将[**在一些测试数据上应用这个模型**]。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "origin_pos": 26,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "d2l.predict_ch3(net, test_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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      ],
      "text/plain": [
       "<Figure size 648x108 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#如果无法显示图形，可以尝试添加下面这行：\n",
    "d2l.plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 27
   },
   "source": [
    "## 小结\n",
    "\n",
    "* 手动实现一个简单的多层感知机是很容易的。然而如果有大量的层，从零开始实现多层感知机会变得很麻烦（例如，要命名和记录模型的参数）。\n",
    "\n",
    "## 练习\n",
    "\n",
    "1. 在所有其他参数保持不变的情况下，更改超参数`num_hiddens`的值，并查看此超参数的变化对结果有何影响。确定此超参数的最佳值。\n",
    "1. 尝试添加更多的隐藏层，并查看它对结果有何影响。\n",
    "1. 改变学习速率会如何影响结果？保持模型架构和其他超参数（包括轮数）不变，学习率设置为多少会带来最好的结果？\n",
    "1. 通过对所有超参数（学习率、轮数、隐藏层数、每层的隐藏单元数）进行联合优化，可以得到的最佳结果是什么？\n",
    "1. 描述为什么涉及多个超参数更具挑战性。\n",
    "1. 如果要构建多个超参数的搜索方法，你能想到的最聪明的策略是什么？\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 29,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "[Discussions](https://discuss.d2l.ai/t/1804)\n"
   ]
  }
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