250_循环神经网络RNN的实现.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. RNN的实现(不用框架)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from d2l import torch as d2l\n",
    "\n",
    "# 定义批量大小和时间步数\n",
    "batch_size, num_steps = 32, 35\n",
    "# 加载时间机器数据并创建词汇表\n",
    "train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0],\n",
       "        [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 独热编码\n",
    "# 打印词汇表的大小\n",
    "print(len(vocab))\n",
    "# 使用独热编码将 [0, 2] 表示的物体下标转换为独热向量，其中0表示第一个元素，2表示第3个元素\n",
    "F.one_hot(torch.tensor([0,2]),len(vocab)) # [0,2] 表示物体下标，0表示第一个元素，2表示第3个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 2, 28])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 小批量形状是(批量大小，时间步数)\n",
    "# 创建一个张量，形状为(2, 5)，表示批量大小为2，时间步数为5\n",
    "X = torch.arange(10).reshape((2,5)) \n",
    "# 对X的转置进行独热编码，其中28表示编码长度，返回独热编码后的形状\n",
    "F.one_hot(X.T, 28).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化循环神经网络模型的模型参数\n",
    "def get_params(vocab_size, num_hiddens, device):\n",
    "    # 设置输入和输出的维度为词汇表大小\n",
    "    num_inputs = num_outputs = vocab_size\n",
    "    \n",
    "    # 定义normal函数用于生成服从正态分布的随机张量，并乘以0.01进行缩放\n",
    "    def normal(shape):\n",
    "        return torch.randn(size=shape, device=device) * 0.01\n",
    "    \n",
    "    # 初始化模型参数\n",
    "    # 输入到隐藏层的权重矩阵，形状为(词汇表大小, 隐藏单元个数)\n",
    "    W_xh = normal((num_inputs, num_hiddens))\n",
    "    # 隐藏层到隐藏层的权重矩阵，形状为(隐藏单元个数, 隐藏单元个数)\n",
    "    W_hh = normal((num_hiddens, num_hiddens))\n",
    "    # 隐藏层的偏置向量，形状为(隐藏单元个数,)\n",
    "    b_h = torch.zeros(num_hiddens, device=device)\n",
    "    # 隐藏层到输出层的权重矩阵，形状为(隐藏单元个数, 词汇表大小)\n",
    "    W_hq = normal((num_hiddens, num_outputs))\n",
    "    # 输出层的偏置向量，形状为(词汇表大小,)\n",
    "    b_q = torch.zeros(num_outputs, device=device)\n",
    "    # 将所有参数放入列表中\n",
    "    params = [W_xh, W_hh, b_h, W_hq, b_q]\n",
    "    # 遍历所有参数\n",
    "    for param in params:\n",
    "        # 设置参数的requires_grad为True，用于梯度计算\n",
    "        param.requires_grad_(True)\n",
    "    # 返回模型的参数\n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一个init_rnn_state函数在初始化时返回隐藏状态\n",
    "def init_rnn_state(batch_size, num_hiddens, device):\n",
    "    # 返回一个包含隐藏状态的元组，元组中的唯一元素是一个形状为(批量大小, 隐藏单元个数)的全零张量   \n",
    "    return (torch.zeros((batch_size, num_hiddens), device=device),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下面的rnn函数定义了如何在一个时间步计算隐藏状态和输出\n",
    "def rnn(inputs, state, params):\n",
    "    # 从参数元组中解包获取输入到隐藏层的权重矩阵 W_xh，\n",
    "    # 隐藏层到隐藏层的权重矩阵 W_hh，\n",
    "    # 隐藏层的偏置向量 b_h，\n",
    "    # 隐藏层到输出层的权重矩阵 W_hq，\n",
    "    # 输出层的偏置向量 b_q\n",
    "    W_xh, W_hh, b_h, W_hq, b_q = params\n",
    "    # 从状态元组中解包获取隐藏状态 H\n",
    "    # 注意这里使用逗号是为了确保 H 为一个元组\n",
    "    H, = state\n",
    "    # 创建一个空列表用于存储输出\n",
    "    outputs = []\n",
    "    # 对于输入序列中的每个输入 X\n",
    "    # 输入序列通常是一个时间步的数据，可以是单个时间步的特征向量或者是嵌入向量\n",
    "    for X in inputs:\n",
    "        # 计算新的隐藏状态 H，使用双曲正切函数作为激活函数\n",
    "        # 根据当前输入 X、上一时间步的隐藏状态 H、以及权重矩阵和偏置向量来计算\n",
    "        H = torch.tanh(torch.mm(X, W_xh) + torch.mm(H, W_hh) + b_h)\n",
    "        # 计算输出 Y，通过隐藏状态 H 与权重矩阵 W_hq 相乘并加上偏置向量 b_q 得到\n",
    "        Y = torch.mm(H, W_hq) + b_q\n",
    "        # 将输出 Y 添加到输出列表中\n",
    "        outputs.append(Y)\n",
    "    # 将输出列表中的输出张量沿着行维度进行拼接，得到一个形状为 (时间步数 * 批量大小, 输出维度) 的张量\n",
    "    # 返回拼接后的输出张量和最后一个时间步的隐藏状态 H\n",
    "    return torch.cat(outputs, dim=0), (H,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个类来包装这些函数\n",
    "class RNNModelScratch:\n",
    "    # 初始化模型参数\n",
    "    def __init__(self, vocab_size, num_hiddens, device, get_params,\n",
    "                init_state, forward_fn):\n",
    "        # 保存词汇表大小和隐藏单元个数作为类的属性\n",
    "        self.vocab_size, self.num_hiddens = vocab_size, num_hiddens\n",
    "        # 调用 get_params 函数初始化模型的参数，并保存为类的属性\n",
    "        # 参数包括输入到隐藏层的权重矩阵、隐藏层到隐藏层的权重矩阵、隐藏层的偏置向量、隐藏层到输出层的权重矩阵、输出层的偏置向量\n",
    "        self.params = get_params(vocab_size, num_hiddens, device)\n",
    "        # 初始化隐藏状态的函数和前向传播函数\n",
    "        self.init_state, self.forward_fn = init_state, forward_fn\n",
    "        \n",
    "    def __call__(self, X, state):\n",
    "        # 将输入序列 X 进行独热编码，形状为 (时间步数, 批量大小, 词汇表大小)\n",
    "        # 并将数据类型转换为浮点型\n",
    "        X = F.one_hot(X.T, self.vocab_size).type(torch.float32)\n",
    "        # 调用前向传播函数进行模型计算，并返回输出\n",
    "        return self.forward_fn(X, state, self.params)\n",
    "    \n",
    "    def begin_state(self, batch_size, device):\n",
    "        # 返回初始化的隐藏状态，用于模型的初始时间步\n",
    "        return self.init_state(batch_size, self.num_hiddens, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([10, 28]), 1, torch.Size([2, 512]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查输出是否具有正确的形状\n",
    "# 设置隐藏单元个数为 512\n",
    "num_hiddens = 512\n",
    "# 创建一个 RNNModelScratch 的实例 net，指定词汇表大小、隐藏单元个数、设备、获取参数函数、初始化隐藏状态函数和前向传播函数\n",
    "net = RNNModelScratch(len(vocab), num_hiddens, d2l.try_gpu(), get_params,\n",
    "                     init_rnn_state, rnn)\n",
    "# 获取模型的初始隐藏状态，输入的批量大小为 X 的行数，设备使用与 X 相同的设备\n",
    "state = net.begin_state(X.shape[0], d2l.try_gpu())\n",
    "# 使用输入 X 和初始隐藏状态进行前向传播计算，得到输出张量 Y 和更新后的隐藏状态 new_state\n",
    "# 将输入和状态都移动到与 X 相同的设备上进行计算\n",
    "Y, new_state = net(X.to(d2l.try_gpu()),state)\n",
    "# 输出 Y 的形状，new_state 的长度（即元素个数）和 new_state 中第一个元素的形状\n",
    "Y.shape, len(new_state), new_state[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'time traveller uupwqaydrq'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 首先定义预测函数来生成用户提供的prefix之后的新字符\n",
    "def predict_ch8(prefix, num_preds, net, vocab, device):\n",
    "    \"\"\"在 'prefix' 后面生成新字符。\"\"\"\n",
    "    # 获取模型的初始隐藏状态，批量大小为 1，设备为指定的设备\n",
    "    state = net.begin_state(batch_size=1, device=device)\n",
    "    # 将 prefix 的第一个字符索引添加到输出列表中\n",
    "    outputs = [vocab[prefix[0]]]\n",
    "    # 定义一个函数 get_input，用于获取输入序列的张量表示\n",
    "    # 输入序列只包含一个字符，将该字符的索引转换为张量，并进行形状调整为 (1, 1)\n",
    "    get_input = lambda: torch.tensor([outputs[-1]],device=device).reshape(1,1)  \n",
    "    # 对于 prefix 中除第一个字符之外的每个字符 y\n",
    "    for y in prefix[1:]:\n",
    "        # 使用当前输入字符和隐藏状态进行前向传播计算，得到输出和更新后的隐藏状态\n",
    "        _, state = net(get_input(), state)\n",
    "        # 将字符 y 的索引添加到输出列表中\n",
    "        outputs.append(vocab[y])\n",
    "    # 生成指定数量的新字符\n",
    "    for _ in range(num_preds):\n",
    "        # 使用当前输入字符和隐藏状态进行前向传播计算，得到输出和更新后的隐藏状态\n",
    "        y, state = net(get_input(), state)\n",
    "        # 将输出张量中概率最大的字符索引添加到输出列表中\n",
    "        outputs.append(int(y.argmax(dim=1).reshape(1)))\n",
    "    # 将输出列表中的字符索引转换为对应的字符，并拼接成一个字符串返回\n",
    "    return ''.join([vocab.idx_to_token[i] for i in outputs])\n",
    "# 生成以 'time traveller ' 为前缀的 10 个新字符\n",
    "# 注意：由于模型尚未训练，这里的预测结果是随机初始化后的预测\n",
    "predict_ch8('time traveller ', 10, net, vocab, d2l.try_gpu()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 梯度裁剪$$\\mathbf{g} \\leftarrow \\min\\left(1, \\frac{\\theta}{\\|\\mathbf{g}\\|}\\right) \\mathbf{g}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grad_clipping(net, theta):\n",
    "    \"\"\"裁剪梯度。\"\"\"\n",
    "    # 如果 net 是 nn.Module 的实例（即使用 PyTorch 构建的模型）\n",
    "    if isinstance(net, nn.Module):\n",
    "        # 获取所有需要计算梯度的参数列表\n",
    "        params = [p for p in net.parameters() if p.requires_grad]\n",
    "    # 如果 net 是自定义的模型（例如上述的 RNNModelScratch）\n",
    "    else:\n",
    "        # 获取自定义模型的参数列表\n",
    "        params = net.params\n",
    "    # 计算参数梯度的范数，即所有参数梯度平方和的平方根\n",
    "    norm = torch.sqrt(sum(torch.sum((p.grad**2)) for p in params))\n",
    "    # 如果梯度范数超过指定阈值 theta\n",
    "    if norm > theta:\n",
    "        # 对于每个参数\n",
    "        for param in params:\n",
    "            # 将参数的梯度值裁剪至指定范围内，保持梯度范数不超过 theta\n",
    "            param.grad[:] *= theta / norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个函数来训练只有一个迭代周期的模型\n",
    "def train_epoch_ch8(net, train_iter, loss, updater, device, use_random_iter):\n",
    "    \"\"\"训练模型一个迭代周期\"\"\"\n",
    "    # 初始化隐藏状态和计时器\n",
    "    state, timer = None, d2l.Timer()\n",
    "    # 初始化度量指标的累加器，用于计算损失和样本数量\n",
    "    metric = d2l.Accumulator(2)\n",
    "    # 遍历训练迭代器中的每个批次数据\n",
    "    for X, Y in train_iter:\n",
    "        # 如果隐藏状态为空或使用随机迭代器\n",
    "        if state is None or use_random_iter:\n",
    "            # 初始化隐藏状态，批量大小为 X 的行数，设备为指定的设备\n",
    "            state = net.begin_state(batch_size=X.shape[0],device=device)\n",
    "        else:\n",
    "            # 如果 net 是 nn.Module 的实例且隐藏状态不是元组类型\n",
    "            if isinstance(net, nn.Module) and not isinstance(state, tuple):\n",
    "                # 分离隐藏状态的计算图\n",
    "                state.detach_()\n",
    "            else:\n",
    "                # 对于隐藏状态中的每个元素\n",
    "                for s in state:\n",
    "                    # 分离隐藏状态的计算图，用于减少内存占用和加速计算\n",
    "                    s.detach_()\n",
    "        # 将目标序列 Y 转置并展平为一维张量\n",
    "        y = Y.T.reshape(-1)\n",
    "        # 将输入序列和目标序列移动到指定的设备上\n",
    "        X, y = X.to(device), y.to(device)\n",
    "        # 使用输入序列和隐藏状态进行前向传播计算，得到预测值和更新后的隐藏状态\n",
    "        y_hat, state = net(X, state)\n",
    "        # 计算预测值与目标值之间的损失\n",
    "        l = loss(y_hat, y.long()).mean()\n",
    "        # 如果使用 PyTorch 内置的优化器\n",
    "        if isinstance(updater, torch.optim.Optimizer):\n",
    "            # 清空优化器中的梯度\n",
    "            updater.zero_grad()\n",
    "            # 反向传播计算梯度\n",
    "            l.backward()\n",
    "            # 裁剪梯度\n",
    "            grad_clipping(net,1)\n",
    "            # 执行一步参数更新\n",
    "            updater.step()\n",
    "        else:\n",
    "            # 反向传播计算梯度\n",
    "            l.backward()\n",
    "            # 裁剪梯度\n",
    "            grad_clipping(net,1)\n",
    "            # 执行自定义的参数更新函数\n",
    "            updater(batch_size=1)\n",
    "        # 累加损失和样本数量\n",
    "        metric.add(l * y.numel(), y.numel())\n",
    "    # 计算平均损失和每秒处理的样本数，返回平均损失的指数形式（以 e 为底）和每秒样本处理速度\n",
    "    return math.exp(metric[0]/metric[1]), metric[1]/timer.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练函数支持从零开始或使用高级API实现的循环神经网络模型\n",
    "def train_ch8(net, train_iter, vocab, lr, num_epochs, device, use_random_iter=False):\n",
    "    \"\"\"训练模型\"\"\"\n",
    "    # 定义损失函数为交叉熵损失\n",
    "    loss = nn.CrossEntropyLoss()\n",
    "    # 创建动画对象，用于可视化训练过程的损失变化\n",
    "    animator = d2l.Animator(xlabel='epoch', ylabel='perplexity', legend=['train'],xlim=[10,num_epochs])    \n",
    "    # 如果模型是 nn.Module 的实例\n",
    "    if isinstance(net, nn.Module):\n",
    "        # 使用 PyTorch 的优化器 SGD 进行参数更新\n",
    "        updater = torch.optim.SGD(net.parameters(), lr)\n",
    "    else:\n",
    "        # # 否则，使用自定义的梯度下降函数进行参数更新\n",
    "        updater = lambda batch_size: d2l.sgd(net.params, lr, batch_size)\n",
    "    # 定义一个预测函数，用于生成给定前缀之后的新字符序列\n",
    "    predict = lambda prefix: predict_ch8(prefix, 50, net, vocab, device)\n",
    "    # 遍历每个迭代周期\n",
    "    for epoch in range(num_epochs):\n",
    "        # 训练一个迭代周期，并返回困惑度和每秒样本处理速度\n",
    "        ppl, speed = train_epoch_ch8(net, train_iter, loss, updater, device, use_random_iter)   \n",
    "        # 每隔 10 个迭代周期生成\n",
    "        if (epoch + 1) % 10 == 0:\n",
    "            # 打印以 'time traveller' 为前缀的新字符序列\n",
    "            print(predict('time traveller'))\n",
    "            # 将当前迭代周期的困惑度添加到动画中进行可视化\n",
    "            animator.add(epoch + 1, [ppl])\n",
    "    # 打印最终的困惑度和每秒样本处理速度\n",
    "    print(f'困惑度 {ppl:.1f}, {speed:.1f} 标记/秒 {str(device)}')\n",
    "    # 生成并打印以 'time traveller' 为前缀的新字符序列\n",
    "    print(predict('time traveller'))\n",
    "    # 生成并打印以 'traveller' 为前缀的新字符序列\n",
    "    print(predict('traveller'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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      "困惑度 1.0, 74455.3 标记/秒 cuda:0\n",
      "time traveller for so it will be convenient to speak of himwas e\n",
      "traveller with a slight accession ofcheerfulness really thi\n"
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   "source": [
    "# 现在我们可以训练循环神经网络模型\n",
    "# 设置迭代周期数和学习率\n",
    "num_epochs, lr = 500, 1\n",
    "# 调用训练函数进行模型训练，使用训练数据迭代器、词汇表、学习率、迭代周期数和设备信息作为输入\n",
    "train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "困惑度 1.3, 69106.4 标记/秒 cuda:0\n",
      "time travellerit s against reason said filbywhat thatlly i so di\n",
      "travellerit s against reason said filbywhat thatlly i so di\n"
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      "text/plain": [
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     "metadata": {
      "needs_background": "light"
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   ],
   "source": [
    "# 最后，让我们检查一下使用随即抽样方法的结果\n",
    "# 调用训练函数进行模型训练，使用训练数据迭代器、词汇表、学习率、迭代周期数、设备信息和随机抽样标志位作为输入\n",
    "# 设置 use_random_iter 参数为 True，表示使用随机抽样方法进行训练\n",
    "train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu(), use_random_iter=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. RNN的实现(用框架)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入 PyTorch 库\n",
    "import torch\n",
    "# 导入 nn 模块，用于定义神经网络模型的基类\n",
    "from torch import nn\n",
    "# 导入 functional 模块，用于定义神经网络模型中的激活函数等功能\n",
    "from torch.nn import functional as F\n",
    "# 导入 d2l.torch 模块，包含了与深度学习相关的工具函数和类\n",
    "from d2l import torch as d2l\n",
    "\n",
    "# 设置批量大小和时间步数\n",
    "batch_size, num_steps = 32, 35\n",
    "# 调用 load_data_time_machine 函数加载时间机器数据集，返回训练数据迭代器和词汇表\n",
    "train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义模型\n",
    "# 设置隐藏单元的数量为 256\n",
    "num_hiddens = 256\n",
    "# 使用 nn.RNN 类定义一个循环神经网络层\n",
    "# 输入大小为词汇表的大小，隐藏单元数量为 num_hiddens\n",
    "# 将该循环神经网络层赋值给 rnn_layer\n",
    "rnn_layer = nn.RNN(len(vocab), num_hiddens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 32, 256])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用张量来初始化隐藏状态\n",
    "# 创建一个形状为 (1, batch_size, num_hiddens) 的张量，用于初始化隐藏状态\n",
    "# 全部元素初始化为 0\n",
    "# 将该张量赋值给变量 state\n",
    "state = torch.zeros((1, batch_size, num_hiddens))\n",
    "# 打印隐藏状态张量的形状\n",
    "state.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([35, 32, 256]), torch.Size([1, 32, 256]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过一个隐藏状态和一个输入，我们可以用更新后的隐藏状态计算输出\n",
    "# 创建一个形状为 (num_steps, batch_size, len(vocab)) 的随机张量 X\n",
    "# 用于表示输入的序列，每个时间步的输入为一个词汇表大小的独热编码向量\n",
    "X = torch.rand(size=(num_steps, batch_size, len(vocab)))\n",
    "# 将输入 X 和初始隐藏状态 state 作为输入传递给循环神经网络层 rnn_layer 进行前向计算\n",
    "# 返回输出张量 Y 和更新后的隐藏状态 state_new\n",
    "Y, state_new = rnn_layer(X, state)\n",
    "# 打印输出张量 Y 和更新后的隐藏状态 state_new 的形状\n",
    "Y.shape, state_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 我们为一个完整的循环神经网络模型定义一个RNNModel类\n",
    "class RNNModel(nn.Module):\n",
    "    \"\"\"循环神经网络模型\"\"\"\n",
    "    # 初始化函数\n",
    "    def __init__(self, rnn_layer, vocab_size, **kwargs):\n",
    "        # 调用父类的构造函数，初始化继承的属性\n",
    "        super(RNNModel, self).__init__(**kwargs)\n",
    "        # 循环神经网络层\n",
    "        self.rnn = rnn_layer\n",
    "        # 词汇表大小\n",
    "        self.vocab_size = vocab_size\n",
    "        # 隐藏状态的大小\n",
    "        self.num_hiddens = self.rnn.hidden_size\n",
    "        # 如果循环神经网络不是双向的\n",
    "        if not self.rnn.bidirectional:\n",
    "            # 方向数量为1\n",
    "            self.num_directions = 1\n",
    "            # 线性层的输入大小为隐藏状态大小，输出大小为词汇表大小\n",
    "            self.linear = nn.Linear(self.num_hiddens, self.vocab_size)\n",
    "        # 如果循环神经网络是双向的\n",
    "        else:\n",
    "            # 方向数量为2\n",
    "            self.num_directions = 2\n",
    "            # 线性层的输入大小为隐藏状态大小的两倍，输出大小为词汇表大小\n",
    "            self.linear = nn.Linear(self.num_hiddens * 2, self.vocab_size)  \n",
    "            \n",
    "    # 前项传播函数\n",
    "    def forward(self, inputs, state):\n",
    "        # 将输入的索引序列转换为独热编码张量 X\n",
    "        X = F.one_hot(inputs.T.long(), self.vocab_size)\n",
    "        # 将 X 转换为 float32 类型\n",
    "        X = X.to(torch.float32)\n",
    "        # 使用循环神经网络层 rnn 进行前向计算，返回输出张量 Y 和更新后的隐藏状态 state\n",
    "        Y, state = self.rnn(X, state)\n",
    "        # 将输出张量 Y 展平并通过线性层 linear 进行变换得到最终的输出\n",
    "        output = self.linear(Y.reshape((-1, Y.shape[-1])))\n",
    "        # 返回最终输出和更新后的隐藏状态\n",
    "        return output, state\n",
    "\n",
    "    # 创建循环神经网络的初始隐藏状态\n",
    "    def begin_state(self, device, batch_size=1):\n",
    "        # 如果循环神经网络不是LSTM类型\n",
    "        if not isinstance(self.rnn, nn.LSTM):\n",
    "            # 创建全零的隐藏状态张量\n",
    "            return torch.zeros((self.num_directions * self.rnn.num_layers,\n",
    "                                batch_size, self.num_hiddens),\n",
    "                               device=device)\n",
    "        # 如果循环神经网络是LSTM类型\n",
    "        else:\n",
    "            # 创建全零的隐藏状态张量和记忆单元张量\n",
    "            # 第一个张量是全零的隐藏状态张量，第二个张量是全零的记忆单元张量\n",
    "            return (torch.zeros((self.num_directions * self.rnn.num_layers,\n",
    "                                 batch_size, self.num_hiddens),\n",
    "                                device=device), \n",
    "                    torch.zeros((self.num_directions * self.rnn.num_layers,\n",
    "                                 batch_size, self.num_hiddens), \n",
    "                                device=device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'time travellerrrrrrrrrrr'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用一个具有随即权重的模型进行预测\n",
    "# 尝试使用GPU设备，如果不可用则使用CPU\n",
    "device = d2l.try_gpu()\n",
    "# 创建RNN模型实例\n",
    "net = RNNModel(rnn_layer, vocab_size=len(vocab))\n",
    "# 将模型移动到指定设备上\n",
    "net = net.to(device)\n",
    "# 对模型进行预测，生成文本\n",
    "d2l.predict_ch8('time traveller', 10, net, vocab, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "perplexity 1.3, 270684.1 tokens/sec on cuda:0\n",
      "time travelleryon oug heally a chifily exper and have at atw yo \n",
      "travelleryon o have e tern whis vewy cand scascolleredit to\n"
     ]
    },
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    "# 使用高级API训练模型\n",
    "# 设置训练的迭代周期数和学习率\n",
    "num_epochs, lr = 500, 1\n",
    "# 使用高级API训练模型，传入模型、训练数据迭代器、词汇表、学习率和迭代周期数进行训练\n",
    "d2l.train_ch8(net, train_iter, vocab, lr, num_epochs, device)"
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