code/55-RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 一、RNN从零开始实现"
   ]
  },
  {
   "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",
    "batch_size, num_steps = 32, 35\n",
    "train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "F.one_hot(torch.tensor([0, 2]), len(vocab)) #将[0, 2]展开为长度为len(vocab)大小的独热向量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 小批量数据形状是 (批量大小, 时间步数)"
   ]
  },
  {
   "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": [
    "X = torch.arange(10).reshape((2, 5)) # (batch_size, n_step)\n",
    "F.one_hot(X.T, 28).shape # (n_step, batch_size, n_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 初始化循环神经网络模型的模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_params(vocab_size, num_hiddens, device):\n",
    "    num_inputs = num_outputs = vocab_size\n",
    "    \n",
    "    def normal(shape):\n",
    "        return torch.randn(size=shape, device=device) * 0.01\n",
    "    \n",
    "    #隐藏层参数\n",
    "    W_xh = normal((num_inputs, num_hiddens))\n",
    "    W_hh = normal((num_hiddens, num_hiddens))\n",
    "    b_h = torch.zeros(num_hiddens, device=device)\n",
    "    #输出参数\n",
    "    W_hq = normal((num_hiddens, num_outputs))\n",
    "    b_q = torch.zeros(num_outputs, device=device)\n",
    "    #附加梯度\n",
    "    params = [W_xh, W_hh, b_h, W_hq, b_q]\n",
    "    for param in params:\n",
    "        param.requires_grad_(True)\n",
    "    return params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### init_rnn_state函数：在初始化时返回隐藏状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_rnn_state(batch_size, num_hiddens, device): #返回初始隐层状态\n",
    "    return (torch.zeros((batch_size, num_hiddens), device=device), )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rnn函数：定义**一个时间步内**计算隐藏状态和输出\n",
    "更新隐藏状态: $$h_t = \\phi(W_{hh}h_{t-1}+W_{hx}x_{t-1}+b_{h})$$\n",
    "输出: $$o_{t}=\\phi(W_{ho}h_{t}+b_{o})$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rnn(inputs, state, params):\n",
    "    W_xh, W_hh, b_h, W_hq, b_q = params\n",
    "    H, = state\n",
    "    outputs = [] #n_step个大小为(batch_size, n_outputs)的torch张量列表\n",
    "    # inputs: (n_step, batch_size, n_features)\n",
    "    for X in inputs: # 按时序遍历\n",
    "        H = torch.tanh(torch.mm(X, W_xh) + torch.mm(H, W_hh) + b_h) # (batch_size, n_hiddens)\n",
    "        Y = torch.mm(H, W_hq) + b_q # (batch_size, n_outputs)\n",
    "        outputs.append(Y) \n",
    "    return torch.cat(outputs, dim=0), (H,) #cat后维数(n_step * batch_size, n_outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 创建一个类来包装这些函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNModelScratch:\n",
    "    \"\"\"从零开始实现的循环神经网络模型\"\"\"\n",
    "    def __init__(self, vocab_size, num_hiddens, device, get_params,\n",
    "                 init_state, forward_fn):\n",
    "        self.vocab_size, self.num_hiddens = vocab_size, num_hiddens\n",
    "        self.params = get_params(vocab_size, num_hiddens, device) #获得模型初始参数\n",
    "        self.init_state, self.forward_fn = init_state, forward_fn #隐层初始函数, 前馈函数 \n",
    "        #注意前馈函数可以换成gru, lstm等\n",
    "        \n",
    "    def __call__(self, X, state):\n",
    "        #输入X: (batch_size, n_step)\n",
    "        #转置＋onehot后 X:(n_step, batch_size, n_features)\n",
    "        X = F.one_hot(X.T, self.vocab_size).type(torch.float32)\n",
    "        return self.forward_fn(X, state, self.params)\n",
    "    \n",
    "    def begin_state(self, batch_size, device):\n",
    "        return self.init_state(batch_size, self.num_hiddens, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 检查输出是否具有正确的形状"
   ]
  },
  {
   "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": [
    "num_hiddens = 512\n",
    "net = RNNModelScratch(len(vocab), num_hiddens, d2l.try_gpu(), \n",
    "                      get_params, init_rnn_state, rnn)\n",
    "# X: (2, 5)  对应(batch_size, n_step)\n",
    "state = net.begin_state(X.shape[0], d2l.try_gpu())\n",
    "Y, new_state = net(X.to(d2l.try_gpu()), state)\n",
    "#Y: (batch_size * n_step, n_outputs) \n",
    "#new_state中一个torch张量(最后一个时间步的隐层)\n",
    "#new_state[0].shape: (batch_size, n_hiddens)\n",
    "Y.shape, len(new_state), new_state[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 首先定义预测函数来生成prefix之后的新字符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'time travellerrrrrrrrrrr'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict_ch8(prefix, num_preds, net, vocab, device):\n",
    "    \"\"\"在`prefix`后面生成新字符\"\"\"\n",
    "    #生成初始隐藏状态\n",
    "    state = net.begin_state(batch_size=1, device=device) \n",
    "    outputs = [vocab[prefix[0]]] #第一个word的整型下标\n",
    "    #将最近预测的词做成tensor, batch_size=1, n_step=1\n",
    "    get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape((1, 1))\n",
    "    for y in prefix[1:]: # 预热操作, 保存真值\n",
    "        _, state = net(get_input(), state)\n",
    "        outputs.append(vocab[y])\n",
    "    for _ in range(num_preds): # 预测num_preds步\n",
    "        y, state = net(get_input(), state)\n",
    "        outputs.append(int(y.argmax(dim=1).reshape(1)))\n",
    "    return ''.join([vocab.idx_to_token[i] for i in outputs])\n",
    "\n",
    "predict_ch8('time traveller', 10, net, vocab, d2l.try_gpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 梯度裁剪\n",
    "$$\\mathbf{g}\\leftarrow min(1, \\frac{\\theta}{\\parallel \\mathbf{g} \\parallel}) \\mathbf{g}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grad_clipping(net, theta):\n",
    "    \"\"\"裁剪梯度\"\"\"\n",
    "    if isinstance(net, nn.Module):#如果使用nn.Module来实现\n",
    "        params = [p for p in net.parameters() if p.requires_grad]\n",
    "    else:\n",
    "        params = net.params\n",
    "    norm = torch.sqrt(sum(torch.sum(\n",
    "        (p.grad**2)) for p in params))\n",
    "    if norm > theta:\n",
    "        for param in params:\n",
    "            param.grad[:] *= theta / norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 查看train_iter数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 35]) torch.Size([32, 35])\n",
      "tensor([[ 1,  3,  5,  ...,  2,  1, 15],\n",
      "        [ 4,  6, 11,  ...,  5, 10,  8],\n",
      "        [ 3,  1,  4,  ...,  2,  8,  8],\n",
      "        ...,\n",
      "        [15,  7,  6,  ..., 21, 14,  3],\n",
      "        [10, 19,  8,  ..., 14,  8,  3],\n",
      "        [ 1, 13,  2,  ..., 10,  1,  4]])\n",
      "tensor([[ 3,  5, 13,  ...,  1, 15,  7],\n",
      "        [ 6, 11, 20,  ..., 10,  8,  1],\n",
      "        [ 1,  4,  6,  ...,  8,  8,  1],\n",
      "        ...,\n",
      "        [ 7,  6, 26,  ..., 14,  3, 21],\n",
      "        [19,  8,  3,  ...,  8,  3,  1],\n",
      "        [13,  2, 15,  ...,  1,  4,  6]])\n",
      " time traveller for so it will be c\n",
      "time traveller for so it will be co\n",
      "andpassed in our glasses our chairs\n",
      "ndpassed in our glasses our chairs \n",
      "\n",
      "onvenient to speak of himwas expoun\n",
      "nvenient to speak of himwas expound\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for X, Y in train_iter:\n",
    "    if count == 0:#第0个batch\n",
    "        print(X.shape, Y.shape)\n",
    "        print(X) # (batch_size(=32), n_step(=35))\n",
    "        print(Y) # (batch_size, n_step)\n",
    "        print(''.join([vocab.idx_to_token[i] for i in X[0]])) #打印第0个样本对应句子\n",
    "        print(''.join([vocab.idx_to_token[i] for i in Y[0]])) #打印第0个样本真值\n",
    "        print(''.join([vocab.idx_to_token[i] for i in X[1]])) #打印第1个样本对应句子\n",
    "        print(''.join([vocab.idx_to_token[i] for i in Y[1]])) #打印第1个样本真值\n",
    "        print()\n",
    "    if count == 1:#第1个batch, 内容和第0个batch上下承接(有时序关系)\n",
    "        print(''.join([vocab.idx_to_token[i] for i in X[0]]))\n",
    "        print(''.join([vocab.idx_to_token[i] for i in Y[0]]))\n",
    "    count += 1\n",
    "print(count) #打印batch数量=8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 定义一个函数在一个迭代周期内训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch_ch8(net, train_iter, loss, updater, device,\n",
    "                   use_random_iter):\n",
    "    \"\"\"训练模型一个迭代周期（定义见第8章）\"\"\"\n",
    "    state, timer = None, d2l.Timer()\n",
    "    metric = d2l.Accumulator(2)\n",
    "    for X, Y in train_iter:\n",
    "        if state is None or use_random_iter:#为第一个batch 或者 batch之间时序上不连续\n",
    "            state = net.begin_state(batch_size=X.shape[0], device=device) #初始化state\n",
    "        else:\n",
    "            if isinstance(net, nn.Module) and not isinstance(state, tuple):\n",
    "                # state对于nn.GRU是个张量\n",
    "                state.detach_() # 对之前的部分取消梯度反向传播计算\n",
    "            else:\n",
    "                # state对于nn.LSTM或者对于我们从零开始实现的模型是个元组(张量构成)\n",
    "                for s in state:\n",
    "                    s.detach_()\n",
    "        y = Y.T.reshape(-1) #reshape真值, 将n_step放在第一维之后拉成一维向量\n",
    "        X, y = X.to(device), y.to(device)\n",
    "        y_hat,state = net(X, state)\n",
    "        l = loss(y_hat, y.long()).mean()\n",
    "        if isinstance(updater, torch.optim.Optimizer):#调用torch优化函数实现\n",
    "            updater.zero_grad()\n",
    "            l.backward()\n",
    "            grad_clipping(net, 1)\n",
    "            updater.step()\n",
    "        else:\n",
    "            l.backward()\n",
    "            grad_clipping(net, 1)\n",
    "            updater(batch_size=1)\n",
    "        metric.add(l * y.numel(), y.numel())\n",
    "    return math.exp(metric[0] / metric[1]), metric[1] / timer.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 循环神经网络模型的训练函数既支持从零开始实现，也可以使用高级API实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_ch8(net, train_iter, vocab, lr, num_epochs, device, use_random_iter=False):\n",
    "    \"\"\"训练模型（定义见第8章）\"\"\"\n",
    "    loss = nn.CrossEntropyLoss()\n",
    "    animator = d2l.Animator(xlabel='epoch', ylabel='perplexity',\n",
    "                           legend=['train'], xlim=[10,num_epochs])\n",
    "    #初始化优化器\n",
    "    if isinstance(net, nn.Module):\n",
    "        updater = torch.optim.SGD(net.parameters(), lr)\n",
    "    else:\n",
    "        updater = lambda batch_size: d2l.sgd(net.params, lr, batch_size)\n",
    "    predict = lambda prefix: predict_ch8(prefix, 50, net, vocab, device)\n",
    "    #训练和预测\n",
    "    for epoch in range(num_epochs):\n",
    "        ppl, speed = train_epoch_ch8(\n",
    "            net, train_iter, loss, updater, device, use_random_iter)\n",
    "        if (epoch + 1) % 10 == 0:\n",
    "            print(predict('time traveller'))\n",
    "            animator.add(epoch+1, [ppl])\n",
    "    print(f'困惑度 {ppl:.1f}, {speed:.1f} 词元/秒 {str(device)}')\n",
    "    print(predict('time traveller'))\n",
    "    print(predict('traveller'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 训练循环神经网络模型(按序迭代batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "困惑度 1.0, 46320.2 词元/秒 cuda:0\n",
      "time travelleryou can show black is white by argument said filby\n",
      "travelleryou can show black is white by argument said filby\n"
     ]
    },
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   "source": [
    "num_epochs, lr = 500, 1\n",
    "train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu())"
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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "### 训练循环神经网络模型(随机迭代batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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     "name": "stdout",
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      "困惑度 1.3, 36524.6 词元/秒 cuda:0\n",
      "time traveller held in his hand was a glitteringmetallic framewo\n",
      "travellerit s against reason said filbycan a cube that does\n"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = RNNModelScratch(len(vocab), num_hiddens, d2l.try_gpu(), get_params,\n",
    "                      init_rnn_state, rnn)\n",
    "train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu(),\n",
    "          use_random_iter=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**从零开始实现**上述循环神经网络模型， 虽然有指导意义，但是并不方便。 在下一节中，我们将学习如何改进循环神经网络模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 二、循环神经网络的简洁实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from d2l import torch as d2l\n",
    "\n",
    "batch_size, num_steps = 32, 35\n",
    "train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps) #加载数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 定义模型\n",
    "nn.RNN(input_size, hidden_size, num_layers=1, nonlinearity=tanh, bias=True, batch_first=False, dropout=0, bidirectional=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_hiddens = 256\n",
    "rnn_layer = nn.RNN(len(vocab), num_hiddens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 使用张量来初始化隐藏状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 32, 256])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = torch.zeros((1, batch_size, num_hiddens))\n",
    "state.shape #(D * num_layers(=1), batch_size, num_hiddens) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 通过一个隐藏状态和一个输入，我们就可以用更新后的隐藏状态计算输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([35, 32, 256]), torch.Size([1, 32, 256]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.rand(size=(num_steps, batch_size, len(vocab)))# (n_step, batch_size, num_inputs)\n",
    "Y, state_new = rnn_layer(X, state)\n",
    "Y.shape, state_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 定义RNNModel类：完整的循环神经网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNModel(nn.Module):\n",
    "    def __init__(self, rnn_layer, vocab_size, **kwargs):\n",
    "        super(RNNModel, self).__init__(**kwargs)\n",
    "        self.rnn = rnn_layer\n",
    "        self.vocab_size = vocab_size\n",
    "        self.num_hiddens = self.rnn.hidden_size\n",
    "        if not self.rnn.bidirectional: #如果是双向\n",
    "            self.num_directions = 1\n",
    "            self.linear = nn.Linear(self.num_hiddens, self.vocab_size) #线性层 /输出层\n",
    "        else:\n",
    "            self.num_directions = 2\n",
    "            self.linear = nn.Linear(self.num_hiddens, self.vocab_size)\n",
    "    \n",
    "    def forward(self, inputs, state):\n",
    "        X = F.one_hot(inputs.T.long(), self.vocab_size)\n",
    "        X = X.to(torch.float32)\n",
    "        Y, state = self.rnn(X, state)\n",
    "        output = self.linear(Y.reshape((-1, Y.shape[-1])))\n",
    "        return output, state\n",
    "    \n",
    "    def begin_state(self, device, batch_size=1):\n",
    "        if not isinstance(self.rnn, nn.LSTM):\n",
    "            # nn.GRU以张量作为隐状态\n",
    "            return torch.zeros((self.num_directions * self.rnn.num_layers, \n",
    "                                batch_size, self.num_hiddens),\n",
    "                                device = device)\n",
    "        else:\n",
    "            # nn.LSTM以元组作为隐状态\n",
    "            return (torch.zeros((\n",
    "                self.num_directions * self.rnn.num_layers,\n",
    "                batch_size, self.num_hiddens), device=device),\n",
    "                     torch.zeros((\n",
    "                         self.num_directions * self.rnn.num_layers,\n",
    "                         batch_size, self.num_hiddens), device=device))#(h_n, c_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 训练与预测\n",
    "在训练模型之前，让我们基于一个具有随机权重的模型进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'time travellerida<unk>nda<unk>nd'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = d2l.try_gpu()\n",
    "net = RNNModel(rnn_layer, vocab_size=len(vocab))\n",
    "net = net.to(device)\n",
    "d2l.predict_ch8('time traveller', 10, net, vocab, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "perplexity 1.3, 152273.6 tokens/sec on cuda:0\n",
      "time traveller coud and inn weridit so mimens of the pramithtred\n",
      "traveller his fictses tor hime hal very is f enghas ow llow\n"
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    "与上一节相比，由于深度学习框架的高级API对代码进行了更多的优化， 该模型在较短的时间内达到了较低的困惑度。"
   ]
  },
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    "### 总结\n",
    "- **深度学习框架**的高级API提供了循环神经网络层的实现。\n",
    "\n",
    "- 高级API的循环神经网络层返回一个输出和一个更新后的隐状态，我们**还需要**计算整个模型的**输出层**。\n",
    "\n",
    "- 相比从零开始实现的循环神经网络，使用**高级API**实现可以**加速训练**。"
   ]
  }
 ],
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