RNN/RNN_pytorch.ipynb

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   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "(torch.Size([35, 32, 256]), torch.Size([1, 32, 256]))"
      ]
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     "execution_count": 7,
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   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "from tools import tool_pytorch_017 as ty\n",
    "\n",
    "batch_size, num_steps = 32, 35\n",
    "train_iter, vocab = ty.load_data_time_machine(batch_size, num_steps)\n",
    "\n",
    "# 定义模型\n",
    "num_hiddens = 256\n",
    "rnn_layer = nn.RNN(len(vocab), num_hiddens)\n",
    "\n",
    "# 使用张量来初始化隐状态，它的形状是（隐藏层数，批量大小，隐藏单元数）\n",
    "state = torch.zeros((1, batch_size, num_hiddens))\n",
    "state.shape\n",
    "\n",
    "# 用更新后的隐状态计算输出\n",
    "X = torch.rand(size=(num_steps, batch_size, len(vocab)))\n",
    "Y, state_new = rnn_layer(X, state)\n",
    "Y.shape, state_new.shape"
   ]
  },
  {
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   "execution_count": 8,
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   "source": [
    "#@save\n",
    "class RNNModel(nn.Module):\n",
    "    \"\"\"循环神经网络模型\"\"\"\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",
    "        # 如果RNN是双向的（之后将介绍），num_directions应该是2，否则应该是1\n",
    "        if not self.rnn.bidirectional:\n",
    "            self.num_directions = 1\n",
    "            self.linear = nn.Linear(self.num_hiddens, self.vocab_size)\n",
    "        else:\n",
    "            self.num_directions = 2\n",
    "            self.linear = nn.Linear(self.num_hiddens * 2, 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",
    "        # 全连接层首先将Y的形状改为(时间步数*批量大小,隐藏单元数)\n",
    "        # 它的输出形状是(时间步数*批量大小,词表大小)。\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))\n",
    "\n",
    "def predict(prefix, num_preds, net, vocab, device):  #@save\n",
    "    \"\"\"\n",
    "    预测函数，在prefix后面生成新字符\"\"\"\n",
    "    state = net.begin_state(batch_size=1, device=device)\n",
    "    outputs = [vocab[prefix[0]]]\n",
    "    get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape((1, 1))\n",
    "    for y in prefix[1:]:  # 预热期\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",
    "def grad_clipping(net, theta):  #@save\n",
    "    \"\"\"裁剪梯度,根据梯度范数来约束梯度\"\"\"\n",
    "    if isinstance(net, nn.Module):\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((p.grad ** 2)) for p in params))\n",
    "    if norm > theta:\n",
    "        for param in params:\n",
    "            param.grad[:] *= theta / norm\n",
    "\n",
    "#@save\n",
    "def train_epoch(net, train_iter, loss, updater, device, use_random_iter):\n",
    "    \"\"\"训练网络一个迭代周期）\"\"\"\n",
    "    state, timer = None, ty.Timer()\n",
    "    metric = ty.Accumulator(2)  # 训练损失之和,词元数量\n",
    "    for X, Y in train_iter:\n",
    "        if state is None or use_random_iter:\n",
    "            # 在第一次迭代或使用随机抽样时初始化state\n",
    "            state = net.begin_state(batch_size=X.shape[0], device=device)\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_()\n",
    "            else:\n",
    "                # state对于nn.LSTM或对于我们从零开始实现的模型是个张量\n",
    "                for s in state:\n",
    "                    s.detach_()\n",
    "        y = Y.T.reshape(-1)\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):\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",
    "            # 因为已经调用了mean函数\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()\n",
    "\n",
    "#@save\n",
    "def train(net, train_iter, vocab, lr, num_epochs, device,\n",
    "              use_random_iter=False):\n",
    "    \"\"\"训练模型\"\"\"\n",
    "    loss = nn.CrossEntropyLoss()\n",
    "    animator = ty.Animator(xlabel='epoch', ylabel='perplexity',\n",
    "                            legend=['train'], xlim=[10, num_epochs])\n",
    "    # 初始化\n",
    "    if isinstance(net, nn.Module):\n",
    "        updater = torch.optim.SGD(net.parameters(), lr)\n",
    "    else:\n",
    "        updater = lambda batch_size: ty.sgd(net.params, lr, batch_size)\n",
    "    predict = lambda prefix: predict(prefix, 50, net, vocab, device)\n",
    "    # 训练和预测\n",
    "    for epoch in range(num_epochs):\n",
    "        ppl, speed = train_epoch(\n",
    "            net, train_iter, loss, updater, device, use_random_iter)\n",
    "        if (epoch + 1) % 10 == 0:\n",
    "            animator.add(epoch + 1, [ppl])\n",
    "    print(f'困惑度 {ppl:.1f}, {speed:.1f} 词元/秒 {str(device)}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "困惑度 1.3, 146035.1 词元/秒 cpu\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'time traveller held in h'"
      ]
     },
     "execution_count": 9,
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   "source": [
    "device = ty.try_gpu()\n",
    "net = RNNModel(rnn_layer, vocab_size=len(vocab))\n",
    "net = net.to(device)\n",
    "\n",
    "num_epochs, lr = 500, 1\n",
    "train(net, train_iter, vocab, lr, num_epochs, device)\n",
    "\n",
    "predict('time traveller', 10, net, vocab, device)"
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