35-循环神经网络RNN-简洁版实现-自然语言处理-pytorch.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "id": "a71e7f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "import dltools\n",
    "\n",
    "batch_size, num_steps = 32, 35\n",
    "train_iter, vocab = dltools.load_data_time_machine(batch_size, num_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ade239e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造一个具有256个隐藏单元的单隐藏层的循环神经网络层 rnn_layer。\n",
    "num_hiddens = 256\n",
    "rnn_layer = nn.RNN(len(vocab), num_hiddens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8645738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 32, 256])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化隐藏状态\n",
    "state = torch.zeros((1, batch_size, num_hiddens))\n",
    "state.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2aadc309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([35, 32, 256]), torch.Size([1, 32, 256]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 需要强调的是，rnn_layer的“输出”（Y）不涉及输出层的计算\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35dbc250",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为一个完整的循环神经网络模型定义了一个RNNModel类\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",
    "    # 初始化隐藏状态\n",
    "    def begin_state(self, device, batch_size=1):\n",
    "        return  torch.zeros((self.num_directions * self.rnn.num_layers,\n",
    "                             batch_size, self.num_hiddens),\n",
    "                            device=device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d479cfd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'time travellerxkkkkkkkkk'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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。\n",
    "device = dltools.try_gpu()\n",
    "net = RNNModel(rnn_layer, vocab_size=len(vocab))\n",
    "net = net.to(device)\n",
    "dltools.predict_ch8('time traveller', 10, net, vocab, device)"
   ]
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     "text": [
      "perplexity 1.3, 34863.3 tokens/sec on cpu\n",
      "time traveller after the pause the thing to thethint you cabnot \n",
      "travelleryou can s of br cone thent in but you and the isth\n"
     ]
    },
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   "source": [
    "# 模型训练阶段\n",
    "num_epochs, lr = 500, 1\n",
    "dltools.train_ch8(net, train_iter, vocab, lr, num_epochs, device)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "6d7d41d1",
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   "outputs": [],
   "source": []
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