code/60_机器翻译与数据集.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "60-机器翻译与数据集.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#机器翻译与数据集\n",
        "机器翻译指的是将序列从一种语言自动翻译成另一种语言。"
      ],
      "metadata": {
        "id": "8TduQRbKaAnl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTvbH-DneRvs",
        "outputId": "f03dfe22-db59-4a6f-b54b-169080869755"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install torch torchvision torchaudio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7u9bteZce0R-",
        "outputId": "870f6238-ec9c-4175-f572-c4e0eab1d94e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.10.0+cu111)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (0.11.1+cu111)\n",
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.7/dist-packages (0.10.0+cu111)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (3.10.0.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision) (1.21.5)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision) (7.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install d2l==0.14"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Od7vqKAJe2TS",
        "outputId": "ba1d1f81-5881-40b0-f54b-8a23814f0b7d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting d2l==0.14\n",
            "  Downloading d2l-0.14.0-py3-none-any.whl (48 kB)\n",
            "\u001b[?25l\r\u001b[K     |██████▊                         | 10 kB 20.0 MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 20 kB 22.9 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 30 kB 10.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▉     | 40 kB 8.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 48 kB 2.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from d2l==0.14) (1.21.5)\n",
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            "Installing collected packages: d2l\n",
            "Successfully installed d2l-0.14.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os \n",
        "import torch\n",
        "from d2l import torch as d2l"
      ],
      "metadata": {
        "id": "f255eLvgei3-"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##下载和预处理数据集\n",
        "Tatoeba项目的双语句子对组成的“英-法”数据集，数据集中的每一行都是制表符分隔的文本序列对"
      ],
      "metadata": {
        "id": "O6qBx3Xmgghh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d2l.DATA_HUB['fra-eng'] = (d2l.DATA_URL + 'fra-eng.zip', '94646ad1522d915e7b0f9296181140edcf86a4f5')\n",
        "\n",
        "def read_data_nmt():\n",
        "    \"\"\"载入 \"英语-法语\" 数据集\"\"\"\n",
        "    data_dir = d2l.download_extract('fra-eng')\n",
        "    with open(os.path.join(data_dir,'fra.txt'), 'r', encoding='utf-8') as f:\n",
        "        return f.read()\n",
        "\n",
        "raw_text = read_data_nmt()\n",
        "print(raw_text[:75])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZD0NjCyvcwNw",
        "outputId": "71056472-1b91-4ddc-b46f-51593c81f5bd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading ../data/fra-eng.zip from http://d2l-data.s3-accelerate.amazonaws.com/fra-eng.zip...\n",
            "Go.\tVa !\n",
            "Hi.\tSalut !\n",
            "Run!\tCours !\n",
            "Run!\tCourez !\n",
            "Who?\tQui ?\n",
            "Wow!\tÇa alors !\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_nmt(text):\n",
        "    \"\"\"预处理 \"英语-法语\" 数据集\"\"\"\n",
        "    def no_space(char, prev_char):\n",
        "        return char in set(',.!?') and prev_char != ' '\n",
        "\n",
        "    # 使用空格替换不间断空格\n",
        "    # 使用小写字母替换大写字母\n",
        "    text = text.replace('\\u202f', ' ').replace('\\xa0', ' ').lower()\n",
        "    # 在单词和标点符号之间插入空格\n",
        "    out = [' ' + char if i > 0 and no_space(char, text[i - 1]) else char for i,char in enumerate(text)]\n",
        "    return ''.join(out)\n",
        "\n",
        "text = preprocess_nmt(raw_text)\n",
        "print(text[:80])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_A_cSE-ud-M4",
        "outputId": "1768fb9b-607c-4e0d-c8db-bb176ff4170f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "go .\tva !\n",
            "hi .\tsalut !\n",
            "run !\tcours !\n",
            "run !\tcourez !\n",
            "who ?\tqui ?\n",
            "wow !\tça alors !\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##词元化"
      ],
      "metadata": {
        "id": "dsLOTMPIg6EW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_nmt(text, num_examples=None):\n",
        "    \"\"\"词元化 \"英语-法语\" 数据数据集\"\"\"\n",
        "    source, target = [], []\n",
        "    for i, line in enumerate(text.split('\\n')):\n",
        "        if num_examples and i > num_examples:\n",
        "            break\n",
        "        parts = line.split('\\t')\n",
        "        if len(parts) == 2:\n",
        "            source.append(parts[0].split(' '))\n",
        "            target.append(parts[1].split(' '))\n",
        "    return source, target\n",
        "\n",
        "source, target = tokenize_nmt(text)"
      ],
      "metadata": {
        "id": "tmA3VhfrgaoZ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "source[:6], target[:6]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKYKK24rh-xA",
        "outputId": "9145945e-81ac-44b9-c132-227dfd287ce4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([['go', '.'],\n",
              "  ['hi', '.'],\n",
              "  ['run', '!'],\n",
              "  ['run', '!'],\n",
              "  ['who', '?'],\n",
              "  ['wow', '!']],\n",
              " [['va', '!'],\n",
              "  ['salut', '!'],\n",
              "  ['cours', '!'],\n",
              "  ['courez', '!'],\n",
              "  ['qui', '?'],\n",
              "  ['ça', 'alors', '!']])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def show_list_len_pair_hist(legend, xlabel, ylabel, xlist, ylist):\n",
        "    \"\"\"绘制列表长度对的直方图\"\"\"\n",
        "    d2l.set_figsize()\n",
        "    _,_,patches = d2l.plt.hist([[len(l) for l in xlist], [len(l) for l in ylist]])\n",
        "    d2l.plt.xlabel(xlabel)\n",
        "    d2l.plt.ylabel(ylabel)\n",
        "    for patch in patches[1].patches:\n",
        "        patch.set_hatch('/')\n",
        "    d2l.plt.legend(legend)\n",
        "\n",
        "show_list_len_pair_hist(['source','target'],'#tokens per sequence','count',source,target);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "NCkfXYCfiCHu",
        "outputId": "f073d017-4cc5-4b15-985c-c04ca49c1831"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
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    },
    {
      "cell_type": "code",
      "source": [
        "src_vocab = d2l.Vocab(source, min_freq=2, reserved_tokens=['<pad>', '<bos>', '<eos>'])\n",
        "len(src_vocab)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpMT8RFXjHbA",
        "outputId": "28693aaa-f684-41fb-dcd9-4658b482b627"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10012"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##加载数据集"
      ],
      "metadata": {
        "id": "THSZVlorjgDQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def truncate_pad(line, num_steps, padding_token):\n",
        "    \"\"\"截断或填充文本序列\"\"\"\n",
        "    if len(line) > num_steps:\n",
        "        return line[:num_steps] # 截断\n",
        "    return line + [padding_token] * (num_steps - len(line)) # 填充\n",
        "\n",
        "truncate_pad(src_vocab[source[0]], 10, src_vocab['<pad>'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05Y0Jr3Aje15",
        "outputId": "83f0773c-4f87-4133-ee3e-09c12e9f0cf8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[47, 4, 1, 1, 1, 1, 1, 1, 1, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_array_nmt(lines,vocab,num_steps):\n",
        "    \"\"\"将机器翻译的文本序列转换成小批量\"\"\"\n",
        "    lines = [vocab[l] for l in lines]\n",
        "    lines = [l + [vocab['<eos>']] for l in lines]\n",
        "    array = torch.tensor([truncate_pad(l,num_steps,vocab['<pad>']) for l in lines])\n",
        "    valid_len = (array != vocab['<pad>']).type(torch.int32).sum(1)\n",
        "    return array, valid_len"
      ],
      "metadata": {
        "id": "EixkhqVPkMkC"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##训练模型"
      ],
      "metadata": {
        "id": "-35TzqErlGRc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_data_nmt(batch_size,num_steps,num_examples=600):\n",
        "    \"\"\"返回翻译数据集的迭代器和词表\"\"\"\n",
        "    text = preprocess_nmt(read_data_nmt())\n",
        "    source, target = tokenize_nmt(text,num_examples)\n",
        "    src_vocab = d2l.Vocab(source,min_freq=2,reserved_tokens=['<pad>','<bos>','<eos>'])\n",
        "    tgt_vocab = d2l.Vocab(target,min_freq=2,reserved_tokens=['<pad>','<bos>','<eos>'])\n",
        "    src_array,src_valid_len = build_array_nmt(source,src_vocab,num_steps)\n",
        "    tgt_array,tgt_valid_len = build_array_nmt(target,tgt_vocab,num_steps)\n",
        "    data_arrays = (src_array,src_valid_len,tgt_array,tgt_valid_len)\n",
        "    data_iter = d2l.load_array(data_arrays,batch_size)\n",
        "    return data_iter,src_vocab,tgt_vocab"
      ],
      "metadata": {
        "id": "ejpKvE2tlFWR"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_iter,src_vocab,tgt_vocab = load_data_nmt(batch_size=2,num_steps=8)\n",
        "for X,X_valid_len,Y,Y_valid_len in train_iter:\n",
        "    print('X:',X.type(torch.int32))\n",
        "    print('X的有效长度:',X_valid_len)\n",
        "    print('Y:',Y.type(torch.int32))\n",
        "    print('Y的有效长度:',Y_valid_len)\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qF-R81Mm1kx",
        "outputId": "d74338a9-2984-4ace-92ad-7872576abf7b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X: tensor([[ 13,  33,   4,   3,   1,   1,   1,   1],\n",
            "        [170,   8,   4,   3,   1,   1,   1,   1]], dtype=torch.int32)\n",
            "X的有效长度: tensor([4, 4])\n",
            "Y: tensor([[187,   4,   3,   1,   1,   1,   1,   1],\n",
            "        [  0,   5,   3,   1,   1,   1,   1,   1]], dtype=torch.int32)\n",
            "Y的有效长度: tensor([3, 3])\n"
          ]
        }
      ]
    }
  ]
}