Embeddinglayer.ipynb

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    "colab": {
      "name": "Embeddinglayer.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMd1made38UhCdGFonlC9P5",
      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BDouchet/Gan_flickr30k/blob/main/Embeddinglayer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DSWKI6I_k0mX"
      },
      "source": [
        "# **Glove Embedding**\n",
        "\n",
        "The dataset is composed of >30k images of size 256x256 mostly representing human interaction. The image format is *.jpg*. A csv file compile the associated captions of the images."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fO5PxOQuZgv"
      },
      "source": [
        "## Useful Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kFrxBM2VO2h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12546b3c-caab-465f-c793-40fde795ebfc"
      },
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from zipfile import ZipFile\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import models,optimizers,layers\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "from tensorflow.keras.callbacks import TensorBoard,ModelCheckpoint\n",
        "import pandas as pd\n",
        "import tensorflow.keras.backend as K\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from os import listdir\n",
        "\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "id='1BzL7oYRJm8wG0yAMamaUP-wZraD7Qjku'\n",
        "\n",
        "downloaded = drive.CreateFile({'id':id})\n",
        "downloaded.GetContentFile('flickr30k_images.zip')\n",
        "\n",
        "with ZipFile('flickr30k_images.zip', 'r') as zip:\n",
        "   zip.extractall()\n",
        "   print('Done')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUI34JCYv6cw"
      },
      "source": [
        "Store the captions into a pandas file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "RkRA7gClv4c7",
        "outputId": "c22ec2a3-7eb5-41c3-c2d6-9ff362c0a17c"
      },
      "source": [
        "df=pd.read_csv('/content/results.csv',sep='|')\n",
        "df=df.rename(columns={' comment_number':'comment_number',' comment':'comment'})\n",
        "print(df.columns)\n",
        "df.loc[19999]['comment_number','comment']=[4,'A dog runs across the grass .']\n",
        "df['comment_number']=pd.to_numeric(df['comment_number'])\n",
        "df"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['image_name', 'comment_number', 'comment'], dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_name</th>\n",
              "      <th>comment_number</th>\n",
              "      <th>comment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000092795.jpg</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>1000092795.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>Two young , White males are outside near many...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000092795.jpg</td>\n",
              "      <td>2</td>\n",
              "      <td>Two men in green shirts are standing in a yard .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000092795.jpg</td>\n",
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              "      <td>A man in a blue shirt standing in a garden .</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000092795.jpg</td>\n",
              "      <td>4</td>\n",
              "      <td>Two friends enjoy time spent together .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>158910</th>\n",
              "      <td>998845445.jpg</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>158913</th>\n",
              "      <td>998845445.jpg</td>\n",
              "      <td>3</td>\n",
              "      <td>A man riding a small boat in a harbor , with ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158914</th>\n",
              "      <td>998845445.jpg</td>\n",
              "      <td>4</td>\n",
              "      <td>A man on a moored blue and white boat with hi...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>158915 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            image_name  ...                                            comment\n",
              "0       1000092795.jpg  ...   Two young guys with shaggy hair look at their...\n",
              "1       1000092795.jpg  ...   Two young , White males are outside near many...\n",
              "2       1000092795.jpg  ...   Two men in green shirts are standing in a yard .\n",
              "3       1000092795.jpg  ...       A man in a blue shirt standing in a garden .\n",
              "4       1000092795.jpg  ...            Two friends enjoy time spent together .\n",
              "...                ...  ...                                                ...\n",
              "158910   998845445.jpg  ...   A man in shorts and a Hawaiian shirt leans ov...\n",
              "158911   998845445.jpg  ...   A young man hanging over the side of a boat ,...\n",
              "158912   998845445.jpg  ...   A man is leaning off of the side of a blue an...\n",
              "158913   998845445.jpg  ...   A man riding a small boat in a harbor , with ...\n",
              "158914   998845445.jpg  ...   A man on a moored blue and white boat with hi...\n",
              "\n",
              "[158915 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "baS2VysoaGTP"
      },
      "source": [
        "To get all the filenames"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5QOwYLSaFAh",
        "outputId": "85e60706-dd2d-4579-a4e5-874da41af49d"
      },
      "source": [
        "filenames=df.image_name.unique()\n",
        "filenames"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1000092795.jpg', '10002456.jpg', '1000268201.jpg', ...,\n",
              "       '997876722.jpg', '99804383.jpg', '998845445.jpg'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "7eNogBGP2lTH",
        "outputId": "3ba682b9-e6c2-45d5-b438-821800e1af03"
      },
      "source": [
        "captions=df.set_index(['image_name','comment_number'])\n",
        "captions"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image_name</th>\n",
              "      <th>comment_number</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">1000092795.jpg</th>\n",
              "      <th>0</th>\n",
              "      <td>Two young guys with shaggy hair look at their...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Two young , White males are outside near many...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Two men in green shirts are standing in a yard .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A man in a blue shirt standing in a garden .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Two friends enjoy time spent together .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">998845445.jpg</th>\n",
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              "      <td>A young man hanging over the side of a boat ,...</td>\n",
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              "      <th>2</th>\n",
              "      <td>A man is leaning off of the side of a blue an...</td>\n",
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              "      <th>3</th>\n",
              "      <td>A man riding a small boat in a harbor , with ...</td>\n",
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              "      <th>4</th>\n",
              "      <td>A man on a moored blue and white boat with hi...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>158915 rows × 1 columns</p>\n",
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            ],
            "text/plain": [
              "                                                                         comment\n",
              "image_name     comment_number                                                   \n",
              "1000092795.jpg 0                Two young guys with shaggy hair look at their...\n",
              "               1                Two young , White males are outside near many...\n",
              "               2                Two men in green shirts are standing in a yard .\n",
              "               3                    A man in a blue shirt standing in a garden .\n",
              "               4                         Two friends enjoy time spent together .\n",
              "...                                                                          ...\n",
              "998845445.jpg  0                A man in shorts and a Hawaiian shirt leans ov...\n",
              "               1                A young man hanging over the side of a boat ,...\n",
              "               2                A man is leaning off of the side of a blue an...\n",
              "               3                A man riding a small boat in a harbor , with ...\n",
              "               4                A man on a moored blue and white boat with hi...\n",
              "\n",
              "[158915 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xlI8FXhDlPWa"
      },
      "source": [
        "Read an image and its associated caption."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKzvf4K8lV_q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "6a254920-a125-4ad4-d6fc-5adf8ef2c3be"
      },
      "source": [
        "def read_image_caption(img,number):\n",
        "    #read the image\n",
        "    img_path = 'images/'+img\n",
        "    im=cv2.imread(img_path)\n",
        "    comment=captions.loc[img,number]['comment']\n",
        "    return im, comment\n",
        "\n",
        "img, text = read_image_caption('1000092795.jpg',3)\n",
        "cv2_imshow(img)\n",
        "print(text)\n",
        "img.shape\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=256x256 at 0x7F95859B7390>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " A man in a blue shirt standing in a garden .\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(256, 256, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82LFZ6PTWg6e"
      },
      "source": [
        "## Glove\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KmiZmcnalgO",
        "outputId": "2bb7ca09-4a82-49ff-e8d6-4b6f77ae3fcb"
      },
      "source": [
        "import nltk  \n",
        "import random  \n",
        "import string\n",
        "import re\n",
        "from os import listdir\n",
        "nltk.download('stopwords')  \n",
        "from nltk.corpus import stopwords\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_xAY030vLRU"
      },
      "source": [
        "clean the caption : Take off stopwords and useless words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3cpYddwah7A"
      },
      "source": [
        "re_punc = re.compile('[%s]' % re.escape(string.punctuation))\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "def clean_caption(caption):\n",
        "  tokens = caption.split()  \n",
        "  tokens = [re_punc.sub('',w) for w in tokens] # enleve les ' et les \n",
        "  tokens = [word for word in tokens if word.isalpha()] # enleve les nombres\n",
        "  tokens = [word for word in tokens if word not in stop_words] # enleve les stopword\n",
        "  tokens = [word for word in tokens if len(word) > 1] # on conserve les mots dont la taille est supérieur à 1\n",
        "  return ' '.join(tokens)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxgExA59tX_X",
        "outputId": "01a6c428-941d-47bc-aa69-ad963b142447"
      },
      "source": [
        "print(captions['comment'][0])\n",
        "print(clean_caption(captions['comment'][0]))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Two young guys with shaggy hair look at their hands while hanging out in the yard .\n",
            "Two young guys shaggy hair look hands hanging yard\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_rASciAv0oW"
      },
      "source": [
        "The Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCyY_J6mWkO9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85e66949-6a27-42c7-dff4-2a2cee0c9ffd"
      },
      "source": [
        "maxlen=10\n",
        "max_words=10000\n",
        "texts=captions['comment'].to_numpy().copy()\n",
        "for i,text in enumerate(texts):\n",
        "    text=clean_caption(text)\n",
        "    texts[i]=text\n",
        "tokenizer=tf.keras.preprocessing.text.Tokenizer(num_words=max_words,lower='True')\n",
        "tokenizer.fit_on_texts(texts)\n",
        "sequences=tokenizer.texts_to_sequences(texts)\n",
        "print(sequences[0])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3, 6, 270, 2018, 79, 144, 121, 269, 426]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S78FNrHuYpVV",
        "outputId": "c5367cdb-2e71-4c58-aacf-d922fb45378d"
      },
      "source": [
        "import itertools\n",
        "\n",
        "\n",
        "word_index=tokenizer.word_index\n",
        "print(dict(itertools.islice(word_index.items(),100)))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'man': 1, 'woman': 2, 'two': 3, 'people': 4, 'wearing': 5, 'young': 6, 'white': 7, 'shirt': 8, 'black': 9, 'blue': 10, 'red': 11, 'girl': 12, 'sitting': 13, 'men': 14, 'boy': 15, 'standing': 16, 'dog': 17, 'playing': 18, 'street': 19, 'group': 20, 'front': 21, 'walking': 22, 'holding': 23, 'one': 24, 'water': 25, 'three': 26, 'the': 27, 'women': 28, 'green': 29, 'child': 30, 'looking': 31, 'little': 32, 'an': 33, 'large': 34, 'outside': 35, 'yellow': 36, 'person': 37, 'children': 38, 'brown': 39, 'hat': 40, 'ball': 41, 'small': 42, 'next': 43, 'dressed': 44, 'another': 45, 'running': 46, 'building': 47, 'jacket': 48, 'riding': 49, 'around': 50, 'orange': 51, 'near': 52, 'field': 53, 'crowd': 54, 'stands': 55, 'beach': 56, 'background': 57, 'pink': 58, 'behind': 59, 'sidewalk': 60, 'jumping': 61, 'table': 62, 'girls': 63, 'sits': 64, 'grass': 65, 'snow': 66, 'bike': 67, 'looks': 68, 'top': 69, 'camera': 70, 'air': 71, 'asian': 72, 'dogs': 73, 'stand': 74, 'city': 75, 'wall': 76, 'player': 77, 'older': 78, 'hair': 79, 'four': 80, 'working': 81, 'soccer': 82, 'dress': 83, 'something': 84, 'several': 85, 'gray': 86, 'talking': 87, 'shorts': 88, 'lady': 89, 'park': 90, 'guitar': 91, 'picture': 92, 'walks': 93, 'pants': 94, 'boys': 95, 'smiling': 96, 'hand': 97, 'jeans': 98, 'plays': 99, 'play': 100}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "2WjOhpxbbVP_",
        "outputId": "a6f9c936-0a22-4e04-938c-0525c951e466"
      },
      "source": [
        "data=tf.keras.preprocessing.sequence.pad_sequences(sequences,maxlen=maxlen,truncating='post')\n",
        "pd.DataFrame(data,index=texts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
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              "<p>158915 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    0   1  ...     8     9\n",
              "Two young guys shaggy hair look hands hanging yard  0   3  ...   269   426\n",
              "Two young White males outside near many bushes      0   0  ...   153  1340\n",
              "Two men green shirts standing yard                  0   0  ...    16   426\n",
              "man blue shirt standing garden                      0   0  ...    16   641\n",
              "Two friends enjoy time spent together               0   0  ...  9880   102\n",
              "...                                                ..  ..  ...   ...   ...\n",
              "man shorts Hawaiian shirt leans rail pilot boat...  1  88  ...  2719   378\n",
              "young man hanging side boat like fog rolling hi...  6   1  ...   229    59\n",
              "man leaning side blue white boat sits body water    0   1  ...   245    25\n",
              "man riding small boat harbor fog mountains back...  0   0  ...   378    57\n",
              "man moored blue white boat hills mist background    0   0  ...  3932    57\n",
              "\n",
              "[158915 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C74tgL5QwD-C"
      },
      "source": [
        "Download Glove matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Bv2HKXwvIjA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b1515b1-fff5-45da-a6b2-6699887f494f"
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "id='1U_SEvo3fFteHG_jvfUmb_RGZB0C3RkOD'\n",
        "\n",
        "downloaded = drive.CreateFile({'id':id})\n",
        "downloaded.GetContentFile('Glove.6B.zip')\n",
        "\n",
        "with ZipFile('Glove.6B.zip', 'r') as zip:\n",
        "   zip.extractall()\n",
        "   print('Done')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5yQyakoyqxZ",
        "outputId": "38b89540-5d06-4348-adc2-ca678e6f404a"
      },
      "source": [
        "embeddings_index={}\n",
        "f =open('glove.6B.100d.txt')\n",
        "for line in f:\n",
        "    values=line.split()\n",
        "    word=values[0]\n",
        "    coefs=np.asarray(values[1:],dtype='float32')\n",
        "    embeddings_index[word]=coefs\n",
        "f.close()\n",
        "\n",
        "print('Found %s word vectors.'% len(embeddings_index))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 400000 word vectors.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VZQ3IiMSzinv",
        "outputId": "c9d52913-bc66-4945-b4a9-bca9255a3423"
      },
      "source": [
        "embedding_dim=100\n",
        "embedding_matrix=np.zeros((10000,100))\n",
        "found=0\n",
        "words=[]\n",
        "for word,i in word_index.items():\n",
        "    if i< max_words:\n",
        "        words.append(word)\n",
        "        embedding_vector=embeddings_index.get(word)\n",
        "        if embedding_vector is not None:\n",
        "            found+=1\n",
        "            embedding_matrix[i]=embedding_vector\n",
        "print(str(found)+'/10000')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9722/10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdanaegH0Zew",
        "outputId": "17620f92-c664-4543-da9d-d192cf476606"
      },
      "source": [
        "print(embedding_matrix[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 3.72929990e-01  3.85030001e-01  7.10860014e-01 -6.59110010e-01\n",
            " -1.01280003e-03  9.27150011e-01  2.76149988e-01 -5.62030002e-02\n",
            " -2.42939994e-01  2.46319994e-01 -1.84489995e-01  3.13980013e-01\n",
            "  4.89829987e-01  9.25600007e-02  3.29580009e-01  1.50560006e-01\n",
            "  5.73170006e-01 -1.85289994e-01 -5.22769988e-01  4.61910009e-01\n",
            "  9.20379996e-01  3.10009997e-02 -1.62459999e-01 -4.05669987e-01\n",
            "  7.86210001e-01  5.77220023e-01 -5.35009980e-01 -6.82280004e-01\n",
            "  1.69870004e-01  3.63099992e-01 -7.17730001e-02  4.72330004e-01\n",
            "  2.78060008e-02 -1.49509996e-01  1.75430000e-01 -3.75730008e-01\n",
            " -7.85170019e-01  5.81709981e-01  8.68589997e-01  3.14450003e-02\n",
            " -4.58970010e-01 -4.09170017e-02  9.58970010e-01 -1.69750005e-01\n",
            "  1.30449995e-01  2.74340004e-01 -6.94850013e-02  2.24019997e-02\n",
            "  2.49770001e-01 -2.15360001e-01 -3.24059993e-01 -3.98669988e-01\n",
            "  6.86129987e-01  1.79229999e+00 -3.78479987e-01 -2.24769998e+00\n",
            " -7.70250022e-01  4.65820014e-01  1.24109995e+00  5.77560008e-01\n",
            "  4.11509991e-01  8.43280017e-01 -5.42590022e-01 -1.67150006e-01\n",
            "  7.39269972e-01 -9.34770033e-02  9.02779996e-01  5.08889973e-01\n",
            " -5.00310004e-01  2.64510006e-01  1.54430002e-01 -2.94319987e-01\n",
            "  1.09059997e-01 -2.66669989e-01  3.54380012e-01  4.90790009e-02\n",
            "  1.80179998e-01 -5.85900009e-01 -5.55419981e-01 -2.89869994e-01\n",
            "  7.42780030e-01  3.45299989e-01 -2.87570003e-02 -2.26459995e-01\n",
            " -1.31130004e+00 -5.71900010e-01 -5.23060024e-01 -1.26699999e-01\n",
            " -9.86780003e-02 -5.34630001e-01  2.86069989e-01 -3.75010014e-01\n",
            "  4.57419991e-01  4.59749997e-02 -2.46749997e-01  4.56559993e-02\n",
            " -3.83020014e-01 -9.37110007e-01  3.91380005e-02 -5.39110005e-01]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "DbNCo-Ac163W",
        "outputId": "4e12c60d-7457-4e5b-b4f6-776f13891caa"
      },
      "source": [
        "matrix=pd.DataFrame(data=embedding_matrix,index=['']+words)\n",
        "matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>72</th>\n",
              "      <th>73</th>\n",
              "      <th>74</th>\n",
              "      <th>75</th>\n",
              "      <th>76</th>\n",
              "      <th>77</th>\n",
              "      <th>78</th>\n",
              "      <th>79</th>\n",
              "      <th>80</th>\n",
              "      <th>81</th>\n",
              "      <th>82</th>\n",
              "      <th>83</th>\n",
              "      <th>84</th>\n",
              "      <th>85</th>\n",
              "      <th>86</th>\n",
              "      <th>87</th>\n",
              "      <th>88</th>\n",
              "      <th>89</th>\n",
              "      <th>90</th>\n",
              "      <th>91</th>\n",
              "      <th>92</th>\n",
              "      <th>93</th>\n",
              "      <th>94</th>\n",
              "      <th>95</th>\n",
              "      <th>96</th>\n",
              "      <th>97</th>\n",
              "      <th>98</th>\n",
              "      <th>99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>man</th>\n",
              "      <td>0.37293</td>\n",
              "      <td>0.38503</td>\n",
              "      <td>0.710860</td>\n",
              "      <td>-0.659110</td>\n",
              "      <td>-0.001013</td>\n",
              "      <td>0.92715</td>\n",
              "      <td>0.27615</td>\n",
              "      <td>-0.056203</td>\n",
              "      <td>-0.24294</td>\n",
              "      <td>0.246320</td>\n",
              "      <td>-0.18449</td>\n",
              "      <td>0.313980</td>\n",
              "      <td>0.48983</td>\n",
              "      <td>0.09256</td>\n",
              "      <td>0.329580</td>\n",
              "      <td>0.150560</td>\n",
              "      <td>0.57317</td>\n",
              "      <td>-0.185290</td>\n",
              "      <td>-0.522770</td>\n",
              "      <td>0.461910</td>\n",
              "      <td>0.92038</td>\n",
              "      <td>0.031001</td>\n",
              "      <td>-0.162460</td>\n",
              "      <td>-0.40567</td>\n",
              "      <td>0.786210</td>\n",
              "      <td>0.577220</td>\n",
              "      <td>-0.535010</td>\n",
              "      <td>-0.682280</td>\n",
              "      <td>0.169870</td>\n",
              "      <td>0.363100</td>\n",
              "      <td>-0.071773</td>\n",
              "      <td>0.47233</td>\n",
              "      <td>0.027806</td>\n",
              "      <td>-0.149510</td>\n",
              "      <td>0.175430</td>\n",
              "      <td>-0.375730</td>\n",
              "      <td>-0.785170</td>\n",
              "      <td>0.58171</td>\n",
              "      <td>0.86859</td>\n",
              "      <td>0.031445</td>\n",
              "      <td>...</td>\n",
              "      <td>0.411510</td>\n",
              "      <td>0.843280</td>\n",
              "      <td>-0.542590</td>\n",
              "      <td>-0.167150</td>\n",
              "      <td>0.739270</td>\n",
              "      <td>-0.093477</td>\n",
              "      <td>0.90278</td>\n",
              "      <td>0.508890</td>\n",
              "      <td>-0.50031</td>\n",
              "      <td>0.264510</td>\n",
              "      <td>0.154430</td>\n",
              "      <td>-0.294320</td>\n",
              "      <td>0.109060</td>\n",
              "      <td>-0.266670</td>\n",
              "      <td>0.354380</td>\n",
              "      <td>0.049079</td>\n",
              "      <td>0.18018</td>\n",
              "      <td>-0.585900</td>\n",
              "      <td>-0.555420</td>\n",
              "      <td>-0.289870</td>\n",
              "      <td>0.742780</td>\n",
              "      <td>0.345300</td>\n",
              "      <td>-0.028757</td>\n",
              "      <td>-0.226460</td>\n",
              "      <td>-1.311300</td>\n",
              "      <td>-0.571900</td>\n",
              "      <td>-0.523060</td>\n",
              "      <td>-0.126700</td>\n",
              "      <td>-0.098678</td>\n",
              "      <td>-0.534630</td>\n",
              "      <td>0.286070</td>\n",
              "      <td>-0.375010</td>\n",
              "      <td>0.45742</td>\n",
              "      <td>0.045975</td>\n",
              "      <td>-0.246750</td>\n",
              "      <td>0.045656</td>\n",
              "      <td>-0.38302</td>\n",
              "      <td>-0.937110</td>\n",
              "      <td>0.039138</td>\n",
              "      <td>-0.539110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>woman</th>\n",
              "      <td>0.59368</td>\n",
              "      <td>0.44825</td>\n",
              "      <td>0.593200</td>\n",
              "      <td>0.074134</td>\n",
              "      <td>0.111410</td>\n",
              "      <td>1.27930</td>\n",
              "      <td>0.16656</td>\n",
              "      <td>0.240700</td>\n",
              "      <td>0.39045</td>\n",
              "      <td>0.327660</td>\n",
              "      <td>-0.75034</td>\n",
              "      <td>0.350070</td>\n",
              "      <td>0.76057</td>\n",
              "      <td>0.38067</td>\n",
              "      <td>0.175170</td>\n",
              "      <td>0.031791</td>\n",
              "      <td>0.46849</td>\n",
              "      <td>-0.216530</td>\n",
              "      <td>-0.462820</td>\n",
              "      <td>0.399670</td>\n",
              "      <td>0.16623</td>\n",
              "      <td>-0.011477</td>\n",
              "      <td>0.044059</td>\n",
              "      <td>0.30325</td>\n",
              "      <td>0.615300</td>\n",
              "      <td>0.470470</td>\n",
              "      <td>-0.440360</td>\n",
              "      <td>-1.596300</td>\n",
              "      <td>0.184330</td>\n",
              "      <td>0.231930</td>\n",
              "      <td>0.204520</td>\n",
              "      <td>0.51617</td>\n",
              "      <td>0.657340</td>\n",
              "      <td>-0.345200</td>\n",
              "      <td>0.234460</td>\n",
              "      <td>-0.620040</td>\n",
              "      <td>-0.687410</td>\n",
              "      <td>0.28575</td>\n",
              "      <td>1.06050</td>\n",
              "      <td>0.469160</td>\n",
              "      <td>...</td>\n",
              "      <td>0.440950</td>\n",
              "      <td>0.725880</td>\n",
              "      <td>-0.092053</td>\n",
              "      <td>-0.022406</td>\n",
              "      <td>0.720390</td>\n",
              "      <td>0.107600</td>\n",
              "      <td>0.84116</td>\n",
              "      <td>0.303120</td>\n",
              "      <td>-0.42544</td>\n",
              "      <td>0.056362</td>\n",
              "      <td>0.131090</td>\n",
              "      <td>-0.071181</td>\n",
              "      <td>-0.105790</td>\n",
              "      <td>0.566770</td>\n",
              "      <td>0.545470</td>\n",
              "      <td>0.841130</td>\n",
              "      <td>0.14861</td>\n",
              "      <td>-0.626280</td>\n",
              "      <td>-0.683910</td>\n",
              "      <td>-1.083100</td>\n",
              "      <td>-0.088385</td>\n",
              "      <td>0.321670</td>\n",
              "      <td>0.477940</td>\n",
              "      <td>0.091868</td>\n",
              "      <td>-1.255900</td>\n",
              "      <td>-1.226800</td>\n",
              "      <td>0.085401</td>\n",
              "      <td>0.368330</td>\n",
              "      <td>0.081566</td>\n",
              "      <td>-0.766110</td>\n",
              "      <td>0.877510</td>\n",
              "      <td>-0.220080</td>\n",
              "      <td>0.82401</td>\n",
              "      <td>-0.092207</td>\n",
              "      <td>-0.459410</td>\n",
              "      <td>0.465710</td>\n",
              "      <td>-0.56018</td>\n",
              "      <td>-0.546480</td>\n",
              "      <td>0.151620</td>\n",
              "      <td>-0.307540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>-0.20154</td>\n",
              "      <td>0.32739</td>\n",
              "      <td>0.000476</td>\n",
              "      <td>-0.224520</td>\n",
              "      <td>0.441100</td>\n",
              "      <td>0.33599</td>\n",
              "      <td>0.40657</td>\n",
              "      <td>0.665270</td>\n",
              "      <td>-0.63852</td>\n",
              "      <td>0.044351</td>\n",
              "      <td>0.59747</td>\n",
              "      <td>-0.086113</td>\n",
              "      <td>0.39787</td>\n",
              "      <td>0.88041</td>\n",
              "      <td>0.170050</td>\n",
              "      <td>-0.142750</td>\n",
              "      <td>-0.17920</td>\n",
              "      <td>-0.116220</td>\n",
              "      <td>-0.561750</td>\n",
              "      <td>0.213780</td>\n",
              "      <td>0.95272</td>\n",
              "      <td>-0.276540</td>\n",
              "      <td>0.225670</td>\n",
              "      <td>0.45870</td>\n",
              "      <td>0.832970</td>\n",
              "      <td>-0.904890</td>\n",
              "      <td>-0.240190</td>\n",
              "      <td>-0.416690</td>\n",
              "      <td>-0.318420</td>\n",
              "      <td>0.335990</td>\n",
              "      <td>0.175160</td>\n",
              "      <td>0.24785</td>\n",
              "      <td>-0.360020</td>\n",
              "      <td>-0.118950</td>\n",
              "      <td>0.412390</td>\n",
              "      <td>0.195490</td>\n",
              "      <td>0.071716</td>\n",
              "      <td>0.31329</td>\n",
              "      <td>-0.36803</td>\n",
              "      <td>0.515460</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.686150</td>\n",
              "      <td>1.149100</td>\n",
              "      <td>0.361290</td>\n",
              "      <td>0.501560</td>\n",
              "      <td>0.339090</td>\n",
              "      <td>0.143700</td>\n",
              "      <td>0.41324</td>\n",
              "      <td>0.751410</td>\n",
              "      <td>0.00799</td>\n",
              "      <td>-0.017592</td>\n",
              "      <td>-0.586090</td>\n",
              "      <td>-0.095992</td>\n",
              "      <td>0.128280</td>\n",
              "      <td>-0.604890</td>\n",
              "      <td>0.272010</td>\n",
              "      <td>0.384970</td>\n",
              "      <td>-0.28595</td>\n",
              "      <td>-0.508070</td>\n",
              "      <td>-1.153900</td>\n",
              "      <td>-0.065888</td>\n",
              "      <td>1.128500</td>\n",
              "      <td>0.284620</td>\n",
              "      <td>-0.247820</td>\n",
              "      <td>0.448590</td>\n",
              "      <td>-1.472600</td>\n",
              "      <td>-0.126820</td>\n",
              "      <td>0.180530</td>\n",
              "      <td>-0.047814</td>\n",
              "      <td>0.016943</td>\n",
              "      <td>-0.225430</td>\n",
              "      <td>-0.227570</td>\n",
              "      <td>0.293410</td>\n",
              "      <td>-0.86753</td>\n",
              "      <td>-0.015261</td>\n",
              "      <td>-1.267500</td>\n",
              "      <td>0.020593</td>\n",
              "      <td>-0.72345</td>\n",
              "      <td>0.023054</td>\n",
              "      <td>0.540160</td>\n",
              "      <td>-0.080331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>people</th>\n",
              "      <td>0.29019</td>\n",
              "      <td>0.80497</td>\n",
              "      <td>0.311870</td>\n",
              "      <td>-0.327060</td>\n",
              "      <td>-0.472370</td>\n",
              "      <td>1.13630</td>\n",
              "      <td>-0.37966</td>\n",
              "      <td>0.115690</td>\n",
              "      <td>0.41912</td>\n",
              "      <td>0.016284</td>\n",
              "      <td>-0.06543</td>\n",
              "      <td>-0.004047</td>\n",
              "      <td>0.77688</td>\n",
              "      <td>-0.24936</td>\n",
              "      <td>0.188170</td>\n",
              "      <td>-0.640080</td>\n",
              "      <td>-0.02027</td>\n",
              "      <td>-0.185280</td>\n",
              "      <td>-1.065100</td>\n",
              "      <td>0.691150</td>\n",
              "      <td>1.04720</td>\n",
              "      <td>0.361710</td>\n",
              "      <td>0.808630</td>\n",
              "      <td>-0.42767</td>\n",
              "      <td>-0.182990</td>\n",
              "      <td>-0.278470</td>\n",
              "      <td>0.438390</td>\n",
              "      <td>-0.023336</td>\n",
              "      <td>0.373590</td>\n",
              "      <td>-0.358800</td>\n",
              "      <td>0.472610</td>\n",
              "      <td>0.73946</td>\n",
              "      <td>0.015123</td>\n",
              "      <td>0.059862</td>\n",
              "      <td>-0.066841</td>\n",
              "      <td>-0.388420</td>\n",
              "      <td>0.223610</td>\n",
              "      <td>0.80570</td>\n",
              "      <td>0.33789</td>\n",
              "      <td>-0.052222</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.352080</td>\n",
              "      <td>1.489300</td>\n",
              "      <td>-0.217010</td>\n",
              "      <td>-0.246150</td>\n",
              "      <td>0.551850</td>\n",
              "      <td>0.972240</td>\n",
              "      <td>0.58526</td>\n",
              "      <td>-0.029754</td>\n",
              "      <td>0.57944</td>\n",
              "      <td>-0.647980</td>\n",
              "      <td>-0.123710</td>\n",
              "      <td>-0.534590</td>\n",
              "      <td>-0.205280</td>\n",
              "      <td>-0.535990</td>\n",
              "      <td>0.308950</td>\n",
              "      <td>0.539350</td>\n",
              "      <td>0.11853</td>\n",
              "      <td>0.427490</td>\n",
              "      <td>-1.087200</td>\n",
              "      <td>-0.234090</td>\n",
              "      <td>0.738820</td>\n",
              "      <td>0.157490</td>\n",
              "      <td>-0.566420</td>\n",
              "      <td>0.162350</td>\n",
              "      <td>-1.911700</td>\n",
              "      <td>-0.019262</td>\n",
              "      <td>-0.502930</td>\n",
              "      <td>-0.175840</td>\n",
              "      <td>-0.538810</td>\n",
              "      <td>-0.393970</td>\n",
              "      <td>0.813300</td>\n",
              "      <td>-0.316540</td>\n",
              "      <td>0.25971</td>\n",
              "      <td>-0.482020</td>\n",
              "      <td>-1.445200</td>\n",
              "      <td>0.274190</td>\n",
              "      <td>-0.72652</td>\n",
              "      <td>-0.336030</td>\n",
              "      <td>0.459980</td>\n",
              "      <td>-0.112780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>steadily</th>\n",
              "      <td>0.18636</td>\n",
              "      <td>0.51958</td>\n",
              "      <td>-0.221940</td>\n",
              "      <td>0.925810</td>\n",
              "      <td>0.454900</td>\n",
              "      <td>-0.60018</td>\n",
              "      <td>-0.06410</td>\n",
              "      <td>-0.307150</td>\n",
              "      <td>0.62070</td>\n",
              "      <td>0.063032</td>\n",
              "      <td>-0.26205</td>\n",
              "      <td>-0.197870</td>\n",
              "      <td>0.45100</td>\n",
              "      <td>-0.53427</td>\n",
              "      <td>-0.078493</td>\n",
              "      <td>-0.926130</td>\n",
              "      <td>-0.77739</td>\n",
              "      <td>-0.050662</td>\n",
              "      <td>0.109400</td>\n",
              "      <td>-0.613850</td>\n",
              "      <td>0.36793</td>\n",
              "      <td>0.579230</td>\n",
              "      <td>0.292000</td>\n",
              "      <td>1.00430</td>\n",
              "      <td>0.019177</td>\n",
              "      <td>-0.062885</td>\n",
              "      <td>-0.210940</td>\n",
              "      <td>-0.877270</td>\n",
              "      <td>-0.060124</td>\n",
              "      <td>0.176770</td>\n",
              "      <td>0.252610</td>\n",
              "      <td>0.22456</td>\n",
              "      <td>-0.007595</td>\n",
              "      <td>0.167660</td>\n",
              "      <td>0.014427</td>\n",
              "      <td>0.363760</td>\n",
              "      <td>0.075120</td>\n",
              "      <td>-0.56594</td>\n",
              "      <td>-0.71894</td>\n",
              "      <td>0.151870</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.018756</td>\n",
              "      <td>0.350650</td>\n",
              "      <td>-0.353310</td>\n",
              "      <td>0.155140</td>\n",
              "      <td>-0.079115</td>\n",
              "      <td>0.899830</td>\n",
              "      <td>-0.29615</td>\n",
              "      <td>-0.135580</td>\n",
              "      <td>1.51500</td>\n",
              "      <td>0.884450</td>\n",
              "      <td>0.586840</td>\n",
              "      <td>-0.019977</td>\n",
              "      <td>-0.101890</td>\n",
              "      <td>0.057419</td>\n",
              "      <td>0.218340</td>\n",
              "      <td>-0.332880</td>\n",
              "      <td>-0.55070</td>\n",
              "      <td>0.297430</td>\n",
              "      <td>0.279240</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.154930</td>\n",
              "      <td>0.702590</td>\n",
              "      <td>-0.093189</td>\n",
              "      <td>-0.142870</td>\n",
              "      <td>-0.358850</td>\n",
              "      <td>-0.795930</td>\n",
              "      <td>0.444610</td>\n",
              "      <td>-0.713670</td>\n",
              "      <td>-1.029800</td>\n",
              "      <td>0.250630</td>\n",
              "      <td>-0.251700</td>\n",
              "      <td>0.201460</td>\n",
              "      <td>-0.79238</td>\n",
              "      <td>0.030509</td>\n",
              "      <td>-0.497580</td>\n",
              "      <td>-0.451760</td>\n",
              "      <td>0.29292</td>\n",
              "      <td>0.287810</td>\n",
              "      <td>0.298000</td>\n",
              "      <td>0.541970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>obtain</th>\n",
              "      <td>0.36833</td>\n",
              "      <td>-0.30518</td>\n",
              "      <td>-0.120690</td>\n",
              "      <td>0.780710</td>\n",
              "      <td>0.307000</td>\n",
              "      <td>-0.27761</td>\n",
              "      <td>-0.43548</td>\n",
              "      <td>0.044172</td>\n",
              "      <td>0.49007</td>\n",
              "      <td>0.485160</td>\n",
              "      <td>0.32991</td>\n",
              "      <td>0.064154</td>\n",
              "      <td>-0.03065</td>\n",
              "      <td>0.34662</td>\n",
              "      <td>-0.074891</td>\n",
              "      <td>-0.430230</td>\n",
              "      <td>0.86498</td>\n",
              "      <td>0.214970</td>\n",
              "      <td>0.016583</td>\n",
              "      <td>0.562780</td>\n",
              "      <td>-0.39904</td>\n",
              "      <td>-0.399830</td>\n",
              "      <td>0.136570</td>\n",
              "      <td>-0.46728</td>\n",
              "      <td>-0.823600</td>\n",
              "      <td>-0.455980</td>\n",
              "      <td>0.192320</td>\n",
              "      <td>-0.871470</td>\n",
              "      <td>0.478650</td>\n",
              "      <td>-0.172790</td>\n",
              "      <td>0.130620</td>\n",
              "      <td>0.47428</td>\n",
              "      <td>-0.692660</td>\n",
              "      <td>0.042365</td>\n",
              "      <td>0.548250</td>\n",
              "      <td>0.037583</td>\n",
              "      <td>0.159710</td>\n",
              "      <td>-0.19138</td>\n",
              "      <td>-0.24573</td>\n",
              "      <td>0.247730</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.499310</td>\n",
              "      <td>-0.001794</td>\n",
              "      <td>-0.224180</td>\n",
              "      <td>-0.285180</td>\n",
              "      <td>0.876930</td>\n",
              "      <td>-0.292210</td>\n",
              "      <td>0.11914</td>\n",
              "      <td>0.399140</td>\n",
              "      <td>0.10156</td>\n",
              "      <td>-0.698290</td>\n",
              "      <td>-0.753620</td>\n",
              "      <td>-0.238740</td>\n",
              "      <td>0.282910</td>\n",
              "      <td>-0.251640</td>\n",
              "      <td>0.577950</td>\n",
              "      <td>0.579940</td>\n",
              "      <td>-0.95142</td>\n",
              "      <td>-0.744590</td>\n",
              "      <td>-1.064000</td>\n",
              "      <td>-0.381640</td>\n",
              "      <td>1.085900</td>\n",
              "      <td>0.283500</td>\n",
              "      <td>-0.646850</td>\n",
              "      <td>-0.529980</td>\n",
              "      <td>-0.793290</td>\n",
              "      <td>0.344510</td>\n",
              "      <td>1.400100</td>\n",
              "      <td>0.248760</td>\n",
              "      <td>-0.016668</td>\n",
              "      <td>-0.037845</td>\n",
              "      <td>-0.448530</td>\n",
              "      <td>-0.351200</td>\n",
              "      <td>0.41547</td>\n",
              "      <td>-0.727790</td>\n",
              "      <td>0.010491</td>\n",
              "      <td>-0.201200</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.015703</td>\n",
              "      <td>0.035639</td>\n",
              "      <td>-0.412950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prunes</th>\n",
              "      <td>-1.10790</td>\n",
              "      <td>0.20695</td>\n",
              "      <td>0.248260</td>\n",
              "      <td>-0.539110</td>\n",
              "      <td>0.197540</td>\n",
              "      <td>0.14902</td>\n",
              "      <td>0.15406</td>\n",
              "      <td>0.280110</td>\n",
              "      <td>0.60230</td>\n",
              "      <td>-0.386800</td>\n",
              "      <td>0.18578</td>\n",
              "      <td>0.401230</td>\n",
              "      <td>0.20701</td>\n",
              "      <td>0.90840</td>\n",
              "      <td>-0.349670</td>\n",
              "      <td>-0.380250</td>\n",
              "      <td>0.86666</td>\n",
              "      <td>0.612100</td>\n",
              "      <td>0.735640</td>\n",
              "      <td>0.494610</td>\n",
              "      <td>-0.60758</td>\n",
              "      <td>-0.426590</td>\n",
              "      <td>0.267420</td>\n",
              "      <td>0.48788</td>\n",
              "      <td>0.411760</td>\n",
              "      <td>0.299390</td>\n",
              "      <td>-0.834810</td>\n",
              "      <td>0.068328</td>\n",
              "      <td>-0.300640</td>\n",
              "      <td>-0.423950</td>\n",
              "      <td>0.729910</td>\n",
              "      <td>-0.79598</td>\n",
              "      <td>-0.340780</td>\n",
              "      <td>0.155590</td>\n",
              "      <td>-1.014900</td>\n",
              "      <td>0.619970</td>\n",
              "      <td>0.258410</td>\n",
              "      <td>0.12764</td>\n",
              "      <td>0.52140</td>\n",
              "      <td>-0.287400</td>\n",
              "      <td>...</td>\n",
              "      <td>0.488420</td>\n",
              "      <td>-0.016244</td>\n",
              "      <td>0.015347</td>\n",
              "      <td>0.006477</td>\n",
              "      <td>0.086378</td>\n",
              "      <td>-0.454650</td>\n",
              "      <td>-0.85898</td>\n",
              "      <td>-0.427430</td>\n",
              "      <td>0.46641</td>\n",
              "      <td>-0.600600</td>\n",
              "      <td>-0.036533</td>\n",
              "      <td>0.629070</td>\n",
              "      <td>-0.278320</td>\n",
              "      <td>0.313930</td>\n",
              "      <td>0.034171</td>\n",
              "      <td>0.131370</td>\n",
              "      <td>0.18333</td>\n",
              "      <td>-0.545280</td>\n",
              "      <td>0.856130</td>\n",
              "      <td>0.040404</td>\n",
              "      <td>0.274480</td>\n",
              "      <td>-0.316430</td>\n",
              "      <td>-0.305890</td>\n",
              "      <td>1.157900</td>\n",
              "      <td>0.310940</td>\n",
              "      <td>0.052292</td>\n",
              "      <td>0.311280</td>\n",
              "      <td>0.723520</td>\n",
              "      <td>-0.675990</td>\n",
              "      <td>-0.130920</td>\n",
              "      <td>-0.794960</td>\n",
              "      <td>-0.350290</td>\n",
              "      <td>-0.58631</td>\n",
              "      <td>-0.249080</td>\n",
              "      <td>-0.589570</td>\n",
              "      <td>-0.358270</td>\n",
              "      <td>-0.51647</td>\n",
              "      <td>0.226850</td>\n",
              "      <td>0.496170</td>\n",
              "      <td>0.252660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>puppeteer</th>\n",
              "      <td>0.50828</td>\n",
              "      <td>0.46999</td>\n",
              "      <td>0.275020</td>\n",
              "      <td>-0.524990</td>\n",
              "      <td>0.082873</td>\n",
              "      <td>0.34529</td>\n",
              "      <td>-0.28691</td>\n",
              "      <td>-0.428900</td>\n",
              "      <td>0.09376</td>\n",
              "      <td>-0.160250</td>\n",
              "      <td>-0.82628</td>\n",
              "      <td>-0.047531</td>\n",
              "      <td>0.62606</td>\n",
              "      <td>-0.14475</td>\n",
              "      <td>-0.567760</td>\n",
              "      <td>0.631480</td>\n",
              "      <td>0.49068</td>\n",
              "      <td>-0.060712</td>\n",
              "      <td>0.307600</td>\n",
              "      <td>0.130510</td>\n",
              "      <td>-0.44620</td>\n",
              "      <td>-0.267460</td>\n",
              "      <td>0.103270</td>\n",
              "      <td>-0.56466</td>\n",
              "      <td>0.938020</td>\n",
              "      <td>-0.077426</td>\n",
              "      <td>-0.034407</td>\n",
              "      <td>0.380900</td>\n",
              "      <td>0.128590</td>\n",
              "      <td>0.087345</td>\n",
              "      <td>-0.748140</td>\n",
              "      <td>-0.39620</td>\n",
              "      <td>-0.084108</td>\n",
              "      <td>-0.003680</td>\n",
              "      <td>-0.095894</td>\n",
              "      <td>-0.185810</td>\n",
              "      <td>-0.160260</td>\n",
              "      <td>-0.14479</td>\n",
              "      <td>0.68451</td>\n",
              "      <td>-0.511910</td>\n",
              "      <td>...</td>\n",
              "      <td>0.590770</td>\n",
              "      <td>0.297180</td>\n",
              "      <td>0.101270</td>\n",
              "      <td>1.121000</td>\n",
              "      <td>-0.070948</td>\n",
              "      <td>-0.299760</td>\n",
              "      <td>0.22915</td>\n",
              "      <td>0.005097</td>\n",
              "      <td>-0.07091</td>\n",
              "      <td>0.904420</td>\n",
              "      <td>-0.450370</td>\n",
              "      <td>0.177550</td>\n",
              "      <td>-0.309520</td>\n",
              "      <td>0.544040</td>\n",
              "      <td>-0.016792</td>\n",
              "      <td>-0.099040</td>\n",
              "      <td>-0.11166</td>\n",
              "      <td>0.053858</td>\n",
              "      <td>0.502150</td>\n",
              "      <td>-0.007959</td>\n",
              "      <td>-0.224490</td>\n",
              "      <td>-0.329750</td>\n",
              "      <td>-0.236120</td>\n",
              "      <td>0.787170</td>\n",
              "      <td>-0.015346</td>\n",
              "      <td>0.087014</td>\n",
              "      <td>-0.005900</td>\n",
              "      <td>0.562570</td>\n",
              "      <td>0.034907</td>\n",
              "      <td>-0.838800</td>\n",
              "      <td>0.040982</td>\n",
              "      <td>0.291260</td>\n",
              "      <td>0.24536</td>\n",
              "      <td>-0.015937</td>\n",
              "      <td>0.346750</td>\n",
              "      <td>0.037715</td>\n",
              "      <td>0.57077</td>\n",
              "      <td>-0.874920</td>\n",
              "      <td>0.241420</td>\n",
              "      <td>-0.399980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>motorists</th>\n",
              "      <td>0.27996</td>\n",
              "      <td>-0.74749</td>\n",
              "      <td>0.216110</td>\n",
              "      <td>-0.589560</td>\n",
              "      <td>0.088315</td>\n",
              "      <td>0.22104</td>\n",
              "      <td>-0.95140</td>\n",
              "      <td>0.803620</td>\n",
              "      <td>0.51855</td>\n",
              "      <td>-0.555150</td>\n",
              "      <td>-0.58722</td>\n",
              "      <td>0.099693</td>\n",
              "      <td>-0.04933</td>\n",
              "      <td>-0.65013</td>\n",
              "      <td>0.242100</td>\n",
              "      <td>-0.886720</td>\n",
              "      <td>0.11503</td>\n",
              "      <td>-0.124470</td>\n",
              "      <td>-0.222950</td>\n",
              "      <td>0.076526</td>\n",
              "      <td>0.80190</td>\n",
              "      <td>-0.164070</td>\n",
              "      <td>0.084479</td>\n",
              "      <td>-0.52405</td>\n",
              "      <td>0.049973</td>\n",
              "      <td>-0.095280</td>\n",
              "      <td>-0.525580</td>\n",
              "      <td>-0.168310</td>\n",
              "      <td>0.269590</td>\n",
              "      <td>-0.522130</td>\n",
              "      <td>0.220510</td>\n",
              "      <td>0.25405</td>\n",
              "      <td>-0.484030</td>\n",
              "      <td>0.232570</td>\n",
              "      <td>0.067601</td>\n",
              "      <td>-0.003825</td>\n",
              "      <td>-0.022231</td>\n",
              "      <td>0.11172</td>\n",
              "      <td>1.01700</td>\n",
              "      <td>0.884600</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.191590</td>\n",
              "      <td>-0.018566</td>\n",
              "      <td>0.489550</td>\n",
              "      <td>0.256480</td>\n",
              "      <td>-0.074682</td>\n",
              "      <td>0.550530</td>\n",
              "      <td>0.24206</td>\n",
              "      <td>-0.695280</td>\n",
              "      <td>0.25798</td>\n",
              "      <td>-0.973900</td>\n",
              "      <td>-1.065800</td>\n",
              "      <td>-0.558870</td>\n",
              "      <td>0.096269</td>\n",
              "      <td>-0.131140</td>\n",
              "      <td>0.112250</td>\n",
              "      <td>0.795210</td>\n",
              "      <td>1.46460</td>\n",
              "      <td>0.417620</td>\n",
              "      <td>-0.094496</td>\n",
              "      <td>-1.221100</td>\n",
              "      <td>1.225300</td>\n",
              "      <td>0.024808</td>\n",
              "      <td>-1.090600</td>\n",
              "      <td>-0.070185</td>\n",
              "      <td>-0.491630</td>\n",
              "      <td>-0.145160</td>\n",
              "      <td>-0.291880</td>\n",
              "      <td>0.150640</td>\n",
              "      <td>-1.008900</td>\n",
              "      <td>-0.142260</td>\n",
              "      <td>-0.478960</td>\n",
              "      <td>0.039734</td>\n",
              "      <td>-0.76455</td>\n",
              "      <td>-1.263300</td>\n",
              "      <td>-1.113500</td>\n",
              "      <td>0.221410</td>\n",
              "      <td>0.15611</td>\n",
              "      <td>0.808430</td>\n",
              "      <td>0.332240</td>\n",
              "      <td>-0.260350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 100 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                0        1         2   ...        97        98        99\n",
              "           0.00000  0.00000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "man        0.37293  0.38503  0.710860  ... -0.937110  0.039138 -0.539110\n",
              "woman      0.59368  0.44825  0.593200  ... -0.546480  0.151620 -0.307540\n",
              "two       -0.20154  0.32739  0.000476  ...  0.023054  0.540160 -0.080331\n",
              "people     0.29019  0.80497  0.311870  ... -0.336030  0.459980 -0.112780\n",
              "...            ...      ...       ...  ...       ...       ...       ...\n",
              "steadily   0.18636  0.51958 -0.221940  ...  0.287810  0.298000  0.541970\n",
              "obtain     0.36833 -0.30518 -0.120690  ...  0.015703  0.035639 -0.412950\n",
              "prunes    -1.10790  0.20695  0.248260  ...  0.226850  0.496170  0.252660\n",
              "puppeteer  0.50828  0.46999  0.275020  ... -0.874920  0.241420 -0.399980\n",
              "motorists  0.27996 -0.74749  0.216110  ...  0.808430  0.332240 -0.260350\n",
              "\n",
              "[10000 rows x 100 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zX3OOhk2FOa"
      },
      "source": [
        "matrix.to_csv('embedding_matrix.csv')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}