Python_Learning_Day_4_try_again_and_repeat.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/4zmyyy/Belajarpyhton/blob/main/Python_Learning_Day_4_try_again_and_repeat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fg2a51Pzan1",
        "outputId": "b576995c-5e4b-44b1-b895-048397e15e23"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.18.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (25.2.10)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (24.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.25.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.2)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.70.0)\n",
            "Requirement already satisfied: tensorboard<2.19,>=2.18 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.18.0)\n",
            "Requirement already satisfied: keras>=3.5.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.8.0)\n",
            "Requirement already satisfied: numpy<2.1.0,>=1.26.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.12.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (13.9.4)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (0.14.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2025.1.31)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow) (3.0.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras>=3.5.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras>=3.5.0->tensorflow) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.10.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.56.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.8)\n",
            "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (24.2)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (11.1.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
            "Get:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,632 B]\n",
            "Get:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease [1,581 B]\n",
            "Get:3 https://r2u.stat.illinois.edu/ubuntu jammy InRelease [6,555 B]\n",
            "Hit:4 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Get:5 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n",
            "Get:7 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  Packages [1,317 kB]\n",
            "Hit:8 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n",
            "Get:9 https://r2u.stat.illinois.edu/ubuntu jammy/main amd64 Packages [2,659 kB]\n",
            "Hit:10 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Hit:11 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Get:12 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]\n",
            "Get:13 https://r2u.stat.illinois.edu/ubuntu jammy/main all Packages [8,682 kB]\n",
            "Get:14 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [2,610 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2,912 kB]\n",
            "Get:16 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,230 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,526 kB]\n",
            "Fetched 21.3 MB in 3s (6,713 kB/s)\n",
            "Reading package lists... Done\n",
            "W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  fonts-droid-fallback fonts-noto-mono fonts-urw-base35 ghostscript gsfonts imagemagick-6-common\n",
            "  imagemagick-6.q16 libdjvulibre-text libdjvulibre21 libfftw3-double3 libgs9 libgs9-common libidn12\n",
            "  libijs-0.35 libjbig2dec0 libjxr-tools libjxr0 liblqr-1-0 libmagickcore-6.q16-6\n",
            "  libmagickcore-6.q16-6-extra libmagickwand-6.q16-6 libnetpbm10 libwmflite-0.2-7 netpbm\n",
            "  poppler-data\n",
            "Suggested packages:\n",
            "  fonts-noto fonts-freefont-otf | fonts-freefont-ttf fonts-texgyre ghostscript-x imagemagick-doc\n",
            "  autotrace cups-bsd | lpr | lprng enscript gimp gnuplot grads hp2xx html2ps libwmf-bin mplayer\n",
            "  povray radiance sane-utils texlive-base-bin transfig ufraw-batch libfftw3-bin libfftw3-dev\n",
            "  inkscape poppler-utils fonts-japanese-mincho | fonts-ipafont-mincho fonts-japanese-gothic\n",
            "  | fonts-ipafont-gothic fonts-arphic-ukai fonts-arphic-uming fonts-nanum\n",
            "The following NEW packages will be installed:\n",
            "  fonts-droid-fallback fonts-noto-mono fonts-urw-base35 ghostscript gsfonts imagemagick\n",
            "  imagemagick-6-common imagemagick-6.q16 libdjvulibre-text libdjvulibre21 libfftw3-double3 libgs9\n",
            "  libgs9-common libidn12 libijs-0.35 libjbig2dec0 libjxr-tools libjxr0 liblqr-1-0\n",
            "  libmagickcore-6.q16-6 libmagickcore-6.q16-6-extra libmagickwand-6.q16-6 libnetpbm10\n",
            "  libwmflite-0.2-7 netpbm poppler-data\n",
            "0 upgraded, 26 newly installed, 0 to remove and 25 not upgraded.\n",
            "Need to get 25.1 MB of archives.\n",
            "After this operation, 87.9 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-droid-fallback all 1:6.0.1r16-1.1build1 [1,805 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-double3 amd64 3.3.8-2ubuntu8 [770 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 liblqr-1-0 amd64 0.4.2-2.1 [27.7 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 imagemagick-6-common all 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5 [64.3 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickcore-6.q16-6 amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5 [1,795 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickwand-6.q16-6 amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5 [328 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy/main amd64 poppler-data all 0.4.11-1 [2,171 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-noto-mono all 20201225-1build1 [397 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-urw-base35 all 20200910-1 [6,367 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgs9-common all 9.55.0~dfsg1-0ubuntu5.10 [752 kB]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libidn12 amd64 1.38-4ubuntu1 [60.0 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu jammy/main amd64 libijs-0.35 amd64 0.35-15build2 [16.5 kB]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjbig2dec0 amd64 0.19-3build2 [64.7 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgs9 amd64 9.55.0~dfsg1-0ubuntu5.10 [5,031 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 ghostscript amd64 9.55.0~dfsg1-0ubuntu5.10 [49.4 kB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu jammy/universe amd64 gsfonts all 1:8.11+urwcyr1.0.7~pre44-4.5 [3,120 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 imagemagick-6.q16 amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5 [224 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 imagemagick amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5 [14.6 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdjvulibre-text all 3.5.28-2build2 [50.9 kB]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdjvulibre21 amd64 3.5.28-2build2 [624 kB]\n",
            "Get:21 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libjxr0 amd64 1.2~git20170615.f752187-5 [174 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libjxr-tools amd64 1.2~git20170615.f752187-5 [16.0 kB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu jammy/main amd64 libwmflite-0.2-7 amd64 0.2.12-5ubuntu1 [68.9 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickcore-6.q16-6-extra amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5 [70.1 kB]\n",
            "Get:25 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libnetpbm10 amd64 2:10.0-15.4 [59.1 kB]\n",
            "Get:26 http://archive.ubuntu.com/ubuntu jammy/universe amd64 netpbm amd64 2:10.0-15.4 [1,007 kB]\n",
            "Fetched 25.1 MB in 2s (11.7 MB/s)\n",
            "Selecting previously unselected package fonts-droid-fallback.\n",
            "(Reading database ... 124926 files and directories currently installed.)\n",
            "Preparing to unpack .../00-fonts-droid-fallback_1%3a6.0.1r16-1.1build1_all.deb ...\n",
            "Unpacking fonts-droid-fallback (1:6.0.1r16-1.1build1) ...\n",
            "Selecting previously unselected package libfftw3-double3:amd64.\n",
            "Preparing to unpack .../01-libfftw3-double3_3.3.8-2ubuntu8_amd64.deb ...\n",
            "Unpacking libfftw3-double3:amd64 (3.3.8-2ubuntu8) ...\n",
            "Selecting previously unselected package liblqr-1-0:amd64.\n",
            "Preparing to unpack .../02-liblqr-1-0_0.4.2-2.1_amd64.deb ...\n",
            "Unpacking liblqr-1-0:amd64 (0.4.2-2.1) ...\n",
            "Selecting previously unselected package imagemagick-6-common.\n",
            "Preparing to unpack .../03-imagemagick-6-common_8%3a6.9.11.60+dfsg-1.3ubuntu0.22.04.5_all.deb ...\n",
            "Unpacking imagemagick-6-common (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Selecting previously unselected package libmagickcore-6.q16-6:amd64.\n",
            "Preparing to unpack .../04-libmagickcore-6.q16-6_8%3a6.9.11.60+dfsg-1.3ubuntu0.22.04.5_amd64.deb ...\n",
            "Unpacking libmagickcore-6.q16-6:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Selecting previously unselected package libmagickwand-6.q16-6:amd64.\n",
            "Preparing to unpack .../05-libmagickwand-6.q16-6_8%3a6.9.11.60+dfsg-1.3ubuntu0.22.04.5_amd64.deb ...\n",
            "Unpacking libmagickwand-6.q16-6:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Selecting previously unselected package poppler-data.\n",
            "Preparing to unpack .../06-poppler-data_0.4.11-1_all.deb ...\n",
            "Unpacking poppler-data (0.4.11-1) ...\n",
            "Selecting previously unselected package fonts-noto-mono.\n",
            "Preparing to unpack .../07-fonts-noto-mono_20201225-1build1_all.deb ...\n",
            "Unpacking fonts-noto-mono (20201225-1build1) ...\n",
            "Selecting previously unselected package fonts-urw-base35.\n",
            "Preparing to unpack .../08-fonts-urw-base35_20200910-1_all.deb ...\n",
            "Unpacking fonts-urw-base35 (20200910-1) ...\n",
            "Selecting previously unselected package libgs9-common.\n",
            "Preparing to unpack .../09-libgs9-common_9.55.0~dfsg1-0ubuntu5.10_all.deb ...\n",
            "Unpacking libgs9-common (9.55.0~dfsg1-0ubuntu5.10) ...\n",
            "Selecting previously unselected package libidn12:amd64.\n",
            "Preparing to unpack .../10-libidn12_1.38-4ubuntu1_amd64.deb ...\n",
            "Unpacking libidn12:amd64 (1.38-4ubuntu1) ...\n",
            "Selecting previously unselected package libijs-0.35:amd64.\n",
            "Preparing to unpack .../11-libijs-0.35_0.35-15build2_amd64.deb ...\n",
            "Unpacking libijs-0.35:amd64 (0.35-15build2) ...\n",
            "Selecting previously unselected package libjbig2dec0:amd64.\n",
            "Preparing to unpack .../12-libjbig2dec0_0.19-3build2_amd64.deb ...\n",
            "Unpacking libjbig2dec0:amd64 (0.19-3build2) ...\n",
            "Selecting previously unselected package libgs9:amd64.\n",
            "Preparing to unpack .../13-libgs9_9.55.0~dfsg1-0ubuntu5.10_amd64.deb ...\n",
            "Unpacking libgs9:amd64 (9.55.0~dfsg1-0ubuntu5.10) ...\n",
            "Selecting previously unselected package ghostscript.\n",
            "Preparing to unpack .../14-ghostscript_9.55.0~dfsg1-0ubuntu5.10_amd64.deb ...\n",
            "Unpacking ghostscript (9.55.0~dfsg1-0ubuntu5.10) ...\n",
            "Selecting previously unselected package gsfonts.\n",
            "Preparing to unpack .../15-gsfonts_1%3a8.11+urwcyr1.0.7~pre44-4.5_all.deb ...\n",
            "Unpacking gsfonts (1:8.11+urwcyr1.0.7~pre44-4.5) ...\n",
            "Selecting previously unselected package imagemagick-6.q16.\n",
            "Preparing to unpack .../16-imagemagick-6.q16_8%3a6.9.11.60+dfsg-1.3ubuntu0.22.04.5_amd64.deb ...\n",
            "Unpacking imagemagick-6.q16 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Selecting previously unselected package imagemagick.\n",
            "Preparing to unpack .../17-imagemagick_8%3a6.9.11.60+dfsg-1.3ubuntu0.22.04.5_amd64.deb ...\n",
            "Unpacking imagemagick (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Selecting previously unselected package libdjvulibre-text.\n",
            "Preparing to unpack .../18-libdjvulibre-text_3.5.28-2build2_all.deb ...\n",
            "Unpacking libdjvulibre-text (3.5.28-2build2) ...\n",
            "Selecting previously unselected package libdjvulibre21:amd64.\n",
            "Preparing to unpack .../19-libdjvulibre21_3.5.28-2build2_amd64.deb ...\n",
            "Unpacking libdjvulibre21:amd64 (3.5.28-2build2) ...\n",
            "Selecting previously unselected package libjxr0:amd64.\n",
            "Preparing to unpack .../20-libjxr0_1.2~git20170615.f752187-5_amd64.deb ...\n",
            "Unpacking libjxr0:amd64 (1.2~git20170615.f752187-5) ...\n",
            "Selecting previously unselected package libjxr-tools.\n",
            "Preparing to unpack .../21-libjxr-tools_1.2~git20170615.f752187-5_amd64.deb ...\n",
            "Unpacking libjxr-tools (1.2~git20170615.f752187-5) ...\n",
            "Selecting previously unselected package libwmflite-0.2-7:amd64.\n",
            "Preparing to unpack .../22-libwmflite-0.2-7_0.2.12-5ubuntu1_amd64.deb ...\n",
            "Unpacking libwmflite-0.2-7:amd64 (0.2.12-5ubuntu1) ...\n",
            "Selecting previously unselected package libmagickcore-6.q16-6-extra:amd64.\n",
            "Preparing to unpack .../23-libmagickcore-6.q16-6-extra_8%3a6.9.11.60+dfsg-1.3ubuntu0.22.04.5_amd64.deb ...\n",
            "Unpacking libmagickcore-6.q16-6-extra:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Selecting previously unselected package libnetpbm10.\n",
            "Preparing to unpack .../24-libnetpbm10_2%3a10.0-15.4_amd64.deb ...\n",
            "Unpacking libnetpbm10 (2:10.0-15.4) ...\n",
            "Selecting previously unselected package netpbm.\n",
            "Preparing to unpack .../25-netpbm_2%3a10.0-15.4_amd64.deb ...\n",
            "Unpacking netpbm (2:10.0-15.4) ...\n",
            "Setting up imagemagick-6-common (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Setting up fonts-noto-mono (20201225-1build1) ...\n",
            "Setting up libwmflite-0.2-7:amd64 (0.2.12-5ubuntu1) ...\n",
            "Setting up libijs-0.35:amd64 (0.35-15build2) ...\n",
            "Setting up libjxr0:amd64 (1.2~git20170615.f752187-5) ...\n",
            "Setting up libnetpbm10 (2:10.0-15.4) ...\n",
            "Setting up fonts-urw-base35 (20200910-1) ...\n",
            "Setting up poppler-data (0.4.11-1) ...\n",
            "Setting up libjbig2dec0:amd64 (0.19-3build2) ...\n",
            "Setting up gsfonts (1:8.11+urwcyr1.0.7~pre44-4.5) ...\n",
            "Setting up libidn12:amd64 (1.38-4ubuntu1) ...\n",
            "Setting up netpbm (2:10.0-15.4) ...\n",
            "Setting up libfftw3-double3:amd64 (3.3.8-2ubuntu8) ...\n",
            "Setting up liblqr-1-0:amd64 (0.4.2-2.1) ...\n",
            "Setting up fonts-droid-fallback (1:6.0.1r16-1.1build1) ...\n",
            "Setting up libdjvulibre-text (3.5.28-2build2) ...\n",
            "Setting up libgs9-common (9.55.0~dfsg1-0ubuntu5.10) ...\n",
            "Setting up libjxr-tools (1.2~git20170615.f752187-5) ...\n",
            "Setting up libgs9:amd64 (9.55.0~dfsg1-0ubuntu5.10) ...\n",
            "Setting up libdjvulibre21:amd64 (3.5.28-2build2) ...\n",
            "Setting up ghostscript (9.55.0~dfsg1-0ubuntu5.10) ...\n",
            "Setting up libmagickcore-6.q16-6:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Setting up libmagickwand-6.q16-6:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Setting up libmagickcore-6.q16-6-extra:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Setting up imagemagick-6.q16 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "update-alternatives: using /usr/bin/compare-im6.q16 to provide /usr/bin/compare (compare) in auto mode\n",
            "update-alternatives: using /usr/bin/compare-im6.q16 to provide /usr/bin/compare-im6 (compare-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/animate-im6.q16 to provide /usr/bin/animate (animate) in auto mode\n",
            "update-alternatives: using /usr/bin/animate-im6.q16 to provide /usr/bin/animate-im6 (animate-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/convert-im6.q16 to provide /usr/bin/convert (convert) in auto mode\n",
            "update-alternatives: using /usr/bin/convert-im6.q16 to provide /usr/bin/convert-im6 (convert-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/composite-im6.q16 to provide /usr/bin/composite (composite) in auto mode\n",
            "update-alternatives: using /usr/bin/composite-im6.q16 to provide /usr/bin/composite-im6 (composite-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/conjure-im6.q16 to provide /usr/bin/conjure (conjure) in auto mode\n",
            "update-alternatives: using /usr/bin/conjure-im6.q16 to provide /usr/bin/conjure-im6 (conjure-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/import-im6.q16 to provide /usr/bin/import (import) in auto mode\n",
            "update-alternatives: using /usr/bin/import-im6.q16 to provide /usr/bin/import-im6 (import-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/identify-im6.q16 to provide /usr/bin/identify (identify) in auto mode\n",
            "update-alternatives: using /usr/bin/identify-im6.q16 to provide /usr/bin/identify-im6 (identify-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/stream-im6.q16 to provide /usr/bin/stream (stream) in auto mode\n",
            "update-alternatives: using /usr/bin/stream-im6.q16 to provide /usr/bin/stream-im6 (stream-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/display-im6.q16 to provide /usr/bin/display (display) in auto mode\n",
            "update-alternatives: using /usr/bin/display-im6.q16 to provide /usr/bin/display-im6 (display-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/montage-im6.q16 to provide /usr/bin/montage (montage) in auto mode\n",
            "update-alternatives: using /usr/bin/montage-im6.q16 to provide /usr/bin/montage-im6 (montage-im6) in auto mode\n",
            "update-alternatives: using /usr/bin/mogrify-im6.q16 to provide /usr/bin/mogrify (mogrify) in auto mode\n",
            "update-alternatives: using /usr/bin/mogrify-im6.q16 to provide /usr/bin/mogrify-im6 (mogrify-im6) in auto mode\n",
            "Setting up imagemagick (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.5) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n",
            "Processing triggers for hicolor-icon-theme (0.17-2) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtcm_debug.so.1 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for mailcap (3.70+nmu1ubuntu1) ...\n",
            "Version: ImageMagick 6.9.11-60 Q16 x86_64 2021-01-25 https://imagemagick.org\n",
            "Copyright: (C) 1999-2021 ImageMagick Studio LLC\n",
            "License: https://imagemagick.org/script/license.php\n",
            "Features: Cipher DPC Modules OpenMP(4.5) \n",
            "Delegates (built-in): bzlib djvu fftw fontconfig freetype heic jbig jng jp2 jpeg lcms lqr ltdl lzma openexr pangocairo png tiff webp wmf x xml zlib\n"
          ]
        }
      ],
      "source": [
        "# Install TensorFlow (Percobaan model Transformer)\n",
        "!pip install tensorflow\n",
        "\n",
        "# Install Matplotlib (untuk visualisasi)\n",
        "!pip install matplotlib\n",
        "\n",
        "# Install Imagemagick untuk visualiasi\n",
        "!apt-get update\n",
        "!apt-get install imagemagick\n",
        "\n",
        "# Re-check Image magick version\n",
        "!convert --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x7g9OUud0pes",
        "outputId": "bea15ebf-58d3-424c-bb0a-28f530b7daa7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.18.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZvN5Q_V0yD0",
        "outputId": "65ef60d2-77ef-491c-c583-ba1a0a4d4729"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.18.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (25.2.10)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (24.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.25.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.2)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.70.0)\n",
            "Requirement already satisfied: tensorboard<2.19,>=2.18 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.18.0)\n",
            "Requirement already satisfied: keras>=3.5.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.8.0)\n",
            "Requirement already satisfied: numpy<2.1.0,>=1.26.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.12.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (13.9.4)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (0.14.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2025.1.31)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow) (3.0.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras>=3.5.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras>=3.5.0->tensorflow) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTYu3IxL03Rj",
        "outputId": "d901fb00-7ec8-425c-ed9c-5878690d825b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MultiHeadAttention is available!\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.layers import MultiHeadAttention\n",
        "print(\"MultiHeadAttention is available!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbZycWfd1Asb",
        "outputId": "344a0a0b-408c-4955-f298-580bd150d698"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input shape: (1, 10, 8)\n"
          ]
        }
      ],
      "source": [
        "# Simulasi input data untuk model sederhana transformer\n",
        "sequence_length = 10  # Panjang urutan (misalnya kalimat dengan 10 kata)\n",
        "embedding_dim = 8     # Dimensi embedding (fitur per kata)\n",
        "\n",
        "# Buat input dummy data\n",
        "x = tf.random.uniform((1, sequence_length, embedding_dim))  # Batch size 1\n",
        "print(\"Input shape:\", x.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9J8AoCmH1CDs",
        "outputId": "73db2e36-dd52-4564-a19a-aa1ba7340762"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input shape: (1, 10, 8)\n",
            "Success!\n",
            "Attention weights shape: (2, 10, 10)\n"
          ]
        }
      ],
      "source": [
        "# Simulasi input dummy data\n",
        "sequence_length = 10  # Panjang urutan (misalnya kalimat dengan 10 kata)\n",
        "embedding_dim = 8     # Dimensi embedding (fitur per kata)\n",
        "\n",
        "# Buat input dummy data\n",
        "x = tf.random.uniform((1, sequence_length, embedding_dim))  # Batch size 1\n",
        "print(\"Input shape:\", x.shape)\n",
        "\n",
        "# Membuat layer MultiHeadAttention\n",
        "multi_head_attention = MultiHeadAttention(num_heads=2, key_dim=embedding_dim)\n",
        "\n",
        "# Forward pass untuk mendapatkan attention weights secara terstruktur\n",
        "try:\n",
        "    output, attention_weights = multi_head_attention(query=x, value=x, return_attention_scores=True)\n",
        "    print(\"Success!\")\n",
        "except Exception as e:\n",
        "    print(\"Error:\", e)\n",
        "\n",
        "# Ekstrak attention weights sederhana\n",
        "attention_weights = attention_weights.numpy().squeeze()  # Shape: (num_heads, seq_len, seq_len)\n",
        "print(\"Attention weights shape:\", attention_weights.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zrt2PUAD1g-j"
      },
      "outputs": [],
      "source": [
        "# Import library utama\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import MultiHeadAttention\n",
        "import matplotlib.pyplot as plt  # Pastikan ini ada!\n",
        "import numpy as np\n",
        "from matplotlib.animation import FuncAnimation\n",
        "\n",
        "# Konfigurasi Matplotlib untuk menggunakan Imagemagick\n",
        "import matplotlib\n",
        "matplotlib.rcParams['animation.writer'] = 'imagemagick'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "VF0WhzVq1tzs",
        "outputId": "e32bbbf4-7c27-422e-bf70-9eba56e7235b"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Re-Test penggunaan plt\n",
        "plt.plot([1, 2, 3], [4, 5, 6])\n",
        "plt.title(\"Tes Plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "NqElLZH41xIj",
        "outputId": "c11ef572-0ce9-475f-ba5f-d3cb1a8a9186"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_a070082e-7603-408b-b9bc-13d3bf338c9d\", \"transformer_attention.gif\", 155393)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualisasi animasi attention weights\n",
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "im = ax.imshow(attention_weights[0], cmap='viridis', interpolation='nearest')\n",
        "ax.set_title(\"Attention is All You Need\", fontsize=12, color='black')  # Judul tetap\n",
        "cbar = fig.colorbar(im)\n",
        "\n",
        "# Fungsi inisialisasi untuk animasi\n",
        "def init():\n",
        "    im.set_data(np.zeros_like(attention_weights[0]))\n",
        "    return [im]\n",
        "\n",
        "# Fungsi update untuk animasi\n",
        "def update(frame):\n",
        "    im.set_data(attention_weights[frame % len(attention_weights)])  # Loop melalui setiap head\n",
        "\n",
        "    # Ganti judul menjadi \"Attention is All You Need\"\n",
        "    ax.set_title(\"Attention is All You Need\", fontsize=12, color='black')\n",
        "\n",
        "    # Tambahkan teks nama akun GitHub di pojok kanan bawah\n",
        "    ax.text(0.95, 0.05, \"GitHub: 4zmyyy\", fontsize=10, color='white',\n",
        "            ha='right', va='bottom', transform=ax.transAxes,\n",
        "            bbox=dict(facecolor='black', alpha=0.5, edgecolor='none'))\n",
        "\n",
        "    return [im]\n",
        "\n",
        "# Buat animasi\n",
        "ani = FuncAnimation(fig, update, frames=len(attention_weights) * 3, init_func=init, blit=True, interval=1000)\n",
        "\n",
        "# Simpan animasi sebagai GIF\n",
        "ani.save('transformer_attention.gif', writer='imagemagick', fps=1)\n",
        "\n",
        "# Tampilkan animasi di notebook\n",
        "plt.show()\n",
        "\n",
        "# Download file GIF jika perlu\n",
        "from google.colab import files\n",
        "files.download('transformer_attention.gif')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hi06vnPDu7g",
        "outputId": "534a24df-cc2d-4e08-f632-40be4a3cc16d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 25 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "# Install ffmpeg\n",
        "!apt-get install ffmpeg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlTmEJmoFWiP",
        "outputId": "0984c9da-14a5-4b88-bbf1-c2003abddd26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ffmpeg version 4.4.2-0ubuntu0.22.04.1 Copyright (c) 2000-2021 the FFmpeg developers\n",
            "built with gcc 11 (Ubuntu 11.2.0-19ubuntu1)\n",
            "configuration: --prefix=/usr --extra-version=0ubuntu0.22.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n",
            "libavutil      56. 70.100 / 56. 70.100\n",
            "libavcodec     58.134.100 / 58.134.100\n",
            "libavformat    58. 76.100 / 58. 76.100\n",
            "libavdevice    58. 13.100 / 58. 13.100\n",
            "libavfilter     7.110.100 /  7.110.100\n",
            "libswscale      5.  9.100 /  5.  9.100\n",
            "libswresample   3.  9.100 /  3.  9.100\n",
            "libpostproc    55.  9.100 / 55.  9.100\n"
          ]
        }
      ],
      "source": [
        "# Re-Check versi ffmpeg yang sesuai\n",
        "!ffmpeg -version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321,
          "resources": {
            "http://localhost:8080/transformer_attention.mp4": {
              "data": "",
              "headers": [
                [
                  "content-length",
                  "0"
                ]
              ],
              "ok": false,
              "status": 404,
              "status_text": ""
            }
          }
        },
        "id": "I0Dc9lPxFYnw",
        "outputId": "d128fd4b-9f05-4161-a02d-81f32f0a7977"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<video width=\"600\" controls>\n",
              "  <source src=\"transformer_attention.mp4\" type=\"video/mp4\">\n",
              "</video>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Buat animasi\n",
        "ani = FuncAnimation(fig, update, frames=len(attention_weights) * 3, init_func=init, blit=True, interval=1000)\n",
        "\n",
        "# Simpan animasi sebagai video MP4\n",
        "ani.save('transformer_attention.mp4', writer='ffmpeg', fps=1)\n",
        "\n",
        "# Tampilkan video di notebook\n",
        "from IPython.display import HTML\n",
        "HTML(\"\"\"\n",
        "<video width=\"600\" controls>\n",
        "  <source src=\"transformer_attention.mp4\" type=\"video/mp4\">\n",
        "</video>\n",
        "\"\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "bBd_sNcJFp6g",
        "outputId": "612a8f5c-6634-4943-98fe-e88011c1b44c"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_156dc158-72ab-4f69-9eec-3692b0d60dac\", \"transformer_attention.mp4\", 21511)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.download('transformer_attention.mp4')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9Ih55MnGBqy",
        "outputId": "096ed67f-4cca-4b3d-eac4-a3b6965ecc1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nilai attention_weights:\n",
            "[[[0.09961708 0.10228734 0.09538539 0.10692652 0.10089505 0.09969258\n",
            "   0.09763838 0.09909516 0.1005157  0.09794678]\n",
            "  [0.09608082 0.10564973 0.09711985 0.1012473  0.09660118 0.10098406\n",
            "   0.09691732 0.10217991 0.10168869 0.1015312 ]\n",
            "  [0.09787328 0.10295233 0.09709651 0.10324103 0.09774479 0.10035385\n",
            "   0.10026299 0.10014977 0.10155346 0.0987719 ]\n",
            "  [0.09765251 0.10364831 0.09576765 0.10434455 0.1022764  0.10159976\n",
            "   0.09359388 0.0969369  0.10385844 0.1003215 ]\n",
            "  [0.09820942 0.10302395 0.09731131 0.10269143 0.10037573 0.10034097\n",
            "   0.09649124 0.09913512 0.10170647 0.10071425]\n",
            "  [0.09558419 0.10460889 0.09922272 0.09832785 0.09734586 0.10203782\n",
            "   0.09747594 0.09847209 0.1052191  0.10170549]\n",
            "  [0.09848349 0.10380394 0.09524936 0.10642353 0.09861881 0.10003492\n",
            "   0.09885823 0.09986853 0.10125413 0.09740497]\n",
            "  [0.09521666 0.10630748 0.09606648 0.10256819 0.096641   0.10171103\n",
            "   0.09718556 0.09990951 0.10414176 0.10025227]\n",
            "  [0.0971319  0.10313478 0.09939175 0.0991485  0.09820361 0.10122167\n",
            "   0.09788071 0.09940536 0.1030726  0.10140917]\n",
            "  [0.09610895 0.10450378 0.09806068 0.10044112 0.09717125 0.10108141\n",
            "   0.0974758  0.10045597 0.10284149 0.10185954]]\n",
            "\n",
            " [[0.09983163 0.10170124 0.10111762 0.09663327 0.09928772 0.09863769\n",
            "   0.09918356 0.09849912 0.10163262 0.10347546]\n",
            "  [0.10190585 0.10105136 0.10241201 0.0966321  0.10245517 0.09641284\n",
            "   0.09832932 0.09801565 0.09919886 0.10358685]\n",
            "  [0.09973954 0.10185644 0.10137945 0.09473154 0.1001182  0.09896668\n",
            "   0.09956358 0.09827006 0.10164546 0.10372896]\n",
            "  [0.10029703 0.10149919 0.10125572 0.098867   0.09967886 0.09734689\n",
            "   0.09731551 0.09879892 0.10059883 0.10434194]\n",
            "  [0.10161484 0.10055784 0.10124061 0.09882869 0.10147152 0.09754723\n",
            "   0.09945037 0.09876318 0.09926174 0.10126401]\n",
            "  [0.10001231 0.1034224  0.10081928 0.09600868 0.10205791 0.09697533\n",
            "   0.09649928 0.09861976 0.09992133 0.10566363]\n",
            "  [0.09958855 0.10356463 0.10162853 0.09412546 0.09937614 0.09727935\n",
            "   0.09821903 0.09859792 0.10133433 0.10628615]\n",
            "  [0.10038646 0.10291853 0.1024687  0.09377009 0.10114568 0.09659961\n",
            "   0.09736238 0.09770487 0.10089082 0.10675283]\n",
            "  [0.10026612 0.10239291 0.10031793 0.09754168 0.10202337 0.09773418\n",
            "   0.09749323 0.09867759 0.09989902 0.10365391]\n",
            "  [0.10157047 0.10159396 0.10116901 0.09636234 0.10333575 0.09721843\n",
            "   0.09872872 0.09734329 0.09986904 0.10280897]]]\n",
            "Min: 0.09359388\n",
            "Max: 0.106926516\n"
          ]
        }
      ],
      "source": [
        "print(\"Nilai attention_weights:\")\n",
        "print(attention_weights)\n",
        "print(\"Min:\", np.min(attention_weights))\n",
        "print(\"Max:\", np.max(attention_weights))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "nlJSIJq4GJdw",
        "outputId": "28e80bb3-9097-4c4a-cb77-8786335d7717"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_7ea6ebb1-7232-40da-ae3f-218f6ff0a6c2\", \"transformer_attention.mp4\", 22630)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualisasi animasi attention weights\n",
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "# Normalisasi attention_weights ke rentang [0, 1] (opsional)\n",
        "attention_weights = (attention_weights - np.min(attention_weights)) / (np.max(attention_weights) - np.min(attention_weights))\n",
        "\n",
        "# Tampilkan heatmap awal\n",
        "im = ax.imshow(attention_weights[0], cmap='plasma', interpolation='nearest', vmin=0, vmax=1)\n",
        "ax.set_title(\"Attention is All You Need\", fontsize=12, color='black')  # Judul tetap\n",
        "cbar = fig.colorbar(im)\n",
        "\n",
        "# Fungsi inisialisasi untuk animasi\n",
        "def init():\n",
        "    im.set_data(np.zeros_like(attention_weights[0]))\n",
        "    return [im]\n",
        "\n",
        "# Fungsi update untuk animasi\n",
        "def update(frame):\n",
        "    im.set_data(attention_weights[frame % len(attention_weights)])  # Loop melalui setiap head\n",
        "\n",
        "    # Ganti judul menjadi \"Attention is All You Need\"\n",
        "    ax.set_title(\"Attention is All You Need\", fontsize=12, color='black')\n",
        "\n",
        "    # Tambahkan teks nama akun GitHub di pojok kanan bawah\n",
        "    ax.text(0.95, 0.05, \"GitHub: 4zmyyy\", fontsize=10, color='white',\n",
        "            ha='right', va='bottom', transform=ax.transAxes,\n",
        "            bbox=dict(facecolor='black', alpha=0.5, edgecolor='none'))\n",
        "\n",
        "    return [im]\n",
        "\n",
        "# Buat animasi\n",
        "ani = FuncAnimation(fig, update, frames=len(attention_weights) * 3, init_func=init, blit=True, interval=1000)\n",
        "\n",
        "# Simpan animasi sebagai video MP4\n",
        "ani.save('transformer_attention.mp4', writer='ffmpeg', fps=1)\n",
        "\n",
        "# Tampilkan video di notebook\n",
        "from IPython.display import HTML\n",
        "HTML(\"\"\"\n",
        "<video width=\"600\" controls>\n",
        "  <source src=\"transformer_attention.mp4\" type=\"video/mp4\">\n",
        "</video>\n",
        "\"\"\")\n",
        "\n",
        "# Download file MP4 jika perlu\n",
        "from google.colab import files\n",
        "files.download('transformer_attention.mp4')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNQ8zi9PyHsHbcbEury05cw",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}