tf2_transfer_learning_v1.ipynb

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      "source": [
        "##  ট্রান্সফার লার্নিং এবং ‘প্রি-ট্রেইনড’ অক্সফোর্ড ইউনিভার্সিটি’র ভিজ্যুয়াল জিওমেট্রি গ্রূপের vgg16 ডাটাসেট\n",
        "\n",
        "মানুষের মধ্যেই সবচেয়ে বেশি ট্রান্সফার লার্নিং ব্যাপারটা লক্ষ্য করা যায়। যেমন, আমরা একটা জিনিস শিখি তবে সেই জিনিসটা যে অন্য কোন কাজে লাগে না সেটা একদম ঠিক নয়। ধরা যাক, এক সময় আমি অংক, অ্যালজেবরা শিখেছি, পাশাপাশি পরিসংখ্যান শিখেছি পরীক্ষায় পাশ করার জন্য। এখন সেই জ্ঞানগুলো ট্রান্সফার হয়েছে মেশিন লার্নিং শিখতে গিয়ে। অংক,অ্যালজেবরা এবং পরিসংখ্যান শেখাটা বৃথা যায়নি। কারণ, আমাকে মেশিন লার্নিং একেবারে কেঁচে গণ্ডূষ করে শুরু করতে হয়নি, কারণ অংক এবং পরিসংখ্যান পাশাপাশি অ্যালজেবরা এই মেশিন লার্নিং শেখার জন্য সাহায্য করেছে। সে কারণেই বলা হয় যদিও একটা কাজের জন্য আমরা কোন কিছু শিখি, দেখা যায় পরে ওই শেখাটা অন্য কাজে লাগছে। এর অর্থ হচ্ছে আমরা অতীতে যা কিছু শিখেছি সেটা এখন কাজে লাগছে নতুন কিছু শিখতে। সেখানে এক ডোমেইন থেকে আরেক ডোমেইনে জ্ঞানটা ট্রান্সফার হচ্ছে।\n",
        "\n",
        "ছোটবেলায় সাইকেল চালানো শিখেছিলাম বলেই মোটরসাইকেল ধরতে খুব একটা সমস্যা হয়নি। আবার কেউ যদি মোটরসাইকেল চালাতে পারেন তাহলে তার জন্য গাড়ি চালানোর শুরুর ব্যাপারগুলো ধরতে সে রকম সমস্যা হবার কথা না। বাচ্চারা যখন কোন মোবাইল ডিভাইস ব্যবহার করা শেখে, তারা হঠাৎ করে আইপ্যাড পেলে তাদের পুরনো জ্ঞান দিয়েই চালিয়ে নেয় নতুন জিনিস। এর অর্থ হচ্ছে একটার নলেজ আরেকটাতে ট্রান্সফার করা যাচ্ছে। এই একই জিনিস ঘটছে ডিপ লার্নিং এর আরেকটা নতুন ধারণায়। ‘ট্রান্সফার লার্নিং’। একটা কাজে একটা মডেলকে ট্রেইন করা হলে সেটাকে যে পাশাপাশি আর একটা কাজে ব্যবহার করা যাবে না সেটা ঠিক নয়। আমরা একটা ডিপ লার্নিং মডেলকে যদি ‘ইমেজ’ রিলেটেড ব্যাপারে ট্রেনিং করাই, তাহলে ওই কাজের কাছাকাছি আরেকটা কাজে ব্যবহার করা যাবে। ওই দুটো কাজের মধ্যে যত ‘সিমিলারিটি’ আছে ততোই মডেল গুলোর মধ্যে লার্নিং গুলোকে ট্রান্সফার করা সহজ হবে। শুরুতে একটা কাজের জন্য আমরা একদম গোড়া থেকে ট্রেইনিং করলেও পরের কাছাকাছি সেই কাজের জন্য একদম গোড়া থেকে ট্রেনিং করানোর প্রয়োজন নেই। এটাই ট্রান্সফার লার্নিং।\n",
        "\n",
        "গোড়ার দিকে মেশিন লার্নিং এবং ডিপ লার্নিং অ্যালগরিদমগুলোকে এমনভাবে তৈরি করা হয়েছিল যাতে সে একটা স্পেসিফিক কাজ করতে পারে। আবার এই মডেলগুলোকে একেবারে গোড়া থেকে তৈরি করতে হতো যখন তাদের ফিচার স্পেস ডিস্ট্রিবিউশনে কোন পরিবর্তন আসতো। এখন দেখা যাচ্ছে ফিচার স্পেসে পরিবর্তন আসলেও কাছাকাছি কাজে একটার লার্নিং আরেকটাতে পাঠানো যাচ্ছে। সে কারণে আমরা গত চ্যাপ্টারে আলাপ করেছি ‘প্রি-ট্রেইনড’ মডেল নিয়ে। ‘প্রি-ট্রেইনড’ মডেল হচ্ছে একটা ‘স্টোরকৃত’ বা ‘সেভকৃত’ মডেল যাকে আগেই ট্রেনিং করানো হয়েছিল বিশাল আরেকটা ডাটাসেটের উপর। আমরা সেই মডেলকে অন্য আরেকটা কাজে সরাসরি অথবা ‘ট্রান্সফার লার্নিং’ এর মাধ্যমে আগের মডেলের পুরো বা কিছু অংশ নিয়ে নতুন মডেলকে কাস্টমাইজ করবো। আমাদের ‘প্রি-ট্রেইনড’ মডেল যদি কোটি কোটি ইমেজের উপর ট্রেনিং করানো থাকে, তাহলে সেই মডেলকে দিয়ে নতুন কাজে ওই ছবিগুলোর ‘লার্নিং’ ব্যবহার করা যাবে। ‘ট্রান্সফার লার্নিং’ এর সবচেয়ে বড় ব্যবহার হচ্ছে যখন আমাদের হাতে পর্যাপ্ত ডাটা থাকে না ট্রেনিং করানোর জন্য। সেই জ্ঞানকে নিয়ে আসছি আগের  প্রি-ট্রেইনড মডেলের প্রাপ্ত জ্ঞান থেকে। \n",
        "\n",
        "ধরা যাক আমাদের কাছে একটা ‘প্রি-ট্রেইনড’ মডেল আছে যেগুলো প্রায় ১০০০ ছবি ক্যাটাগরিতে কাজ করেছে। এখন আমার নতুন কাজে যেখানে মাত্র দুটো ছবির ক্যাটাগরি নিয়ে কাজ করছি সেখানে আগের একটা ‘প্রি-ট্রেইনড’ মডেল কোন রকম পরিবর্তন ছাড়াই ব্যবহার করা যায়। আসল কথা হচ্ছে আমাদের ডিপ লার্নিং মডেলগুলো যে ফিচার ম্যাপ তৈরি করে - সেগুলোকে নতুন কাজে আবার গোড়া থেকে ‘ফিচার ম্যাপ’ তৈরি না করে শেষে ক্লাসিফায়ার অংশটুকু ফেলে দিয়ে নতুন ক্লাসিফায়ার যোগ করলেই কাজ হয়ে যায়। মানে একজনের শরীর, আরেকজনের মাথা। মজার কথা, জিনিসটা কাজ করে।"
      ]
    },
    {
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        "id": "p7-Scy30mlbF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3ffe6adf-0cb7-4852-cc16-b4ff73a262d2"
      },
      "source": [
        "try:\n",
        "  # শুধুমাত্র টেন্সর-ফ্লো ২.x ব্যবহার করবো \n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf\n",
        "keras = tf.keras"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
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    {
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      "metadata": {
        "id": "FnmZL-yJmy1e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "import tensorflow_datasets as tfds\n",
        "import matplotlib.pylab as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Sg9XD0cCkBh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext tensorboard\n",
        "LOG_DIR = './log'"
      ],
      "execution_count": 0,
      "outputs": []
    },
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.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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        },
        "outputId": "f81f612e-dbda-4475-a58d-e97cc17906fe"
      },
      "source": [
        "split = (80, 10, 10)\n",
        "splits = tfds.Split.TRAIN.subsplit(weighted=split)\n",
        "\n",
        "(cat_train, cat_valid, cat_test), info = tfds.load('cats_vs_dogs', split=list(splits), with_info=True, as_supervised=True)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset cats_vs_dogs (786.68 MiB) to /root/tensorflow_datasets/cats_vs_dogs/2.0.1...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
            "  InsecureRequestWarning)\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n"
          ],
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        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:1738 images were corrupted and were skipped\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
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          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use eager execution and: \n",
            "`tf.data.TFRecordDataset(path)`\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use eager execution and: \n",
            "`tf.data.TFRecordDataset(path)`\n"
          ],
          "name": "stderr"
        },
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            "\r\u001b[1mDataset cats_vs_dogs downloaded and prepared to /root/tensorflow_datasets/cats_vs_dogs/2.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
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      "source": [
        "get_label_name = info.features['label'].int2str\n",
        "for image, label in cat_test.take(2):\n",
        "  plt.figure()\n",
        "  plt.imshow(image)\n",
        "  plt.title(get_label_name(label))\n",
        "  image_ = image"
      ],
      "execution_count": 5,
      "outputs": [
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SBEoqLAopFIVyuPL5JSMuay8OcWVtDVLP1Lzk67A2GPqDjh3VkqIokCpUnQ0S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\nX9RVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k++QktI3nnXfe4frqwOXllvGjPQZqSZqFogTXcmPlGuZZQe+zM3VgcZ5XUH1N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\nJ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0Qng+tlUbXOcT1sOJ8Du+3Aw7v3bDcdp+PI1f6G3faKw+HA4eHI9c0e5z1aWbabK/Go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YrcYMTTNhU3nzrii5/7POKFerWGbLCLOe/pvNOgaYKtdTZjOpnSBgeb8542MHhBBXART2Mb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\nhnpN13RN1/SQ6JqhXtM1XdM1PST6/wCM9unhInCNVwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1UH2Husuhxm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "f25f5415-8cdc-433b-8351-21412f28c759"
      },
      "source": [
        "print(cat_train)\n",
        "print(cat_valid)\n",
        "print(cat_test)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<_OptionsDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n",
            "<_OptionsDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n",
            "<_OptionsDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3YWtvNyq7QI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# IMAGE_SIZE = 100\n",
        "# def pre_process_image(image, label):\n",
        "# image = tf.cast(image, tf.float32)\n",
        "# image = image / 255.0\n",
        "# image = tf.image.resize(image, (IMAGE_SIZE, IMAGE_SIZE))\n",
        "# return image, label\n",
        "\n",
        "IMAGE_SIZE = 160 \n",
        "\n",
        "def pre_process_image(image, label):\n",
        "  image = tf.cast(image, tf.float32)\n",
        "  image = (image/127.5) - 1\n",
        "  image = tf.image.resize(image, (IMAGE_SIZE, IMAGE_SIZE))\n",
        "  return image, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ydCtg446_2y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# TRAIN_BATCH_SIZE = 64\n",
        "# cat_train = cat_train.map(pre_process_image).shuffle(1000).repeat().batch(TRAIN_BATCH_SIZE)\n",
        "# cat_valid = cat_valid.map(pre_process_image).repeat().batch(1000)\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "SHUFFLE_BUFFER_SIZE = 1000\n",
        "\n",
        "cat_train = cat_train.map(pre_process_image)\n",
        "cat_valid = cat_valid.map(pre_process_image)\n",
        "cat_test = cat_test.map(pre_process_image)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hznXKEKcxd51",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_batches = cat_train.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "validation_batches = cat_valid.batch(BATCH_SIZE)\n",
        "test_batches = cat_test.batch(BATCH_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aobrHMm9xwUM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "724d1a61-351e-4c92-c28d-400ddd7cba9e"
      },
      "source": [
        "for image_batch, label_batch in train_batches.take(1):\n",
        "   pass\n",
        "\n",
        "image_batch.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([32, 160, 160, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4J_ILpHV7-B",
        "colab_type": "text"
      },
      "source": [
        "## একটা কনভলিউশন বেইজ, আরেকটা ক্লাসিফায়ার হেড\n",
        "\n",
        "একটা ‘কনভলিউশনাল নিউরাল’ নেটওয়ার্কে কি আছে? একটা কনভলিউশন বেইজ, আরেকটা ক্লাসিফায়ার হেড। বেশিরভাগ সময় ‘প্রি-ট্রেইনড’ মডেলের কনভলিউশন বেইজ ঠিক রেখে নতুন কাজের নতুন ক্লাসিফায়ার হেড যোগ করলেই ট্রান্সফার লার্নিং এর ব্যবহার দেখতে পারি সহজেই। ট্রান্সফার লার্নিং এর শুরুতেই আমাদের আগের অন্য কাজের জন্য তৈরি মডেলকে ব্যবচ্ছেদ করবো আমরা। তখন বুঝব সেই ‘প্রি-ট্রেইনড’ মডেলের কতটুকু আমরা ‘আনকোরা’ ব্যবহার করব আর কতটুকু নতুন করে ট্রেইন করে নেব সেটা হাতেকলমে দেখবো সামনে। \n",
        "\n",
        "অনেক গল্প হল, এই মুহূর্তে আমরা একটা  ‘প্রি-ট্রেইনড’  মডেলের কিভাবে কাস্টমাইজেশন করব সেটা নিয়ে আলাপ করছি এখানে।  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TN69lAEB8Yy8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "head = tf.keras.Sequential()\n",
        "head.add(layers.Conv2D(32, (3, 3), input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))\n",
        "head.add(layers.BatchNormalization())\n",
        "head.add(layers.Activation('relu'))\n",
        "head.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "head.add(layers.Conv2D(32, (3, 3)))\n",
        "head.add(layers.BatchNormalization())\n",
        "head.add(layers.Activation('relu'))\n",
        "head.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "head.add(layers.Conv2D(64, (3, 3)))\n",
        "head.add(layers.BatchNormalization())\n",
        "head.add(layers.Activation('relu'))\n",
        "head.add(layers.MaxPooling2D(pool_size=(2, 2)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQyJRwnN3EzN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# average_pool = tf.keras.Sequential()\n",
        "# average_pool.add(layers.AveragePooling2D())\n",
        "# average_pool.add(layers.Flatten())\n",
        "# average_pool.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "\n",
        "average_pool = tf.keras.layers.GlobalAveragePooling2D()\n",
        "prediction = keras.layers.Dense(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3X1uMRpeW2sY",
        "colab_type": "text"
      },
      "source": [
        "১. ‘প্রি-ট্রেইনড’ মডেল থেকে ফিচার এক্সট্রাকশন: আমরা রিপ্রেজেন্টেশন লার্নিং এর মাধ্যমে আগের নেটওয়ার্ক থেকে শিখে সেটা দিয়ে নতুন নেটওয়ার্কের দরকারি ফিচারগুলোকে এক্সট্রাক্ট করব নতুন ডাটা থেকে। এর শেষে আমরা একটা নতুন ক্লাসিফায়ার হেড যোগ করে দেব যাকে ট্রেনিং করালেই বাকি মডেলকে আনকোরা রাখলেও কাজ হয়ে যাবে। এর অর্থ হচ্ছে আমাদের আগের  প্রি-ট্রেইনড মডেলকে ব্যবহার করছি নতুন একটা কাজে। আমরা কিন্তু পুরো মডেলকে ট্রেইন করছিনা, শুধুমাত্র দরকারি ক্লাসিফায়ার হেডকে সেই কয়েকটা ক্লাসিফিকেশন কাজের জন্য ট্রেইন করলেই চলছে। পেছনে আগের মডেলের কনভলিউশন বেইজ একই থাকছে। মানে এক নেটওয়ার্ক থেকে কনভলিউশন বেইজ এবং নতুন কাজের নেটওয়ার্ক ক্লাসিফায়ার হেড (নতুন কাজের শেষ অংশ) যোগ করে দিলেই কাজ হয়ে যাবে।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IS02e1euaCef",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "standard_model = tf.keras.Sequential([\n",
        "    head, \n",
        "    average_pool,\n",
        "    prediction\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbhQBUy88eYC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# standard_model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "#              loss='binary_crossentropy',\n",
        "#              metrics=['accuracy'])\n",
        "\n",
        "base_learning_rate = 0.0001\n",
        "standard_model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VagLLgXeaOB8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = [tf.keras.callbacks.TensorBoard(log_dir='./log/standard_model', update_freq='batch')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLzJCwtZ_Enw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# standard_model.fit(cat_train, steps_per_epoch = 23262//TRAIN_BATCH_SIZE, epochs=7, \n",
        "#               validation_data=cat_valid, validation_steps=10, callbacks=callbacks)\n",
        "\n",
        "num_train, num_val, num_test = (\n",
        "  info.splits['train'].num_examples*weight/10\n",
        "  for weight in split\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCHFxXaO2pai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "275e6024-a73d-48aa-ce5a-80a78fdf169d"
      },
      "source": [
        "initial_epochs = 10\n",
        "steps_per_epoch = round(num_train)//BATCH_SIZE\n",
        "validation_steps = 20\n",
        "\n",
        "loss0,accuracy0 = standard_model.evaluate(validation_batches, steps = validation_steps)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20/20 [==============================] - 6s 301ms/step - loss: 1.4064 - accuracy: 0.5125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2RwkTco3BaL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4131c47e-dfaa-46e9-e45a-f43254a48c9e"
      },
      "source": [
        "print(\"initial loss: {:.2f}\".format(loss0))\n",
        "print(\"initial accuracy: {:.2f}\".format(accuracy0))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "initial loss: 1.41\n",
            "initial accuracy: 0.51\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8X0H_qmn3Gi3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "c0606532-dacd-471f-a5ec-59d8662ef4bf"
      },
      "source": [
        "history = standard_model.fit(train_batches,\n",
        "                    epochs=initial_epochs,\n",
        "                    validation_data=validation_batches,\n",
        "                    callbacks=callbacks)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "      1/Unknown - 5s 5s/step - loss: 2.0392 - accuracy: 0.5625WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.211291). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.211291). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r      2/Unknown - 5s 3s/step - loss: 2.6911 - accuracy: 0.5781WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.209699). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.209699). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "582/582 [==============================] - 102s 176ms/step - loss: 0.7131 - accuracy: 0.6410 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "582/582 [==============================] - 98s 168ms/step - loss: 0.6399 - accuracy: 0.6812 - val_loss: 0.6015 - val_accuracy: 0.6711\n",
            "Epoch 3/10\n",
            "582/582 [==============================] - 96s 166ms/step - loss: 0.6189 - accuracy: 0.7018 - val_loss: 0.7550 - val_accuracy: 0.6638\n",
            "Epoch 4/10\n",
            "582/582 [==============================] - 97s 166ms/step - loss: 0.6180 - accuracy: 0.7072 - val_loss: 0.5734 - val_accuracy: 0.7263\n",
            "Epoch 5/10\n",
            "582/582 [==============================] - 97s 167ms/step - loss: 0.6052 - accuracy: 0.7217 - val_loss: 0.5728 - val_accuracy: 0.7297\n",
            "Epoch 6/10\n",
            "582/582 [==============================] - 97s 167ms/step - loss: 0.5993 - accuracy: 0.7275 - val_loss: 0.5841 - val_accuracy: 0.7336\n",
            "Epoch 7/10\n",
            "582/582 [==============================] - 96s 165ms/step - loss: 0.6082 - accuracy: 0.7294 - val_loss: 0.7019 - val_accuracy: 0.6901\n",
            "Epoch 8/10\n",
            "582/582 [==============================] - 97s 167ms/step - loss: 0.5929 - accuracy: 0.7339 - val_loss: 0.5725 - val_accuracy: 0.7397\n",
            "Epoch 9/10\n",
            "582/582 [==============================] - 97s 167ms/step - loss: 0.5849 - accuracy: 0.7389 - val_loss: 0.5467 - val_accuracy: 0.7599\n",
            "Epoch 10/10\n",
            "582/582 [==============================] - 97s 167ms/step - loss: 0.5796 - accuracy: 0.7437 - val_loss: 0.5811 - val_accuracy: 0.7371\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q81K1MiTaf2b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4c492b7c-9cea-4e90-832a-af4463cdbf5c"
      },
      "source": [
        "%tensorboard --logdir log/"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div id=\"root\"></div>\n",
              "    <script>\n",
              "      (function() {\n",
              "        window.TENSORBOARD_ENV = window.TENSORBOARD_ENV || {};\n",
              "        window.TENSORBOARD_ENV[\"IN_COLAB\"] = true;\n",
              "        document.querySelector(\"base\").href = \"https://localhost:6006\";\n",
              "        function fixUpTensorboard(root) {\n",
              "          const tftb = root.querySelector(\"tf-tensorboard\");\n",
              "          // Disable the fragment manipulation behavior in Colab. Not\n",
              "          // only is the behavior not useful (as the iframe's location\n",
              "          // is not visible to the user), it causes TensorBoard's usage\n",
              "          // of `window.replace` to navigate away from the page and to\n",
              "          // the `localhost:<port>` URL specified by the base URI, which\n",
              "          // in turn causes the frame to (likely) crash.\n",
              "          tftb.removeAttribute(\"use-hash\");\n",
              "        }\n",
              "        function executeAllScripts(root) {\n",
              "          // When `script` elements are inserted into the DOM by\n",
              "          // assigning to an element's `innerHTML`, the scripts are not\n",
              "          // executed. Thus, we manually re-insert these scripts so that\n",
              "          // TensorBoard can initialize itself.\n",
              "          for (const script of root.querySelectorAll(\"script\")) {\n",
              "            const newScript = document.createElement(\"script\");\n",
              "            newScript.type = script.type;\n",
              "            newScript.textContent = script.textContent;\n",
              "            root.appendChild(newScript);\n",
              "            script.remove();\n",
              "          }\n",
              "        }\n",
              "        function setHeight(root, height) {\n",
              "          // We set the height dynamically after the TensorBoard UI has\n",
              "          // been initialized. This avoids an intermediate state in\n",
              "          // which the container plus the UI become taller than the\n",
              "          // final width and cause the Colab output frame to be\n",
              "          // permanently resized, eventually leading to an empty\n",
              "          // vertical gap below the TensorBoard UI. It's not clear\n",
              "          // exactly what causes this problematic intermediate state,\n",
              "          // but setting the height late seems to fix it.\n",
              "          root.style.height = `${height}px`;\n",
              "        }\n",
              "        const root = document.getElementById(\"root\");\n",
              "        fetch(\".\")\n",
              "          .then((x) => x.text())\n",
              "          .then((html) => void (root.innerHTML = html))\n",
              "          .then(() => fixUpTensorboard(root))\n",
              "          .then(() => executeAllScripts(root))\n",
              "          .then(() => setHeight(root, 800));\n",
              "      })();\n",
              "    </script>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osKycGEkbHou",
        "colab_type": "text"
      },
      "source": [
        "আমরা আজকে একটা বেইজ মডেল তৈরি করব অক্সফোর্ড ইউনিভার্সিটি’র ভিজ্যুয়াল জিওমেট্রি গ্রূপের vgg16 ডাটাসেট থেকে। এটা একটা প্রি-ট্রেইনড মডেল যা আসলে ‘ইমেজনেট’ নামে একটা বিশাল ডাটাসেট  থেকে তৈরি করা। আমরা জানি যে ‘ইমেজনেট’ আসলে ১৪ লক্ষ ছবির একটা ডাটা সেট যার মধ্যে হাজারো ইন্টারনেটের ছবির ১০০০ ক্লাস বা ক্যাটেগরি আছে। এরমধ্যে কি ছবি নেই সেটাই অনেক চিন্তা করে বলতে হবে। আমরা যে ছবি প্রেডিক্ট করবো সেই ছবি ইমেজনেটে আছে কি নেই তার থেকে বড় ব্যাপার হচ্ছে ইমেজনেটকে ব্যবহার করছি ছবির বিভিন্ন ফিচার এক্সট্রাক্ট করার জন্য। \n",
        "\n",
        "এখন আমাদেরকে চিন্তা করতে হবে প্রি-ট্রেইনড মডেল হিসেবে কোন লেয়ারটাকে ব্যবহার করব ফিচার এক্সট্রাকশন এর জন্য। অবশ্যই সবচেয়ে শেষের যে ক্লাসিফিকেশন লেয়ার (সবচেয়ে শুরু’র, কারণ মেশিন লার্নিং মডেলগুলোর ডায়াগ্রাম শুরু হয় নিচ থেকে উপরে) সেটা ফিচার এক্সট্রাকশন এর জন্য ভালো নয়। আমরা শেষের আগের লেয়ারটা ব্যবহার করি আগের লেয়ারের আউটপুটকে ‘ফ্ল্যাটেন’ করার জন্য। তার ঠিক আগের লেয়ার থেকে শুরু করে সবচেয়ে উপরের লেয়ার পর্যন্ত ফিচার এক্সট্রাক্ট করা যায়। ক্লাসিফায়ার লেয়ার এর আগেরটা ফ্ল্যাটেন লেয়ার। আর তার ঠিক আগের আগের লেয়ারটাই স্পেশালাইজড লেয়ার। উপর থেকে নিচ পর্যন্ত আস্তে আস্তে ফিচারগুলো কমপ্লেক্স হতে থাকে বলে শেষের দিকের লেয়ারগুলো স্পেশালাইজড লেয়ার। এই লেয়ারকে বলা হচ্ছে ‘বটলনেক’ লেয়ার। এখানে ‘বটলনেক’ ফিচারগুলো খুবই স্পেসিফিক একটা কাজের জন্য থাকে। আমরা একটা মডেলকে যখন ইনস্ট্যান্সিয়েট করব তখন include_top=False আর্গুমেন্ট দিলেই ক্লাসিফিকেশন লেয়ার থাকবেনা শুরুতে। টপ লেয়ার ছাড়া একটা নেটওয়ার্ক লোড হবে। এটাই ফিচার ‘এক্সট্রাকশন’ এর জন্য সবচেয়ে ভালো অপশন।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEEhpnXj_OQq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9d1ccbb8-94c4-48df-b99b-af3880fc2dfc"
      },
      "source": [
        "IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)\n",
        "vgg = tf.keras.applications.vgg16.VGG16(weights='imagenet', include_top=False, input_shape=IMG_SHAPE)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAB4HQdCSmKX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "average_pool = tf.keras.layers.GlobalAveragePooling2D()\n",
        "prediction = keras.layers.Dense(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49dX9qkZbPnD",
        "colab_type": "text"
      },
      "source": [
        "আমাদের vgg16 মডেলে ১৩টা কনভলিউশন লেয়ার আছে। এরমধ্যে ৩,৩ কনভলিউশন ফিল্টার এবং দরকারি ম্যাক্স পুলিং লেয়ার আছে ডাউন স্যাম্পলিং এর জন্য। এর মধ্যে দুটো ফুললি কানেক্টেড হিডেন লেয়ার যার  ৪০৯৬ নোড ইউনিট আছে প্রতিটা লেয়ারে। এরপরে ১০০০ ইউনিটের ‘ডেন্স লেয়ার’ যা আসলে ১০০০ ক্যাটাগরির ছবির ক্লাসকে আইডেন্টিফাই করছে। আমাদের আসলে শেষের ৩ লেয়ার দরকার পড়ছে না, কারণ নিজেদের ফুললি কানেক্টেড ডেন্স লেয়ার ব্যবহার করব প্রেডিক্ট করতে - যে আসলে ছবিটা একটা কুকুরের না বিড়ালের? আমরা শুরুর পাঁচটা ব্লক নিয়ে ব্যস্ত থাকবো যেগুলো আসলে ফিচার এক্সট্রাকটর হিসেবে ব্যবহার হবে। ছবি দেখুন। \n",
        "\n",
        "আমাদের শুরুর মডেলে যেহেতু শুধুমাত্র ফিচার এক্সট্রাক্ট করব সে কারণে পাঁচটা কনভলিউশন ব্লককে ‘ফ্রিজ’ মানে ‘লক’ করে ফেলব যাতে তাদের ওয়েটগুলো আপডেট হতে না পারে প্রতিটা ইপকের পর। যেহেতু ট্রেনিং এর সময় লেয়ারগুলোর ‘ওয়েট’ দৈব-চয়নের ভিত্তিতে ইনিশিয়ালাইজ হয়, তাই সেই জিনিসটা ব্লকগুলোর আগের শেখা ওয়েটগুলো ভুলিয়ে দিতে পারে। ভুলিয়ে দিতে পারে বলছি ,এ কারণে আগের ট্রেনিংকৃত ফিচার ম্যাপগুলো উল্টাপাল্টা হয়ে যেতে পারে। সে কারণেই পাঁচটা ব্লক, মানে সব ব্লককেই ‘লক’ করে দিচ্ছি।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KupG3fT_PQ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vgg.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUu5WvhNoXy8",
        "colab_type": "code",
        "outputId": "ac8affa5-078c-4f44-f6b4-010d9864b882",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "import pandas as pd\n",
        "pd.set_option('max_colwidth', -1)\n",
        "\n",
        "layer_vgg = [(layer, layer.name, layer.trainable) for layer in vgg.layers]\n",
        "pd.DataFrame(layer_vgg, columns=['Layer Type', 'Layer Name', 'Layer Trainable'])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Layer Type</th>\n",
              "      <th>Layer Name</th>\n",
              "      <th>Layer Trainable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f037114f358&gt;</td>\n",
              "      <td>input_1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0323472dd8&gt;</td>\n",
              "      <td>block1_conv1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f031124f550&gt;</td>\n",
              "      <td>block1_conv2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f03112057f0&gt;</td>\n",
              "      <td>block1_pool</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03112058d0&gt;</td>\n",
              "      <td>block2_conv1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0311219ef0&gt;</td>\n",
              "      <td>block2_conv2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032251c358&gt;</td>\n",
              "      <td>block2_pool</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f032251c438&gt;</td>\n",
              "      <td>block3_conv1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0322528ba8&gt;</td>\n",
              "      <td>block3_conv2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0322537e80&gt;</td>\n",
              "      <td>block3_conv3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032254af98&gt;</td>\n",
              "      <td>block3_pool</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224db278&gt;</td>\n",
              "      <td>block4_conv1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224e99e8&gt;</td>\n",
              "      <td>block4_conv2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224f5cc0&gt;</td>\n",
              "      <td>block4_conv3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032250af98&gt;</td>\n",
              "      <td>block4_pool</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f032249a0b8&gt;</td>\n",
              "      <td>block5_conv1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224ac828&gt;</td>\n",
              "      <td>block5_conv2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224b6b00&gt;</td>\n",
              "      <td>block5_conv3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f03224c8dd8&gt;</td>\n",
              "      <td>block5_pool</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                          Layer Type  ... Layer Trainable\n",
              "0   <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f037114f358>  ...  False         \n",
              "1   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0323472dd8>    ...  False         \n",
              "2   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f031124f550>    ...  False         \n",
              "3   <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f03112057f0>    ...  False         \n",
              "4   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03112058d0>    ...  False         \n",
              "5   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0311219ef0>    ...  False         \n",
              "6   <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032251c358>    ...  False         \n",
              "7   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f032251c438>    ...  False         \n",
              "8   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0322528ba8>    ...  False         \n",
              "9   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0322537e80>    ...  False         \n",
              "10  <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032254af98>    ...  False         \n",
              "11  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224db278>    ...  False         \n",
              "12  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224e99e8>    ...  False         \n",
              "13  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224f5cc0>    ...  False         \n",
              "14  <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032250af98>    ...  False         \n",
              "15  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f032249a0b8>    ...  False         \n",
              "16  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224ac828>    ...  False         \n",
              "17  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224b6b00>    ...  False         \n",
              "18  <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f03224c8dd8>    ...  False         \n",
              "\n",
              "[19 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLA3Kw1FozXh",
        "colab_type": "code",
        "outputId": "af0b3d86-58cb-461f-bb53-6c1fedf69588",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Trainable layers:\", vgg.trainable_weights)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Trainable layers: []\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1ankGpTY2kx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tl_model = tf.keras.Sequential([\n",
        "  vgg,\n",
        "  average_pool,\n",
        "  prediction,\n",
        "  ])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0jeApWsUO35",
        "colab_type": "code",
        "outputId": "c397cbce-5c9e-48f4-8eab-ba920b5f9341",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "tl_model.summary()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Model)                (None, 5, 5, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 14,715,201\n",
            "Trainable params: 513\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDu7zFYFZbum",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tl_model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXStXBoLe7gV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = [tf.keras.callbacks.TensorBoard(log_dir='./log/transer_learning_model', update_freq='batch')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgGLQaLt8z7Q",
        "colab_type": "code",
        "outputId": "944d1ff3-2e19-46b7-a85f-bd2aa4d6ad82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "# tl_model.fit(cat_train, steps_per_epoch = 23262//TRAIN_BATCH_SIZE, epochs=7, \n",
        "#             validation_data=cat_valid, validation_steps=10, callbacks=callbacks)\n",
        "\n",
        "history = tl_model.fit(train_batches,\n",
        "                    epochs=initial_epochs,\n",
        "                    validation_data=validation_batches,\n",
        "                    callbacks=callbacks)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "582/582 [==============================] - 192s 330ms/step - loss: 7.7042 - accuracy: 0.5005 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "582/582 [==============================] - 182s 313ms/step - loss: 7.6903 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 3/10\n",
            "582/582 [==============================] - 182s 312ms/step - loss: 7.6870 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 4/10\n",
            "582/582 [==============================] - 182s 313ms/step - loss: 7.6878 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 5/10\n",
            "582/582 [==============================] - 182s 312ms/step - loss: 7.6795 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 6/10\n",
            "582/582 [==============================] - 182s 313ms/step - loss: 7.6895 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 7/10\n",
            "582/582 [==============================] - 181s 311ms/step - loss: 7.6895 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 8/10\n",
            "582/582 [==============================] - 182s 313ms/step - loss: 7.6986 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 9/10\n",
            "582/582 [==============================] - 181s 312ms/step - loss: 7.6994 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 10/10\n",
            "582/582 [==============================] - 182s 313ms/step - loss: 7.6920 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yjap2hIr38am",
        "colab_type": "code",
        "outputId": "32f0c381-3868-4817-aa68-da262f2f0a5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "%tensorboard --logdir log/ --port=8008"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div id=\"root\"></div>\n",
              "    <script>\n",
              "      (function() {\n",
              "        window.TENSORBOARD_ENV = window.TENSORBOARD_ENV || {};\n",
              "        window.TENSORBOARD_ENV[\"IN_COLAB\"] = true;\n",
              "        document.querySelector(\"base\").href = \"https://localhost:8008\";\n",
              "        function fixUpTensorboard(root) {\n",
              "          const tftb = root.querySelector(\"tf-tensorboard\");\n",
              "          // Disable the fragment manipulation behavior in Colab. Not\n",
              "          // only is the behavior not useful (as the iframe's location\n",
              "          // is not visible to the user), it causes TensorBoard's usage\n",
              "          // of `window.replace` to navigate away from the page and to\n",
              "          // the `localhost:<port>` URL specified by the base URI, which\n",
              "          // in turn causes the frame to (likely) crash.\n",
              "          tftb.removeAttribute(\"use-hash\");\n",
              "        }\n",
              "        function executeAllScripts(root) {\n",
              "          // When `script` elements are inserted into the DOM by\n",
              "          // assigning to an element's `innerHTML`, the scripts are not\n",
              "          // executed. Thus, we manually re-insert these scripts so that\n",
              "          // TensorBoard can initialize itself.\n",
              "          for (const script of root.querySelectorAll(\"script\")) {\n",
              "            const newScript = document.createElement(\"script\");\n",
              "            newScript.type = script.type;\n",
              "            newScript.textContent = script.textContent;\n",
              "            root.appendChild(newScript);\n",
              "            script.remove();\n",
              "          }\n",
              "        }\n",
              "        function setHeight(root, height) {\n",
              "          // We set the height dynamically after the TensorBoard UI has\n",
              "          // been initialized. This avoids an intermediate state in\n",
              "          // which the container plus the UI become taller than the\n",
              "          // final width and cause the Colab output frame to be\n",
              "          // permanently resized, eventually leading to an empty\n",
              "          // vertical gap below the TensorBoard UI. It's not clear\n",
              "          // exactly what causes this problematic intermediate state,\n",
              "          // but setting the height late seems to fix it.\n",
              "          root.style.height = `${height}px`;\n",
              "        }\n",
              "        const root = document.getElementById(\"root\");\n",
              "        fetch(\".\")\n",
              "          .then((x) => x.text())\n",
              "          .then((html) => void (root.innerHTML = html))\n",
              "          .then(() => fixUpTensorboard(root))\n",
              "          .then(() => executeAllScripts(root))\n",
              "          .then(() => setHeight(root, 800));\n",
              "      })();\n",
              "    </script>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7WTY4wy6x-9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tl_model.save('cats_dogs_tlearn.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJTCajMvaq0H",
        "colab_type": "text"
      },
      "source": [
        "২.  প্রি-ট্রেইনড মডেলের ফাইন টিউনিং: পুরনো মডেলের কনভলিউশন বেইজের শুরুর দিকের লেয়ারগুলোকে ‘লক’ করে রেখে নিচের দিকের লেয়ারগুলোকে যদি ‘আনলক’ রাখি, তাহলে ক্লাসিফায়ার হেড এর ট্রেনিং এর সাথে সাথে আনলক লেয়ারগুলোর ট্রেনিং করানো সম্ভব। ফলে  প্রি-ট্রেইনড মডেলের শেষের দিকের লেয়ারগুলোকে আমাদের কাজ অনুসারে দরকারি ফাইন টিউনিং করানো সম্ভব। এতে মডেল আমার স্পেসিফিক কাজের জন্য ভালোভাবে কাজ করবে। \n",
        "\n",
        "পরের অংশে আমরা যখন vgg16কে ফাইন টিউনিং করব, তখন ক্লাসিফায়ারের এর সাথে সাথে নিচের দুটো ব্লককে আনলক করে দেব। আমরা বলছি ব্লক ৪ এবং ৫ - যেগুলো আনলক করার ফলে তাদের ওয়েটগুলো আপডেট হবে প্রতি ইপকে, মডেল ট্রেনিং এর সাথে সাথে। নিচের ছবিটা একটু দেখি। এখানে কম্প্লেক্সিটি এড়ানোর জন্য আমরা ‘গ্লোবাল এভারেজ পুলিং’ আর যোগ করছিনা। কোডে সেটা দেখলে বুঝতে পারবেন। এই লেয়ারগুলোর ফিচার ম্যাপ থেকে যে আউটপুট পাব সেটাকে পাঠিয়ে দেব ‘ফ্ল্যাটেন’ লেয়ারে। এরপরের আউটপুট চলে যাবে আমাদের ক্লাসিফায়ারের ‘ডেন্স’ লেয়ারে।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTADpo5-7Hr1",
        "colab_type": "code",
        "outputId": "4f17a0ee-5208-4c8b-d6f6-91ff749e742b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "tl_model.trainable = True\n",
        "\n",
        "set_trainable = False\n",
        "for layer in vgg.layers:\n",
        "    if layer.name in ['block5_conv1', 'block4_conv1']:\n",
        "        set_trainable = True\n",
        "    if set_trainable:\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False\n",
        "        \n",
        "pd.set_option('max_colwidth', -1)\n",
        "\n",
        "layer_vgg2 = [(layer, layer.name, layer.trainable) for layer in vgg.layers]\n",
        "pd.DataFrame(layer_vgg2, columns=['Layer Type', 'Layer Name', 'Layer Trainable'])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Layer Type</th>\n",
              "      <th>Layer Name</th>\n",
              "      <th>Layer Trainable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f037114f358&gt;</td>\n",
              "      <td>input_1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0323472dd8&gt;</td>\n",
              "      <td>block1_conv1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f031124f550&gt;</td>\n",
              "      <td>block1_conv2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f03112057f0&gt;</td>\n",
              "      <td>block1_pool</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03112058d0&gt;</td>\n",
              "      <td>block2_conv1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0311219ef0&gt;</td>\n",
              "      <td>block2_conv2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032251c358&gt;</td>\n",
              "      <td>block2_pool</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f032251c438&gt;</td>\n",
              "      <td>block3_conv1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0322528ba8&gt;</td>\n",
              "      <td>block3_conv2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0322537e80&gt;</td>\n",
              "      <td>block3_conv3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032254af98&gt;</td>\n",
              "      <td>block3_pool</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224db278&gt;</td>\n",
              "      <td>block4_conv1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224e99e8&gt;</td>\n",
              "      <td>block4_conv2</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224f5cc0&gt;</td>\n",
              "      <td>block4_conv3</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032250af98&gt;</td>\n",
              "      <td>block4_pool</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f032249a0b8&gt;</td>\n",
              "      <td>block5_conv1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224ac828&gt;</td>\n",
              "      <td>block5_conv2</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224b6b00&gt;</td>\n",
              "      <td>block5_conv3</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>&lt;tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f03224c8dd8&gt;</td>\n",
              "      <td>block5_pool</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                          Layer Type  ... Layer Trainable\n",
              "0   <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f037114f358>  ...  False         \n",
              "1   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0323472dd8>    ...  False         \n",
              "2   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f031124f550>    ...  False         \n",
              "3   <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f03112057f0>    ...  False         \n",
              "4   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03112058d0>    ...  False         \n",
              "5   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0311219ef0>    ...  False         \n",
              "6   <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032251c358>    ...  False         \n",
              "7   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f032251c438>    ...  False         \n",
              "8   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0322528ba8>    ...  False         \n",
              "9   <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f0322537e80>    ...  False         \n",
              "10  <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032254af98>    ...  False         \n",
              "11  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224db278>    ...  True          \n",
              "12  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224e99e8>    ...  True          \n",
              "13  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224f5cc0>    ...  True          \n",
              "14  <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f032250af98>    ...  True          \n",
              "15  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f032249a0b8>    ...  True          \n",
              "16  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224ac828>    ...  True          \n",
              "17  <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7f03224b6b00>    ...  True          \n",
              "18  <tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7f03224c8dd8>    ...  True          \n",
              "\n",
              "[19 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1irzgAlNUfUu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tl_model.compile(loss='binary_crossentropy',\n",
        "              optimizer = tf.keras.optimizers.RMSprop(lr=base_learning_rate/10),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pz_A8J5U8Ddt",
        "colab_type": "code",
        "outputId": "a085dfc1-b3a9-4113-e7e0-c9e75ff17870",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "tl_model.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Model)                (None, 5, 5, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 14,715,201\n",
            "Trainable params: 12,979,713\n",
            "Non-trainable params: 1,735,488\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZxu5DiCV1dz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = [tf.keras.callbacks.TensorBoard(log_dir='./log/transer_learning_fine_tuning', update_freq='batch')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8bhxkFrVFiM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "00471600-cf87-4472-b13a-75f62178defe"
      },
      "source": [
        "fine_tune_epochs = 10\n",
        "total_epochs =  initial_epochs + fine_tune_epochs\n",
        "\n",
        "fine_tuning = tl_model.fit(train_batches,\n",
        "                         epochs=total_epochs,\n",
        "                         initial_epoch = initial_epochs,\n",
        "                         validation_data=validation_batches,\n",
        "                         callbacks=callbacks)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 11/20\n",
            "      1/Unknown - 5s 5s/step - loss: 8.1945 - accuracy: 0.4688WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.902772). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.902772). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "582/582 [==============================] - 254s 437ms/step - loss: 7.7041 - accuracy: 0.5005 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
            "Epoch 12/20\n",
            "582/582 [==============================] - 248s 426ms/step - loss: 7.6953 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 13/20\n",
            "582/582 [==============================] - 247s 424ms/step - loss: 7.6986 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 14/20\n",
            "582/582 [==============================] - 247s 424ms/step - loss: 7.6878 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 15/20\n",
            "582/582 [==============================] - 246s 424ms/step - loss: 7.6895 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 16/20\n",
            "582/582 [==============================] - 248s 427ms/step - loss: 7.6878 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 17/20\n",
            "582/582 [==============================] - 247s 424ms/step - loss: 7.7011 - accuracy: 0.5005 - val_loss: 7.3625 - val_accuracy: 0.5216\n",
            "Epoch 18/20\n",
            "155/73 [===============================================================] - 65s 419ms/step - loss: 7.6723 - accuracy: 0.5026\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-877736cfbd82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m                          \u001b[0minitial_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minitial_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m                          \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_batches\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m                          callbacks=callbacks)\n\u001b[0m",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m   def evaluate(self,\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m                 total_epochs=epochs)\n\u001b[0m\u001b[1;32m    325\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[0;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[1;32m    121\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[1;32m    122\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[0;34m(input_fn)\u001b[0m\n\u001b[1;32m     84\u001b[0m     \u001b[0;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[0;32m---> 86\u001b[0;31m                               distributed_function(input_fn))\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_counter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalled_without_tracing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    485\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 487\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    488\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1821\u001b[0m     \u001b[0;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1822\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1823\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1825\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1139\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1140\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1141\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1222\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[0;32m-> 1224\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1225\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    512\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[1;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                                                num_outputs)\n\u001b[0m\u001b[1;32m     62\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ES7k4WpUb_3Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorboard --logdir log/ --port=8009"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}