classification_book.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/raqueeb/TensorFlow2/blob/master/classification_book.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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        "id": "jYysdyb-CaWM"
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      "source": [
        "## লাইফস্টাইল প্রোডাক্টের ক্লাসিফিকেশন\n",
        "###  ফ্যাশন MNIST ডেটাসেট\n",
        "\n",
        "(ছবিগুলো আপডেট হবে বাংলায় - ধন্যবাদ উডাসিটি, কোর্সেরা, ডিপমাইন্ড, টেন্সর-ফ্লো সাইট)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "id": "FbVhjPpzn6BM"
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      "source": [
        "আমরা যে এক্সারসাইজটা করলাম ঝিঝিপোকার ডাক নিয়ে সেটা কিন্তু ডিপ লার্নিং এর কোন অংশ নয়, তবে সাধারণ একটা মেশিন লার্নিং সমস্যাকে ডিপ লার্নিং ফ্রেমওয়ার্কে সমাধান করার চেষ্টা করেছি মাত্র। ডিপ লার্নিং ফ্রেমওয়ার্কের কাজ আমরা দেখব অন্যান্য ক্ষেত্রে। এর আগের সমস্যাটা ছিল রিগ্রেশন, যেখানে আমরা কন্টিনিউয়াস ভেরিয়েবলের ইনপুট এর সাপেক্ষে আরেকটা কন্টিনিউয়াস আউটপুট পেয়েছি। ১৫ সেকেন্ড ডাকের সংখ্যার সাথে তাপমাত্রা। দুটোই কন্টিনিয়াস ভেরিয়েবল। এখন আমরা একটা ক্লাসিফিকেশন প্রবলেম দেখব। তবে সেটা সাধারণ ক্লাসিফিকেশন নয়, বরং ইমেজ সংক্রান্ত। ডিপ লার্নিং এর হ্যালো ওয়ার্ল্ড, মানে যে এক্সারসাইজটা দিয়ে সবাই শুরু করে সেটাকে আমরা বলি MNIST ডাটা সেট। হাতে লেখা ইংরেজি অক্ষরকে ঠিকমতো চেনার জন্য এই এক্সারসাইজটা শুরুর জন্য সবারই পছন্দের। কম্পিউটার ভিশনের সবচেয়ে প্রথম এক্সাইজ বলে সবাই চেনে তাকে। তবে, আমরা একই জিনিস করছি একটু নতুন কিছুটা কমপ্লেক্স ডেটাসেটে। \n",
        "\n",
        "আমাদের এই এক্সারসাইজটা ব্যবহার ব্যবহার করছে কেরাস [tf.keras](https://www.tensorflow.org/guide/keras), একটা হাই-লেভেল এপিআই যা টেন্সর-ফ্লোএর কমপ্লেক্সিটি লুকিয়ে রাখে। টেন্সর-ফ্লো এর শুরুতেই একটা হাই-লেভেল এপিআই দরকার ছিলো যাতে নতুন ব্যবহারকারীরা ভয় না পান। আসলেই তাই। আমাদের এই হাই-লেভেল এপিআই হিসেবে কেরাস এতো জনপ্রিয় যে টেন্সর-ফ্লো ২.০ থেকে এটা অফিসিয়াল হয়ে গেছে। সেকারণে এটা tf.keras, মানে টেন্সর-ফ্লো এর সাথে চলে এসেছে ইনস্টলেশনের সাথে সাথে। "
      ]
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          "base_uri": "https://localhost:8080/",
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      "source": [
        "try:\n",
        "  # শুধুমাত্র কোলাব এবং জুপিটার নোটবুকে চেষ্টা করবো টেন্সর-ফ্লো ২.০ এর জন্য\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjSdrl-XCB7O",
        "colab_type": "text"
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      "source": [
        "টেন্সর-ফ্লো ১.x এ জিনিসটা সেরকম পাইথনিক (মানে কাজের সঙ্গে সঙ্গে রেজাল্ট দেখা) ছিলো না। তবে এখন সেটা অনেক পাইথনিক, অর্থাৎ কমান্ডের সাথে সাথে রেজাল্ট দেখতে পারছি tf.executing_eagerly() এর জন্য। সেকারণে tf.executing_eagerly()কে ‘সত্যি’ বলে ধরে নিচ্ছি। "
      ]
    },
    {
      "cell_type": "code",
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        "id": "dzLKpmZICaWN",
        "outputId": "c714eca5-9784-4549-b7c1-4f61e4279a15",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "source": [
        "# টেন্সর-ফ্লো এবং কেরাসকে ইমপোর্ট করছি\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# কিছু হেলপার লাইব্রেরি\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# স্ট্যাটাস দেখি সিস্টেমের \n",
        "print(\"Version: \", tf.__version__)\n",
        "print(\"Eager mode: \", tf.executing_eagerly())\n",
        "# print(\"Hub version: \", hub.__version__)\n",
        "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Version:  2.1.0-rc1\n",
            "Eager mode:  True\n",
            "GPU is available\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yR0EdgrLCaWR"
      },
      "source": [
        "## ফ্যাশন MNIST ডেটাসেট ইমপোর্ট করছি"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DLdCchMdCaWQ"
      },
      "source": [
        "এখন আমরা পুরোদস্তুর লাইফস্টাইল ব্যবসায়ী হয়ে গেছি। আমাদের কাছে হাজারো হাজারো কাপড়চোপড়, টিশার্ট, জুতা, স্নিকার্স, আরো বেশ কিছু জিনিস চলে এসেছে শোরুমে দেবার জন্য। কিন্তু আপনি নাছোড়বান্দা,বললেন ক্যাটালগিং ছাড়া এগুলো শোরুমে উঠানোটা ঠিক হবে না। আর এই ক্যাটালগিং আমরা হাতেকলমে করতে চাইনা। এক একটা জিনিস ধরবো ক্যামেরার সামনে, সে নিজে থেকেই ঠিকমতো ক্লাসিফাই করবে কোনটা কি জিনিস। মেশিনে ঠিকমতো আইডেন্টিফাই করতে পারলে আমাদের সমস্যা মিটে যাবে। আপনার দরকার একটা ডিপ লার্নিং মডেল। আপনার উর্দ্ধতন কর্মকর্তা বললেন, আমি ব্যাপারটা ঠিক বুঝি না, আমাকে কি একটা ডেমো দেখানো যাবে? অবশ্যই দেখানো যাবে। ৭০,০০০ গ্রে-স্কেল স্যাম্পল আছে আমাদের [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) ডেটাসেটে। \n",
        "\n",
        "** ২৮ X ২৮ পিক্সেল = ৭৬৮ বাইট **\n",
        "\n",
        "** ১০টা ক্যাটেগরি **\n",
        "<table>\n",
        "  <tr><td>\n",
        "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
        "         alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
        "  </td></tr>\n",
        "  <tr><td align=\"center\">\n",
        "    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
        "  </td></tr>\n",
        "</table>\n",
        "আর সে কারণেই আমরা দ্বারস্থ হলাম ফ্যাশন MNIST ডাটাসেটের কাছে। ব্যাপারটা সাধারণ MNIST এর সকল কোড সাপোর্ট করে, তবে ফ্যাশন MNIST বেশকিছু কমপ্লেক্স। এখানে প্রায় ৭০ হাজার স্যাম্পল আছে, তার মধ্যে দশ হাজার ইমেজকে ব্যবহার করব টেস্টসেট হিসেবে, আমাদের নেটওয়ার্ক কতটুকু শিখেছে ইমেজকে ঠিকমতো ক্লাসিফাই করার জন্য। এই ইমেজগুলোর রেজুলেশন ২৮x২৮ পিক্সেল। ছবিতে দেখুন শুরুতেই টি-শার্ট টপ, মাঝখানে স্যান্ডেল, শেষের দিকে এঙ্কেল বুটস। নিচের ছবিতে দেখুন যে দশটা আইটেম আছে আমাদের এই ডাটাসেটে। এর পাশাপাশি আমরা সংখ্যায় লেবেল দিয়েছি ০ থেকে ৯ পর্যন্ত। আপনি বুঝে গেছেন যে আমাদের ডিপ লার্নিং নেটওয়ার্ককে ট্রেইন করার জন্য ইমেজ আছে ৬০০০০। \n",
        "\n",
        "যেহেতু প্রতিটা ইমেজ ২৮x২৮ গ্রেস্কেল ইমেজ, এর সাইজ হচ্ছে ৭৮৪ বাইট (দুটোকে গুন দিলে যা হয়)। তার মানে হচ্ছে এর পিক্সএল ইনটেনসিটি শূন্য থেকে ২৫৫ পর্যন্ত। ইনপুট হিসেবে থাকবে এই ২৮x২৮ পিক্সেলের ছবি, মাঝখানে থাকবে আমাদের ডিপ নিউরাল নেটওয়ার্ক। এখন আমাদের নিউরাল নেটওয়ার্ক ইনপুট হিসেবে ৭৮৪ বাইটকে নেবে, মাঝখানে সে জাদুর ছড়ি ঘুরিয়ে শেষে ক্লাসিফাই করে দেবে একেকটা ইমেজ - যা আসলে যেকোন দশটা আইটেমের একটা আইটেম হবে।"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fKRao7xG8mT",
        "colab_type": "text"
      },
      "source": [
        "## ছবিতে কি কি আছে?\n",
        "\n",
        "ভালো করে নিচের ছবিটা দেখি। ছবিতে আলাদা করতে পারবেন এলিমেন্টগুলোকে?\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/start-page/cat1.png\"\n",
        "         alt=\"Fashion MNIST sprite\"  width=\"600\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OX78HVzGzsDv"
      },
      "source": [
        "## ডেটাসেট আছে tf.keras.datasets এ fashion_mnist হিসেবে "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6Y3zaQEBzsDw"
      },
      "source": [
        "আমরা লোড ডাটাকে কল করলে নিয়ে আসবে দুই সেটের দুটো লিস্ট। ট্রেনিং এবং টেস্ট সেট। তার ভেতরে একটা কাপড় চোপড়ের ছবি, আরেকটা তার লেবেল মানে কোনটা কি?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7MqDQO0KCaWS",
        "colab": {}
      },
      "source": [
        "# কেরাসের দিয়ে স্যাম্পল ডেটাসেটে অ্যাক্সেস করছি, সাইকিট লার্নের মতো করে, ট্রেইন / টেস্ট স্প্লিট করে\n",
        "\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpirGcn3Ac61",
        "colab_type": "text"
      },
      "source": [
        "## কেরাস দিয়ে স্যাম্পল ডেটাসেটে অ্যাক্সেস\n",
        "\n",
        "আমি একটু সময় নেব টেন্সর-ফ্লো ডেটাসেট (TFDS) নিয়ে কাজ করতে। আমি চাইবো আপনারা যাতে এই ফরম্যাটের সাথে পরিচিতি পান, পাশাপাশি এটা নিয়ে একটা চ্যাপ্টার থাকবে। এই চ্যাপ্টারের নিচের দিকে কিছু প্র্যাকটিস করবো এই ডেটাসেটে।\n",
        "\n",
        "টেন্সর-ফ্লো অফিসিয়াল গাইডে এই ধারণাটা ভালো লেগেছে। X_trainকে আমরা বলছি train_images যেখানে আসলেই ট্রেনিং ইমেজ আছে। আসল ছবি। পাশাপাশি y_trainকে বলছি train_labels যা আসলে লেবেলের অ্যারে যার মধ্যে সংখ্যা আছে। মানে ওই জিনিসটা কোন প্রোডাক্ট। সে হিসেবে X_test এবং y_testকে আমরা test_images এবং test_labels হিসেবে বলছি যাতে ব্যাপারটা সহজে বোঝা যায়। আমি (X_train, y_train), (X_test, y_test) কনসেপ্টটা বাদ দেবো না, কারণ এটা এখনো ট্রেইন/টেস্ট স্প্লিটের ডিফ্যাক্টো স্ট্যান্ডার্ড।\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) মানে - এই load_data() পাইথন দুটো ‘টুপল’ জেনারেট করছে, যা লিস্টের মতো তবে পরিবর্তনযোগ্য নয়। মনে আছে আমরা এই কাজটি সাইকিট লার্নে করেছিলাম প্রি-ডিফাইন্ড ডেটাসেট লোড করার জন্য।\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbM3x7eVBXjL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "# (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3uc7WwU5zsD3"
      },
      "source": [
        "## ডেটাসেট লোড করে কি পাবো?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "t9FDsUlxCaWW"
      },
      "source": [
        "ডেটাসেট লোড করতে গিয়ে চারটা নামপাই অ্যারে পাচ্ছি। \n",
        "\n",
        "\n",
        "* এখানে `train_images` এবং `train_labels` অ্যারে দুটো আমাদের ট্রেনিং সেট — এগুলোকে আমরা ব্যবহার করবো মডেলকে ট্রেনিং করানোর জন্য। \n",
        "* মডেলকে টেস্ট করবো টেস্ট সেট দিয়ে। এই দুটো অ্যারে হিসেবে `test_images`, এবং `test_labels`।\n",
        "\n",
        "আমাদের ছবিগুলো ২৮x২৮ নামপাই অ্যারে যার পিক্সেল ভ্যালু ০ থেকে ২৫৫ এর মধ্যে। এখানে লেবেলগুলো ইন্টেজারের অ্যারে, ০ থেকে ৯ পর্যন্ত। তার করেসপন্ডিং ছবিগুলোর নাম দেখুন এখানে। \n",
        "\n",
        "<table>\n",
        "  <tr>\n",
        "    <th>Label</th>\n",
        "    <th>Class</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>0</td>\n",
        "    <td>T-shirt/top</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>1</td>\n",
        "    <td>Trouser</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>2</td>\n",
        "    <td>Pullover</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>3</td>\n",
        "    <td>Dress</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>4</td>\n",
        "    <td>Coat</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>5</td>\n",
        "    <td>Sandal</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>6</td>\n",
        "    <td>Shirt</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>7</td>\n",
        "    <td>Sneaker</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>8</td>\n",
        "    <td>Bag</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>9</td>\n",
        "    <td>Ankle boot</td>\n",
        "  </tr>\n",
        "</table>\n",
        "\n",
        "প্রতিটা ছবি কিন্তু ম্যাপিং করা আছে একেকটা লেবেলে। একটা ক্লাস নাম দিয়ে রাখি পরে ব্যবহার করতে। প্লটিং এবং টেন্সর-ফ্লো লাইটের সময়ে লাগবে। মনে থাকবে তো \"টেন্সর-ফ্লো লাইট\" এর জন্য? পরে আসবে।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IjnLH5S2CaWx",
        "colab": {}
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Brm0b_KACaWX"
      },
      "source": [
        "## এক্সপ্লোরেটরি ডেটা অ্যানালাইসিস\n",
        "\n",
        "ডেটাগুলোকে একটু নেড়েচেড়ে দেখি। এটা একটা ভালো প্র্যাকটিস - ডেটাকে চিনতে। এখানে দেখছি ৬০,০০০ ছবি, ট্রেনিং সেটে। ২৮x২৮ পিক্সেল, ০ থেকে ৯ লেবেল। বাকি ১০,০০০ টেস্ট ছবি, পরীক্ষা করার জন্য। \n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/start-page/cat2.png\"\n",
        "         alt=\"Fashion MNIST sprite\"  width=\"600\">"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cupTUYW_DcFs",
        "colab_type": "code",
        "outputId": "573b6517-e8b4-4de3-c660-2ddcfe6a47ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# অবজেক্টটা কি? \n",
        "# এটা ডাউনলোড হয়েছে টেন্সর-ফ্লো মডিউল TFModuleWrapper হিসেবে।\n",
        "\n",
        "type(fashion_mnist)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "module"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zW5k_xz1CaWX",
        "outputId": "6d8b0d5f-cda1-4bfe-f8da-fc9221e1e4ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cIAcvQqMCaWf"
      },
      "source": [
        "৬০,০০০ লেবেলও আছে সঙ্গে।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TRFYHB2mCaWb",
        "outputId": "2e881f40-ba43-4693-fe20-275ff54ef3e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(train_labels)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YSlYxFuRCaWk"
      },
      "source": [
        "০ থেকে ৯ লেবেল সব ইন্টেজার ভ্যালু। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XKnCTHz4CaWg",
        "outputId": "fb38aafb-c2a3-46b3-9dff-892a5480687f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train_labels"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TMPI88iZpO2T"
      },
      "source": [
        "১০,০০০ টেস্ট ছবি। ২৮x২৮ পিক্সেল আছে এখানেও। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2KFnYlcwCaWl",
        "outputId": "6b797007-b9f5-44b6-8da1-baf6dac1e8c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_images.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rd0A0Iu0CaWq"
      },
      "source": [
        "আছে ১০,০০০ লেবেলও। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iJmPr5-ACaWn",
        "outputId": "c586ca5d-840f-4bb9-b72b-6f84e1e14806",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(test_labels)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCZSrr0YzsER",
        "colab_type": "text"
      },
      "source": [
        "## ট্রেনিং ছবি এবং তার লেবেল\n",
        "\n",
        "আচ্ছা, এর ভেতরের ডেটাগুলো কেমন দেখতে? আমরা একটা ট্রেনিং ছবি দেখি।  তার সাথে তার লেবেল। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Sfs4yNNxzsEV",
        "outputId": "aff77584-d601-4dd2-caec-e9c503f3931f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print('Training Lebel:')\n",
        "print(train_labels[0])\n",
        "print('Training Image:')\n",
        "print(train_images[0])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Lebel:\n",
            "9\n",
            "Training Image:\n",
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
            "    0   1   4   0   0   0   0   1   1   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
            "   54   0   0   0   1   3   4   0   0   3]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
            "  144 123  23   0   0   0   0  12  10   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
            "  107 156 161 109  64  23  77 130  72  15]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
            "  216 163 127 121 122 146 141  88 172  66]\n",
            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
            "  223 223 215 213 164 127 123 196 229   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
            "  235 227 224 222 224 221 223 245 173   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
            "  180 212 210 211 213 223 220 243 202   0]\n",
            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
            "  169 227 208 218 224 212 226 197 209  52]\n",
            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
            "  198 221 215 213 222 220 245 119 167  56]\n",
            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
            "  232 213 218 223 234 217 217 209  92   0]\n",
            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
            "  222 221 216 223 229 215 218 255  77   0]\n",
            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
            "  211 218 224 223 219 215 224 244 159   0]\n",
            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
            "  224 234 176 188 250 248 233 238 215   0]\n",
            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
            "  255 255 221 234 221 211 220 232 246   0]\n",
            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
            "  188 154 191 210 204 209 222 228 225   0]\n",
            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
            "  168 219 221 215 217 223 223 224 229  29]\n",
            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
            "  239 223 218 212 209 222 220 221 230  67]\n",
            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
            "  199 206 186 181 177 172 181 205 206 115]\n",
            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
            "  195 191 198 192 176 156 167 177 210  92]\n",
            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
            "  210 210 211 188 188 194 192 216 170   0]\n",
            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
            "  182 182 181 176 166 168  99  58   0   0]\n",
            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6sa4ffzDocj",
        "colab_type": "code",
        "outputId": "4d2ccad1-ce8d-48ac-f1aa-bbcd09664720",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.max(train_images)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ES6uQoLKCaWr"
      },
      "source": [
        "## ডেটার প্রি-প্রসেসিং \n",
        "\n",
        "মনে আছে ডেটা নরমালাইজ করার কথা? যখন ডেটা বেশি ডিস্ট্রিবিউটেড থাকে তখন সেটা কাজ করানো সমস্যা হয়। আমরা যদি প্রথম ছবিটা দেখি, তাহলে বুঝবো সেটার পিক্সেল রেঞ্জ ০ থেকে ২৫৫ পর্যন্ত। \n",
        "\n",
        "ছবিটা দেখি কালারবার সহ। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "m4VEw8Ud9Quh",
        "outputId": "731b5713-0a8d-4d75-f2d2-83a6b5745388",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dS16ZtPBuGrjvzKyNSqK4390fSVY3fN8N1q9m2m9F1IgkNuKPIQyXmXUkF1wx\nsw7gK8DquNWoexxYmLxeCDzWwL7005cgEpfRoH1nZgbcA6xz9zuqQg3dd2n9apb9VlQNqdhPbiH/\nTz58DOHWUe/EIMzsZCpHX1B5JOuBRvbNzH4MnE9lqJZtwLeB/ws8BHwMeBu43N1H/QJ7St/Op3JK\n5MBG4Jqqa1Cj2bfzgF8Cq4C+kftupnL9qWH7LujXlTTBfisqPXYkIoWmC/siUmhKYiJSaEpiIlJo\nSmIiUmhKYiJSaEpiIlJoSmIiUmj/H4BqExLuMX2fAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsLdjHLlD3Wb",
        "colab_type": "code",
        "outputId": "6e47133f-fda2-436b-86fe-df7f28423741",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[1])\n",
        "plt.colorbar()\n",
        "plt.grid(False)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KvJK1PyUxERlHy0osxqszXWlmy6mdy+0Bbgdw951m9izwOjAC3OEeDJaXUBITkUYtqsZP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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Wz7l27Lz9S1P"
      },
      "source": [
        "আমাদের ডাটাগুলোকে এমনভাবে প্রি-প্রসেস করতে হবে যাতে সেটা ঠিকমতো কাজ করতে পারে। যেহেতু আমাদের ইনপুট ভ্যারিয়েবলব্গুলো গ্রেস্কেল মানে ০ থেকে ২৫৫ পর্যন্ত রেঞ্জ, সে কারণে আমরা সেটাকে ০ থেকে ১ এর মধ্যে নিয়ে আসব। আমাদের ট্রেনিং এবং টেস্ট সেট দুটোকেই এই নরমালাইজেশন এর মধ্যে নিয়ে আসতে হবে। এটা করার জন্য আমাদেরকে প্রতিটা ইমেজকে ২৫৫ দিয়ে ভাগ করতে হবে। তাহলে সেটা ০ থেকে ১ এর মধ্যে চলে আসবে।\n",
        "\n",
        "পাইথনে সুবিধা হচ্ছে একটা লিস্টকে নর্মালাইজ করতে লুপিং করতে হবে না। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bW5WzIPlCaWv",
        "colab": {}
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFq6YGW0AVGb",
        "colab_type": "text"
      },
      "source": [
        "ভালো করে লক্ষ্য করুন এবার কালার-বারে, ০ থেকে  ২৫৫ হয়ে গেছে ০ থেকে ১ এর মধ্যে। এই নর্মালাইজ ডিপ লার্নিং মডেলকে ভালোভাবে কাজ করতে দেয়।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9_va8FI8l2I",
        "colab_type": "code",
        "outputId": "440868a2-54cd-4f8e-ae28-c33473e9c2dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[1])\n",
        "plt.colorbar()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f8f55f42be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZOudFpIR0xCUipRP3IOROiUtEhlMdl4iUka4qikj5FDxxFfdat4hIhuPniCtRN1Pt75/w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\nKHGJSOkocYlI6fx/wFtho7BtllkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1Yu7t_jpzsEh",
        "colab": {}
      },
      "source": [
        "# আমরা এভাবেও করতে পারি, একটা ফাংশন ডিক্লেয়ার করে, তবে সেটা এখন নয়\n",
        "\n",
        "# def normalize(images, labels):\n",
        "#  images = tf.cast(images, tf.float32)\n",
        "#  images /= 255\n",
        "#  return images, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Ee638AlnCaWz"
      },
      "source": [
        "আমরা দেখতে চাচ্ছি ডেটাগুলো ঠিকমতো ফরম্যাটে আছে কিনা? যেহেতু আমরা এই ডাটাগুলোকে ট্রেনিংয়ে ব্যবহার করবো, সেকারণে প্রথম ২৫টা ছবিকে ডিসপ্লে করি। যেহেতু আমরা আগেই ক্লাস ডিক্লেয়ার করেছিলাম সেকারণে নিচে সেটার নাম দেখা যাবে। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oZTImqg_CaW1",
        "outputId": "aaa9590d-440c-4a68-c859-600729a17254",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ih81bYnbt2jinhgJ/V3tN6upzrY+WSCfNHw4I/TbY7xEI93SsejbvGX6P9Wds166dl9m/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\nECIX6KFHCCGEELmgJD49aeUfLLH00mz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f5MmTvXzWWWd5efTo0ZnOx/r53XXXXV6uqKgI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iuH5mJKWNf3x83p1WWFYzUWLVoUbZ/KzFVt77zzzmQbx2jxOM3FEXA7r/Xz5zgt25eJ\n4HPie8fHGHD8Qa0xgB3NJptsEr8Dx9UAaawjp337FdL333//ZrfxeAPS1G5fBoCrWXPsXG6ler72\nPhWd511fQZnhNHVeBd6nQ/fr1y/aPs6IU7Y5Vdqn2/vV2esBn+rP8DXwfc7bcvMbz6X+t5DHBLfL\nrXbA+PFWtL9cbGfu/moL5OkRQgghRCnQQ48QQgghSkHd+vjZ3eVddezirTX9jqn1Mzn3t0+RrPVz\nZWfgwIHJa04l5zIARRWYPb4qKae/cj/7e0jy5PpwyjrLGSw3AGk/eXd2rpIzk0tZZdglzp85++yz\nk3bHHXdctD/3uc9FmyUQT61V2juatWvXRtnJp9zzeHnooYeive+++ybtxowZE21OZ3/88ceTdlxW\nwEtfnHLOi5b6RVzfeOONaHMIAKfXA6n0xfKpl2n4O/J96NOfWZry5RF4Qcujjjoq2pzyDaTyWb3g\nyzGw7MjbuEwDUHtF8VoroBeVlcjtw0ukfA/xWPZ9znIk/763B/L0CCGEEKIU6KFHCCGEEKVADz1C\nCCGEKAV1G9PDeP2PV2FtzXICXsdkrZHT/nyKJB/Ll31nWhNn1JXhUvc+tZRXSeeU5AMPPLCmffuY\nDe4z1oZ9PEA9avmdDcdF8HX1Gjv3k7+utS4vsdNOO0V7yZIl0c4tK8Jj7sc//nHS7rvf/W60R4wY\nEe3ddtstacdxMO29mnNr2XLLLbHXXnsBWD++g2PT/uZv/ibafq7iJTa4rIMv8cDX6u677062cTwR\nx3X5eMZhw4ZFm5eN8Eu/8H3EsXj+nPhYPDf7e4Pjgvh+AtLV6Hl5Db9S+2mnnYZ6w/8+cSwUx0/5\nPueYHr80CI+/ovIfQBo3V7Qye3Ovm/D9wCURuE9qXUm+PZCnRwghhBClQA89QgghhCgFDSFvsfvb\nk6v2W0StaXreJc+uZT5uS/ZfRji11Kes77LLLtF+7bXXoj1y5Mia9j18+PDk9Q477BBtlmu8K/iY\nY46paf9lglPR2S3tV8tmWcjLi+x+ZxnMX39OHX7//fej7eVPPjaPP+8eL0pf9ivEc2p7rSm+Hc1W\nW20VV0P3q6K3J2eeeWaHHUvUDstbLD/5quQTJ06MtpduOUSESzX4ccnUGqaRq7TMc/phhx0WbV9C\nhD/nywq0NfL0CCGEEKIU6KFHCCGEEKWgU+WtWt1nnBEArF+Jsgm/UBm/5ohwHx1etDibrzabcwUy\nHyxagAAABwdJREFUyt5KYUmB7baAXaYAMHny5GjnshTE+rALnKvucoYdAPTp0yfa48ePL9zfjBkz\nou0lapaxeGHK448/PmnHYy63mCVnafFnTj755KQdn8eoUaMKz12IzsJXNV64cGG0Wd7yoQIs2fvK\n2/xbxvvwldGLFgjNZUnzNi+rcRYuLwrsM0JZ4l62bFnhsdoCeXqEEEIIUQr00COEEEKIUqCHHiGE\nEEKUgoaI6fEraXMVWE4d97EHnNbKlU29Zso6JuuTnHILpDpkbpV1kcIpiD7VuFb42nMMlo/HKorj\n8fFYnCLpK36XFY6Puu6666Ltx8u1115b0/642i/bOfxq4a2B7wE/d/AcwauxC1Ev+LhHriLOMTi+\n+vF5553XrF2PnHDCCclrnp9POeWUdj22PD1CCCGEKAV66BFCCCFEKbCWVA82s3cBLNxgQ9GW9A8h\n9Nxws5ahvuw01J9dB/Vl16LN+1N92WkU9mWLHnqEEEIIIRoVyVtCCCGEKAV66BFCCCFEKWi4hx4z\n+8TMppvZHDObYWb/aGYN9z3KhpntWO236Wb2tpm9Sa9bl8cu6hoz28XMbjOz+Wb2nJnda2Z7tHAf\n25vZN9rrHEXt0Nw7w8yeN7MDN/wpUW+UfVw2XEyPma0KIWxTtXcCMB7AlBDC/3TtNgshfNzcPkTn\nYmb/C8CqEMIP3fuGyj25ttkPtv156B5pJ6p9+SSAfw8h3Fh9bwSA7UIIj2c/nO5nAIC7QwjD2uM8\nRe24ufcYAP8cQjhsAx8TdYTGZQN6epgQwlIA5wL4plU428wmmNkjAB4GADO72MyeNbOZZva/q+99\n2szuqf7FMtvMTqu+/wMzm1tt+8PCA4s2w8x2q17z3wKYA6CXmX3FzGZV++Zfqu02M7MV9LkvmtlN\nZM+u9uckav8jM3um2p9/X33/aDObbGZ3A5jV4V+4PBwBYE3TxAoAIYQZAJ4ws2ur/TWLxt42ZvZw\n1YMwy8xOrH7sBwAGVz0MtVVFFB3BdgCWA9m+g5ldbmbzzOwJM/udmf1Tp52xADQuO7cic1sQQnjN\nzDYF0FSe8jMAhocQ3jezsQB2BzAGgAGYYGaHAugJYEkI4QsAYGbdzGxHACcBGBJCCGa2fYd/mfIy\nBMCZIYRpZtYHwFUARgNYCeAhMzsOwP2Zz/9PAIeHEN6hfjsXwNIQwhgz2wLAU2Y2sbptNIC9Qghv\ntMu3EQAwDMBzzbx/MoCRAEYA6AHgWTN7DMC7AE4KIfzJzHqg0l8TAFwKYFgIYWQHnbcoZiszmw5g\nSwC9ABxZfX81mu+70QBOQaWvNwfwPJq/J0THUfpx2dCengIeDCE0rVM/tvrvBVQG3BBUHoJmAfic\nmV1jZoeEEFai8gO7GsCvzOxkAH/u+FMvLfNDCNOq9v4AHgkhLAshrEFFvjx0A5+fAuCWqjen6Z4e\nC+Cr1Un6aQDbo9L3ADBVDzydxsEAfhdC+CSE8A6ARwHsh8ofJf9iZjMBPASgN4CdO+80RTN8FEIY\nGUIYAmAcKmPOUNx3BwH4YwhhdQjhAwB3ddaJiw1SmnHZ8J4eMxsE4BMAS6tvfcibAVwdQvh5M5/7\nDIBjAVxlZg+HEK4wszEAjgJwKoBvYt1fMqJ9+XDDTbAWlf5sYkuyv4bKw9JxAJ43s32rbb8RQniY\nd2JmR9d4PLFxzEFlHNXKl1HxwI4KIawxswVI+1jUESGEqdW//HuiMo+q7xqD0o/Lhvb0mFlPADcC\n+NfQfET2AwDOMbOm4LveZraTme0K4M8hhN8AuBbAZ6ptuoUQ7gVwESpuPtHxPA3gCKtke20G4IsA\nHq0GNy83s92tkq13En1mUAjhKQCXoxJn0BuVvv9GdR8wsz3NbKsO/Sbl5hEAW5jZuU1vmNlwACsA\nnGZmm1bH76EAngHQDRU5co2ZHQGgf/VjHwDYtmNPXWwIMxsCYFMA76G476YAON7MtqzOr8c1vzfR\ngZR+XDaip6dJV94cwMcAbgXwo+YahhAmmtlQAFMrXlisAvAVALsBuNbM1gJYA+A8VDrwj2a2JSpe\ngm+39xcR6xNCWGxmlwOYjEo/3BVCuKe6+RJUHmaWoqJLNy2P/mMzG1htPzGEMNvMXgTQD8D0at8v\nBRADLEX7Uo2LOwnAdWZ2CSrS8QIAFwLYBsAMAAHAd0IIb1slkP0uM5sFYBqAl6r7ec/MppjZbAD3\nhRAu7oSvIyo0zb1AZaydFUL4JNN3z1bjP2YCeAeVsIKVnXDeoorGZQOmrAshhGgMzGybEMIqM9sa\nwGMAzg0hPN/Z5yXKSyN6eoQQQjQGvzCzvVCJA/l3PfCIzkaeHiGEEEKUgoYOZBZCCCGEqBU99Agh\nhBCiFOihRwghhBClQA89QgghhCgFeugRQgghRCnQQ48QQgghSsH/B+EGxouiWKkNAAAAAElFTkSu\nQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "59veuiEZCaW4"
      },
      "source": [
        "## মডেলকে বিল্ড করি \n",
        "\n",
        "আগেও দেখেছি একটা নিউরাল নেটওয়ার্ক তৈরি করতে প্রথমে মডেলের লেয়ার বানাতে হবে। এরপর আসবে কম্পাইলিং। মডেলের কাজ হচ্ছে ইনপুট থেকে ২৮ x ২৮ পিক্সেল মানে ৭৮৪ পিক্সেল থেকে এই নিউরাল নেটওয়ার্কের লেয়ারগুলো শার্ট/ব্যাগ এর মতো আইটেমগুলোকে ক্লাসিফাই করবো। \n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/start-page/cat3.png\"\n",
        "         alt=\"Fashion MNIST sprite\"  width=\"600\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Gxg1XGm0eOBy"
      },
      "source": [
        "### লেয়ারগুলোকে সেট করি\n",
        "\n",
        "একটা নিউরাল নেটওয়ার্কের বেসিক বিল্ডিং ব্লক হচ্ছে তার লেয়ার। এই লেয়ারগুলো বিভিন্ন ফিচার এক্সট্রাক্ট করে রিপ্রেজেন্টেশন এর মাধ্যমে। ইনপুট ডাটা থেকে। যেহেতু একেকটার লেয়ার আরেকটা লেয়ারের সাথে সিকুয়েন্সিয়াল, সে কারণেই ডেন্স `tf.keras.layers.Dense` ব্যবহার করছি। এর ভেতরের ইন্টারনাল প্যারামিটারগুলো নিজে থেকেই এডজাস্ট হয় মানে শেখে এই ট্রেনিং এর সময়। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9ODch-OFCaW4",
        "colab": {}
      },
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    keras.layers.Dense(128, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gut8A_7rCaW6"
      },
      "source": [
        "আমাদের এই নেটওয়ার্কটা তিনটা লেয়ারের, ছবি দেখুন। \n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/start-page/cat5.1.png\"\n",
        "         alt=\"Fashion MNIST sprite\"  width=\"600\"> ছবি: সবগুলো ক্লাসের প্রোবাবিলিটি যোগ করলে সেটা ১ হবে মানে ১০০%\n",
        "\n",
        "* **ইনপুট লেয়ার** - প্রথম লেয়ারটার কাজ বেশ মজার। এর কাজ হচ্ছে ইনপুটকে ফ্ল্যাট করে দেয়া - `tf.keras.layers.Flatten`। আমাদের ইনপুট হিসেবে যে ইমেজ আছে সেটা কিন্তু একটা ২ ডাইমেনশনের অ্যারে মানে ২৮ x ২৮ পিক্সেল। বেসিক মডেলগুলোতে ডাইমেনশনের অ্যারের লাইনগুলোকে একসাথে প্রসেস করা কঠিন, তাই সেগুলোকে আমরা পাশাপাশি খুলে এক লাইনে দাঁড় করাবো। এই ২৮ x ২৮ পিক্সেলকে এক লাইনে দাঁড় করালে ৭৮৪ পিক্সেল হয়। এই লেয়ারের কোন লার্নিং প্যারামিটার নেই, তার কাজ হচ্ছে এটাকে রিফর্মেট মানে নতুন ফরম্যাটে ট্রান্সফরম করে দেয়।\n",
        "\n",
        "* **\"হিডেন লেয়ার\"** — এটা 'ডেন্স' লেয়ার, একটার সাথে আরেকটা 'ডেন্সলি কানেক্টেড' মানে `tf.keras.layers.Dense`। ইনপুট লেয়ারের পর থেকে আমরা হিডেন লেয়ার বলতে পারি। এই লেয়ারটাকে আমরা কনফিগার করেছি ১২৮টা নিউরন দিয়ে। আরো কম বেশি করতে পারতাম। প্রতিটা নিউরন যাকে আমরা নোড বলছি সেটা কিন্তু ৭৮৪টা নোডকে কানেক্ট করছে আগের লেয়ার থেকে নতুন লেয়ারে। ইনপুট এর ওয়েট এর উপর ভিত্তি করে এই হিডেন প্যারামিটারগুলো শেখে ট্রেনিং এর সময়। তবে তাদের আউটপুটের যে সিঙ্গেল ভ্যালু সেটাকে সে পাঠায় পরবর্তী লেয়ারে। এখানে আমরা ইচ্ছা করলে আরো কয়েকটা লেয়ার দিতে পারতাম, তবে শেখার জন্য আমরা এটাকে একটু সিম্প্লিস্টিক রাখছি। আমাদের অ্যাক্টিভেশন ফাংশন হিসেবে এখানে ব্যবহার করেছি রেলু, যা আসলে নন লিনিয়ার ফাংশনকে ভালোভাবে সামনে এগিয়ে দিতে পারে।\n",
        "\n",
        "* আমাদের এখানে activation='relu' এর কাজ হচ্ছে \"যদি X>0 তাহলে রিটার্ন X, নাহলে রিটার্ন 0\"। এর মানে হচ্ছে এই অ্যাক্টিভেশন ফাংশন লেয়ারে X শূন্য এর বড়ো হলে সামনের লেয়ারে X এর ভ্যালু পাঠাবে। \n",
        "\n",
        "* **আউটপুট লেয়ার** `tf.keras.layers.Dense` — এর কনফিগারেশন আগের মতো। ডেন্স। আউটপুট লেয়ারে আপনাদের অবশ্যই মনে আছে আমাদেরকে ক্লাসিফাই করতে হবে দশটা অবজেক্ট। ১০ ধরনের কাপড়, চোপড়, জুতা এগুলোকে যখন ক্লাসিফাই করব তখন অ্যাক্টিভেশন লেয়ার হিসাবে ব্যবহার করব 'সফটম্যাক্স'। 'সফটম্যাক্স' এর কাজ হচ্ছে তাকে যদি দশটা ভ্যালু দেয়া হয় সে সবার ম্যাক্সিমাম ভ্যালু, সবচেয়ে বড় ভ্যালুটা পিক করবে পরের লেয়ারে পাঠাতে। এই ফাংশন আমাদের অনেক কোডিং বাঁচায়। \n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/start-page/cat6.png\"\n",
        "         alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
        "* প্রথম লেয়ারে ইনপুট ছিল ৭৮৪ বাইট, এরপরের লেয়ার থেকে শেষ লেয়ারে আমাদের ইনপুট হবে ১২৮ নোড থেকে। প্রতিটা নোডের ওয়েট ইনপুট এর সাথে যুক্ত হয়ে সেটা একটা শেখানো প্যারামিটার হিসেবে দাঁড়াবে। আমাদের আউটপুট ভ্যালু কিন্তু ০ থেকে ১ এর মধ্যে, আমরা যেটাকে বলি প্রবাবিলিটি ডিস্ট্রিবিউশন যেটা আমাদেরকে ওই ছবিটা কে কোন ক্লাসে পড়বে সেটা দেখাবে। এর মধ্যে সবচেয়ে বড়োটা পিক করবে সফটম্যাক্স। ছবি দেখুন, এই দশটা নোটের সবগুলোর ভ্যালু যোগ করলে এক হবে।"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JamJ00chzsE0"
      },
      "source": [
        "### মডেলকে কম্পাইল করা\n",
        "\n",
        "ট্রেনিং করানোর আগে আমাদেরকে কিছু সেটিং বুঝতে হবে। এগুলোকে আমরা যোগ করবো মডেল কম্পাইলিং স্টেজে। \n",
        "\n",
        "* *লস ফাংশন* — আগেও আলাপ করেছিলাম, তবে মডেলকে ঠিক জায়গায় মানে ঠিক অ্যাক্যুরেসিতে পৌঁছাতে হলে এই ফাংশন দরকার। আমাদের এখানে আউটপুট ১০ টা ক্যাটেগরীতে একটা আরেকটার সাথে মিউচুয়ালি এক্সক্লুসিভ (যেখানে একেকটা স্যাম্পল একদম একটা ক্লাসেই থাকবে), সেকারণে ব্যবহার করছি 'sparse_categorical_crossentropy' ব্যবহার করছি লস হিসেবে। \n",
        "\n",
        "* *অপটিমাইজার* — লস ক্যালকুলেট করে সেটার কারেকশন/আপডেট পাঠানোই এর কাজ। \n",
        "\n",
        "* *মেট্রিকস* — আমরা যখন ট্রেনিং এবং টেস্ট একসাথে মনিটর করবো, তখন অ্যাক্যুরেসির দরকার পড়বে। আমাদের এখানে যখন কিছু ছবি ঠিকমতো ক্লাসিফাই হয়েছে, তখন অ্যাক্যুরেসিটা কার্যকরী। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Lhan11blCaW7",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qKF6uW-BCaW-"
      },
      "source": [
        "## মডেলকে ট্রেনিং করাই\n",
        "\n",
        "শুরুতেই একটা ছবি। কখন কোনটা ব্যবহার করবেন? কখন শুধুমাত্র `model.fit` অথবা কলব্যাক, অথবা অনেকগুলো ডেটা একসাথে ব্যাচে। তবে, নতুন ব্যবহারকারী হিসেবে শুরুতেই শুধুমাত্র `model.fit` দিয়ে আজকে কাজ করি। এরপরে আমরা আজকেই কলব্যাক এবং পরে ব্যাচে ট্রেনিং করাবো।\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/TensorFlow2/master/assets/training-test2.jpg\"> চিত্রঃ কখন কোন `model.fit` ব্যবহার হবে?\n",
        "\n",
        "আজকে আমাদের এই নিউরাল নেটওয়ার্ককে ট্রেনিং করাতে সামনের কয়েকটা স্টেপ দরকার পড়বে;\n",
        "\n",
        "১. শুরতেই ডেটাকে ফিড করতে হবে মডেলকে। আমাদের এখানে ট্রেনিং ডেটা আছে `train_images` এবং `train_labels` অ্যারেগুলোতে।\n",
        "২. মডেল শেখে তার ছবি আর লেবেলের মধ্যে সম্পর্ক থেকে। \n",
        "৩. এখন আমাদের মডেল টেস্ট করার পালা। `train_images` থেকে প্রেডিকশন করাবো? ভুলেও না। তাহলে পরীক্ষায় প্রশ্ন ফাঁস হয়ে যাবে। বরং আমরা ব্যবহার করবো আনকোরা টেস্ট সেট থেকে যাদের আলাদা করে রেখেছিলাম। আমরা ব্যবহার করবো `test_images` অ্যারে। আমাদেরকে ভেরিফাই করতে হবে `test_images` অ্যারে ম্যাচ করে কিনা `test_labels`কে। \n",
        "৪. `epochs=10` মানে পুরো ট্রেনিংসেটের ওপর ১০ বার পুরো আইটারেশন চালাবে, মানে সর্বমোট  ১০ * ৬০০০০ = ৬০০০০০ এক্সাম্পল।\n",
        "\n",
        "ট্রেনিং শুরুতেই কল করবো `model.fit` মেথডকে — এর কাজ হচ্ছে train_images এবং  train_labels এর ভেতরের সম্পর্ককে শেখা। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xvwvpA64CaW_",
        "outputId": "41b25fff-d17b-4fbc-df7a-d24c22a224aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=10)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 6s 102us/sample - loss: 0.4979 - accuracy: 0.8252\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.3753 - accuracy: 0.8650\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 5s 87us/sample - loss: 0.3376 - accuracy: 0.8766\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 5s 89us/sample - loss: 0.3132 - accuracy: 0.8844\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.2945 - accuracy: 0.8912\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.2793 - accuracy: 0.8965\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.2666 - accuracy: 0.9009\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 5s 90us/sample - loss: 0.2578 - accuracy: 0.9038\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 5s 89us/sample - loss: 0.2484 - accuracy: 0.9069\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 5s 90us/sample - loss: 0.2392 - accuracy: 0.9119\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8f54088198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "W3ZVOhugCaXA"
      },
      "source": [
        "এই প্রসেসটা ভালো করে লক্ষ্য করুন। মডেল ট্রেনিং এর পাশাপাশি তার লস এবং অ্যাক্যুরেসির দিকে নজর রাখুন। লস কমার সাথে সাথে অ্যাক্যুরেসি বাড়ছে। এই মডেলের অ্যাক্যুরেসি বেড়ে চলে এসেছে ০.৮৮ মানে ৮৮% ট্রেনিং ডেটার ওপর। "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oEw4bZgGCaXB"
      },
      "source": [
        "## অ্যাক্যুরেসিকে টেস্ট ডেটা দিয়ে ইভাল্যুয়েট করানো\n",
        "\n",
        "ট্রেনিং দেখলাম, এখন দেখতে হবে কেমন কাজ করে টেস্ট ডেটার ওপর?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VflXLEeECaXC",
        "outputId": "7d7e8a4a-6c6b-4d6c-a598-d6ce76365098",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "# model.evaluate(test_images, test_labels)\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
        "\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 87us/sample - loss: 0.3467 - accuracy: 0.8805\n",
            "\n",
            "Test accuracy: 0.8805\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yWfgsmVXCaXG"
      },
      "source": [
        "টেস্ট ডাটাসেটের ওপর আমরা যখন অ্যাক্যুরেসি টেস্ট করলাম, তখন দেখা গেল এখানে তারা কিছুটা খারাপ করেছে। মানে ট্রেনিং ডাটাসেট থেকে টেস্টডাটা সেটের অ্যাক্যুরেসি কিছুটা খারাপ। এই ট্রেনিং অ্যাক্যুরেসি এবং টেস্টসেটের মধ্যে যে গ্যাপ সেটাকে আমরা বলি 'ওভার ফিটিং'। 'ওভার ফিটিং' হচ্ছে এমন একটা স্ট্যাটাস, যখন মেশিন লার্নিং মডেল নতুন ডাটা, যে ডাটা আগে দেখেনি - সেই ডাটাতে ট্রেনিং ডাটার অ্যাক্যুরেসি থেকে খারাপ করে। \n",
        "\n",
        "তবে এই অ্যাক্যুরেসি বাড়ানোর উপায় নিয়ে আলাপ করবো সামনে। "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lPe1awm3Fd4S",
        "colab_type": "text"
      },
      "source": [
        "## সাইকিট লার্নে কি করতাম?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49i1KIqUFSWm",
        "colab_type": "code",
        "outputId": "74372077-3d17-4084-8961-d8862d305ca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "pred_lebel = model.predict_classes(test_images)\n",
        "accuracy_score(test_labels, pred_lebel)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8805"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xsoS7CPDCaXH"
      },
      "source": [
        "## এখন প্রেডিকশনের পালা \n",
        "\n",
        "যিহেতু আমাদের মডেলের ট্রেনিং হয়ে গেছে, এখন কিছু পরীক্ষা নিরীক্ষার পালা। কিছু ছবি প্রেডিকশন করি এখন। প্রেডিক্ট করি টেস্ট ছবি নিয়ে। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Gl91RPhdCaXI",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "x9Kk1voUCaXJ"
      },
      "source": [
        "প্রেডিকশন শেষ। আমাদের মডেল প্রতিটা ছবিকে ঠিকমতো লেবেল করেছে কিনা, সেটা দেখা দরকার। শুরুতেই প্রথম প্রেডিকশনটা দেখি।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3DmJEUinCaXK",
        "outputId": "49acd837-8d10-4bad-9164-933fde34d5c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7.5772544e-10, 2.4105393e-11, 8.0843354e-10, 1.0038004e-12,\n",
              "       1.5201566e-08, 1.2795480e-03, 3.8289167e-09, 5.9792902e-02,\n",
              "       3.4744371e-10, 9.3892759e-01], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-hw1hgeSCaXN"
      },
      "source": [
        "ভালো করে লক্ষ্য করুন। আমাদের আউটপুট লেয়ারে যে ১০টা প্রোবাবিলিটি ডিস্ট্রিবিউশন দিচ্ছে, সেখানে সবচেয়ে বেশি সংখ্যাটাই হচ্ছে তার লেবেলের কনফিডেন্স। সবচেয়ে বেশি সংখ্যাটা কি হতে পারে? এখানে একটা ছবি দেখতে পারেন বোঝার সুবিধার জন্য। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qsqenuPnCaXO",
        "outputId": "cafdfeaa-ac1a-49f9-c1a4-bf749d15fa4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "E51yS7iCCaXO"
      },
      "source": [
        "আমাদের মডেল সবচেয়ে বেশি কনফিডেন্ট অ্যাঙ্কেল বুট হিসেবে যার ক্লাস নাম হচ্ছে `class_names[9]`. আমরা যখন চেক করলাম, তখন বোঝা গেল আমাদের ক্লাসিফিকেশন ঠিক আছে।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Sd7Pgsu6CaXP",
        "outputId": "6c79bb37-e1f2-4d19-e019-9170d8a33a4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ygh2yYC972ne"
      },
      "source": [
        "এই কোড দেখে ভয় পাবেন না। এই মুহূর্তে আপনার এটা জানতে হবে না। দশটা স্যাম্পলকে আমরা গ্রাফে দেখতে চাচ্ছি ১০টা আলাদা আলাদা ক্লাস প্রেডিকশনে। ডিপ লার্নিং শিখতে এই মুহুর্তে এই কোড জানতে হবে না। আস্তে আস্তে আমরা বুঝতে পারবো।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DvYmmrpIy6Y1",
        "colab": {}
      },
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array, true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "d4Ov9OFDMmOD"
      },
      "source": [
        "ভালো করে লক্ষ্য করুন, ০তম ছবিটা, তার প্রেডিকশনএবং তার প্রেডিকশন অ্যারে। ঠিক প্রেডিকশন লেবেলগুলো নীল (মানে হালকা কালো) আর যেগুলো ঠিক প্রেডিকশন নয়, সেগুলোর লেবেল লাল (হালকা গ্রে)। শেষে ১০০ এর মধ্যে শতাংশ আকারে প্রেডিক্টেড লেবেলের ভ্যালু।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HV5jw-5HwSmO",
        "outputId": "990542ea-a862-4022-e559-e8ee09dbc3c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "source": [
        "i = 0\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gHW3dHYAXjREvndMdaCY7X9Ayc6X+E1rv+dNmjWMcbTwS8OHAmaNi36JfsgD4IDC5vYp8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\nYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ko-uzOufSCSe",
        "outputId": "c7ac6391-33f4-421a-9fb4-67cf9a4483fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "source": [
        "i = 12\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZE/61zPxSD+12Bn5Kc4toNHB5Zs7u47wDNE/Yj2RmT8s5I+JhmtU7a4DVva4UiIjdaJ74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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kgdvGD52CaXR"
      },
      "source": [
        "কয়েকটা ছবির পাশাপাশি তার কয়েকটা প্রেডিকশন প্লট করে দেখি। অনেক সময় দেখা যায় - মডেল মাঝে মাঝে ভুল করে যদিও তারা এই ব্যাপারে বেশ 'কনফিডেন্ট' থাকে। প্রথমে প্লট করছি টেস্ট ছবিগুলোকে, তারপর প্রেডিকটেড লেবেল, তারপর আসল, ট্রু লেবেল। রংয়ে হালকা গ্রে ঠিক প্রেডিকশন, কালোটা ভুল প্রেডিকশন। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hQlnbqaw2Qu_",
        "outputId": "e59537b5-a42d-4cc2-ae13-3128bf8c541d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        }
      },
      "source": [
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions[i], test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions[i], test_labels)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4fB+W9AkzTZPUV1m3IUn6srKkuCHeOAStDUHvDkET8nM/T9JEM/23ZU/ryv0o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\nv1OcryDtmBsQ5yBs2rQpqfN5TT5fK87lktJ8rDifSpJ69Oi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9drwUflTlfXaRwuNSaC+F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ik0Vlv74s53Wf69XCqF30SPL1c2deh5KfxRCsPyx0/L277fSWEvKfy5WvuWP+c1KezZGO9NCvtI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\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "R32zteKHCaXT"
      },
      "source": [
        "সবশেষে আমাদের এই ট্রেইনড মডেলকে দিয়ে একটা ছবি প্রেডিক্ট করাই। টেস্ট ডেটাসেট থেকে অবশ্যই। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yRJ7JU7JCaXT",
        "outputId": "671b6503-4335-4b0a-a06c-6ff0ec329f4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "img = test_images[1]\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vz3bVp21CaXV"
      },
      "source": [
        "`tf.keras` মডেল ব্যাচে প্রেডিকশন করার জন্য অপ্টিমাইজ। মানে একটা একটা করে না। এইজন্য ব্যাচে প্রেডিকশন সবসময় সময় বাঁচায়। এখন আমরা যদি একটা ছবিকে ব্যবহার করি সেটাকেও একটা লিস্টে ফেলতে হবে। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lDFh5yF_CaXW",
        "outputId": "54a49231-bbc1-456b-af2c-3ce2cc4de4e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "img = (np.expand_dims(img,0))\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EQ5wLTkcCaXY"
      },
      "source": [
        "সঠিক লেবেলের জন্য প্রেডিক্ট করি -"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "o_rzNSdrCaXY",
        "outputId": "75319800-d236-4904-8627-113ae0803fa6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "predictions_single = model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[4.0305869e-05 3.3975964e-12 9.9847370e-01 7.6855168e-11 9.3581108e-04\n",
            "  2.2720197e-08 5.5011554e-04 6.2563029e-16 2.3616828e-09 3.7662083e-12]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6Ai-cpLjO-3A",
        "outputId": "0455c34e-235b-408c-c8ee-27a8346407e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "plot_value_array(1, predictions_single[0], test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q3MBEV8TGxFnE1kMuzxNyGZ5OHNAOBa7ovP844FuDoBrlTsjf9/PNA95JzEQYnG6fB/w/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\nAEtSI/8f8KYtg1JrU0QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cU1Y2OAMCaXb"
      },
      "source": [
        "`model.predict` আমাদেরকে একটা লিস্টের লিস্ট দেয়, যেখানে প্রতিটা ছবি ওই ব্যাচে আছে। এখন ওই ব্যাচের একটা ছবির জন্য প্রেডিকশন দেখি। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2tRmdq_8CaXb",
        "outputId": "22e4cae2-8e82-4f78-d76e-f5b1e896c150",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YFc2HbEVCaXd"
      },
      "source": [
        "কি বোঝা গেলো? মডেল ঠিকমতো প্রেডিক্ট করতে পেরেছে বলতে হবে। "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9v7D7O_4zsGB"
      },
      "source": [
        "## কেন কলব্যাক? \n",
        "\n",
        "মডেলকে ট্রেইন করতে সময় লাগে। বিশেষ করে আমরা যখন বিভিন্ন ইপক সংখ্যা নিয়ে কাজ করি। একেকটা ইপক চালাতে যেরকম কম্পিউটিং রিসোর্স ব্যবহার হয়, সেখানে আমাদেরকে পয়সা গুনতে হয় ক্লাউডএর জন্য। এমন যদি হতো আমরা একটা ট্রেনিং বন্ধ করতে পারতাম যখন আমরা একটা দরকারি অ্যাক্যুরেসি পয়েন্টে পৌঁছে গেছি। এই মুহূর্তে আমাদের প্রতিবারই একটা ইপক সংখ্যা পর্যন্ত যেতে হচ্ছে। ভালো খবর হচ্ছে, আমাদের একটা কলব্যাক ফাংশন আছে যেটা এই ট্রেনিং লুপকে দরকারি অ্যাক্যুরেসি এলেই আপনা আপনি বন্ধ করতে পারে। আমরা আসলে ওই পর্যন্ত পৌঁছাতে চাই ওই লেভেলের অ্যাক্যুরেসি পৌঁছে গেলে ট্রেনিং বন্ধ করে দিতে পারি।\n",
        "\n",
        "এখন ফ্যাশন MNIST এর মধ্যে কলব্যাক ফিচারটা চালাই। আমাদের 'মডেল.ফিট' ফাংশনটা যখন ট্রেনিং লুপের উপর কাজ করে তখন আমরা লক্ষ্য রাখি সেটার আউটপুটে। পাইথনে কাজটা করা বেশ সোজা। যদিও জিনিসটা ইমপ্লিমেন্ট করা হচ্ছে একটা আলাদা ক্লাস হিসেবে তবে সেটা ইনলাইনে থাকছে আমাদের সেই একই কোডে। ধরা যাক, আমরা চাইছি যখন লস ০.২ এর নিচে থাকতে হবে, তাহলে সেই ট্রেনিংটা এমনিতেই ক্যান্সেল হয়ে যাবে।\n",
        "\n",
        "দেখি কখন আমাদের ট্রেনিং লুপ থামে!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pa1Zw-lczsGC",
        "outputId": "ceaa0cd6-9b05-48cc-d07e-46e80c2e1f8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>0.9):\n",
        "      print(\"\\nReached 90% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "# আগের সব কোড এক জায়গায় \n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "# শুধুমাত্র এটা নতুন অংশ\n",
        "callbacks = myCallback()\n",
        "\n",
        "# আগের লেয়ার\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "\n",
        "# আগের মডেল কম্পাইলেশন\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# আগের মডেল ফিট\n",
        "model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 6s 96us/sample - loss: 0.4750 - accuracy: 0.8282\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 6s 93us/sample - loss: 0.3607 - accuracy: 0.8691\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 6s 92us/sample - loss: 0.3252 - accuracy: 0.8797\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 6s 92us/sample - loss: 0.3000 - accuracy: 0.8893\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 6s 93us/sample - loss: 0.2812 - accuracy: 0.8954\n",
            "Epoch 6/10\n",
            "59456/60000 [============================>.] - ETA: 0s - loss: 0.2665 - accuracy: 0.9006\n",
            "Reached 90% accuracy so cancelling training!\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.2663 - accuracy: 0.9007\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8f214b6438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_bgZl_QfD2_",
        "colab_type": "text"
      },
      "source": [
        "## মডেলের একটা সামারি\n",
        "\n",
        "কি মনে হচ্ছে? এখানে ৩ লেয়ারের একটা ছোট্ট সামারি টেবিল দিয়েছে টাইপ, শেপ এবং প্যারামিটার দিয়ে। ট্রেনিং এর জন্য প্যারামিটার সংখ্যা ৪০৭০৫০, মানে ৪ লক্ষ। একটা ছোট ডেটাসেট অথচ অনেক প্যারামিটার।\n",
        "\n",
        "এই সামারি দিয়ে মডেলের প্রতিটা লেয়ারে কি হচ্ছে সেটা জানা যায়। সেটার সাথে লেয়ারগুলোর নাম যদি না মডেল নিজে থেকে দেয়। অনেক সময় সেগুলোর নাম আমরা দিয়ে দিতে পারি। আউটপুট শেপ None কেন? সামনে দেখবেন ডিপ লার্নিং মডেলগুলো ট্রেনিং করানোর সময় ডেটাসেটগুলোকে ব্যাচে করাবো। এখানে এর মানে হচ্ছে ব্যাচ সাইজ যে কোন কিছু হতে পারে। সামনে আমরা দেখবো নন-ট্রেইনেবল প্যারামিটার নিয়ে কিভাবে কাজ করা যায়।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Se_338WFG8oY",
        "colab_type": "code",
        "outputId": "562666e2-5a04-423c-d106-7bc9396cbad6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 512)               401920    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 407,050\n",
            "Trainable params: 407,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dUHA6MK3O4Wx",
        "colab_type": "text"
      },
      "source": [
        "আমরা দেখছি ডেন্স লেয়ারে অনেক প্যারামিটার। এখানে প্রথম হিডেন লেয়ারে ৭৮৪ x ৫১২ কানেকশন ওয়েট সঙ্গে ৫১২ বায়াস নিলে ৮০২,৮১৬ প্যারামিটার হয়ে যায়। ভালো কথা, এই মডেলটাকে কি ছবি হিসেবে জেনারেট করা যায়?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRYWZnr9WUYM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "e3eb15ac-0510-4e10-f9c2-9a5718e05576"
      },
      "source": [
        "keras.utils.plot_model(model, \"mnist_model.png\", show_shapes=True)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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ztbWl0+kWFhZr1qwpLi4+ffo0fpR9ypQpZWVlZ86cwe/nnTRp0i+//NLnDfu7d+++//77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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7mt0CwtV51h",
        "colab_type": "text"
      },
      "source": [
        "## মডেলের লেয়ারগুলোর নাম ইনডেক্স ধরে"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3vCIgEqQcex",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "88ab1438-7be9-419b-f37c-81b6833639d7"
      },
      "source": [
        "model.layers"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tensorflow.python.keras.layers.core.Flatten at 0x7f8f55c272b0>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7f8f55c27710>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7f8f55c27978>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FD9iv6eRRmt-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0bb7d697-3fdd-433d-fb1c-eb0716011b67"
      },
      "source": [
        "hidden1 = model.layers[1]\n",
        "hidden1.name"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'dense_2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-KHz-SSR8zN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9bf9f6c5-2878-4a93-b732-8ea5c183d74b"
      },
      "source": [
        "model.get_layer(hidden1.name) is hidden1"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffhCGYMDTnk9",
        "colab_type": "text"
      },
      "source": [
        "একটা লেয়ারের প্রতিটা প্যারামিটার এক্সেস করা যায় সেটার get_weights() এবং set_weights() মেথড দিয়ে। একটা ডেন্স লেয়ারের জন্য সেটা তার প্রতিটা কানেকশন এর ওয়েট এবং বায়াসের জন্য সেটা প্রযোজ্য।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8Mjdq7sTMm8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weights, biases = hidden1.get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5EJLMtNTPmu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "dcce7747-6245-4a4e-ad6a-aeaed7241950"
      },
      "source": [
        "weights"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.03092452, -0.07504503, -0.13763687, ...,  0.1057985 ,\n",
              "         0.07987928,  0.06646112],\n",
              "       [-0.03415161, -0.01011258,  0.06298686, ..., -0.00679969,\n",
              "        -0.00428082, -0.01032218],\n",
              "       [-0.00104201, -0.00733904, -0.3240066 , ..., -0.13431765,\n",
              "         0.01226498,  0.03361627],\n",
              "       ...,\n",
              "       [ 0.18274958, -0.0153599 , -0.32968813, ..., -0.16499789,\n",
              "        -0.23007339, -0.00274525],\n",
              "       [ 0.06125874, -0.2426279 , -0.31404632, ...,  0.01373851,\n",
              "        -0.10446495,  0.03723101],\n",
              "       [ 0.01087047, -0.01767496, -0.21919104, ...,  0.16324499,\n",
              "        -0.013962  , -0.04339984]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blTR21enTSPh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6eb3b950-350c-49c2-f6ef-b1136735f874"
      },
      "source": [
        "weights.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(784, 512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWD3ZwCdTVgU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "45bec170-3bab-4d7e-b8e3-3a10724907c2"
      },
      "source": [
        "biases"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.25886723e-01,  2.13907227e-01, -9.80531052e-02,  1.18458144e-01,\n",
              "       -2.11972281e-01, -1.80441719e-02,  3.71187717e-01,  1.98542148e-01,\n",
              "        1.18108280e-01,  2.16348737e-01,  2.83454090e-01,  3.79868224e-02,\n",
              "        2.80779153e-01,  4.59733494e-02,  1.25797391e-01,  2.33179599e-01,\n",
              "        2.40252092e-01,  1.08046696e-01,  2.73400456e-01,  2.60755062e-01,\n",
              "        2.42609903e-01,  1.64256692e-01,  2.14972168e-01, -4.73761745e-02,\n",
              "        1.51032165e-01,  3.03067893e-01,  3.62261713e-01,  4.22253698e-01,\n",
              "       -6.03409037e-02,  1.91008464e-01,  1.99588552e-01,  1.18786216e-01,\n",
              "        9.83842313e-02,  5.58407344e-02,  1.43366516e-01,  5.07925935e-02,\n",
              "        1.58827409e-01,  1.09077156e-01,  1.13929652e-01, -3.25769454e-01,\n",
              "       -2.82146316e-02,  1.20326295e-01,  2.59599686e-01,  3.15617591e-01,\n",
              "        2.14895129e-01,  1.80912912e-01, -9.26189646e-02, -2.98572071e-02,\n",
              "       -8.94159451e-03, -9.15721525e-03,  3.75347227e-01, -2.32045591e-01,\n",
              "       -2.53463387e-02,  7.21785054e-03, -2.77692109e-01, -9.90898907e-02,\n",
              "        8.81810784e-02, -1.10327750e-02, -8.22040662e-02, -1.05065763e-01,\n",
              "        1.12036698e-01,  2.88687408e-01, -5.00892522e-03, -8.95195268e-03,\n",
              "        1.40814558e-01, -4.82428260e-02,  8.68727192e-02, -8.39016680e-03,\n",
              "       -3.12986150e-02, -2.93279201e-01,  2.52183259e-01, -5.35922721e-02,\n",
              "        6.28331974e-02,  2.96453029e-01,  3.27713192e-01,  2.59188056e-01,\n",
              "        5.05853258e-02,  2.19842121e-01,  2.51833528e-01, -9.75737162e-03,\n",
              "       -3.54820513e-03,  3.18930507e-01,  1.01525463e-01,  5.89164905e-02,\n",
              "        4.38640922e-01,  2.23438293e-01,  1.30564094e-01,  4.76228744e-01,\n",
              "        3.73356551e-01, -1.30673066e-01,  2.84261167e-01,  4.43581678e-02,\n",
              "       -6.10574111e-02, -3.55487019e-02, -6.84999824e-02, -4.92695868e-02,\n",
              "       -1.92908607e-02,  6.83786348e-02,  2.36618906e-01,  2.84296095e-01,\n",
              "       -4.02765013e-02,  9.28878710e-02,  1.91573963e-01,  3.99491876e-01,\n",
              "        2.28353605e-01,  1.07774287e-01,  2.68926099e-02,  7.61952996e-02,\n",
              "        1.58211440e-02,  8.15742239e-02, -1.86223742e-02,  1.25605380e-02,\n",
              "       -1.73789524e-02, -1.07492857e-01, -3.08612199e-03,  1.06519172e-02,\n",
              "        6.23488091e-02, -3.28006037e-02,  5.34423590e-02,  1.11911789e-01,\n",
              "       -1.93542615e-02,  1.88335165e-01, -2.09009349e-01,  2.39734471e-01,\n",
              "        2.99377024e-01,  4.75190021e-02,  2.21432373e-01,  3.98434289e-02,\n",
              "        2.70836294e-01,  2.23631337e-01,  1.02158196e-01,  3.54893237e-01,\n",
              "       -2.35657487e-02,  3.12954485e-01,  1.26610640e-02,  2.59558618e-01,\n",
              "        1.41475841e-01, -2.27317829e-02,  3.59335452e-01,  1.84565857e-01,\n",
              "        6.77047074e-02,  2.18576729e-01, -1.55849397e-01,  5.08943573e-02,\n",
              "        2.56839901e-01,  2.63117105e-01,  4.75446507e-02, -1.96384303e-02,\n",
              "        9.76224914e-02, -1.87391147e-01,  1.01191923e-01, -4.37028781e-02,\n",
              "       -1.43302724e-01,  5.35215139e-02,  3.26957628e-02,  1.01038538e-01,\n",
              "       -8.15147161e-03,  1.92770615e-01,  6.07930347e-02,  3.26805785e-02,\n",
              "        1.07654743e-01, -2.59618647e-02, -1.39367133e-02,  1.89742520e-01,\n",
              "        4.95366216e-01, -4.67346124e-02,  1.48721904e-01,  2.15321541e-01,\n",
              "       -1.08233728e-02, -1.50751621e-02,  1.77065596e-01,  1.64003864e-01,\n",
              "       -2.10356545e-02,  1.23086713e-01, -2.61788756e-01,  3.04727197e-01,\n",
              "        8.95419866e-02,  1.11793198e-01,  1.81979060e-01,  2.77219921e-01,\n",
              "        2.81064302e-01,  1.49175584e-01,  1.39427662e-01,  1.62479490e-01,\n",
              "        3.01655859e-01,  4.97099161e-02, -3.61545570e-02,  4.20112163e-02,\n",
              "        1.60445645e-02, -3.74554545e-02, -1.25938505e-01, -1.63213890e-02,\n",
              "       -2.30975952e-02, -4.46033577e-04,  9.32052955e-02,  3.51893216e-01,\n",
              "       -3.73982601e-02,  4.02994603e-02,  1.76703185e-01,  3.34332347e-01,\n",
              "        1.49438053e-01,  3.50425661e-01, -7.86138922e-02, -2.80360401e-01,\n",
              "       -1.10039739e-02,  1.71338901e-01,  2.86721379e-01,  2.57998198e-01,\n",
              "        2.34010637e-01,  4.54076588e-01, -2.16229632e-01,  2.04289839e-01,\n",
              "        2.30417639e-01,  2.93096185e-01,  1.26535267e-01,  1.70833647e-01,\n",
              "        2.08981022e-01,  4.24034238e-01,  1.39519960e-01,  5.39441220e-03,\n",
              "        2.74643064e-01, -1.48211457e-02, -1.01324581e-01,  2.27378532e-02,\n",
              "        1.20825820e-01,  3.80249888e-01, -2.16826927e-02,  2.23529741e-01,\n",
              "       -1.90211356e-01,  4.36370730e-01,  1.71342775e-01, -2.24140733e-02,\n",
              "        1.43658787e-01,  1.56217039e-01,  3.77161503e-01, -8.48915707e-03,\n",
              "       -5.34788668e-02, -1.20929964e-02,  5.72207756e-02, -1.40726687e-02,\n",
              "        4.28708404e-01,  2.06134930e-01,  6.72007501e-02, -1.46676013e-02,\n",
              "       -7.54063204e-03, -1.09363347e-01,  8.20806902e-03,  2.60380030e-01,\n",
              "       -1.06208632e-02,  1.05041087e-01,  2.04135150e-01,  3.19657981e-01,\n",
              "       -1.99197158e-01,  8.36214349e-02, -1.34593606e-01,  5.36810696e-01,\n",
              "       -1.86585225e-02,  1.73311770e-01,  2.12134153e-01, -9.61540174e-03,\n",
              "        5.23845479e-03,  2.07612216e-01,  1.87863454e-01,  3.13629895e-01,\n",
              "        3.92977923e-01, -3.49369422e-02, -1.28193926e-02,  3.64920199e-02,\n",
              "        1.35867789e-01, -1.24274604e-02, -8.25007483e-02,  4.10072282e-02,\n",
              "        2.71850824e-01,  1.13948576e-01, -1.78395752e-02,  3.50857317e-01,\n",
              "        3.33951227e-02,  1.19140618e-01, -2.62760576e-02,  2.83869565e-01,\n",
              "        2.70071086e-02,  5.22712953e-02,  6.09205067e-01, -3.10263969e-02,\n",
              "       -1.89843342e-01, -1.03999469e-02,  1.48822561e-01,  3.22858274e-01,\n",
              "        9.60339755e-02, -3.38310897e-01,  1.63255170e-01, -1.31636374e-02,\n",
              "       -1.24331333e-01,  2.78957486e-02,  5.81052341e-03,  1.62719592e-01,\n",
              "        1.91903144e-01, -9.33009200e-03,  1.09740488e-01,  7.10190982e-02,\n",
              "       -4.27278206e-02,  4.30741638e-01, -2.59810418e-01,  3.91409397e-02,\n",
              "        5.81655502e-02,  1.84298918e-01, -1.00494092e-02, -4.30638380e-02,\n",
              "        1.55372545e-01, -5.53914197e-02,  1.99669808e-01,  4.53635566e-02,\n",
              "        1.85020730e-01, -9.97748878e-03,  2.90572122e-02,  3.12883556e-01,\n",
              "       -7.04385862e-02,  1.75260589e-01, -1.11011406e-02, -9.50886589e-03,\n",
              "        3.04866970e-01,  4.11549071e-03, -1.36713292e-02,  6.50038719e-02,\n",
              "        2.61938304e-01,  1.08043261e-01, -8.35261308e-03,  4.63615268e-01,\n",
              "       -2.91984938e-02,  1.07116558e-01,  2.33722553e-02,  1.18434548e-01,\n",
              "       -6.83209896e-02,  4.93263751e-01, -1.44451559e-01,  3.43986005e-01,\n",
              "        8.67046863e-02,  2.46367991e-01,  2.66130537e-01,  1.34123400e-01,\n",
              "        1.77106187e-01, -1.52987558e-02,  2.39472866e-01, -9.27915890e-03,\n",
              "       -7.72879869e-02, -4.01731402e-01,  1.73934564e-01,  2.00967297e-01,\n",
              "       -3.03038619e-02, -7.61678501e-04,  2.14086786e-01,  3.58685017e-01,\n",
              "       -1.33300856e-01, -4.59887348e-02,  2.12977365e-01,  1.34568987e-03,\n",
              "        7.18460009e-02, -4.01217565e-02, -1.04163177e-02,  2.88994074e-01,\n",
              "       -9.20293182e-02,  7.23323151e-02, -2.74981260e-02,  9.46915150e-02,\n",
              "       -1.50201740e-02,  3.61653626e-01,  2.52445638e-01, -2.43416671e-02,\n",
              "        3.56824547e-01,  2.07259804e-01,  3.24565768e-01, -1.05647422e-01,\n",
              "       -3.88597518e-01,  2.38225460e-01,  3.33366424e-01,  1.75654322e-01,\n",
              "        4.57855463e-02, -1.06345512e-01, -1.14005255e-02, -4.74197753e-02,\n",
              "        2.64198393e-01, -1.59744285e-02, -1.29160568e-01, -6.96748942e-02,\n",
              "       -2.47806564e-01,  4.00302708e-01,  1.44770574e-02,  3.31702788e-04,\n",
              "       -1.10766459e-02, -3.88029441e-02, -1.11600928e-01,  3.01511168e-01,\n",
              "        2.43792862e-01,  4.51289386e-01,  1.99337155e-01,  6.36204481e-02,\n",
              "       -2.49218456e-02,  1.00864343e-01,  1.27281815e-01,  2.37246841e-01,\n",
              "        2.78149039e-01,  2.24866092e-01, -8.71316344e-03,  1.83543965e-01,\n",
              "        1.68926448e-01,  3.08455408e-01, -9.60134268e-02,  3.63421738e-01,\n",
              "       -1.13639280e-01,  1.52598307e-01, -2.89007314e-02, -9.96809360e-03,\n",
              "        4.35572982e-01,  1.08859546e-01,  2.98261140e-02, -1.07701989e-02,\n",
              "        2.38084137e-01,  6.95741475e-02, -2.31942266e-01,  9.35596675e-02,\n",
              "       -1.01982644e-02, -1.22091444e-02,  2.18806013e-01,  9.30256993e-02,\n",
              "        2.60790288e-01,  2.47776702e-01,  2.69381940e-01,  1.13121264e-01,\n",
              "       -2.09944677e-02,  2.26818487e-01,  2.34444886e-01,  1.74053296e-01,\n",
              "        1.54679373e-01,  2.07526743e-01,  1.82029277e-01,  3.24247312e-03,\n",
              "        4.46787700e-02,  1.62087884e-02,  3.75479236e-02,  1.78914562e-01,\n",
              "        4.18981224e-01, -6.09360784e-02,  2.95475841e-01,  9.82703548e-03,\n",
              "        4.64513414e-02,  6.73660785e-02, -1.28521234e-01,  9.62261483e-02,\n",
              "       -5.08104153e-02,  1.54364318e-01,  1.68301929e-02,  2.48791613e-02,\n",
              "        1.24096759e-02,  1.24827951e-01,  3.85377169e-01,  2.34378904e-01,\n",
              "        3.94466631e-02,  2.30291814e-01,  4.04159039e-01, -8.27096589e-03,\n",
              "        2.30164200e-01,  1.74776107e-01, -9.08801053e-03,  2.51532942e-01,\n",
              "        3.43557686e-01, -7.12217577e-03,  3.17612290e-01,  4.45640236e-01,\n",
              "        4.76848483e-02, -1.13833006e-02, -1.23706860e-02,  1.30011275e-01,\n",
              "        2.62798280e-01,  3.32627982e-01,  1.78902850e-01, -3.96689884e-02,\n",
              "        1.22285731e-01,  4.86666888e-01,  4.47556466e-01,  5.89868240e-02,\n",
              "        7.82715678e-02, -4.59612161e-03,  4.72350210e-01, -5.00269718e-02,\n",
              "        8.04118514e-02,  3.51777017e-01,  6.30071536e-02, -1.58591364e-02,\n",
              "       -4.05127481e-02, -4.14409162e-03,  4.35575098e-02, -2.78798580e-01,\n",
              "        2.24586770e-01,  3.70146451e-03,  2.22373009e-01,  2.21236907e-02,\n",
              "       -2.35944036e-02,  2.78141081e-01,  6.57311678e-02, -1.48257427e-02,\n",
              "        2.84432650e-01,  8.26860815e-02,  3.43976647e-01,  4.44761544e-01,\n",
              "        4.43316460e-01,  1.61185693e-02,  1.23792239e-01,  2.70007670e-01,\n",
              "       -1.00653777e-02,  3.53714198e-01, -2.44197948e-03,  1.39813170e-01],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fy3XM5fHTWdw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2811828f-160c-4f0e-ecf8-b7367dda274f"
      },
      "source": [
        "biases.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XJp0iKf3ZgK",
        "colab_type": "text"
      },
      "source": [
        "## মডেলের টেন্সর-ফ্লো লাইট ভার্সন \n",
        "\n",
        "(১২ তম চ্যাপ্টারে আলাপ হয়েছে)\n",
        "\n",
        "আচ্ছা, কেমন হয় আমরা যদি এই মডেলকে আমাদের মোবাইলে অথবা রাস্পবেরি পাইয়ের ওপর চালাতে চাই? অথবা আমাদের যেকোন অ্যাপে? অন্য কোন এমবেডিং ডিভাইসে?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yz10p6fZ32H0",
        "colab_type": "code",
        "outputId": "0283cb70-d251-4d93-ffbb-8928e496acda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "export_dir = 'saved_model/1'\n",
        "tf.saved_model.save(model, export_dir)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: saved_model/1/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EwpGAVV44L3",
        "colab_type": "text"
      },
      "source": [
        "## মডেলকে কনভার্ট করি "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYUXcrXW49Uw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert the model.\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n",
        "tflite_model = converter.convert()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_hStTIy5yM3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tflite_model_file = 'model.tflite'\n",
        "\n",
        "with open(tflite_model_file, \"wb\") as f:\n",
        "  f.write(tflite_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NAW1ApRSxA-0",
        "colab_type": "text"
      },
      "source": [
        "মনে আছে আমাদের ক্লাসের কথা? পেছনে দেখুন। তৈরি করি দরকারি labels.txt ফাইল।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRs2bN5vxuhn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('labels.txt', 'w') as f:\n",
        "  f.write('\\n'.join(class_names))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWHUnQAU6WSb",
        "colab_type": "text"
      },
      "source": [
        "## মডেলকে ডাউনলোড করে রাখি\n",
        "\n",
        "আমাদের দরকার দুটো ফাইল। tflite_model_file এবং labels.txt। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mFUwJME6FVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "try:\n",
        "  from google.colab import files\n",
        "\n",
        "  files.download(tflite_model_file)\n",
        "  files.download('labels.txt')\n",
        "except:\n",
        "  pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEM5sDSXznyR",
        "colab_type": "text"
      },
      "source": [
        "এদুটো ফাইল লাগবে সামনে মোবাইল অ্যাপ তৈরি করতে।"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIv5RyKyK6ld",
        "colab_type": "text"
      },
      "source": [
        "## টেন্সর-ফ্লো এর ডেটাসেট এপিআই\n",
        "\n",
        "টেন্সর-ফ্লো ২ থেকে আমরা চেষ্টা করবো tf.data এপিআই ব্যবহার করতে tf.keras এর সাথে। সাইকিট লার্নের মতো আগে থেকে দেয়া ডেটাসেটের সাথে এই tf.data.Dataset ব্যবহার করলে ম্যাপ কল দিয়ে ইমেজ অগমেন্টেশন (এই বইয়ের বাইরে) থেকে শুরু করে অনেককিছুই একবারে করা সম্ভব।\n",
        "\n",
        "আমাদের tfds.load সঙ্গে সঙ্গে ডেটা ডাউনলোড করে ট্রেইন এবং টেস্ট ভাগ করে দেবে আগের উদাহরণের কেরাসের মতো। তবে এখানে ডেটাটাইপটা একটু ভিন্ন। ডেটাসেট বিল্ডার হিসেবে tfds.load অনেক অপারেশন সহজ করে দেবে দরকার মতো।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KHk65KPszsD0",
        "colab": {}
      },
      "source": [
        "# আমরা আমরা এভাবেও চেষ্টা করতে পারি, টেন্সর-ফ্লো এর ডেটাসেট এপিআই দিয়ে\n",
        "# সামনে আমরা এটা নিয়ে বড় আলাপ করবো, তবে এখানে একটু পরীক্ষা করতে ক্ষতি কি? \n",
        "# tfds হচ্ছে টেন্সর ফ্লোতে প্র্যাকটিস করার জন্য তৈরি করা ডেটাসেট, সাইকিট লার্নে যেভাবে দেখেছিলাম\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "train, test = tfds.load(\"fashion_mnist\", as_supervised=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LF9vWscp3eTz",
        "colab_type": "text"
      },
      "source": [
        "টেন্সর-ফ্লো ডেটাসেট নিয়ে কাজ করলে স্প্লিটিং এ সুবিধা অনেক। এ ব্যাপারগুলোকে tfds.Split অবজেক্ট ব্যবহার করে ডিফাইন করা যায়। ডিফল্ট হিসেবে ৬০০০০ ট্রেইন অবজেক্ট এবং ১০০০০ টেস্ট অবজেক্ট স্প্লিট এর জন্য tfds.Split.TRAIN এবং tfds.Split.TEST অবজেক্ট ব্যবহার করা যায়। পাশাপাশি অনেকে ভ্যালিডেশন অবজেক্টের জন্য tfds.Split.VALID ব্যবহার করেন।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSQQJbxNFY4V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# শুধুমাত্র টেস্ট করার জন্য\n",
        "\n",
        "# train_half_1, train_half_2 = tfds.Split.TRAIN.subsplit(2)\n",
        "# test1, test2, test3, test4 = tfds.Split.TEST.subsplit(4)\n",
        "\n",
        "# train_first_half = tfds.load(\"fashion-mnist\", split=train_half_1)\n",
        "# test_second_quarter = tfds.load(\"fashion-mnist\", split=test2)\n",
        "\n",
        "# আচ্ছা শতাংশ হিসেবে কি স্প্লিট করা যায়?\n",
        "# first_10_percent = tfds.Split.TRAIN.subsplit(tfds.percent[:10])\n",
        "\n",
        "# আচ্ছা, দুটো স্প্লিটকে যোগ করা যায়?\n",
        "# train_and_test = tfds.Split.TRAIN + tfds.Split.TEST"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ap4onKAhFiLA",
        "colab_type": "text"
      },
      "source": [
        "আরেকটা জিনিস দেখা যায়। tfds.builder দিয়ে।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDB8zhQsFoIP",
        "colab_type": "code",
        "outputId": "9cdfec82-dccf-4b97-c3ca-c969438e8c9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        }
      },
      "source": [
        "mnist_builder = tfds.builder(\"fashion_mnist\")\n",
        "mnist_builder.download_and_prepare()\n",
        "mnist_data = mnist_builder.as_dataset(split = tfds.Split.TRAIN)\n",
        "info = mnist_builder.info\n",
        "\n",
        "# নামপাই অ্যারে দিয়ে\n",
        "np_mnist_data = tfds.as_numpy(mnist_data)\n",
        "\n",
        "print(mnist_data)\n",
        "\n",
        "print(np_mnist_data)\n",
        "\n",
        "print(info)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<_OptionsDataset shapes: {image: (28, 28, 1), label: ()}, types: {image: tf.uint8, label: tf.int64}>\n",
            "<generator object _eager_dataset_iterator at 0x7f8f202314c0>\n",
            "tfds.core.DatasetInfo(\n",
            "    name='fashion_mnist',\n",
            "    version=1.0.0,\n",
            "    description='Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.',\n",
            "    homepage='https://github.com/zalandoresearch/fashion-mnist',\n",
            "    features=FeaturesDict({\n",
            "        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
            "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
            "    }),\n",
            "    total_num_examples=70000,\n",
            "    splits={\n",
            "        'test': 10000,\n",
            "        'train': 60000,\n",
            "    },\n",
            "    supervised_keys=('image', 'label'),\n",
            "    citation=\"\"\"@article{DBLP:journals/corr/abs-1708-07747,\n",
            "      author    = {Han Xiao and\n",
            "                   Kashif Rasul and\n",
            "                   Roland Vollgraf},\n",
            "      title     = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning\n",
            "                   Algorithms},\n",
            "      journal   = {CoRR},\n",
            "      volume    = {abs/1708.07747},\n",
            "      year      = {2017},\n",
            "      url       = {http://arxiv.org/abs/1708.07747},\n",
            "      archivePrefix = {arXiv},\n",
            "      eprint    = {1708.07747},\n",
            "      timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},\n",
            "      biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},\n",
            "      bibsource = {dblp computer science bibliography, https://dblp.org}\n",
            "    }\"\"\",\n",
            "    redistribution_info=,\n",
            ")\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zk0lZNEM_fqP",
        "colab_type": "text"
      },
      "source": [
        "## ডেটাসেটে কি আছে?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRCpuGxXFyfM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ফিচার ডিকশনারি দেখার জন্য\n",
        "for features in mnist_data.take(1):\n",
        "    image, label = features[\"image\"], features[\"label\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2KJYTRVHQD-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# অথবা numpy দিয়ে\n",
        "for features in tfds.as_numpy(mnist_data):\n",
        "    image, label = features[\"image\"], features[\"label\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nR3huz8HWlP",
        "colab_type": "code",
        "outputId": "b1b5aad4-1de0-4f78-db35-7fffc44c3602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.imshow(image[:, :, 0].astype(np.float32), \n",
        "           cmap = plt.get_cmap(\"gray\"))\n",
        "print(\"Label: %d\" % label)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label: 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/enZpLTP7B0lH3P3B6j/Kme7+N30yt/v0BZfx7tLc6pYZ/3M1eN91cvnzVjTxyH6tpL3u\n/oa7j0j6qaRVDcyj77n7c5KOfGbzKkmbqq83afQfS8/VzK0vuPsBd3+p+vq4pLPLjDd63wXz6okm\nwr5A0jtjvn9X/bXeu0v6lZm9aGbrm57MOAbGLLN1UNJAk5MZR3EZ7176zDLjfXPftbL8ebt4g+7z\nrnf3P5H0Z5K+Uz1d7Us++hqsn3qnP5R0uUbXADwg6QdNTqZaZnyzpHvc/djYWpP33Tjz6sn91kTY\nhyQtHPP9V6ptfcHdh6rPw5Ke1ujLjn5y6OwKutXn4Ybn8//c/ZC7n3b3M5J+pAbvu2qZ8c2SfuLu\nT1WbG7/vxptXr+63JsK+VdJSM1tkZhdIWiPpmQbm8TlmNrV640RmNlXS19V/S1E/I2ld9fU6ST9r\ncC6/p1+W8a5bZlwN33eNL3/u7j3/kLRSo+/Ivy7pb5uYQ828Fkv63+rj1abnJulxjT6tO6nR9za+\nJelSSc9Kek3Sf0ma1Udz+zeNLu29XaPBmt/Q3K7X6FP07ZJeqT5WNn3fBfPqyf3G4bJAErxBByRB\n2IEkCDuQBGEHkiDsQBKEHUiCsANJ/B+imzWE1lygYwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}