deep_learning_decision_boundary.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "colab": {
      "name": "deep_learning_decision_boundary.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raqueeb/TensorFlow2/blob/master/deep_learning_decision_boundary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tTHOELyYaau8",
        "colab_type": "text"
      },
      "source": [
        "#  ডিপ লার্নিং দিয়ে ডিসিশন বাউন্ডারি, চাঁদ আকারের ডাটাসেট\n",
        "\n",
        "যেকোনো প্রজেক্ট এর কাজের শুরুতে আমার একটা অভ্যাস হচ্ছে সেই ডাটাসেটকে প্লট করে দেখা। একটা ডাটাসেটের ঠিকমতো প্লটিং দেখতে পারলে তার সমস্যার অনেকটাইকুল কিনারা করা যায়। এজন্যই ডাটা ভিজুয়ালাইজেশন এতটাই দরকারি। আমরা যখন সাইকিট-লার্ন নিয়ে কাজ করছিলাম তখন এধরনের ডাটার অনেক প্লটিং দেখেছিলাম। \n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/assets/generator.png\"> চিত্রঃ বিভিন্ন ধরনের ডেটার প্লটিং "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msN8Wuy6aau-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pKwEWPS7aavB",
        "colab_type": "text"
      },
      "source": [
        "## অল্প এবং বেশি গভীর ডিপ নিউরাল নেটওয়ার্কের কিছু কাজ\n",
        "\n",
        "ডাক্তার প্লটিং এর পাশাপাশি প্রয়োজনে দরকারি ডাটাসেট না পেলে অনেক ধরনের ডাটাসেট স্যাম্পল জেনারেট করতে হয় যা আসলে আমাদের দরকারি সাইজ এবং কম্প্লেক্সিটি বুঝেই করতে হয়। এই ধরনের জেনারেটর আমাদের ফিচার এবং তার করেসপন্ডিং ‘ডিসক্রিট’ টার্গেটের একটা ম্যাট্রিক্স তৈরি করে দেয়। পাশাপাশি কিছু নয়েজও ঢোকাতে হয় রিয়ালিস্টিক করার জন্য। লিনিয়ার ডাটাসেটের পাশাপাশি কিছু নন-লিনিয়ার ডাটা নিয়ে কাজ করতে গেলে আলাদা জেনারেটর ব্যবহার করি আমরা। যেমন, দুইটা বৃত্তাংশ একটা আরেকটার মধ্যে ঢুকে গেছে। দুটা অর্ধেক চাঁদ। সাইকিট-লার্নে এটার একটা ফাংশন আছে, দেখুন এই উদাহরণে।"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7r6bqn8U9hSL",
        "colab_type": "text"
      },
      "source": [
        "আমাদের কাজ হবে এই ধরনের একটা নন-লিনিয়ার ডাটাসেটে কিভাবে ডিপ লার্নিং দিয়ে সুন্দর ক্লাসিফিকেশন করা যায়। এর আগেও করেছি, তবে এই ডেটা জেনারেটর দিয়ে তৈরি, এবং আরো কমপ্লেক্স। একে sklearn.datasets থেকে ইমপোর্ট করছি এই নতুন ফাংশন দিয়ে। ফিচার এবং টার্গেট ভ্যারিয়েবল হিসেবে দুটো আলাদা আলাদা স্যাম্পল জেনারেট করছি। এরপর প্লটিং। \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwu3F2vfaavB",
        "colab_type": "code",
        "outputId": "d6fdbe94-1240-41a1-e405-ea40b99a6179",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "from sklearn.datasets import make_moons\n",
        "\n",
        "X, y = make_moons(n_samples=1000, noise=0.1, random_state=0)\n",
        "\n",
        "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
        "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
        "plt.legend(['0', '1'])"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f21c7be5908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MjyImxkQChYfz3PPn0zQifRLmzKF5RDQXXyGTQnilaF1kJJ3lyckkiLNm0b8h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zrFIR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\nHALgFIODg4ODQwCcYnBwcHBwCMD/B8EefoxettCRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zBN-6uC9m1l",
        "colab_type": "text"
      },
      "source": [
        "স্যাম্পল এর সংখ্যা দেখি।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-saMjk2aavH",
        "colab_type": "code",
        "outputId": "7b8f5149-ad25-43a0-b95a-2226eabf99b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10DfXFGH9s_p",
        "colab_type": "text"
      },
      "source": [
        "টেস্ট এবং ট্রেনিং ডাটা স্প্লিট করি।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7o5HCghaavL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eELcaeFFaavO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    test_size=0.3,\n",
        "                                                    random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXbVTWGn-BeB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "777e1e16-535d-4775-d97f-8bb7b30afa91"
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  # শুধুমাত্র জুপিটার নোটবুক/কোলাবে চেষ্টা করবো টেন্সর-ফ্লো ২.০ এর জন্য\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6Y0KKJVF_79",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYABTf8HaavS",
        "colab_type": "text"
      },
      "source": [
        "### অগভীর একটা নিউরাল নেটওয়ার্ক \n",
        "\n",
        "এরপর আমরা এর ইনপুটকে এক লাইনের/লেয়ারের নিউরাল নেটওয়ার্কের দিয়ে দেই। কি ঘটছে?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6FiKAwi7aavT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Dense(1, input_dim=2, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(tf.keras.optimizers.Adam(lr=0.05), 'binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX0cKSnTaavX",
        "colab_type": "code",
        "outputId": "d36b25bb-4e72-4523-b5fd-b65c5b7dead5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "model.fit(X_train, y_train, epochs=200, verbose=0)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-2.0.0-rc2/python3.6/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f21ab9359b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYPquacnA-6j",
        "colab_type": "text"
      },
      "source": [
        "মডেলকে ইভালুয়েট করে দেখি অ্যাক্যুরেসি কত এসেছে। লস এবং অ্যাক্যুরেসি দেখতে পাচ্ছি এখানে।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxJUsGHgaavb",
        "colab_type": "code",
        "outputId": "30238ab9-389b-4dea-a358-af32755b3fab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "results = model.evaluate(X_test, y_test)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r300/1 [========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 268us/sample - loss: 0.2587 - accuracy: 0.8367\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9lGLs5Iaave",
        "colab_type": "code",
        "outputId": "cc23bf8f-db31-423b-f5ff-6c69083f2453",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "results"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.31679999371369677, 0.83666664]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7-n52LSBRYt",
        "colab_type": "text"
      },
      "source": [
        "ডাটা প্লটিং এর পাশাপাশি আমরা ডিসিশন বাউন্ডারি দেখার চেষ্টা করি। এখানে ব্যবহার করেছি mlxtend.plotting এর মতো হেল্পার লাইব্রেরি। এর plot_decision_regions কাজ হচ্ছে মডেল থেকে যা ইনপুট দেয় সেটা সে প্লট করে দেয়। যেমন এখানে \n",
        "ডিসিশন বাউন্ডারি করে দিয়েছে ঠিকই তবে সেটা কাজ করছেনা। সত্যিই তো, এখানকার ডিসিশন বাউন্ডারি লিনিয়ার, কিন্তু ডাটা তো নন-লিনিয়ার। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eN7zwQ9bHxK",
        "colab_type": "code",
        "outputId": "914f26e2-d45e-4ab2-8e2c-9264cbd13c86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "from mlxtend.plotting import plot_decision_regions\n",
        "\n",
        "plot_decision_regions(X=X, y=y, clf=model, legend=2)\n",
        "plt.xlabel(\"x\", size=5)\n",
        "plt.ylabel(\"y\", size=5)\n",
        "plt.title('Plot Decision Region Boundary', size=10)\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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55YBCzBDVs6/BjccAqFq9nJaGSL9zJYRXglhbI7Uv3EFzdh77XDRts9aj0ewMaCGh6cH2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\nzkNiE7LbIqyZ7j1huq5D2Wm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NbMCLpTdzSjwNA7z6SwtuRiBwiEcjawNzR1OkroeGsaM1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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2J7dTD0aavq",
        "colab_type": "text"
      },
      "source": [
        "### গভীর একটা নিউরাল নেটওয়ার্ক \n",
        "\n",
        "আমরা একটু তিন লাইনের একটা ডিপ লার্নিং মডেল থেকে আউটকাম দেখার চেষ্টা করি। ৩ লেয়ার, পাশাপাশি নিউরনের সংখ্যাও বেশি। কি ঘটছে এখন?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDcwAfdKaavr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Dense(4, input_shape=(2,), activation='tanh'))\n",
        "model.add(tf.keras.layers.Dense(2, activation='tanh'))\n",
        "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
        "model.compile(tf.keras.optimizers.Adam(lr=0.05), 'binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2svDBCCaavt",
        "colab_type": "code",
        "outputId": "6d56e93a-adca-4b3c-d875-b55952ebcf2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "model.fit(X_train, y_train, epochs=100, verbose=0)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f21a2ba0ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tHjP6cQaavv",
        "colab_type": "code",
        "outputId": "c6fd14d5-2d82-43c5-9845-1f80b5cd45de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r300/1 [========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 325us/sample - loss: 5.6714e-04 - accuracy: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0009398714792526637, 1.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnHg60RTaavx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHX6Rz6PFH74",
        "colab_type": "text"
      },
      "source": [
        "দেখতে পাচ্ছি অ্যাকুরেসি বেড়েছে। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tElfVrTSaavz",
        "colab_type": "code",
        "outputId": "dae28e34-3358-40bf-97dd-997f432d2a46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "y_train_pred = model.predict_classes(X_train)\n",
        "y_test_pred = model.predict_classes(X_test)\n",
        "\n",
        "print(\"The Accuracy score on the Train set is:\\t{:0.3f}\".format(accuracy_score(y_train, y_train_pred)))\n",
        "print(\"The Accuracy score on the Test set is:\\t{:0.3f}\".format(accuracy_score(y_test, y_test_pred)))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Accuracy score on the Train set is:\t0.999\n",
            "The Accuracy score on the Test set is:\t1.000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rGT-Rt9TFgQW",
        "colab_type": "text"
      },
      "source": [
        "### নতুন ডিসিশন বাউন্ডারি\n",
        "নতুন করে ডিসিশন বাউন্ডারি প্লটিং করে দেখি। অসাধারণ কাজ হয়েছে। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ew2w686_aav2",
        "colab_type": "code",
        "outputId": "6251704a-5b43-47b7-b957-36ac2be8077d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "plot_decision_regions(X=X, y=y, clf=model, legend=2)\n",
        "plt.xlabel(\"x\", size=5)\n",
        "plt.ylabel(\"y\", size=5)\n",
        "plt.title('Plot Decision Region Boundary', size=10)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5tPxmZ2OvKCT7xKvjNnUILL8FI2uAF8/QU7Mcz48bQ4imwwqhxRXUEbOmXHmi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\neygaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}