material/deep_learning/dnn_iris_classification.ipynb

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  "metadata": {
    "colab": {
      "name": "dnn_iris_classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giMgHzNY8861"
      },
      "source": [
        "# IRIS 데이터 분류"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ID7lfGHi8g6u"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "is1Jygwv8leP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b020ab2a-a105-4ede-dda4-45d2e2f27b1d"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/iris.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-09 09:00:45--  https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/iris.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2720 (2.7K) [text/plain]\n",
            "Saving to: ‘iris.csv.3’\n",
            "\n",
            "\riris.csv.3            0%[                    ]       0  --.-KB/s               \riris.csv.3          100%[===================>]   2.66K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-09-09 09:00:45 (65.7 MB/s) - ‘iris.csv.3’ saved [2720/2720]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Uw1AwaxbTm7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1231f039-8fc4-45e5-c14d-8dfe5929dada"
      },
      "source": [
        "!ls -al\n",
        "!head iris.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 40\n",
            "drwxr-xr-x 1 root root 4096 Sep  9 09:00 .\n",
            "drwxr-xr-x 1 root root 4096 Sep  9 08:53 ..\n",
            "drwxr-xr-x 4 root root 4096 Sep  1 19:26 .config\n",
            "-rw-r--r-- 1 root root 2720 Sep  9 08:57 iris.csv\n",
            "-rw-r--r-- 1 root root 2720 Sep  9 08:59 iris.csv.1\n",
            "-rw-r--r-- 1 root root 2720 Sep  9 09:00 iris.csv.2\n",
            "-rw-r--r-- 1 root root 2720 Sep  9 09:00 iris.csv.3\n",
            "-rw-r--r-- 1 root root 2218 Sep  9 08:57 iris_with_category_index.csv\n",
            "-rw-r--r-- 1 root root 2218 Sep  9 09:00 iris_with_category_index.csv.1\n",
            "drwxr-xr-x 1 root root 4096 Sep  1 19:26 sample_data\n",
            "septal_length,septal_width,petal_length,petal_width,setosa,versicolor,virginica\n",
            "6.4,2.8,5.6,2.2,0,0,1\n",
            "5.0,2.3,3.3,1.0,0,1,0\n",
            "4.9,2.5,4.5,1.7,0,0,1\n",
            "4.9,3.1,1.5,0.1,1,0,0\n",
            "5.7,3.8,1.7,0.3,1,0,0\n",
            "4.4,3.2,1.3,0.2,1,0,0\n",
            "5.4,3.4,1.5,0.4,1,0,0\n",
            "6.9,3.1,5.1,2.3,0,0,1\n",
            "6.7,3.1,4.4,1.4,0,1,0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1fqqYEm8n2t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "57a42a0f-b43b-4c60-9318-0458001854d6"
      },
      "source": [
        "iris = pd.read_csv(\"iris.csv\")\n",
        "iris.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>setosa</th>\n",
              "      <th>versicolor</th>\n",
              "      <th>virginica</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   septal_length  septal_width  petal_length  ...  setosa  versicolor  virginica\n",
              "0            6.4           2.8           5.6  ...       0           0          1\n",
              "1            5.0           2.3           3.3  ...       0           1          0\n",
              "2            4.9           2.5           4.5  ...       0           0          1\n",
              "3            4.9           3.1           1.5  ...       1           0          0\n",
              "4            5.7           3.8           1.7  ...       1           0          0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5G66SaI8rKZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37c0f5e2-cc37-46a6-bf25-e55a7f617947"
      },
      "source": [
        "iris.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120 entries, 0 to 119\n",
            "Data columns (total 7 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   septal_length  120 non-null    float64\n",
            " 1   septal_width   120 non-null    float64\n",
            " 2   petal_length   120 non-null    float64\n",
            " 3   petal_width    120 non-null    float64\n",
            " 4   setosa         120 non-null    int64  \n",
            " 5   versicolor     120 non-null    int64  \n",
            " 6   virginica      120 non-null    int64  \n",
            "dtypes: float64(4), int64(3)\n",
            "memory usage: 6.7 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-ZtqXYD8s1O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fc9a3a4-ede4-4feb-fd21-5cf1130dbc98"
      },
      "source": [
        "data = iris.to_numpy()\n",
        "print(data.shape)\n",
        "print(data[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(120, 7)\n",
            "[[6.4 2.8 5.6 2.2 0.  0.  1. ]\n",
            " [5.  2.3 3.3 1.  0.  1.  0. ]\n",
            " [4.9 2.5 4.5 1.7 0.  0.  1. ]\n",
            " [4.9 3.1 1.5 0.1 1.  0.  0. ]\n",
            " [5.7 3.8 1.7 0.3 1.  0.  0. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUqZeQAm8s3t"
      },
      "source": [
        "x = data[:,:4]\n",
        "y = data[:,4:]\n",
        "\n",
        "split_index = 100\n",
        "\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9PVE6VJ8s9V"
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Input(4))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(3, activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gWZl5rG8s6k"
      },
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncEY6nOd80X8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "104f550d-1319-46d0-edd7-27c7329d6eb7"
      },
      "source": [
        "model.fit(train_x, train_y, epochs=200, verbose=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7f6ec83ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dOxrqlB82OM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3748c6bf-1cd8-4c12-994a-2508d91b67c9"
      },
      "source": [
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "\n",
        "print(\"loss :\", loss)\n",
        "print(\"acc :\", acc)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 100ms/step - loss: 0.0656 - accuracy: 1.0000\n",
            "loss : 0.0655723512172699\n",
            "acc : 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpHP3Ds884OT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "675ad328-7c9b-456b-ba56-7fdc76c14f4e"
      },
      "source": [
        "y_ = model.predict(test_x)\n",
        "print(y_)\n",
        "print(np.argmax(y_, axis=1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.96748424 0.03117989 0.00133594]\n",
            " [0.02293573 0.94575524 0.03130902]\n",
            " [0.9077266  0.09010788 0.00216558]\n",
            " [0.01398178 0.9496784  0.03633992]\n",
            " [0.9711345  0.02753814 0.00132739]\n",
            " [0.9758733  0.02286812 0.00125855]\n",
            " [0.9786174  0.02012268 0.00125977]\n",
            " [0.9790127  0.01972041 0.0012669 ]\n",
            " [0.02888522 0.9498904  0.02122442]\n",
            " [0.9665249  0.0321051  0.00136992]\n",
            " [0.00428514 0.14600466 0.84971017]\n",
            " [0.01630112 0.91485196 0.06884687]\n",
            " [0.9597133  0.03886941 0.00141727]\n",
            " [0.00132677 0.021859   0.9768142 ]\n",
            " [0.9702928  0.02836292 0.00134428]\n",
            " [0.01232413 0.69508094 0.2925949 ]\n",
            " [0.01996948 0.9426117  0.03741875]\n",
            " [0.96785575 0.03076326 0.00138095]\n",
            " [0.9698758  0.02881452 0.00130976]\n",
            " [0.01362896 0.9422324  0.04413872]]\n",
            "[0 1 0 1 0 0 0 0 1 0 2 1 0 2 0 1 1 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSRas3Po_ZO3"
      },
      "source": [
        "# loss categorical_crossentropy\n",
        "\n",
        "2가지 crossentropy 사용 방법\n",
        "- categorical_crossentropy\n",
        "- sparse_categorical_crossentropy\n",
        "\n",
        "## categorical_crossentropy\n",
        "y의 값이 one hot encoding인 경우\n",
        "```\n",
        "1,0,0\n",
        "0,1,0\n",
        "0,0,1\n",
        "```\n",
        "\n",
        "출력 레이어 설정\n",
        "```\n",
        "model.add(Dense(3, activation=\"softmax\")) # 출력 레이어\n",
        "```\n",
        "\n",
        "loss 설정\n",
        "```\n",
        "model.compile(..., loss='categorical_crossentropy')\n",
        "```\n",
        "\n",
        "\n",
        "## sparse_categorical_crossentropy\n",
        "y의 값이 one hot encoding인 경우\n",
        "```\n",
        "0\n",
        "1\n",
        "2\n",
        "```\n",
        "\n",
        "출력 레이어 설정\n",
        "```\n",
        "model.add(Dense(3, activation=\"softmax\")) # 출력 레이어. 1이 아니라 클래스 수 3\n",
        "```\n",
        "\n",
        "loss 설정\n",
        "```\n",
        "model.compile(..., loss='sparse_categorical_crossentropy')\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TS6kKsxs_dS9"
      },
      "source": [
        "# iris_dnn with category index\n",
        "\n",
        "아래의 코드는 dnn_iris_and_optimizer.ipynb의 코드를 기반으로 한다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROWsFbJ5852r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e25b9ddf-325b-4f94-fefb-d24504e416fb"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/iris_with_category_index.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-09 09:00:48--  https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/iris_with_category_index.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2218 (2.2K) [text/plain]\n",
            "Saving to: ‘iris_with_category_index.csv.2’\n",
            "\n",
            "\r          iris_with   0%[                    ]       0  --.-KB/s               \riris_with_category_ 100%[===================>]   2.17K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-09-09 09:00:48 (45.2 MB/s) - ‘iris_with_category_index.csv.2’ saved [2218/2218]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqCzFLp-gh7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3648bf2e-1c1b-49fc-84ba-3c7ec5a4bcd6"
      },
      "source": [
        "!ls -al\n",
        "!head iris_with_category_index.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 44\n",
            "drwxr-xr-x 1 root root 4096 Sep  9 09:00 .\n",
            "drwxr-xr-x 1 root root 4096 Sep  9 08:53 ..\n",
            "drwxr-xr-x 4 root root 4096 Sep  1 19:26 .config\n",
            "-rw-r--r-- 1 root root 2720 Sep  9 08:57 iris.csv\n",
            "-rw-r--r-- 1 root root 2720 Sep  9 08:59 iris.csv.1\n",
            "-rw-r--r-- 1 root root 2720 Sep  9 09:00 iris.csv.2\n",
            "-rw-r--r-- 1 root root 2720 Sep  9 09:00 iris.csv.3\n",
            "-rw-r--r-- 1 root root 2218 Sep  9 08:57 iris_with_category_index.csv\n",
            "-rw-r--r-- 1 root root 2218 Sep  9 09:00 iris_with_category_index.csv.1\n",
            "-rw-r--r-- 1 root root 2218 Sep  9 09:00 iris_with_category_index.csv.2\n",
            "drwxr-xr-x 1 root root 4096 Sep  1 19:26 sample_data\n",
            "septal_length,septal_width,petal_length,petal_width,class\n",
            "6.4,2.8,5.6,2.2,2\n",
            "5.0,2.3,3.3,1.0,1\n",
            "4.9,2.5,4.5,1.7,2\n",
            "4.9,3.1,1.5,0.1,0\n",
            "5.7,3.8,1.7,0.3,0\n",
            "4.4,3.2,1.3,0.2,0\n",
            "5.4,3.4,1.5,0.4,0\n",
            "6.9,3.1,5.1,2.3,2\n",
            "6.7,3.1,4.4,1.4,1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OuplbOU_ls3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "4adf4bdd-de1e-46be-9bda-11b8c0734eb7"
      },
      "source": [
        "iris = pd.read_csv(\"iris_with_category_index.csv\")\n",
        "iris.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>septal_length</th>\n",
              "      <th>septal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.7</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   septal_length  septal_width  petal_length  petal_width  class\n",
              "0            6.4           2.8           5.6          2.2      2\n",
              "1            5.0           2.3           3.3          1.0      1\n",
              "2            4.9           2.5           4.5          1.7      2\n",
              "3            4.9           3.1           1.5          0.1      0\n",
              "4            5.7           3.8           1.7          0.3      0"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTZ0g9Ax_mMf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4a1668e-6f08-4cce-d3df-c40eefbb5f4e"
      },
      "source": [
        "data = iris.to_numpy()\n",
        "print(data.shape)\n",
        "print(data[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(120, 5)\n",
            "[[6.4 2.8 5.6 2.2 2. ]\n",
            " [5.  2.3 3.3 1.  1. ]\n",
            " [4.9 2.5 4.5 1.7 2. ]\n",
            " [4.9 3.1 1.5 0.1 0. ]\n",
            " [5.7 3.8 1.7 0.3 0. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u30kuIr0_mWC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82bf4b6b-4d4d-4180-bc9f-1effa107ee83"
      },
      "source": [
        "x = data[:,:4]\n",
        "y = data[:,4:]\n",
        "\n",
        "split_index = 100\n",
        "\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]\n",
        "\n",
        "print(train_x.shape)\n",
        "print(train_y.shape)\n",
        "print(test_x.shape)\n",
        "print(test_y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 4)\n",
            "(100, 1)\n",
            "(20, 4)\n",
            "(20, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-wBUWBH_mTQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06fb6342-ee3c-49d1-d4d5-fc4f8b8d6de9"
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Input(4))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(3, activation='softmax')) # 1이 아니고 클래스 수 3이다\n",
        "\n",
        "# model.compile(optimizer=\"SGD\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.compile(optimizer=\"SGD\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "              \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_18 (Dense)             (None, 10)                50        \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 3)                 33        \n",
            "=================================================================\n",
            "Total params: 193\n",
            "Trainable params: 193\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_test_function.<locals>.test_function at 0x7f7f6ea5c7a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "1/1 [==============================] - 0s 93ms/step - loss: 0.0311 - accuracy: 1.0000\n",
            "loss= 0.03113712929189205\n",
            "acc= 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzXyJg3__mQE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e288e09c-0eff-4240-b68a-d5e4e12e3f1e"
      },
      "source": [
        "y_ = model.predict(test_x)\n",
        "print(y_)\n",
        "print(np.argmax(y_, axis=1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f7f6e83f9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "[[9.9334103e-01 6.3550966e-03 3.0381305e-04]\n",
            " [9.2408275e-03 9.8865664e-01 2.1025997e-03]\n",
            " [9.8414570e-01 1.5356159e-02 4.9819116e-04]\n",
            " [4.3511586e-03 9.9381411e-01 1.8347246e-03]\n",
            " [9.9340773e-01 6.2909001e-03 3.0132959e-04]\n",
            " [9.9464071e-01 5.0886516e-03 2.7059036e-04]\n",
            " [9.9490935e-01 4.8286561e-03 2.6201320e-04]\n",
            " [9.9497485e-01 4.7637178e-03 2.6144611e-04]\n",
            " [1.4877894e-02 9.8303586e-01 2.0861388e-03]\n",
            " [9.9252677e-01 7.1533378e-03 3.1989816e-04]\n",
            " [7.6917064e-04 2.8348857e-01 7.1574223e-01]\n",
            " [5.0608129e-03 9.9008501e-01 4.8542586e-03]\n",
            " [9.9165475e-01 8.0060568e-03 3.3911286e-04]\n",
            " [2.3343464e-04 7.2743259e-02 9.2702329e-01]\n",
            " [9.9326634e-01 6.4252894e-03 3.0834330e-04]\n",
            " [3.0556251e-03 9.3160123e-01 6.5343179e-02]\n",
            " [6.4292504e-03 9.9027681e-01 3.2939943e-03]\n",
            " [9.9297571e-01 6.7101964e-03 3.1416645e-04]\n",
            " [9.9365968e-01 6.0456078e-03 2.9466441e-04]\n",
            " [4.5873965e-03 9.9363756e-01 1.7750665e-03]]\n",
            "[0 1 0 1 0 0 0 0 1 0 2 1 0 2 0 1 1 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhrTS4Zb_u8G"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}