{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1W5ygAfuMCE"
      },
      "source": [
        "# 머신 러닝 교과서 3판"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTRFtb1_uMCL"
      },
      "source": [
        "# 14장 - 텐서플로의 구조 자세히 알아보기 (1/3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BNi5wasxuMCL"
      },
      "source": [
        "**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://nbviewer.org/github/rickiepark/python-machine-learning-book-3rd-edition/blob/master/ch14/ch14_part1.ipynb\"><img src=\"https://jupyter.org/assets/share.png\" width=\"60\" />주피터 노트북 뷰어로 보기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/python-machine-learning-book-3rd-edition/blob/master/ch14/ch14_part1.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AnsROqoOuMCL"
      },
      "source": [
        "### 목차"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3fPFO6KuMCM"
      },
      "source": [
        "- 텐서플로의 주요 특징\n",
        "- 텐서플로의 계산 그래프: 텐서플로 v2로 이전하기\n",
        "  - 계산 그래프 이해하기\n",
        "  - 텐서플로 v1.x에서 그래프 만들기\n",
        "  - 텐서플로 v2로 이전하기\n",
        "  - 입력 데이터를 모델에 주입하기: 텐서플로 v1.x 스타일\n",
        "  - 입력 데이터를 모델에 주입하기: 텐서플로 v2 스타일\n",
        "  - 함수 데코레이터로 계산 성능 높이기\n",
        "- 모델 파라미터를 저장하고 업데이트하기 위한 텐서플로 변수 객체\n",
        "- 자동 미분과 GradientTape을 사용해 그레이디언트 계산하기\n",
        "  - 훈련 가능한 변수에 대한 손실의 그레이디언트 계산하기\n",
        "  - 훈련하지 않는 변수에 대한 그레이디언트 계산하기\n",
        "  - 여러 개의 그레이디언트 계산하기\n",
        "- 케라스 API를 사용해 간단하게 일반적인 구조 구현하기\n",
        "  - XOR 분류 문제 풀어보기\n",
        "  - 케라스 함수형 API로 유연성이 높은 모델 만들기\n",
        "  - 케라스의 Model 클래스 기반으로 모델 만들기\n",
        "  - 사용자 정의 케라스 층 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMDCB5rAGPwo",
        "outputId": "756e1adc-90ce-4306-f93d-548f2efd496e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.12/dist-packages (0.23.4)\n",
            "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.12/dist-packages (from mlxtend) (1.16.1)\n",
            "Requirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.12/dist-packages (from mlxtend) (2.0.2)\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.12/dist-packages (from mlxtend) (2.2.2)\n",
            "Requirement already satisfied: scikit-learn>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from mlxtend) (1.6.1)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.12/dist-packages (from mlxtend) (3.10.0)\n",
            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.12/dist-packages (from mlxtend) (1.5.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.0.0->mlxtend) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.0.0->mlxtend) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.0.0->mlxtend) (4.59.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.0.0->mlxtend) (1.4.9)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.0.0->mlxtend) (25.0)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.0.0->mlxtend) (11.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.0.0->mlxtend) (3.2.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.0.0->mlxtend) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.24.2->mlxtend) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.24.2->mlxtend) (2025.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.3.1->mlxtend) (3.6.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.17.0)\n"
          ]
        }
      ],
      "source": [
        "# 코랩에서 실행할 경우 최신 mlxtend 라이브러리를 설치합니다.\n",
        "!pip install --upgrade mlxtend"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "PoZvSxpiuMCM"
      },
      "outputs": [],
      "source": [
        "from IPython.display import Image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCV-Fq80uMCM"
      },
      "source": [
        "## 텐서플로의 주요 특징\n",
        "\n",
        "## 텐서플로의 계산 그래프: 텐서플로 v2로 이전하기\n",
        "\n",
        "### 계산 그래프 이해하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "e-lGbx5xuMCN",
        "outputId": "3e47508d-da4e-442f-8cf3-6c24d778556b",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JL52v\" width=\"500\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "Image(url='https://git.io/JL52v', width=500)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcR05RhnuMCO"
      },
      "source": [
        "### 텐서플로 v1.x에서 그래프 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "zhIE7wfwuMCO"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "-Peo6UCHuOMv",
        "outputId": "caeb62c0-d0fe-4b63-b843-e8e0ed9bde67"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.19.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66ESkEJJuMCO",
        "outputId": "92a52165-792f-4795-9f9d-e31b3ca8dbf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "결과: z = 1\n",
            "결과: z = 1\n"
          ]
        }
      ],
      "source": [
        "## TF-v1.x 스타일\n",
        "g = tf.Graph()\n",
        "with g.as_default():\n",
        "    a = tf.constant(1, name='a')\n",
        "    b = tf.constant(2, name='b')\n",
        "    c = tf.constant(3, name='c')\n",
        "    z = 2*(a - b) + c\n",
        "\n",
        "with tf.compat.v1.Session(graph=g) as sess:\n",
        "    print('결과: z =', sess.run(z))\n",
        "    print('결과: z =', z.eval())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hig3NY6duMCO"
      },
      "source": [
        "### TensorFlow v2로 이전하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-IM6ScYuMCP",
        "outputId": "344c7e4f-62fa-4251-8c1f-31719fb09a57"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "결과: z = 1\n"
          ]
        }
      ],
      "source": [
        "## TF v2 스타일\n",
        "a = tf.constant(1, name='a')\n",
        "b = tf.constant(2, name='b')\n",
        "c = tf.constant(3, name='c')\n",
        "\n",
        "z = 2*(a - b) + c\n",
        "tf.print('결과: z =', z)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ntV4OZUuMCP"
      },
      "source": [
        "### 입력 데이터를 모델에 주입하기: 텐서플로 v1.x 스타일"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxNEr_nEuMCP",
        "outputId": "d63190ed-4ac7-4e66-91be-33feca5aab16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "결과: z = 1\n"
          ]
        }
      ],
      "source": [
        "## TF-v1.x 스타일\n",
        "g = tf.Graph()\n",
        "with g.as_default():\n",
        "    a = tf.compat.v1.placeholder(shape=None, dtype=tf.int32, name='tf_a')\n",
        "    b = tf.compat.v1.placeholder(shape=None, dtype=tf.int32, name='tf_b')\n",
        "    c = tf.compat.v1.placeholder(shape=None, dtype=tf.int32, name='tf_c')\n",
        "    z = 2*(a - b) + c\n",
        "\n",
        "with tf.compat.v1.Session(graph=g) as sess:\n",
        "    feed_dict = {a:1, b:2, c:3}\n",
        "    print('결과: z =', sess.run(z, feed_dict=feed_dict))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4U2TFRIuMCP"
      },
      "source": [
        "### 입력 데이터를 모델에 주입하기: 텐서플로 v2 스타일"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e9DxkQuuMCQ",
        "outputId": "5c2f65f9-f3d2-42df-d055-b87cb26d468a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "스칼라 입력: 1\n",
            "랭크 1 입력: [1]\n",
            "랭크 2 입력: [[1]]\n"
          ]
        }
      ],
      "source": [
        "## TF-v2 스타일\n",
        "def compute_z(a, b, c):\n",
        "    r1 = tf.subtract(a, b)\n",
        "    r2 = tf.multiply(2, r1)\n",
        "    z = tf.add(r2, c)\n",
        "    return z\n",
        "\n",
        "tf.print('스칼라 입력:', compute_z(1, 2, 3))\n",
        "tf.print('랭크 1 입력:', compute_z([1], [2], [3]))\n",
        "tf.print('랭크 2 입력:', compute_z([[1]], [[2]], [[3]]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjPbqg5ruMCQ"
      },
      "source": [
        "### 함수 데코레이터로 계산 성능 높이기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKR_Rk_vuMCR",
        "outputId": "1efe8c09-f3ea-426a-ca86-f776f0690db2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "스칼라 입력: 1\n",
            "랭크 1 입력: [1]\n",
            "랭크 2 입력: [[1]]\n"
          ]
        }
      ],
      "source": [
        "@tf.function\n",
        "def compute_z(a, b, c):\n",
        "    r1 = tf.subtract(a, b)\n",
        "    r2 = tf.multiply(2, r1)\n",
        "    z = tf.add(r2, c)\n",
        "    return z\n",
        "\n",
        "tf.print('스칼라 입력:', compute_z(1, 2, 3))\n",
        "tf.print('랭크 1 입력:', compute_z([1], [2], [3]))\n",
        "tf.print('랭크 2 입력:', compute_z([[1]], [[2]], [[3]]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFY-qx2vuMCR",
        "outputId": "138a368b-ec9f-41c4-9ace-63beb851b63f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "랭크 1 입력: [1]\n",
            "랭크 1 입력: [1 2]\n"
          ]
        }
      ],
      "source": [
        "@tf.function(input_signature=(tf.TensorSpec(shape=[None], dtype=tf.int32),\n",
        "                              tf.TensorSpec(shape=[None], dtype=tf.int32),\n",
        "                              tf.TensorSpec(shape=[None], dtype=tf.int32),))\n",
        "def compute_z(a, b, c):\n",
        "    r1 = tf.subtract(a, b)\n",
        "    r2 = tf.multiply(2, r1)\n",
        "    z = tf.add(r2, c)\n",
        "    return z\n",
        "\n",
        "tf.print('랭크 1 입력:', compute_z([1], [2], [3]))\n",
        "tf.print('랭크 1 입력:', compute_z([1, 2], [2, 4], [3, 6]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xLOE7O0uMCR"
      },
      "source": [
        "```python\n",
        "## 에러가 발생합니다.\n",
        "tf.print('Rank 2 Inputs:', compute_z([[1], [2]], [[2], [4]], [[3], [6]]))\n",
        "\n",
        "\n",
        "## >> 에러:\n",
        "#ValueError: Python inputs incompatible with input_signature:\n",
        "#inputs (([[1], [2]], [[2], [4]], [[3], [6]])), input_signature\n",
        "#((TensorSpec(shape=(None,), dtype=tf.int32, name=None),\n",
        "#  TensorSpec(shape=(None,), dtype=tf.int32, name=None),\n",
        "#  TensorSpec(shape=(None,), dtype=tf.int32, name=None)))\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulMQ6tpEuMCR",
        "outputId": "67059020-d83a-4f3f-8064-b49f75ceda65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorSpec(shape=(None,), dtype=tf.int32, name=None)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tf.TensorSpec(shape=[None], dtype=tf.int32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzjOhKiiuMCS"
      },
      "source": [
        "## 모델 파라미터를 저장하고 업데이트하기 위한 텐서플로 변수 객체"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7g0mPwLuMCS",
        "outputId": "4db710c3-6d1a-497f-ff14-c7baa10b464f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<tf.Variable 'var_a:0' shape=() dtype=float32, numpy=3.140000104904175>\n",
            "<tf.Variable 'var_b:0' shape=(3,) dtype=int32, numpy=array([1, 2, 3], dtype=int32)>\n",
            "<tf.Variable 'Variable:0' shape=(2,) dtype=bool, numpy=array([ True, False])>\n",
            "<tf.Variable 'Variable:0' shape=(1,) dtype=string, numpy=array([b'abc'], dtype=object)>\n"
          ]
        }
      ],
      "source": [
        "a = tf.Variable(initial_value=3.14, name='var_a')\n",
        "b = tf.Variable(initial_value=[1, 2, 3], name='var_b')\n",
        "c = tf.Variable(initial_value=[True, False], dtype=tf.bool)\n",
        "d = tf.Variable(initial_value=['abc'], dtype=tf.string)\n",
        "print(a)\n",
        "print(b)\n",
        "print(c)\n",
        "print(d)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wgfpn9ZhuMCS",
        "outputId": "04f1dfe4-c7e0-4d4d-98dc-53fef0e42e99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "a.trainable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u37Qx69-uMCS",
        "outputId": "df0675fc-76c3-4f88-c301-9ded040d6d8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        }
      ],
      "source": [
        "w = tf.Variable([1, 2, 3], trainable=False)\n",
        "\n",
        "print(w.trainable)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0wkJpXRuMCT",
        "outputId": "c30e71a7-6a77-4972-9981-6f7169d4d516"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<tf.Variable 'UnreadVariable' shape=(3,) dtype=int32, numpy=array([3, 1, 4], dtype=int32)>\n",
            "tf.Tensor([5 0 6], shape=(3,), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "print(w.assign([3, 1, 4], read_value=True))\n",
        "w.assign_add([2, -1, 2], read_value=False)\n",
        "\n",
        "print(w.value())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XF3eRs6PuMCT",
        "outputId": "f9a7cc49-4f8c-4837-b168-798003c29106"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.480880678 0.55129087 0.200916022]\n"
          ]
        }
      ],
      "source": [
        "tf.random.set_seed(1)\n",
        "init = tf.keras.initializers.GlorotNormal()\n",
        "\n",
        "tf.print(init(shape=(3,)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ilsY1xKuMCT",
        "outputId": "1537b7df-c423-4ced-b1b4-e16ae4b26953"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.5267784 0.603908896 0.220092475]\n",
            " [0.699662 0.276737273 1.13213742]]\n"
          ]
        }
      ],
      "source": [
        "v = tf.Variable(init(shape=(2, 3)))\n",
        "tf.print(v)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNHCQd7WuMCT",
        "outputId": "527b57d8-e930-4a73-a044-1f8a031e9885",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "모든 변수:  [TensorShape([2, 3]), TensorShape([1, 2])]\n",
            "훈련 가능한 변수:    [TensorShape([2, 3])]\n"
          ]
        }
      ],
      "source": [
        "class MyModule(tf.Module):\n",
        "    def __init__(self):\n",
        "        init = tf.keras.initializers.GlorotNormal()\n",
        "        self.w1 = tf.Variable(init(shape=(2, 3)), trainable=True)\n",
        "        self.w2 = tf.Variable(init(shape=(1, 2)), trainable=False)\n",
        "\n",
        "m = MyModule()\n",
        "print('모든 변수: ', [v.shape for v in m.variables])\n",
        "print('훈련 가능한 변수:   ', [v.shape for v in\n",
        "                        m.trainable_variables])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXoNe9XCuMCU"
      },
      "source": [
        "#### `tf.function`과 함께 `Variable` 사용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNZD7eDQuMCU"
      },
      "source": [
        "```python\n",
        "\n",
        "## 이렇게 하면 에러가 발생합니다.\n",
        "## ==> tf.function 데코레이트가 적용된 함수 안에서는\n",
        "##     변수를 만들 수 없습니다.\n",
        "\n",
        "@tf.function\n",
        "def f(x):\n",
        "    w = tf.Variable([1, 2, 3])\n",
        "\n",
        "f([1])\n",
        "\n",
        "## ==> 에러\n",
        "## ValueError: tf.function-decorated function tried to create variables on non-first call.\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1UL0kSMuMCU",
        "outputId": "745e4c0b-6b72-4fdd-f76d-1ba8c20dba8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3.8610158]\n",
            " [2.94593048]\n",
            " [3.82629013]]\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.random.set_seed(1)\n",
        "w = tf.Variable(tf.random.uniform((3, 3)))\n",
        "\n",
        "@tf.function\n",
        "def compute_z(x):\n",
        "    return tf.matmul(w, x)\n",
        "\n",
        "x = tf.constant([[1], [2], [3]], dtype=tf.float32)\n",
        "tf.print(compute_z(x))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_1iMzZVuMCU"
      },
      "source": [
        "## 자동 미분과 GradientTape을 사용해 그레이디언트 계산하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3B7fvhS5uMCV"
      },
      "source": [
        "### 훈련 가능한 변수에 대한 손실의 그레이디언트 계산하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otgkgVFOuMCW",
        "outputId": "f8c883ea-3b63-4074-9043-654161c66076"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True True\n",
            "dL/dw :  -0.559999764\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "w = tf.Variable(1.0)\n",
        "b = tf.Variable(0.5)\n",
        "print(w.trainable, b.trainable)\n",
        "\n",
        "x = tf.convert_to_tensor([1.4])\n",
        "y = tf.convert_to_tensor([2.1])\n",
        "\n",
        "with tf.GradientTape() as tape:\n",
        "    z = tf.add(tf.multiply(w, x), b)\n",
        "    loss = tf.reduce_sum(tf.square(y - z))\n",
        "\n",
        "dloss_dw = tape.gradient(loss, w)\n",
        "\n",
        "tf.print('dL/dw : ', dloss_dw)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XpIX3BunuMCW",
        "outputId": "59081bf7-1a81-4b8e-d25b-043134f9476a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.559999764]\n"
          ]
        }
      ],
      "source": [
        "# 계산된 그레이디언트 확인\n",
        "#tf.print(-2*x * (-b - w*x + y))\n",
        "\n",
        "tf.print(2*x * ((w*x + b) - y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4Lj0UJ1uMCX"
      },
      "source": [
        "### 훈련하지 않는 변수에 대한 그레이디언트 계산하기\n",
        "\n",
        "`tape.watch()`를 사용해 훈련하지 않는 텐서를 모니터링합니다"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HmT93umuMCX",
        "outputId": "aa072e44-3f2e-4e99-94ab-f5222e775f0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dL/dx: [-0.399999857]\n"
          ]
        }
      ],
      "source": [
        "with tf.GradientTape() as tape:\n",
        "    tape.watch(x)\n",
        "    z = tf.add(tf.multiply(w, x), b)\n",
        "    loss = tf.square(y - z)\n",
        "\n",
        "dloss_dx = tape.gradient(loss, x)\n",
        "\n",
        "tf.print('dL/dx:', dloss_dx)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgOO24ENuMCX",
        "outputId": "b043ec35-0a97-4bfd-b157-ff8cd4670486"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.399999857]\n"
          ]
        }
      ],
      "source": [
        "# 계산된 그레이디언트 검증\n",
        "tf.print(2*w * ((w*x + b) - y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hdetaLHuMCX"
      },
      "source": [
        "### 여러 개의 그레이디언트 계산하기\n",
        "\n",
        "`persistent=True`를 사용합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_IPqjgRuMCX",
        "outputId": "2e647914-cd41-4b95-8a9d-f7264f8dd0a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dL/dw: -0.559999764\n",
            "dL/db: -0.399999857\n"
          ]
        }
      ],
      "source": [
        "with tf.GradientTape(persistent=True) as tape:\n",
        "    z = tf.add(tf.multiply(w, x), b)\n",
        "    loss = tf.reduce_sum(tf.square(y - z))\n",
        "\n",
        "dloss_dw = tape.gradient(loss, w)\n",
        "dloss_db = tape.gradient(loss, b)\n",
        "\n",
        "tf.print('dL/dw:', dloss_dw)\n",
        "tf.print('dL/db:', dloss_db)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rE-prplFuMCY"
      },
      "source": [
        "#### 변수 업데이트: `optimizer.apply_gradients()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clKdTjgouMCY",
        "outputId": "f7f5653d-3064-493d-c07a-dc6bc18c23fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "업데이트된 w: 1.0056\n",
            "업데이트된 b: 0.504\n"
          ]
        }
      ],
      "source": [
        "optimizer = tf.keras.optimizers.SGD()\n",
        "\n",
        "optimizer.apply_gradients(zip([dloss_dw, dloss_db], [w, b]))\n",
        "\n",
        "tf.print('업데이트된 w:', w)\n",
        "tf.print('업데이트된 b:', b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJQHF5-fuMCY"
      },
      "source": [
        "## 케라스 API를 사용해 간단하게 일반적인 구조 구현하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "qikbGL6FuMCZ",
        "outputId": "699823c1-020b-4d66-bdaf-bc14313224cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │            \u001b[38;5;34m80\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m544\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">544</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m624\u001b[0m (2.44 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">624</span> (2.44 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m624\u001b[0m (2.44 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">624</span> (2.44 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(units=16, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=32, activation='relu'))\n",
        "\n",
        "## 변수 지연 생성\n",
        "model.build(input_shape=(None, 4))\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__q8lNy_uMCZ",
        "outputId": "6c0866f4-ae29-4746-f3f3-2a7e16ebe619"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kernel               True (4, 16)\n",
            "bias                 True (16,)\n",
            "kernel               True (16, 32)\n",
            "bias                 True (32,)\n"
          ]
        }
      ],
      "source": [
        "## 모델의 변수 출력하기\n",
        "for v in model.variables:\n",
        "    print('{:20s}'.format(v.name), v.trainable, v.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZlTglQtouMCZ"
      },
      "source": [
        "#### 층 설정\n",
        "\n",
        "* `tf.keras.activations`에서 활성화 함수를 선택하기: https://www.tensorflow.org/api_docs/python/tf/keras/activations\n",
        "* `tf.keras.initializers`로 층의 파라미터 초기화하기: https://www.tensorflow.org/api_docs/python/tf/keras/initializers\n",
        "* `tf.keras.regularizers`로 (과대적합을 막기 위해) 층의 파라미터에 규제 적용하기: https://www.tensorflow.org/api_docs/python/tf/keras/regularizers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "YBDGYfikuMCZ",
        "outputId": "18c298d9-4233-49fd-cdaa-f63f84a7d2c3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │            \u001b[38;5;34m80\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m544\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">544</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m624\u001b[0m (2.44 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">624</span> (2.44 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m624\u001b[0m (2.44 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">624</span> (2.44 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = tf.keras.Sequential()\n",
        "\n",
        "model.add(\n",
        "    tf.keras.layers.Dense(\n",
        "        units=16,\n",
        "        activation=tf.keras.activations.relu,\n",
        "        kernel_initializer=tf.keras.initializers.GlorotNormal(),\n",
        "        bias_initializer=tf.keras.initializers.Constant(2.0)\n",
        "    ))\n",
        "\n",
        "model.add(\n",
        "    tf.keras.layers.Dense(\n",
        "        units=32,\n",
        "        activation=tf.keras.activations.sigmoid,\n",
        "        kernel_regularizer=tf.keras.regularizers.l1\n",
        "    ))\n",
        "\n",
        "model.build(input_shape=(None, 4))\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHaMQhIZuMCZ"
      },
      "source": [
        "#### 모델 컴파일\n",
        "\n",
        "* `tf.keras.optimizers`에 있는 옵티마이저: https://www.tensorflow.org/api_docs/python/tf/keras/optimizers\n",
        "* `tf.keras.losses`에 있는 손실 함수: https://www.tensorflow.org/api_docs/python/tf/keras/losses\n",
        "* `tf.keras.metrics`에 있는 성능 지표: https://www.tensorflow.org/api_docs/python/tf/keras/metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "3RBcRZdduMCZ"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "    metrics=[tf.keras.metrics.Accuracy(),\n",
        "             tf.keras.metrics.Precision(),\n",
        "             tf.keras.metrics.Recall(),])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZLIQM0xYuMCZ"
      },
      "source": [
        "### XOR 분류 문제 풀어보기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "vJxkx_TAuMCa",
        "outputId": "a5132115-b935-45b1-9ad8-5e31bbd94ff6",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tf.random.set_seed(1)\n",
        "np.random.seed(1)\n",
        "\n",
        "x = np.random.uniform(low=-1, high=1, size=(200, 2))\n",
        "y = np.ones(len(x))\n",
        "y[x[:, 0] * x[:, 1]<0] = 0\n",
        "\n",
        "x_train = x[:100, :]\n",
        "y_train = y[:100]\n",
        "x_valid = x[100:, :]\n",
        "y_valid = y[100:]\n",
        "\n",
        "fig = plt.figure(figsize=(6, 6))\n",
        "plt.plot(x[y==0, 0],\n",
        "         x[y==0, 1], 'o', alpha=0.75, markersize=10)\n",
        "plt.plot(x[y==1, 0],\n",
        "         x[y==1, 1], '<', alpha=0.75, markersize=10)\n",
        "plt.xlabel(r'$x_1$', size=15)\n",
        "plt.ylabel(r'$x_2$', size=15)\n",
        "# plt.savefig('images/14_1.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "F8uAWPsguMCa",
        "outputId": "7af0deef-67f2-47a6-a296-a15624af8eb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m3\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3\u001b[0m (12.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3</span> (12.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3\u001b[0m (12.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3</span> (12.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(units=1,\n",
        "                                input_shape=(2,),\n",
        "                                activation='sigmoid'))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "uDX1wO92uMCa",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.SGD(),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=[tf.keras.metrics.BinaryAccuracy()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "2otPTzMxuMCa"
      },
      "outputs": [],
      "source": [
        "hist = model.fit(x_train, y_train,\n",
        "                 validation_data=(x_valid, y_valid),\n",
        "                 epochs=200, batch_size=2, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "TdofpXaAuMCa",
        "outputId": "47159afd-775a-4035-adf9-3ca5f94a8f7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-3286167664.py:19: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20000/20000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from mlxtend.plotting import plot_decision_regions\n",
        "\n",
        "history = hist.history\n",
        "\n",
        "fig = plt.figure(figsize=(16, 4))\n",
        "ax = fig.add_subplot(1, 3, 1)\n",
        "plt.plot(history['loss'], lw=4)\n",
        "plt.plot(history['val_loss'], lw=4)\n",
        "plt.legend(['Train loss', 'Validation loss'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 2)\n",
        "plt.plot(history['binary_accuracy'], lw=4)\n",
        "plt.plot(history['val_binary_accuracy'], lw=4)\n",
        "plt.legend(['Train Acc.', 'Validation Acc.'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 3)\n",
        "plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n",
        "                      clf=model)\n",
        "ax.set_xlabel(r'$x_1$', size=15)\n",
        "ax.xaxis.set_label_coords(1, -0.025)\n",
        "ax.set_ylabel(r'$x_2$', size=15)\n",
        "ax.yaxis.set_label_coords(-0.025, 1)\n",
        "# plt.savefig('images/14_2.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "WINOBAGSuMCa",
        "outputId": "b7fd6815-5f07-4589-f48a-e45ac0e7f69e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m12\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m20\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m20\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m5\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m57\u001b[0m (228.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">57</span> (228.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m57\u001b[0m (228.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">57</span> (228.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "tf.random.set_seed(1)\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(units=4, input_shape=(2,), activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=4, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=4, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "## 컴파일:\n",
        "model.compile(optimizer=tf.keras.optimizers.SGD(),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
        "\n",
        "## 훈련:\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 validation_data=(x_valid, y_valid),\n",
        "                 epochs=200, batch_size=2, verbose=0)\n",
        "\n",
        "history = hist.history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "Ukg6EnHsuMCb",
        "outputId": "8966f9d0-742d-461d-acd8-a22238db5556"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-217449913.py:15: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20000/20000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(16, 4))\n",
        "ax = fig.add_subplot(1, 3, 1)\n",
        "plt.plot(history['loss'], lw=4)\n",
        "plt.plot(history['val_loss'], lw=4)\n",
        "plt.legend(['Train loss', 'Validation loss'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 2)\n",
        "plt.plot(history['binary_accuracy'], lw=4)\n",
        "plt.plot(history['val_binary_accuracy'], lw=4)\n",
        "plt.legend(['Train Acc.', 'Validation Acc.'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 3)\n",
        "plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n",
        "                      clf=model)\n",
        "ax.set_xlabel(r'$x_1$', size=15)\n",
        "ax.xaxis.set_label_coords(1, -0.025)\n",
        "ax.set_ylabel(r'$x_2$', size=15)\n",
        "ax.yaxis.set_label_coords(-0.025, 1)\n",
        "# plt.savefig('images/14_3.png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a80uG6IruMCb"
      },
      "source": [
        "### 케라스 함수형 API로 유연성이 높은 모델 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "SWsKMeXguMCb",
        "outputId": "6ba96283-0398-480d-ae4f-2403f7cd0c07"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_7\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_7\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_4 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m12\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m20\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m20\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m5\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m57\u001b[0m (228.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">57</span> (228.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m57\u001b[0m (228.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">57</span> (228.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "tf.random.set_seed(1)\n",
        "\n",
        "## 입력층\n",
        "inputs = tf.keras.Input(shape=(2,))\n",
        "\n",
        "## 은닉층\n",
        "h1 = tf.keras.layers.Dense(units=4, activation='relu')(inputs)\n",
        "h2 = tf.keras.layers.Dense(units=4, activation='relu')(h1)\n",
        "h3 = tf.keras.layers.Dense(units=4, activation='relu')(h2)\n",
        "\n",
        "## 출력층\n",
        "outputs = tf.keras.layers.Dense(units=1, activation='sigmoid')(h3)\n",
        "\n",
        "## 모델 만들기\n",
        "model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "_UJWTq9luMCb",
        "outputId": "f21ae579-e5ec-4561-8ec2-0c2984b00cbc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-1659542413.py:28: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20000/20000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "## 컴파일\n",
        "model.compile(optimizer=tf.keras.optimizers.SGD(),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
        "\n",
        "## 훈련\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 validation_data=(x_valid, y_valid),\n",
        "                 epochs=200, batch_size=2, verbose=0)\n",
        "\n",
        "## 그래프 그리기\n",
        "history = hist.history\n",
        "\n",
        "fig = plt.figure(figsize=(16, 4))\n",
        "ax = fig.add_subplot(1, 3, 1)\n",
        "plt.plot(history['loss'], lw=4)\n",
        "plt.plot(history['val_loss'], lw=4)\n",
        "plt.legend(['Train loss', 'Validation loss'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 2)\n",
        "plt.plot(history['binary_accuracy'], lw=4)\n",
        "plt.plot(history['val_binary_accuracy'], lw=4)\n",
        "plt.legend(['Train Acc.', 'Validation Acc.'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 3)\n",
        "plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n",
        "                      clf=model)\n",
        "ax.set_xlabel(r'$x_1$', size=15)\n",
        "ax.xaxis.set_label_coords(1, -0.025)\n",
        "ax.set_ylabel(r'$x_2$', size=15)\n",
        "ax.yaxis.set_label_coords(-0.025, 1)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_Mj06wOuMCb"
      },
      "source": [
        "### 케라사의 Model 클래스 기반으로 모델 만들기\n",
        "\n",
        "#### `tf.keras.Model` 클래스 상속\n",
        "\n",
        " * `__init__()` 구현\n",
        " * `call()` 구현"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "8rqJ4pisuMCc",
        "outputId": "94f6d29a-0f2a-423b-b7c6-c03a7683bf33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/layer.py:421: UserWarning: `build()` was called on layer 'my_model', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"my_model\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"my_model\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_16 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-2193701909.py:50: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20000/20000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "class MyModel(tf.keras.Model):\n",
        "    def __init__(self):\n",
        "        super(MyModel, self).__init__()\n",
        "        self.hidden_1 = tf.keras.layers.Dense(units=4, activation='relu')\n",
        "        self.hidden_2 = tf.keras.layers.Dense(units=4, activation='relu')\n",
        "        self.hidden_3 = tf.keras.layers.Dense(units=4, activation='relu')\n",
        "        self.output_layer = tf.keras.layers.Dense(units=1, activation='sigmoid')\n",
        "\n",
        "    def call(self, inputs):\n",
        "        h = self.hidden_1(inputs)\n",
        "        h = self.hidden_2(h)\n",
        "        h = self.hidden_3(h)\n",
        "        return self.output_layer(h)\n",
        "\n",
        "tf.random.set_seed(1)\n",
        "\n",
        "## 테스트\n",
        "model = MyModel()\n",
        "model.build(input_shape=(None, 2))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "## 컴파일\n",
        "model.compile(optimizer=tf.keras.optimizers.SGD(),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
        "\n",
        "## 훈련\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 validation_data=(x_valid, y_valid),\n",
        "                 epochs=200, batch_size=2, verbose=0)\n",
        "\n",
        "## 그래프 그리기\n",
        "history = hist.history\n",
        "\n",
        "fig = plt.figure(figsize=(16, 4))\n",
        "ax = fig.add_subplot(1, 3, 1)\n",
        "plt.plot(history['loss'], lw=4)\n",
        "plt.plot(history['val_loss'], lw=4)\n",
        "plt.legend(['Train loss', 'Validation loss'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 2)\n",
        "plt.plot(history['binary_accuracy'], lw=4)\n",
        "plt.plot(history['val_binary_accuracy'], lw=4)\n",
        "plt.legend(['Train Acc.', 'Validation Acc.'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 3)\n",
        "plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n",
        "                      clf=model)\n",
        "ax.set_xlabel(r'$x_1$', size=15)\n",
        "ax.xaxis.set_label_coords(1, -0.025)\n",
        "ax.set_ylabel(r'$x_2$', size=15)\n",
        "ax.yaxis.set_label_coords(-0.025, 1)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "vpsH7aM8uMCc"
      },
      "outputs": [],
      "source": [
        "class MyCustomStepModel(MyModel):\n",
        "\n",
        "    def train_step(self, data):\n",
        "        # fit()에서 전달된 데이터\n",
        "        x, y = data\n",
        "\n",
        "        # 그레이디언트 기록 시작\n",
        "        with tf.GradientTape() as tape:\n",
        "            # 정방향 계산\n",
        "            y_pred = self(x)\n",
        "            # compile() 메서드에서 지정한 손실 계산\n",
        "            loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)\n",
        "\n",
        "        # 훈련가능한 파라미터에 대한 그레이디언트 계산\n",
        "        gradients = tape.gradient(loss, self.trainable_variables)\n",
        "        # 파라미터 업데이트\n",
        "        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n",
        "\n",
        "        # TF 2.4에서는\n",
        "        # self.optimizer.minimize(loss, self.trainable_variables, tape=tape)\n",
        "\n",
        "        # compile() 메서드에서 지정한 지표 계산\n",
        "        self.compiled_metrics.update_state(y, y_pred)\n",
        "\n",
        "        # 현재까지 지표와 결괏값을 딕셔너리로 반환\n",
        "        return {m.name: m.result() for m in self.metrics}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "IrsJ2EijuMCc",
        "outputId": "1db8d1d6-ee9c-4ae4-a76d-a8e0bfb8116c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/layer.py:421: UserWarning: `build()` was called on layer 'my_custom_step_model', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"my_custom_step_model\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"my_custom_step_model\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_17 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_18 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_19 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/backend/tensorflow/trainer.py:671: UserWarning: `model.compiled_loss()` is deprecated. Instead, use `model.compute_loss(x, y, y_pred, sample_weight, training)`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.12/dist-packages/keras/src/backend/tensorflow/trainer.py:646: UserWarning: `model.compiled_metrics()` is deprecated. Instead, use e.g.:\n",
            "```\n",
            "for metric in self.metrics:\n",
            "    metric.update_state(y, y_pred)\n",
            "```\n",
            "\n",
            "  return self._compiled_metrics_update_state(\n"
          ]
        }
      ],
      "source": [
        "tf.random.set_seed(1)\n",
        "\n",
        "## 테스트\n",
        "model = MyCustomStepModel()\n",
        "model.build(input_shape=(None, 2))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "## 컴파일\n",
        "model.compile(optimizer='sgd',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "## 훈련\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 validation_data=(x_valid, y_valid),\n",
        "                 epochs=200, batch_size=2, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "cziX0sTXuMCc",
        "outputId": "d6f90a44-bec9-4c30-9441-7116343e5dda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-2800025416.py:18: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20000/20000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "## 그래프 그리기\n",
        "history = hist.history\n",
        "\n",
        "fig = plt.figure(figsize=(16, 4))\n",
        "ax = fig.add_subplot(1, 3, 1)\n",
        "plt.plot(history['loss'], lw=4)\n",
        "plt.plot(history['val_loss'], lw=4)\n",
        "plt.legend(['Train loss', 'Validation loss'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 2)\n",
        "plt.plot(history['accuracy'], lw=4)\n",
        "plt.plot(history['val_accuracy'], lw=4)\n",
        "plt.legend(['Train Acc.', 'Validation Acc.'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 3)\n",
        "plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n",
        "                      clf=model)\n",
        "ax.set_xlabel(r'$x_1$', size=15)\n",
        "ax.xaxis.set_label_coords(1, -0.025)\n",
        "ax.set_ylabel(r'$x_2$', size=15)\n",
        "ax.yaxis.set_label_coords(-0.025, 1)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUgDHIsYuMCd"
      },
      "source": [
        "### 사용자 정의 케라스 층 만들기\n",
        "\n",
        "\n",
        "#### 사용자 정의 층 만들기\n",
        " * `__init__()` 구현\n",
        " * 변수 지연 생성을 위해 `build()` 구현\n",
        " * `call()` 구현\n",
        " * 직렬화를 위해 `get_config()` 구현"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2mUtGMxUuMCd",
        "outputId": "98cd6ff6-4533-4079-e204-efb1a61de897"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.000911796466 0.0179151781 0.00289400481 0]]\n",
            "[[0 0.0111579672 0.00275074062 0]]\n"
          ]
        }
      ],
      "source": [
        "class NoisyLinear(tf.keras.layers.Layer):\n",
        "    def __init__(self, output_dim, noise_stddev=0.1, **kwargs):\n",
        "        self.output_dim = output_dim\n",
        "        self.noise_stddev = noise_stddev\n",
        "        super(NoisyLinear, self).__init__(**kwargs)\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        self.w = self.add_weight(name='weights',\n",
        "                                 shape=(input_shape[1], self.output_dim),\n",
        "                                 initializer='random_normal',\n",
        "                                 trainable=True)\n",
        "\n",
        "        self.b = self.add_weight(shape=(self.output_dim,),\n",
        "                                 initializer='zeros',\n",
        "                                 trainable=True)\n",
        "\n",
        "    def call(self, inputs, training=False):\n",
        "        if training:\n",
        "            batch = tf.shape(inputs)[0]\n",
        "            dim = tf.shape(inputs)[1]\n",
        "            noise = tf.random.normal(shape=(batch, dim),\n",
        "                                     mean=0.0,\n",
        "                                     stddev=self.noise_stddev)\n",
        "\n",
        "            noisy_inputs = tf.add(inputs, noise)\n",
        "        else:\n",
        "            noisy_inputs = inputs\n",
        "        z = tf.matmul(noisy_inputs, self.w) + self.b\n",
        "        return tf.keras.activations.relu(z)\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super(NoisyLinear, self).get_config()\n",
        "        config.update({'output_dim': self.output_dim,\n",
        "                       'noise_stddev': self.noise_stddev})\n",
        "        return config\n",
        "\n",
        "\n",
        "## 테스트\n",
        "\n",
        "tf.random.set_seed(1)\n",
        "\n",
        "noisy_layer = NoisyLinear(4)\n",
        "noisy_layer.build(input_shape=(None, 4))\n",
        "\n",
        "x = tf.zeros(shape=(1, 4))\n",
        "tf.print(noisy_layer(x, training=True))\n",
        "\n",
        "## 설정을 사용해 모델 만들기\n",
        "config = noisy_layer.get_config()\n",
        "new_layer = NoisyLinear.from_config(config)\n",
        "tf.print(new_layer(x, training=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "dRPl5VqXuMCd",
        "outputId": "c9551d40-a847-4407-a75f-e581637b9462"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ noisy_linear_1 (\u001b[38;5;33mNoisyLinear\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m12\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m20\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_22 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │            \u001b[38;5;34m20\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_23 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m5\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ noisy_linear_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NoisyLinear</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m57\u001b[0m (228.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">57</span> (228.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m57\u001b[0m (228.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">57</span> (228.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-2398563354.py:40: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20000/20000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tf.random.set_seed(1)\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    NoisyLinear(4, noise_stddev=0.1),\n",
        "    tf.keras.layers.Dense(units=4, activation='relu'),\n",
        "    tf.keras.layers.Dense(units=4, activation='relu'),\n",
        "    tf.keras.layers.Dense(units=1, activation='sigmoid')])\n",
        "\n",
        "model.build(input_shape=(None, 2))\n",
        "model.summary()\n",
        "\n",
        "## 컴파일\n",
        "model.compile(optimizer=tf.keras.optimizers.SGD(),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
        "\n",
        "## 훈련\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 validation_data=(x_valid, y_valid),\n",
        "                 epochs=200, batch_size=2,\n",
        "                 verbose=0)\n",
        "\n",
        "## 그래프 그리기\n",
        "history = hist.history\n",
        "\n",
        "fig = plt.figure(figsize=(16, 4))\n",
        "ax = fig.add_subplot(1, 3, 1)\n",
        "plt.plot(history['loss'], lw=4)\n",
        "plt.plot(history['val_loss'], lw=4)\n",
        "plt.legend(['Train loss', 'Validation loss'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 2)\n",
        "plt.plot(history['binary_accuracy'], lw=4)\n",
        "plt.plot(history['val_binary_accuracy'], lw=4)\n",
        "plt.legend(['Train Acc.', 'Validation Acc.'], fontsize=15)\n",
        "ax.set_xlabel('Epochs', size=15)\n",
        "\n",
        "ax = fig.add_subplot(1, 3, 3)\n",
        "plot_decision_regions(X=x_valid, y=y_valid.astype(np.integer),\n",
        "                      clf=model)\n",
        "ax.set_xlabel(r'$x_1$', size=15)\n",
        "ax.xaxis.set_label_coords(1, -0.025)\n",
        "ax.set_ylabel(r'$x_2$', size=15)\n",
        "ax.yaxis.set_label_coords(-0.025, 1)\n",
        "# plt.savefig('images/14_4.png', dpi=300)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "ch14_part1.ipynb",
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}