chapter03_introduction-to-keras-and-tf.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LfldcZzRjR1L"
      },
      "source": [
        "이 노트북은 [케라스 창시자에게 배우는 딥러닝 2판](https://tensorflow.blog/kerasdl2/)의 예제 코드를 담고 있습니다.\n",
        "\n",
        "<table align=\"left\"><tr><td>\n",
        "<a href=\"https://colab.research.google.com/github/rickiepark/deep-learning-with-python-2nd/blob/master/chapter03_introduction-to-keras-and-tf.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "</td></tr></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKORndCjjR1N"
      },
      "source": [
        "# 케라스와 텐서플로 소개"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bnvrkq6pjR1O"
      },
      "source": [
        "## 텐서플로란?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXa_qKgAjR1O"
      },
      "source": [
        "## 케라스란?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Ti2lq6PjR1O"
      },
      "source": [
        "## 케라스와 텐서플로의 간략한 역사"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RS5aSY-JjR1O"
      },
      "source": [
        "## 딥러닝 작업 환경 설정하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tpAPYi2NjR1P"
      },
      "source": [
        "### 주피터 노트북: 권장하는 딥러닝 실험 도구"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0JRUfctjR1P"
      },
      "source": [
        "### 코랩 사용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S99ikTFVjR1Q"
      },
      "source": [
        "#### 코랩 시작하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X58JBJtljR1Q"
      },
      "source": [
        "#### pip로 패키지 설치하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCnG27L6jR1R"
      },
      "source": [
        "#### GPU 런타임 사용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djDuvOKVjR1R"
      },
      "source": [
        "## 텐서플로 시작하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T887Zne6jR1R"
      },
      "source": [
        "#### 상수 텐서와 변수"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8ykr8bMjR1R"
      },
      "source": [
        "**모두 1 또는 모두 0인 텐서**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:14.681100Z",
          "iopub.status.busy": "2022-05-21T00:58:14.680540Z",
          "iopub.status.idle": "2022-05-21T00:58:16.481810Z",
          "shell.execute_reply": "2022-05-21T00:58:16.480769Z"
        },
        "id": "0Yh_78IzjR1R",
        "outputId": "365afa47-9fbd-42e5-bb9a-26f06519d73d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[1.]\n",
            " [1.]], shape=(2, 1), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "x = tf.ones(shape=(2, 1))\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.485729Z",
          "iopub.status.busy": "2022-05-21T00:58:16.485088Z",
          "iopub.status.idle": "2022-05-21T00:58:16.490852Z",
          "shell.execute_reply": "2022-05-21T00:58:16.490149Z"
        },
        "id": "63xkTvoXjR1T",
        "outputId": "91ac9f0f-ac17-4c0e-f4fb-14fcd5c9e026",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[0.]\n",
            " [0.]], shape=(2, 1), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "x = tf.zeros(shape=(2, 1))\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NthSViHdjR1T"
      },
      "source": [
        "**랜덤 텐서**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.494374Z",
          "iopub.status.busy": "2022-05-21T00:58:16.493742Z",
          "iopub.status.idle": "2022-05-21T00:58:16.501220Z",
          "shell.execute_reply": "2022-05-21T00:58:16.500464Z"
        },
        "id": "Ktevr051jR1T",
        "outputId": "a1dd27fb-4ef2-4d3c-caed-46aae3ec0e6d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[-0.7189114 ]\n",
            " [ 0.00264509]\n",
            " [-1.543211  ]], shape=(3, 1), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "x = tf.random.normal(shape=(3, 1), mean=0., stddev=1.)\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.504901Z",
          "iopub.status.busy": "2022-05-21T00:58:16.504410Z",
          "iopub.status.idle": "2022-05-21T00:58:16.510480Z",
          "shell.execute_reply": "2022-05-21T00:58:16.509458Z"
        },
        "id": "lcm6ziA-jR1T",
        "outputId": "7dc091a4-16b1-41ad-e502-c62c684af476",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[0.7547444 ]\n",
            " [0.00125206]\n",
            " [0.9056035 ]], shape=(3, 1), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "x = tf.random.uniform(shape=(3, 1), minval=0., maxval=1.)\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xr-7w0jHjR1U"
      },
      "source": [
        "**넘파이 배열에 값 할당하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.514734Z",
          "iopub.status.busy": "2022-05-21T00:58:16.514199Z",
          "iopub.status.idle": "2022-05-21T00:58:16.518209Z",
          "shell.execute_reply": "2022-05-21T00:58:16.517567Z"
        },
        "id": "0HnGGxzJjR1U"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "x = np.ones(shape=(2, 2))\n",
        "x[0, 0] = 0."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exaFII_wjR1U"
      },
      "source": [
        "**텐서플로 변수 만들기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.522164Z",
          "iopub.status.busy": "2022-05-21T00:58:16.521381Z",
          "iopub.status.idle": "2022-05-21T00:58:16.529208Z",
          "shell.execute_reply": "2022-05-21T00:58:16.528387Z"
        },
        "id": "W3dtiJ7hjR1U",
        "outputId": "e0749d76-9268-4ec9-97b5-1440194b7b78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<tf.Variable 'Variable:0' shape=(3, 1) dtype=float32, numpy=\n",
            "array([[0.58872557],\n",
            "       [0.73657054],\n",
            "       [1.0027444 ]], dtype=float32)>\n"
          ]
        }
      ],
      "source": [
        "v = tf.Variable(initial_value=tf.random.normal(shape=(3, 1)))\n",
        "print(v)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylun-78QjR1V"
      },
      "source": [
        "**텐서플로 변수에 값 할당하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.532960Z",
          "iopub.status.busy": "2022-05-21T00:58:16.532657Z",
          "iopub.status.idle": "2022-05-21T00:58:16.545443Z",
          "shell.execute_reply": "2022-05-21T00:58:16.544477Z"
        },
        "id": "3L7pyEarjR1V",
        "outputId": "90d63258-5150-4696-fe0d-e00931c73599",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'UnreadVariable' shape=(3, 1) dtype=float32, numpy=\n",
              "array([[1.],\n",
              "       [1.],\n",
              "       [1.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "v.assign(tf.ones((3, 1)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slQzosWHjR1V"
      },
      "source": [
        "**변수 일부에 값 할당하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.549025Z",
          "iopub.status.busy": "2022-05-21T00:58:16.548733Z",
          "iopub.status.idle": "2022-05-21T00:58:16.557519Z",
          "shell.execute_reply": "2022-05-21T00:58:16.556482Z"
        },
        "id": "RM9JbVFMjR1V",
        "outputId": "31c85721-e2e0-4565-9160-1b2e2810e9ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'UnreadVariable' shape=(3, 1) dtype=float32, numpy=\n",
              "array([[3.],\n",
              "       [1.],\n",
              "       [1.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "v[0, 0].assign(3.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2PtAFsgjR1V"
      },
      "source": [
        "**`assign_add` 사용하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.561236Z",
          "iopub.status.busy": "2022-05-21T00:58:16.560971Z",
          "iopub.status.idle": "2022-05-21T00:58:16.568357Z",
          "shell.execute_reply": "2022-05-21T00:58:16.567523Z"
        },
        "id": "nRvjF0NKjR1W",
        "outputId": "552cfb67-9861-4201-efb8-dc9fd87dd166",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'UnreadVariable' shape=(3, 1) dtype=float32, numpy=\n",
              "array([[4.],\n",
              "       [2.],\n",
              "       [2.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "v.assign_add(tf.ones((3, 1)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdevQ8z-jR1W"
      },
      "source": [
        "#### 텐서 연산: 텐서플로에서 수학 계산하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tckVPX4tjR1W"
      },
      "source": [
        "**기본적인 수학 연산**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.572052Z",
          "iopub.status.busy": "2022-05-21T00:58:16.571786Z",
          "iopub.status.idle": "2022-05-21T00:58:16.599467Z",
          "shell.execute_reply": "2022-05-21T00:58:16.598533Z"
        },
        "id": "2cfJ4xb9jR1W"
      },
      "outputs": [],
      "source": [
        "a = tf.ones((2, 2))\n",
        "b = tf.square(a)\n",
        "c = tf.sqrt(a)\n",
        "d = b + c\n",
        "e = tf.matmul(a, b)\n",
        "e *= d"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yD4QBR3LjR1W"
      },
      "source": [
        "#### GradientTape API 다시 살펴 보기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wf9L61oejR1W"
      },
      "source": [
        "**`GradientTape` 사용하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.603842Z",
          "iopub.status.busy": "2022-05-21T00:58:16.603513Z",
          "iopub.status.idle": "2022-05-21T00:58:16.612102Z",
          "shell.execute_reply": "2022-05-21T00:58:16.611176Z"
        },
        "id": "r2NyvJv1jR1W"
      },
      "outputs": [],
      "source": [
        "input_var = tf.Variable(initial_value=3.)\n",
        "with tf.GradientTape() as tape:\n",
        "   result = tf.square(input_var)\n",
        "gradient = tape.gradient(result, input_var)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9CCUOnqjR1W"
      },
      "source": [
        "**상수 입력 텐서와 함께 `GradientTape` 사용하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.615692Z",
          "iopub.status.busy": "2022-05-21T00:58:16.615413Z",
          "iopub.status.idle": "2022-05-21T00:58:16.620532Z",
          "shell.execute_reply": "2022-05-21T00:58:16.619752Z"
        },
        "id": "GeWtkJ5ijR1X"
      },
      "outputs": [],
      "source": [
        "input_const = tf.constant(3.)\n",
        "with tf.GradientTape() as tape:\n",
        "   tape.watch(input_const)\n",
        "   result = tf.square(input_const)\n",
        "gradient = tape.gradient(result, input_const)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOoe3Zp8jR1X"
      },
      "source": [
        "**그레이디언트 테이프를 중첩하여 이계도 그레이디언트를 계산하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.624217Z",
          "iopub.status.busy": "2022-05-21T00:58:16.623600Z",
          "iopub.status.idle": "2022-05-21T00:58:16.630991Z",
          "shell.execute_reply": "2022-05-21T00:58:16.629989Z"
        },
        "id": "e5gXa8SVjR1X"
      },
      "outputs": [],
      "source": [
        "time = tf.Variable(0.)\n",
        "with tf.GradientTape() as outer_tape:\n",
        "    with tf.GradientTape() as inner_tape:\n",
        "        position =  4.9 * time ** 2\n",
        "    speed = inner_tape.gradient(position, time)\n",
        "acceleration = outer_tape.gradient(speed, time)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1MVfacbjR1X"
      },
      "source": [
        "#### 엔드-투-엔드 예제: 텐서플로 선형 분류기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8RNP89rjR1X"
      },
      "source": [
        "**2D 평면에 두 클래스의 랜덤한 포인트 생성하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.635105Z",
          "iopub.status.busy": "2022-05-21T00:58:16.634828Z",
          "iopub.status.idle": "2022-05-21T00:58:16.641379Z",
          "shell.execute_reply": "2022-05-21T00:58:16.640512Z"
        },
        "id": "ukRDkZjcjR1X"
      },
      "outputs": [],
      "source": [
        "num_samples_per_class = 1000\n",
        "negative_samples = np.random.multivariate_normal(\n",
        "    mean=[0, 3],\n",
        "    cov=[[1, 0.5],[0.5, 1]],\n",
        "    size=num_samples_per_class)\n",
        "positive_samples = np.random.multivariate_normal(\n",
        "    mean=[3, 0],\n",
        "    cov=[[1, 0.5],[0.5, 1]],\n",
        "    size=num_samples_per_class)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNW_TQz7jR1X"
      },
      "source": [
        "**두 클래스를 (2000, 2) 크기의 한 배열로 쌓기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.645184Z",
          "iopub.status.busy": "2022-05-21T00:58:16.644657Z",
          "iopub.status.idle": "2022-05-21T00:58:16.649566Z",
          "shell.execute_reply": "2022-05-21T00:58:16.648645Z"
        },
        "id": "mKsVyGg0jR1Y"
      },
      "outputs": [],
      "source": [
        "inputs = np.vstack((negative_samples, positive_samples)).astype(np.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JR70BhDujR1Y"
      },
      "source": [
        "**(0과 1로 구성된) 타깃 생성하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.653376Z",
          "iopub.status.busy": "2022-05-21T00:58:16.652893Z",
          "iopub.status.idle": "2022-05-21T00:58:16.656858Z",
          "shell.execute_reply": "2022-05-21T00:58:16.656239Z"
        },
        "id": "jMEgZXkMjR1Y"
      },
      "outputs": [],
      "source": [
        "targets = np.vstack((np.zeros((num_samples_per_class, 1), dtype=\"float32\"),\n",
        "                     np.ones((num_samples_per_class, 1), dtype=\"float32\")))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3joL3w9qjR1Y"
      },
      "source": [
        "**두 클래스의 포인트를 그래프로 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:16.660370Z",
          "iopub.status.busy": "2022-05-21T00:58:16.659928Z",
          "iopub.status.idle": "2022-05-21T00:58:17.496563Z",
          "shell.execute_reply": "2022-05-21T00:58:17.495565Z"
        },
        "id": "9TXfBsUJjR1Y",
        "outputId": "a9c6e519-3089-4ace-d61f-c3fcd517ee82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(inputs[:, 0], inputs[:, 1], c=targets[:, 0])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fZlBGjfjR1Y"
      },
      "source": [
        "**선형 분류기의 변수 만들기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.501331Z",
          "iopub.status.busy": "2022-05-21T00:58:17.500625Z",
          "iopub.status.idle": "2022-05-21T00:58:17.507479Z",
          "shell.execute_reply": "2022-05-21T00:58:17.506630Z"
        },
        "id": "Lr6-tTFtjR1Y"
      },
      "outputs": [],
      "source": [
        "input_dim = 2\n",
        "output_dim = 1\n",
        "W = tf.Variable(initial_value=tf.random.uniform(shape=(input_dim, output_dim)))\n",
        "b = tf.Variable(initial_value=tf.zeros(shape=(output_dim,)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nepUS0hcjR1Y"
      },
      "source": [
        "**정방향 패스 함수**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.511530Z",
          "iopub.status.busy": "2022-05-21T00:58:17.510880Z",
          "iopub.status.idle": "2022-05-21T00:58:17.514807Z",
          "shell.execute_reply": "2022-05-21T00:58:17.513996Z"
        },
        "id": "NENOsp6wjR1Z"
      },
      "outputs": [],
      "source": [
        "def model(inputs):\n",
        "    return tf.matmul(inputs, W) + b"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PS4bb2IFjR1Z"
      },
      "source": [
        "**평균 제곱 오차 손실 함수**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.518429Z",
          "iopub.status.busy": "2022-05-21T00:58:17.518140Z",
          "iopub.status.idle": "2022-05-21T00:58:17.522316Z",
          "shell.execute_reply": "2022-05-21T00:58:17.521551Z"
        },
        "id": "TfRT4wnCjR1Z"
      },
      "outputs": [],
      "source": [
        "def square_loss(targets, predictions):\n",
        "    per_sample_losses = tf.square(targets - predictions)\n",
        "    return tf.reduce_mean(per_sample_losses)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnMMSTVojR1a"
      },
      "source": [
        "**훈련 스텝 함수**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.526391Z",
          "iopub.status.busy": "2022-05-21T00:58:17.525809Z",
          "iopub.status.idle": "2022-05-21T00:58:17.531136Z",
          "shell.execute_reply": "2022-05-21T00:58:17.530318Z"
        },
        "id": "pfnfnGcbjR1a"
      },
      "outputs": [],
      "source": [
        "learning_rate = 0.1\n",
        "\n",
        "def training_step(inputs, targets):\n",
        "    with tf.GradientTape() as tape:\n",
        "        predictions = model(inputs)\n",
        "        loss = square_loss(targets, predictions)\n",
        "    grad_loss_wrt_W, grad_loss_wrt_b = tape.gradient(loss, [W, b])\n",
        "    W.assign_sub(grad_loss_wrt_W * learning_rate)\n",
        "    b.assign_sub(grad_loss_wrt_b * learning_rate)\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdzwgKLIjR1a"
      },
      "source": [
        "**배치 훈련 루프**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.535011Z",
          "iopub.status.busy": "2022-05-21T00:58:17.534413Z",
          "iopub.status.idle": "2022-05-21T00:58:17.601552Z",
          "shell.execute_reply": "2022-05-21T00:58:17.600577Z"
        },
        "id": "EXeU2olUjR1a",
        "outputId": "6aaaa79e-bc42-4379-bc5c-a9771e31c56f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0번째 스텝의 손실: 3.0461\n",
            "1번째 스텝의 손실: 0.2291\n",
            "2번째 스텝의 손실: 0.1168\n",
            "3번째 스텝의 손실: 0.0948\n",
            "4번째 스텝의 손실: 0.0869\n",
            "5번째 스텝의 손실: 0.0814\n",
            "6번째 스텝의 손실: 0.0767\n",
            "7번째 스텝의 손실: 0.0724\n",
            "8번째 스텝의 손실: 0.0684\n",
            "9번째 스텝의 손실: 0.0648\n",
            "10번째 스텝의 손실: 0.0615\n",
            "11번째 스텝의 손실: 0.0584\n",
            "12번째 스텝의 손실: 0.0556\n",
            "13번째 스텝의 손실: 0.0530\n",
            "14번째 스텝의 손실: 0.0507\n",
            "15번째 스텝의 손실: 0.0485\n",
            "16번째 스텝의 손실: 0.0465\n",
            "17번째 스텝의 손실: 0.0447\n",
            "18번째 스텝의 손실: 0.0430\n",
            "19번째 스텝의 손실: 0.0415\n",
            "20번째 스텝의 손실: 0.0400\n",
            "21번째 스텝의 손실: 0.0387\n",
            "22번째 스텝의 손실: 0.0376\n",
            "23번째 스텝의 손실: 0.0365\n",
            "24번째 스텝의 손실: 0.0355\n",
            "25번째 스텝의 손실: 0.0345\n",
            "26번째 스텝의 손실: 0.0337\n",
            "27번째 스텝의 손실: 0.0329\n",
            "28번째 스텝의 손실: 0.0322\n",
            "29번째 스텝의 손실: 0.0315\n",
            "30번째 스텝의 손실: 0.0309\n",
            "31번째 스텝의 손실: 0.0304\n",
            "32번째 스텝의 손실: 0.0299\n",
            "33번째 스텝의 손실: 0.0294\n",
            "34번째 스텝의 손실: 0.0290\n",
            "35번째 스텝의 손실: 0.0286\n",
            "36번째 스텝의 손실: 0.0282\n",
            "37번째 스텝의 손실: 0.0279\n",
            "38번째 스텝의 손실: 0.0276\n",
            "39번째 스텝의 손실: 0.0273\n"
          ]
        }
      ],
      "source": [
        "for step in range(40):\n",
        "    loss = training_step(inputs, targets)\n",
        "    print(f\"{step}번째 스텝의 손실: {loss:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.605276Z",
          "iopub.status.busy": "2022-05-21T00:58:17.604747Z",
          "iopub.status.idle": "2022-05-21T00:58:17.748955Z",
          "shell.execute_reply": "2022-05-21T00:58:17.746773Z"
        },
        "id": "JR1aqLU5jR1b",
        "outputId": "41af979f-1988-4afc-aab3-847256e105e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predictions = model(inputs)\n",
        "plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.753051Z",
          "iopub.status.busy": "2022-05-21T00:58:17.752361Z",
          "iopub.status.idle": "2022-05-21T00:58:17.897123Z",
          "shell.execute_reply": "2022-05-21T00:58:17.895862Z"
        },
        "id": "iJrHw9WSjR1b",
        "outputId": "7f266a10-6a24-4725-b72f-d530266f1736",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.linspace(-1, 4, 100)\n",
        "# 사실 100개의 x 축 좌표를 만들 필요 없이 시작과 종료 위치만 있어도 됩니다.\n",
        "# x = [-1, 4]\n",
        "y = - W[0] /  W[1] * x + (0.5 - b) / W[1]\n",
        "plt.plot(x, y, \"-r\")\n",
        "plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Z2drwSqjR1b"
      },
      "source": [
        "## 신경망의 구조: 핵심 Keras API 이해하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOk_UcQyjR1b"
      },
      "source": [
        "### 층: 딥러닝의 구성 요소"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2XTNDbxjR1b"
      },
      "source": [
        "#### 케라스의 Layer 클래스"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCGiAeb3jR1b"
      },
      "source": [
        "**`Layer`의 서브클래스로 구현한 `Dense` 층**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.901835Z",
          "iopub.status.busy": "2022-05-21T00:58:17.901476Z",
          "iopub.status.idle": "2022-05-21T00:58:17.908604Z",
          "shell.execute_reply": "2022-05-21T00:58:17.907592Z"
        },
        "id": "0gwFDo70jR1b"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "class SimpleDense(keras.layers.Layer):\n",
        "\n",
        "    def __init__(self, units, activation=None):\n",
        "        super().__init__()\n",
        "        self.units = units\n",
        "        self.activation = activation\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        input_dim = input_shape[-1]\n",
        "        self.W = self.add_weight(shape=(input_dim, self.units),\n",
        "                                 initializer=\"random_normal\")\n",
        "        self.b = self.add_weight(shape=(self.units,),\n",
        "                                 initializer=\"zeros\")\n",
        "\n",
        "    def call(self, inputs):\n",
        "        y = tf.matmul(inputs, self.W) + self.b\n",
        "        if self.activation is not None:\n",
        "            y = self.activation(y)\n",
        "        return y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.912833Z",
          "iopub.status.busy": "2022-05-21T00:58:17.912083Z",
          "iopub.status.idle": "2022-05-21T00:58:17.923080Z",
          "shell.execute_reply": "2022-05-21T00:58:17.922276Z"
        },
        "id": "Z2KTF3FijR1c",
        "outputId": "cc1703e6-3e96-4002-89a2-102e41186854",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 32)\n"
          ]
        }
      ],
      "source": [
        "my_dense = SimpleDense(units=32, activation=tf.nn.relu)\n",
        "input_tensor = tf.ones(shape=(2, 784))\n",
        "output_tensor = my_dense(input_tensor)\n",
        "print(output_tensor.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VnPv_4djR1c"
      },
      "source": [
        "#### 자동 크기 추론: 동적으로 층 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.926464Z",
          "iopub.status.busy": "2022-05-21T00:58:17.926171Z",
          "iopub.status.idle": "2022-05-21T00:58:17.933973Z",
          "shell.execute_reply": "2022-05-21T00:58:17.932941Z"
        },
        "id": "uO_ctIKmjR1c"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras import layers\n",
        "layer = layers.Dense(32, activation=\"relu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.937598Z",
          "iopub.status.busy": "2022-05-21T00:58:17.937320Z",
          "iopub.status.idle": "2022-05-21T00:58:17.945965Z",
          "shell.execute_reply": "2022-05-21T00:58:17.944897Z"
        },
        "id": "MZxM9YAxjR1c"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras import models\n",
        "from tensorflow.keras import layers\n",
        "model = models.Sequential([\n",
        "    layers.Dense(32, activation=\"relu\"),\n",
        "    layers.Dense(32)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.949491Z",
          "iopub.status.busy": "2022-05-21T00:58:17.949222Z",
          "iopub.status.idle": "2022-05-21T00:58:17.957588Z",
          "shell.execute_reply": "2022-05-21T00:58:17.956818Z"
        },
        "id": "7x4nViHrjR1c"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    SimpleDense(32, activation=\"relu\"),\n",
        "    SimpleDense(64, activation=\"relu\"),\n",
        "    SimpleDense(32, activation=\"relu\"),\n",
        "    SimpleDense(10, activation=\"softmax\")\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AnOIxT_pjR1c"
      },
      "source": [
        "### 층에서 모델로"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GuWb-UATjR1c"
      },
      "source": [
        "### “컴파일” 단계: 학습 과정 설정"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.961340Z",
          "iopub.status.busy": "2022-05-21T00:58:17.961064Z",
          "iopub.status.idle": "2022-05-21T00:58:17.973759Z",
          "shell.execute_reply": "2022-05-21T00:58:17.972882Z"
        },
        "id": "a6R5ru-3jR1c"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([keras.layers.Dense(1)])\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"mean_squared_error\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.977477Z",
          "iopub.status.busy": "2022-05-21T00:58:17.977176Z",
          "iopub.status.idle": "2022-05-21T00:58:17.989690Z",
          "shell.execute_reply": "2022-05-21T00:58:17.988911Z"
        },
        "id": "uA44NTPejR1c"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=keras.optimizers.RMSprop(),\n",
        "              loss=keras.losses.MeanSquaredError(),\n",
        "              metrics=[keras.metrics.BinaryAccuracy()])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lnKn8lZjR1d"
      },
      "source": [
        "### 손실 함수 선택하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDOSr9pXjR1d"
      },
      "source": [
        "### fit() 메서드 이해하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmBftilFjR1d"
      },
      "source": [
        "**넘파이 데이터로 `fit()` 메서드 호출하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:17.993795Z",
          "iopub.status.busy": "2022-05-21T00:58:17.993504Z",
          "iopub.status.idle": "2022-05-21T00:58:18.510003Z",
          "shell.execute_reply": "2022-05-21T00:58:18.509248Z"
        },
        "id": "TCD2GNU2jR1d",
        "outputId": "5d8f5477-1046-477b-ac43-e9cac8ed509a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "16/16 [==============================] - 3s 5ms/step - loss: 1.4070 - binary_accuracy: 0.6650\n",
            "Epoch 2/5\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 1.2352 - binary_accuracy: 0.6805\n",
            "Epoch 3/5\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 1.0976 - binary_accuracy: 0.6935\n",
            "Epoch 4/5\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.9735 - binary_accuracy: 0.7135\n",
            "Epoch 5/5\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.8597 - binary_accuracy: 0.7285\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(\n",
        "    inputs,\n",
        "    targets,\n",
        "    epochs=5,\n",
        "    batch_size=128\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:18.513804Z",
          "iopub.status.busy": "2022-05-21T00:58:18.513289Z",
          "iopub.status.idle": "2022-05-21T00:58:18.518984Z",
          "shell.execute_reply": "2022-05-21T00:58:18.518076Z"
        },
        "id": "7g0R_STajR1d",
        "outputId": "573d6f71-a1ce-408f-dde9-b4a0990203f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'loss': [1.4069768190383911,\n",
              "  1.235182523727417,\n",
              "  1.0976046323776245,\n",
              "  0.9735286235809326,\n",
              "  0.8597371578216553],\n",
              " 'binary_accuracy': [0.6650000214576721,\n",
              "  0.6804999709129333,\n",
              "  0.6934999823570251,\n",
              "  0.7135000228881836,\n",
              "  0.7285000085830688]}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "history.history"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eg7A4QwCjR1d"
      },
      "source": [
        "### 검증 데이터에서 손실과 측정 지표 모니터링하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcScui7rjR1d"
      },
      "source": [
        "**`validation_data` 매개변수 사용하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:18.522664Z",
          "iopub.status.busy": "2022-05-21T00:58:18.522375Z",
          "iopub.status.idle": "2022-05-21T00:58:19.764061Z",
          "shell.execute_reply": "2022-05-21T00:58:19.763028Z"
        },
        "id": "KaP6UYfNjR1d",
        "outputId": "b2294514-c30e-4ee8-e1fd-b4eeda31fcd0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "88/88 [==============================] - 3s 13ms/step - loss: 0.2763 - binary_accuracy: 0.8943 - val_loss: 0.0400 - val_binary_accuracy: 0.9950\n",
            "Epoch 2/5\n",
            "88/88 [==============================] - 1s 10ms/step - loss: 0.0727 - binary_accuracy: 0.9600 - val_loss: 0.0478 - val_binary_accuracy: 0.9933\n",
            "Epoch 3/5\n",
            "88/88 [==============================] - 1s 6ms/step - loss: 0.0741 - binary_accuracy: 0.9607 - val_loss: 0.0782 - val_binary_accuracy: 0.9667\n",
            "Epoch 4/5\n",
            "88/88 [==============================] - 1s 6ms/step - loss: 0.0690 - binary_accuracy: 0.9571 - val_loss: 0.1165 - val_binary_accuracy: 0.9583\n",
            "Epoch 5/5\n",
            "88/88 [==============================] - 1s 10ms/step - loss: 0.0792 - binary_accuracy: 0.9464 - val_loss: 0.0233 - val_binary_accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x79b95e138a90>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "model = keras.Sequential([keras.layers.Dense(1)])\n",
        "model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=0.1),\n",
        "              loss=keras.losses.MeanSquaredError(),\n",
        "              metrics=[keras.metrics.BinaryAccuracy()])\n",
        "\n",
        "indices_permutation = np.random.permutation(len(inputs))\n",
        "shuffled_inputs = inputs[indices_permutation]\n",
        "shuffled_targets = targets[indices_permutation]\n",
        "\n",
        "num_validation_samples = int(0.3 * len(inputs))\n",
        "val_inputs = shuffled_inputs[:num_validation_samples]\n",
        "val_targets = shuffled_targets[:num_validation_samples]\n",
        "training_inputs = shuffled_inputs[num_validation_samples:]\n",
        "training_targets = shuffled_targets[num_validation_samples:]\n",
        "model.fit(\n",
        "    training_inputs,\n",
        "    training_targets,\n",
        "    epochs=5,\n",
        "    batch_size=16,\n",
        "    validation_data=(val_inputs, val_targets)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKS1lIrmjR1d"
      },
      "source": [
        "### 추론: 훈련한 모델 사용하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:19.768128Z",
          "iopub.status.busy": "2022-05-21T00:58:19.767816Z",
          "iopub.status.idle": "2022-05-21T00:58:19.874956Z",
          "shell.execute_reply": "2022-05-21T00:58:19.873756Z"
        },
        "id": "O6b3X5hUjR1e",
        "outputId": "fa8e671a-6196-4e98-c14a-9d09d532adb4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 3ms/step\n",
            "[[ 0.1304169 ]\n",
            " [ 1.0192415 ]\n",
            " [ 0.9584348 ]\n",
            " [ 1.150435  ]\n",
            " [ 0.36042613]\n",
            " [ 0.07586262]\n",
            " [ 0.01445282]\n",
            " [-0.10492018]\n",
            " [-0.12498769]\n",
            " [ 0.28297824]]\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(val_inputs, batch_size=128)\n",
        "print(predictions[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jc1OJ7sJjR1e"
      },
      "source": [
        "## 요약"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "chapter03_introduction-to-keras-and-tf.i",
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}