{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9y222HgfM-OA"
      },
      "source": [
        "# 머신 러닝 교과서 3판"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5SXCZX_M-OD"
      },
      "source": [
        "# 17장 - 새로운 데이터 합성을 위한 생성적 적대 신경망 (1/2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hx1MAB0FbouH"
      },
      "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/ch17/ch17_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/ch17/ch17_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": "4wzfKSEhbouH"
      },
      "source": [
        "### 목차"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vc8_zr4bouH"
      },
      "source": [
        "- 생성적 적대 신경망 소개\n",
        "    - 오토인코더\n",
        "    - 새로운 데이터 합성을 위한 생성 모델\n",
        "    - GAN으로 새로운 샘플 생성하기\n",
        "    - GAN의 생성자와 판별자 손실 함수 이해하기\n",
        "- 밑바닥부터 GAN 모델 구현하기\n",
        "    - 구글 코랩에서 GAN 모델 훈련하기\n",
        "    - 생성자와 판별자 신경망 구현하기\n",
        "    - 훈련 데이터셋 정의하기\n",
        "    - GAN 모델 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2AfJgihdM-OE"
      },
      "outputs": [],
      "source": [
        "from IPython.display import Image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41bk3eM5M-OE"
      },
      "source": [
        "# 생성적 적대 신경망 소개\n",
        "\n",
        "## 오토인코더"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "phbH6zGQM-OE",
        "outputId": "d5b1da00-0247-4804-8e91-ff6e98021e09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQ2\" width=\"500\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQ2', width=500)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cuw0DGR2M-OF"
      },
      "source": [
        "## 새로운 데이터 합성을 위한 생성 모델"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "ziTpZ3G5M-OF",
        "outputId": "ed04f6ef-3583-4f8f-9b21-ca0cbb82d732"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQH\" width=\"700\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQH', width=700)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1DsTJvLM-OF"
      },
      "source": [
        "## GAN으로 새로운 샘플 생성하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "2DijKimVM-OF",
        "outputId": "464dabe6-0665-484a-af2a-97b0395c9a66"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQ7\" width=\"700\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQ7', width=700)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iamOayQfM-OF"
      },
      "source": [
        "## GAN의 생성자와 판별자 손실 함수 이해하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "P0SEKNGpM-OG",
        "outputId": "1676837d-c866-41f3-9924-0325f1cfb8cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQF\" width=\"800\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQF', width=800)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgleEEF1M-OG"
      },
      "source": [
        "# 밑바닥부터 GAN 모델 구현하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1UBoKm_M-OG"
      },
      "source": [
        "## 구글 코랩에서 GAN 모델 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "sBdI0g22M-OG",
        "outputId": "7070c24d-01fa-453f-b5cb-15573512ceb1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQb\" width=\"700\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQb', width=700)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "Mf0pXqraM-OH",
        "outputId": "d00e2d19-01a5-49ea-f029-4ce5c6395f10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQN\" width=\"600\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQN', width=600)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "Nd67sNNIM-OH",
        "outputId": "eb4bc708-1646-4104-f4e6-5c1113a8b412"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQA\" width=\"600\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQA', width=600)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_khN3hi1xxQ-",
        "outputId": "d7c9ae45-b74d-42d7-d71e-545c082474b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.14.0\n",
            "GPU 여부: True\n",
            "/device:GPU:0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "print(\"GPU 여부:\", len(tf.config.list_physical_devices('GPU')) > 0)\n",
        "\n",
        "if tf.config.list_physical_devices('GPU'):\n",
        "    device_name = tf.test.gpu_device_name()\n",
        "else:\n",
        "    device_name = 'cpu:0'\n",
        "\n",
        "print(device_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ceNFHOHHNVwz",
        "outputId": "7dad837e-9aa0-4092-cf65-dc1a50cd3505"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From <ipython-input-10-b9c5066405f4>:1: get_memory_usage (from tensorflow.python.framework.config) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.config.experimental.get_memory_info(device)['current'] instead.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "tf.config.experimental.get_memory_usage('GPU:0')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5sNcfuwHPQMs",
        "outputId": "eccafefa-7b91-40e8-ebed-d15cf4d9e3c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Nov 11 01:34:19 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P0    49W / 400W |    627MiB / 40960MiB |      1%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_yH3L4wTOLb",
        "outputId": "f295b2c7-1774-443f-fa5f-60001fc3b7d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No LSB modules are available.\n",
            "Distributor ID:\tUbuntu\n",
            "Description:\tUbuntu 22.04.2 LTS\n",
            "Release:\t22.04\n",
            "Codename:\tjammy\n"
          ]
        }
      ],
      "source": [
        "!lsb_release -a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ehoy80rqwNnl"
      },
      "outputs": [],
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHHcQfM2M-OI"
      },
      "source": [
        "## 생성자와 판별자 신경망 구현하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "ig_rZtXqM-OI",
        "outputId": "0c4fb239-941b-4290-d0f0-a24dda6214f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQp\" width=\"600\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQp', width=600)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "uc4FQzczM-OJ",
        "outputId": "d0dc1c80-8658-4250-e0e1-74de6cd2b9ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://git.io/JLAQh\" width=\"600\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "Image(url='https://git.io/JLAQh', width=600)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "FRh8E66HwXRL"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "tOL56FmXwoL-"
      },
      "outputs": [],
      "source": [
        "## 생성자 함수를 정의합니다:\n",
        "def make_generator_network(\n",
        "        num_hidden_layers=1,\n",
        "        num_hidden_units=100,\n",
        "        num_output_units=784):\n",
        "    model = tf.keras.Sequential()\n",
        "    for i in range(num_hidden_layers):\n",
        "        model.add(\n",
        "            tf.keras.layers.Dense(\n",
        "                units=num_hidden_units,\n",
        "                use_bias=False)\n",
        "            )\n",
        "        model.add(tf.keras.layers.LeakyReLU())\n",
        "\n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=num_output_units, activation='tanh'))\n",
        "    return model\n",
        "\n",
        "## 판별자 함수를 정의합니다:\n",
        "def make_discriminator_network(\n",
        "        num_hidden_layers=1,\n",
        "        num_hidden_units=100,\n",
        "        num_output_units=1):\n",
        "    model = tf.keras.Sequential()\n",
        "    for i in range(num_hidden_layers):\n",
        "        model.add(tf.keras.layers.Dense(units=num_hidden_units))\n",
        "        model.add(tf.keras.layers.LeakyReLU())\n",
        "        model.add(tf.keras.layers.Dropout(rate=0.5))\n",
        "\n",
        "    model.add(\n",
        "        tf.keras.layers.Dense(\n",
        "            units=num_output_units,\n",
        "            activation=None)\n",
        "        )\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Z4JQb9MxD5p",
        "outputId": "523b360d-2016-4ea2-d258-e5721fa5307e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 100)               2000      \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 100)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 784)               79184     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 81184 (317.12 KB)\n",
            "Trainable params: 81184 (317.12 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "image_size = (28, 28)\n",
        "z_size = 20\n",
        "mode_z = 'uniform'  # 'uniform' vs. 'normal'\n",
        "gen_hidden_layers = 1\n",
        "gen_hidden_size = 100\n",
        "disc_hidden_layers = 1\n",
        "disc_hidden_size = 100\n",
        "\n",
        "tf.random.set_seed(1)\n",
        "\n",
        "gen_model = make_generator_network(\n",
        "    num_hidden_layers=gen_hidden_layers,\n",
        "    num_hidden_units=gen_hidden_size,\n",
        "    num_output_units=np.prod(image_size))\n",
        "\n",
        "gen_model.build(input_shape=(None, z_size))\n",
        "gen_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAhbOUZHxN1b",
        "outputId": "bcdd325d-802e-4bdf-9d5a-271c3d6aae6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 100)               78500     \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 100)               0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 78601 (307.04 KB)\n",
            "Trainable params: 78601 (307.04 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "disc_model = make_discriminator_network(\n",
        "    num_hidden_layers=disc_hidden_layers,\n",
        "    num_hidden_units=disc_hidden_size)\n",
        "\n",
        "disc_model.build(input_shape=(None, np.prod(image_size)))\n",
        "disc_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "edHUQPQxM-OK"
      },
      "source": [
        "## 훈련 데이터셋 정의하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "7c09dda7eeb241508cb2cd6592fcf80c",
            "4031a96ff1774e8bbe71a02da617faf1",
            "70173d794bf14346a57eb8224a198e34",
            "eadf9fcc81ce4955a0ffc627ed3969b2",
            "6b94e9c7a4fd4993b1f8c91a216da696",
            "0c1ac0f5831c4fb7b5d300a831cb53c6",
            "23af028da94e47539c7ec6268869fa3f",
            "7321144c23ca4587afd927a717dc2483",
            "63ce20e955e6458196a771f56bd66163",
            "074e16370b554088884fc89a4c265a04",
            "06e14526222044b08b6468f1bcc0a401"
          ]
        },
        "id": "ApQ1ICJixf2w",
        "outputId": "e6a2d6e4-8a80-478e-dd6c-6de7c3f3427c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 11.06 MiB (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...:   0%|          | 0/5 [00:00<?, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c09dda7eeb241508cb2cd6592fcf80c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\n",
            "전처리 전:  \n",
            "dtype:  <dtype: 'uint8'>  최소: 0 최대: 255\n",
            "전처리 후:  \n",
            "dtype:  <dtype: 'float32'>  최소: -0.8737728595733643 최대: 0.9460210800170898\n"
          ]
        }
      ],
      "source": [
        "mnist_bldr = tfds.builder('mnist')\n",
        "mnist_bldr.download_and_prepare()\n",
        "mnist = mnist_bldr.as_dataset(shuffle_files=False)\n",
        "\n",
        "def preprocess(ex, mode='uniform'):\n",
        "    image = ex['image']\n",
        "    image = tf.image.convert_image_dtype(image, tf.float32)\n",
        "    image = tf.reshape(image, [-1])\n",
        "    image = image*2 - 1.0\n",
        "    if mode == 'uniform':\n",
        "        input_z = tf.random.uniform(\n",
        "            shape=(z_size,), minval=-1.0, maxval=1.0)\n",
        "    elif mode == 'normal':\n",
        "        input_z = tf.random.normal(shape=(z_size,))\n",
        "    return input_z, image\n",
        "\n",
        "\n",
        "\n",
        "mnist_trainset = mnist['train']\n",
        "\n",
        "print('전처리 전:  ')\n",
        "example = next(iter(mnist_trainset))['image']\n",
        "print('dtype: ', example.dtype, ' 최소: {} 최대: {}'.format(np.min(example), np.max(example)))\n",
        "\n",
        "mnist_trainset = mnist_trainset.map(preprocess)\n",
        "\n",
        "print('전처리 후:  ')\n",
        "example = next(iter(mnist_trainset))[0]\n",
        "print('dtype: ', example.dtype, ' 최소: {} 최대: {}'.format(np.min(example), np.max(example)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HT57CGAz0RDr"
      },
      "source": [
        " * **데이터 흐름을 단계별로 밟아보기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdAXXUtryFGs",
        "outputId": "b1cd6041-4e8c-4cfc-bed6-489da17bf3cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input-z -- 크기: (32, 20)\n",
            "input-real -- 크기: (32, 784)\n",
            "생성자 출력 -- 크기: (32, 784)\n",
            "판별자 (진짜) -- 크기: (32, 1)\n",
            "판별자 (가짜) -- 크기: (32, 1)\n"
          ]
        }
      ],
      "source": [
        "mnist_trainset = mnist_trainset.batch(32, drop_remainder=True)\n",
        "input_z, input_real = next(iter(mnist_trainset))\n",
        "print('input-z -- 크기:', input_z.shape)\n",
        "print('input-real -- 크기:', input_real.shape)\n",
        "\n",
        "g_output = gen_model(input_z)\n",
        "print('생성자 출력 -- 크기:', g_output.shape)\n",
        "\n",
        "d_logits_real = disc_model(input_real)\n",
        "d_logits_fake = disc_model(g_output)\n",
        "print('판별자 (진짜) -- 크기:', d_logits_real.shape)\n",
        "print('판별자 (가짜) -- 크기:', d_logits_fake.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nb2H-Ac3M-OK"
      },
      "source": [
        "## GAN 모델 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-c9xVvjJySZk",
        "outputId": "374d9387-1b4d-47ea-a261-2dd9913654f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "생성자 손실: 0.6984\n",
            "판별자 손실: 진짜 0.2477 가짜 0.6916\n"
          ]
        }
      ],
      "source": [
        "loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "\n",
        "## 생성자 손실\n",
        "g_labels_real = tf.ones_like(d_logits_fake)\n",
        "g_loss = loss_fn(y_true=g_labels_real, y_pred=d_logits_fake)\n",
        "print('생성자 손실: {:.4f}'.format(g_loss))\n",
        "\n",
        "## 판별자 손실\n",
        "d_labels_real = tf.ones_like(d_logits_real)\n",
        "d_labels_fake = tf.zeros_like(d_logits_fake)\n",
        "\n",
        "d_loss_real = loss_fn(y_true=d_labels_real, y_pred=d_logits_real)\n",
        "d_loss_fake = loss_fn(y_true=d_labels_fake, y_pred=d_logits_fake)\n",
        "print('판별자 손실: 진짜 {:.4f} 가짜 {:.4f}'\n",
        "      .format(d_loss_real.numpy(), d_loss_fake.numpy()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtxdTVyF0KCF"
      },
      "source": [
        " * **최종 훈련**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeFKLGNfAF5J",
        "outputId": "882a1558-4b11-4605-a277-bd06fc4ca6d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function _BaseOptimizer._update_step_xla at 0x79fddd1cbbe0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:6 out of the last 6 calls to <function _BaseOptimizer._update_step_xla at 0x79fddd1cbbe0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "에포크 001 | 시간 0.72 min | 평균 손실 >> 생성자/판별자 2.9682/0.2987 [판별자-진짜: 0.0330 판별자-가짜: 0.2658]\n",
            "에포크 002 | 시간 1.39 min | 평균 손실 >> 생성자/판별자 4.9700/0.3043 [판별자-진짜: 0.0969 판별자-가짜: 0.2074]\n",
            "에포크 003 | 시간 2.05 min | 평균 손실 >> 생성자/판별자 3.7117/0.6227 [판별자-진짜: 0.2698 판별자-가짜: 0.3529]\n",
            "에포크 004 | 시간 2.72 min | 평균 손실 >> 생성자/판별자 2.1959/0.8859 [판별자-진짜: 0.4327 판별자-가짜: 0.4532]\n",
            "에포크 005 | 시간 3.38 min | 평균 손실 >> 생성자/판별자 2.2290/0.7819 [판별자-진짜: 0.4272 판별자-가짜: 0.3548]\n",
            "에포크 006 | 시간 4.05 min | 평균 손실 >> 생성자/판별자 1.6863/0.9263 [판별자-진짜: 0.5147 판별자-가짜: 0.4116]\n",
            "에포크 007 | 시간 4.71 min | 평균 손실 >> 생성자/판별자 1.7057/0.9675 [판별자-진짜: 0.5167 판별자-가짜: 0.4508]\n",
            "에포크 008 | 시간 5.37 min | 평균 손실 >> 생성자/판별자 1.4655/1.0108 [판별자-진짜: 0.5518 판별자-가짜: 0.4590]\n",
            "에포크 009 | 시간 6.04 min | 평균 손실 >> 생성자/판별자 1.5040/0.9920 [판별자-진짜: 0.5426 판별자-가짜: 0.4494]\n",
            "에포크 010 | 시간 6.71 min | 평균 손실 >> 생성자/판별자 1.4708/0.9936 [판별자-진짜: 0.5464 판별자-가짜: 0.4471]\n",
            "에포크 011 | 시간 7.38 min | 평균 손실 >> 생성자/판별자 1.4586/1.0476 [판별자-진짜: 0.5591 판별자-가짜: 0.4885]\n",
            "에포크 012 | 시간 8.05 min | 평균 손실 >> 생성자/판별자 1.2618/1.0958 [판별자-진짜: 0.5840 판별자-가짜: 0.5118]\n",
            "에포크 013 | 시간 8.71 min | 평균 손실 >> 생성자/판별자 1.3207/1.1025 [판별자-진짜: 0.5822 판별자-가짜: 0.5203]\n",
            "에포크 014 | 시간 9.37 min | 평균 손실 >> 생성자/판별자 1.2535/1.1556 [판별자-진짜: 0.5972 판별자-가짜: 0.5584]\n",
            "에포크 015 | 시간 10.04 min | 평균 손실 >> 생성자/판별자 1.1510/1.1871 [판별자-진짜: 0.6151 판별자-가짜: 0.5721]\n",
            "에포크 016 | 시간 10.71 min | 평균 손실 >> 생성자/판별자 1.0960/1.2090 [판별자-진짜: 0.6248 판별자-가짜: 0.5842]\n",
            "에포크 017 | 시간 11.37 min | 평균 손실 >> 생성자/판별자 1.2611/1.1628 [판별자-진짜: 0.5985 판별자-가짜: 0.5644]\n",
            "에포크 018 | 시간 12.04 min | 평균 손실 >> 생성자/판별자 1.0957/1.2118 [판별자-진짜: 0.6239 판별자-가짜: 0.5879]\n",
            "에포크 019 | 시간 12.70 min | 평균 손실 >> 생성자/판별자 1.1300/1.1819 [판별자-진짜: 0.6089 판별자-가짜: 0.5730]\n",
            "에포크 020 | 시간 13.37 min | 평균 손실 >> 생성자/판별자 1.1829/1.1976 [판별자-진짜: 0.6099 판별자-가짜: 0.5877]\n",
            "에포크 021 | 시간 14.04 min | 평균 손실 >> 생성자/판별자 1.1001/1.2283 [판별자-진짜: 0.6267 판별자-가짜: 0.6016]\n",
            "에포크 022 | 시간 14.71 min | 평균 손실 >> 생성자/판별자 1.0800/1.2179 [판별자-진짜: 0.6228 판별자-가짜: 0.5952]\n",
            "에포크 023 | 시간 15.39 min | 평균 손실 >> 생성자/판별자 1.1102/1.2162 [판별자-진짜: 0.6203 판별자-가짜: 0.5960]\n",
            "에포크 024 | 시간 16.05 min | 평균 손실 >> 생성자/판별자 1.0613/1.2296 [판별자-진짜: 0.6253 판별자-가짜: 0.6043]\n",
            "에포크 025 | 시간 16.72 min | 평균 손실 >> 생성자/판별자 1.0747/1.2356 [판별자-진짜: 0.6281 판별자-가짜: 0.6075]\n",
            "에포크 026 | 시간 17.39 min | 평균 손실 >> 생성자/판별자 1.1006/1.2295 [판별자-진짜: 0.6225 판별자-가짜: 0.6070]\n",
            "에포크 027 | 시간 18.06 min | 평균 손실 >> 생성자/판별자 1.0458/1.2295 [판별자-진짜: 0.6270 판별자-가짜: 0.6025]\n",
            "에포크 028 | 시간 18.73 min | 평균 손실 >> 생성자/판별자 1.0213/1.2526 [판별자-진짜: 0.6363 판별자-가짜: 0.6163]\n",
            "에포크 029 | 시간 19.40 min | 평균 손실 >> 생성자/판별자 1.0016/1.2773 [판별자-진짜: 0.6415 판별자-가짜: 0.6357]\n",
            "에포크 030 | 시간 20.06 min | 평균 손실 >> 생성자/판별자 0.9722/1.2945 [판별자-진짜: 0.6516 판별자-가짜: 0.6429]\n",
            "에포크 031 | 시간 20.73 min | 평균 손실 >> 생성자/판별자 0.9942/1.2642 [판별자-진짜: 0.6401 판별자-가짜: 0.6242]\n",
            "에포크 032 | 시간 21.39 min | 평균 손실 >> 생성자/판별자 1.0377/1.2560 [판별자-진짜: 0.6335 판별자-가짜: 0.6225]\n",
            "에포크 033 | 시간 22.06 min | 평균 손실 >> 생성자/판별자 0.9786/1.2734 [판별자-진짜: 0.6439 판별자-가짜: 0.6295]\n",
            "에포크 034 | 시간 22.72 min | 평균 손실 >> 생성자/판별자 0.9801/1.2733 [판별자-진짜: 0.6426 판별자-가짜: 0.6307]\n",
            "에포크 035 | 시간 23.39 min | 평균 손실 >> 생성자/판별자 0.9964/1.2705 [판별자-진짜: 0.6423 판별자-가짜: 0.6282]\n",
            "에포크 036 | 시간 24.05 min | 평균 손실 >> 생성자/판별자 1.0205/1.2722 [판별자-진짜: 0.6366 판별자-가짜: 0.6356]\n",
            "에포크 037 | 시간 24.72 min | 평균 손실 >> 생성자/판별자 0.9517/1.2897 [판별자-진짜: 0.6497 판별자-가짜: 0.6400]\n",
            "에포크 038 | 시간 25.38 min | 평균 손실 >> 생성자/판별자 0.9446/1.3009 [판별자-진짜: 0.6549 판별자-가짜: 0.6460]\n",
            "에포크 039 | 시간 26.05 min | 평균 손실 >> 생성자/판별자 1.0219/1.2726 [판별자-진짜: 0.6393 판별자-가짜: 0.6334]\n",
            "에포크 040 | 시간 26.71 min | 평균 손실 >> 생성자/판별자 0.9654/1.2797 [판별자-진짜: 0.6439 판별자-가짜: 0.6359]\n",
            "에포크 041 | 시간 27.38 min | 평균 손실 >> 생성자/판별자 0.9571/1.2818 [판별자-진짜: 0.6456 판별자-가짜: 0.6362]\n",
            "에포크 042 | 시간 28.04 min | 평균 손실 >> 생성자/판별자 0.9663/1.2874 [판별자-진짜: 0.6461 판별자-가짜: 0.6414]\n",
            "에포크 043 | 시간 28.71 min | 평균 손실 >> 생성자/판별자 1.0119/1.2833 [판별자-진짜: 0.6457 판별자-가짜: 0.6376]\n",
            "에포크 044 | 시간 29.38 min | 평균 손실 >> 생성자/판별자 0.9797/1.2826 [판별자-진짜: 0.6471 판별자-가짜: 0.6355]\n",
            "에포크 045 | 시간 30.04 min | 평균 손실 >> 생성자/판별자 0.9551/1.2850 [판별자-진짜: 0.6474 판별자-가짜: 0.6375]\n",
            "에포크 046 | 시간 30.71 min | 평균 손실 >> 생성자/판별자 0.9485/1.3029 [판별자-진짜: 0.6521 판별자-가짜: 0.6508]\n",
            "에포크 047 | 시간 31.38 min | 평균 손실 >> 생성자/판별자 0.9814/1.2957 [판별자-진짜: 0.6482 판별자-가짜: 0.6475]\n",
            "에포크 048 | 시간 32.04 min | 평균 손실 >> 생성자/판별자 0.9731/1.2807 [판별자-진짜: 0.6466 판별자-가짜: 0.6341]\n",
            "에포크 049 | 시간 32.71 min | 평균 손실 >> 생성자/판별자 0.9403/1.2871 [판별자-진짜: 0.6473 판별자-가짜: 0.6398]\n",
            "에포크 050 | 시간 33.38 min | 평균 손실 >> 생성자/판별자 0.9500/1.3004 [판별자-진짜: 0.6519 판별자-가짜: 0.6485]\n",
            "에포크 051 | 시간 34.05 min | 평균 손실 >> 생성자/판별자 0.9708/1.2962 [판별자-진짜: 0.6507 판별자-가짜: 0.6455]\n",
            "에포크 052 | 시간 34.71 min | 평균 손실 >> 생성자/판별자 0.9720/1.2942 [판별자-진짜: 0.6500 판별자-가짜: 0.6442]\n",
            "에포크 053 | 시간 35.38 min | 평균 손실 >> 생성자/판별자 0.9284/1.2902 [판별자-진짜: 0.6495 판별자-가짜: 0.6407]\n",
            "에포크 054 | 시간 36.04 min | 평균 손실 >> 생성자/판별자 0.9052/1.3117 [판별자-진짜: 0.6579 판별자-가짜: 0.6538]\n",
            "에포크 055 | 시간 36.70 min | 평균 손실 >> 생성자/판별자 0.9440/1.3121 [판별자-진짜: 0.6572 판별자-가짜: 0.6549]\n",
            "에포크 056 | 시간 37.37 min | 평균 손실 >> 생성자/판별자 0.9472/1.3049 [판별자-진짜: 0.6539 판별자-가짜: 0.6510]\n",
            "에포크 057 | 시간 38.04 min | 평균 손실 >> 생성자/판별자 0.9292/1.3026 [판별자-진짜: 0.6556 판별자-가짜: 0.6471]\n",
            "에포크 058 | 시간 38.71 min | 평균 손실 >> 생성자/판별자 0.9418/1.3132 [판별자-진짜: 0.6584 판별자-가짜: 0.6548]\n",
            "에포크 059 | 시간 39.37 min | 평균 손실 >> 생성자/판별자 0.9499/1.2961 [판별자-진짜: 0.6510 판별자-가짜: 0.6451]\n",
            "에포크 060 | 시간 40.04 min | 평균 손실 >> 생성자/판별자 0.9483/1.3055 [판별자-진짜: 0.6545 판별자-가짜: 0.6509]\n",
            "에포크 061 | 시간 40.71 min | 평균 손실 >> 생성자/판별자 0.9042/1.3180 [판별자-진짜: 0.6598 판별자-가짜: 0.6582]\n",
            "에포크 062 | 시간 41.38 min | 평균 손실 >> 생성자/판별자 0.9129/1.3102 [판별자-진짜: 0.6591 판별자-가짜: 0.6511]\n",
            "에포크 063 | 시간 42.04 min | 평균 손실 >> 생성자/판별자 0.9112/1.3132 [판별자-진짜: 0.6583 판별자-가짜: 0.6549]\n",
            "에포크 064 | 시간 42.71 min | 평균 손실 >> 생성자/판별자 0.9533/1.3041 [판별자-진짜: 0.6540 판별자-가짜: 0.6500]\n",
            "에포크 065 | 시간 43.37 min | 평균 손실 >> 생성자/판별자 0.9316/1.2983 [판별자-진짜: 0.6519 판별자-가짜: 0.6463]\n",
            "에포크 066 | 시간 44.03 min | 평균 손실 >> 생성자/판별자 0.9133/1.3127 [판별자-진짜: 0.6599 판별자-가짜: 0.6529]\n",
            "에포크 067 | 시간 44.69 min | 평균 손실 >> 생성자/판별자 0.9513/1.3086 [판별자-진짜: 0.6563 판별자-가짜: 0.6523]\n",
            "에포크 068 | 시간 45.36 min | 평균 손실 >> 생성자/판별자 0.9390/1.3004 [판별자-진짜: 0.6516 판별자-가짜: 0.6488]\n",
            "에포크 069 | 시간 46.02 min | 평균 손실 >> 생성자/판별자 0.8923/1.3192 [판별자-진짜: 0.6617 판별자-가짜: 0.6574]\n",
            "에포크 070 | 시간 46.69 min | 평균 손실 >> 생성자/판별자 0.9945/1.2940 [판별자-진짜: 0.6478 판별자-가짜: 0.6462]\n",
            "에포크 071 | 시간 47.35 min | 평균 손실 >> 생성자/판별자 0.9231/1.3035 [판별자-진짜: 0.6546 판별자-가짜: 0.6489]\n",
            "에포크 072 | 시간 48.02 min | 평균 손실 >> 생성자/판별자 0.8941/1.3198 [판별자-진짜: 0.6623 판별자-가짜: 0.6575]\n",
            "에포크 073 | 시간 48.69 min | 평균 손실 >> 생성자/판별자 0.9696/1.2985 [판별자-진짜: 0.6513 판별자-가짜: 0.6472]\n",
            "에포크 074 | 시간 49.35 min | 평균 손실 >> 생성자/판별자 0.9070/1.3158 [판별자-진짜: 0.6595 판별자-가짜: 0.6563]\n",
            "에포크 075 | 시간 50.02 min | 평균 손실 >> 생성자/판별자 0.9233/1.3115 [판별자-진짜: 0.6577 판별자-가짜: 0.6538]\n",
            "에포크 076 | 시간 50.69 min | 평균 손실 >> 생성자/판별자 0.9441/1.2990 [판별자-진짜: 0.6518 판별자-가짜: 0.6472]\n",
            "에포크 077 | 시간 51.35 min | 평균 손실 >> 생성자/판별자 0.8913/1.3201 [판별자-진짜: 0.6647 판별자-가짜: 0.6554]\n",
            "에포크 078 | 시간 52.01 min | 평균 손실 >> 생성자/판별자 0.9400/1.3134 [판별자-진짜: 0.6584 판별자-가짜: 0.6551]\n",
            "에포크 079 | 시간 52.68 min | 평균 손실 >> 생성자/판별자 0.9224/1.3160 [판별자-진짜: 0.6599 판별자-가짜: 0.6561]\n",
            "에포크 080 | 시간 53.34 min | 평균 손실 >> 생성자/판별자 0.9180/1.3117 [판별자-진짜: 0.6595 판별자-가짜: 0.6521]\n",
            "에포크 081 | 시간 54.01 min | 평균 손실 >> 생성자/판별자 0.9286/1.3145 [판별자-진짜: 0.6591 판별자-가짜: 0.6554]\n",
            "에포크 082 | 시간 54.67 min | 평균 손실 >> 생성자/판별자 0.9180/1.3100 [판별자-진짜: 0.6560 판별자-가짜: 0.6540]\n",
            "에포크 083 | 시간 55.33 min | 평균 손실 >> 생성자/판별자 0.8849/1.3169 [판별자-진짜: 0.6593 판별자-가짜: 0.6576]\n",
            "에포크 084 | 시간 55.99 min | 평균 손실 >> 생성자/판별자 0.9320/1.3058 [판별자-진짜: 0.6547 판별자-가짜: 0.6510]\n",
            "에포크 085 | 시간 56.66 min | 평균 손실 >> 생성자/판별자 0.8832/1.3228 [판별자-진짜: 0.6631 판별자-가짜: 0.6598]\n",
            "에포크 086 | 시간 57.33 min | 평균 손실 >> 생성자/판별자 0.9631/1.2975 [판별자-진짜: 0.6511 판별자-가짜: 0.6464]\n",
            "에포크 087 | 시간 58.00 min | 평균 손실 >> 생성자/판별자 0.9304/1.3057 [판별자-진짜: 0.6557 판별자-가짜: 0.6500]\n",
            "에포크 088 | 시간 58.66 min | 평균 손실 >> 생성자/판별자 0.8759/1.3222 [판별자-진짜: 0.6637 판별자-가짜: 0.6585]\n",
            "에포크 089 | 시간 59.33 min | 평균 손실 >> 생성자/판별자 0.9384/1.3129 [판별자-진짜: 0.6559 판별자-가짜: 0.6570]\n",
            "에포크 090 | 시간 59.99 min | 평균 손실 >> 생성자/판별자 0.9403/1.3117 [판별자-진짜: 0.6583 판별자-가짜: 0.6534]\n",
            "에포크 091 | 시간 60.66 min | 평균 손실 >> 생성자/판별자 0.8884/1.3227 [판별자-진짜: 0.6658 판별자-가짜: 0.6569]\n",
            "에포크 092 | 시간 61.32 min | 평균 손실 >> 생성자/판별자 0.9144/1.3193 [판별자-진짜: 0.6619 판별자-가짜: 0.6575]\n",
            "에포크 093 | 시간 61.99 min | 평균 손실 >> 생성자/판별자 0.9396/1.3042 [판별자-진짜: 0.6523 판별자-가짜: 0.6518]\n",
            "에포크 094 | 시간 62.65 min | 평균 손실 >> 생성자/판별자 0.8942/1.3268 [판별자-진짜: 0.6645 판별자-가짜: 0.6623]\n",
            "에포크 095 | 시간 63.32 min | 평균 손실 >> 생성자/판별자 0.8894/1.3291 [판별자-진짜: 0.6665 판별자-가짜: 0.6626]\n",
            "에포크 096 | 시간 63.99 min | 평균 손실 >> 생성자/판별자 0.9248/1.3169 [판별자-진짜: 0.6609 판별자-가짜: 0.6560]\n",
            "에포크 097 | 시간 64.65 min | 평균 손실 >> 생성자/판별자 0.8919/1.3169 [판별자-진짜: 0.6624 판별자-가짜: 0.6545]\n",
            "에포크 098 | 시간 65.32 min | 평균 손실 >> 생성자/판별자 0.9275/1.3183 [판별자-진짜: 0.6610 판별자-가짜: 0.6573]\n",
            "에포크 099 | 시간 65.99 min | 평균 손실 >> 생성자/판별자 0.9424/1.3100 [판별자-진짜: 0.6585 판별자-가짜: 0.6515]\n",
            "에포크 100 | 시간 66.65 min | 평균 손실 >> 생성자/판별자 0.8891/1.3146 [판별자-진짜: 0.6596 판별자-가짜: 0.6550]\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "\n",
        "num_epochs = 100\n",
        "batch_size = 64\n",
        "image_size = (28, 28)\n",
        "z_size = 20\n",
        "mode_z = 'uniform'\n",
        "gen_hidden_layers = 1\n",
        "gen_hidden_size = 100\n",
        "disc_hidden_layers = 1\n",
        "disc_hidden_size = 100\n",
        "\n",
        "tf.random.set_seed(1)\n",
        "np.random.seed(1)\n",
        "\n",
        "\n",
        "if mode_z == 'uniform':\n",
        "    fixed_z = tf.random.uniform(\n",
        "        shape=(batch_size, z_size),\n",
        "        minval=-1, maxval=1)\n",
        "elif mode_z == 'normal':\n",
        "    fixed_z = tf.random.normal(\n",
        "        shape=(batch_size, z_size))\n",
        "\n",
        "\n",
        "def create_samples(g_model, input_z):\n",
        "    g_output = g_model(input_z, training=False)\n",
        "    images = tf.reshape(g_output, (batch_size, *image_size))\n",
        "    return (images+1)/2.0\n",
        "\n",
        "## 데이터셋 준비\n",
        "mnist_trainset = mnist['train']\n",
        "mnist_trainset = mnist_trainset.map(\n",
        "    lambda ex: preprocess(ex, mode=mode_z))\n",
        "\n",
        "mnist_trainset = mnist_trainset.shuffle(10000)\n",
        "mnist_trainset = mnist_trainset.batch(\n",
        "    batch_size, drop_remainder=True)\n",
        "\n",
        "## 모델 준비\n",
        "with tf.device(device_name):\n",
        "    gen_model = make_generator_network(\n",
        "        num_hidden_layers=gen_hidden_layers,\n",
        "        num_hidden_units=gen_hidden_size,\n",
        "        num_output_units=np.prod(image_size))\n",
        "    gen_model.build(input_shape=(None, z_size))\n",
        "\n",
        "    disc_model = make_discriminator_network(\n",
        "        num_hidden_layers=disc_hidden_layers,\n",
        "        num_hidden_units=disc_hidden_size)\n",
        "    disc_model.build(input_shape=(None, np.prod(image_size)))\n",
        "\n",
        "## 손실 함수와 옵티마이저:\n",
        "loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "g_optimizer = tf.keras.optimizers.Adam()\n",
        "d_optimizer = tf.keras.optimizers.Adam()\n",
        "\n",
        "all_losses = []\n",
        "all_d_vals = []\n",
        "epoch_samples = []\n",
        "\n",
        "start_time = time.time()\n",
        "for epoch in range(1, num_epochs+1):\n",
        "    epoch_losses, epoch_d_vals = [], []\n",
        "    for i,(input_z,input_real) in enumerate(mnist_trainset):\n",
        "\n",
        "        ## 생성자 손실을 계산합니다\n",
        "        with tf.GradientTape() as g_tape:\n",
        "            g_output = gen_model(input_z)\n",
        "            d_logits_fake = disc_model(g_output, training=True)\n",
        "            labels_real = tf.ones_like(d_logits_fake)\n",
        "            g_loss = loss_fn(y_true=labels_real, y_pred=d_logits_fake)\n",
        "\n",
        "        # g_loss의 그래디언트를 계산합니다\n",
        "        g_grads = g_tape.gradient(g_loss, gen_model.trainable_variables)\n",
        "\n",
        "        # 최적화: 그래디언트를 적용합니다\n",
        "        g_optimizer.apply_gradients(\n",
        "            grads_and_vars=zip(g_grads, gen_model.trainable_variables))\n",
        "\n",
        "        ## 판별자 손실을 계산합니다\n",
        "        with tf.GradientTape() as d_tape:\n",
        "            d_logits_real = disc_model(input_real, training=True)\n",
        "\n",
        "            d_labels_real = tf.ones_like(d_logits_real)\n",
        "\n",
        "            d_loss_real = loss_fn(\n",
        "                y_true=d_labels_real, y_pred=d_logits_real)\n",
        "\n",
        "            d_logits_fake = disc_model(g_output, training=True)\n",
        "            d_labels_fake = tf.zeros_like(d_logits_fake)\n",
        "\n",
        "            d_loss_fake = loss_fn(\n",
        "                y_true=d_labels_fake, y_pred=d_logits_fake)\n",
        "\n",
        "            d_loss = d_loss_real + d_loss_fake\n",
        "\n",
        "        ## d_loss의 그래디언트를 계산합니다\n",
        "        d_grads = d_tape.gradient(d_loss, disc_model.trainable_variables)\n",
        "\n",
        "        ## 최적화: 그래디언트를 적용합니다\n",
        "        d_optimizer.apply_gradients(\n",
        "            grads_and_vars=zip(d_grads, disc_model.trainable_variables))\n",
        "\n",
        "        epoch_losses.append(\n",
        "            (g_loss.numpy(), d_loss.numpy(),\n",
        "             d_loss_real.numpy(), d_loss_fake.numpy()))\n",
        "\n",
        "        d_probs_real = tf.reduce_mean(tf.sigmoid(d_logits_real))\n",
        "        d_probs_fake = tf.reduce_mean(tf.sigmoid(d_logits_fake))\n",
        "        epoch_d_vals.append((d_probs_real.numpy(), d_probs_fake.numpy()))\n",
        "    all_losses.append(epoch_losses)\n",
        "    all_d_vals.append(epoch_d_vals)\n",
        "    print(\n",
        "        '에포크 {:03d} | 시간 {:.2f} min | 평균 손실 >>'\n",
        "        ' 생성자/판별자 {:.4f}/{:.4f} [판별자-진짜: {:.4f} 판별자-가짜: {:.4f}]'\n",
        "        .format(\n",
        "            epoch, (time.time() - start_time)/60,\n",
        "            *list(np.mean(all_losses[-1], axis=0))))\n",
        "    epoch_samples.append(\n",
        "        create_samples(gen_model, fixed_z).numpy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "TQyQ8deLaHmw",
        "outputId": "eb2af2ba-65f2-4bbd-dadc-0abd2e5e0623"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import itertools\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(16, 6))\n",
        "\n",
        "## 손실 그래프\n",
        "ax = fig.add_subplot(1, 2, 1)\n",
        "g_losses = [item[0] for item in itertools.chain(*all_losses)]\n",
        "d_losses = [item[1]/2.0 for item in itertools.chain(*all_losses)]\n",
        "plt.plot(g_losses, label='Generator loss', alpha=0.95)\n",
        "plt.plot(d_losses, label='Discriminator loss', alpha=0.95)\n",
        "plt.legend(fontsize=20)\n",
        "ax.set_xlabel('Iteration', size=15)\n",
        "ax.set_ylabel('Loss', size=15)\n",
        "\n",
        "epochs = np.arange(1, 101)\n",
        "epoch2iter = lambda e: e*len(all_losses[-1])\n",
        "epoch_ticks = [1, 20, 40, 60, 80, 100]\n",
        "newpos = [epoch2iter(e) for e in epoch_ticks]\n",
        "ax2 = ax.twiny()\n",
        "ax2.set_xticks(newpos)\n",
        "ax2.set_xticklabels(epoch_ticks)\n",
        "ax2.xaxis.set_ticks_position('bottom')\n",
        "ax2.xaxis.set_label_position('bottom')\n",
        "ax2.spines['bottom'].set_position(('outward', 60))\n",
        "ax2.set_xlabel('Epoch', size=15)\n",
        "ax2.set_xlim(ax.get_xlim())\n",
        "ax.tick_params(axis='both', which='major', labelsize=15)\n",
        "ax2.tick_params(axis='both', which='major', labelsize=15)\n",
        "\n",
        "## 판별자의 출력\n",
        "ax = fig.add_subplot(1, 2, 2)\n",
        "d_vals_real = [item[0] for item in itertools.chain(*all_d_vals)]\n",
        "d_vals_fake = [item[1] for item in itertools.chain(*all_d_vals)]\n",
        "plt.plot(d_vals_real, alpha=0.75, label=r'Real: $D(\\mathbf{x})$')\n",
        "plt.plot(d_vals_fake, alpha=0.75, label=r'Fake: $D(G(\\mathbf{z}))$')\n",
        "plt.legend(fontsize=20)\n",
        "ax.set_xlabel('Iteration', size=15)\n",
        "ax.set_ylabel('Discriminator output', size=15)\n",
        "\n",
        "ax2 = ax.twiny()\n",
        "ax2.set_xticks(newpos)\n",
        "ax2.set_xticklabels(epoch_ticks)\n",
        "ax2.xaxis.set_ticks_position('bottom')\n",
        "ax2.xaxis.set_label_position('bottom')\n",
        "ax2.spines['bottom'].set_position(('outward', 60))\n",
        "ax2.set_xlabel('Epoch', size=15)\n",
        "ax2.set_xlim(ax.get_xlim())\n",
        "ax.tick_params(axis='both', which='major', labelsize=15)\n",
        "ax2.tick_params(axis='both', which='major', labelsize=15)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iENdPX_gPoJ7",
        "outputId": "9abf0c8e-f7bd-4aa2-b6f1-962fedc40c61"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1400 with 30 Axes>"
            ],
            "image/png": 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          "metadata": {}
        }
      ],
      "source": [
        "selected_epochs = [1, 2, 4, 10, 50, 100]\n",
        "fig = plt.figure(figsize=(10, 14))\n",
        "for i,e in enumerate(selected_epochs):\n",
        "    for j in range(5):\n",
        "        ax = fig.add_subplot(6, 5, i*5+j+1)\n",
        "        ax.set_xticks([])\n",
        "        ax.set_yticks([])\n",
        "        if j == 0:\n",
        "            ax.text(\n",
        "                -0.06, 0.5, 'Epoch {}'.format(e),\n",
        "                rotation=90, size=18, color='red',\n",
        "                horizontalalignment='right',\n",
        "                verticalalignment='center',\n",
        "                transform=ax.transAxes)\n",
        "\n",
        "        image = epoch_samples[e-1][j]\n",
        "        ax.imshow(image, cmap='gray_r')\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
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