Chapter_8_CGAN(edited).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zergswim/GAN/blob/main/Chapter_8_CGAN(edited).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2wKzIFWH_35"
      },
      "source": [
        "# 8장: CGAN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zpo3u6PmH_39"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/gans-in-action/blob/master/chapter-8/Chapter_8_CGAN.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "o0YGQzKpH_3-"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.layers import (Activation, BatchNormalization, Concatenate, Dense,\n",
        "                                     Embedding, Flatten, Input, Multiply, Reshape)\n",
        "from tensorflow.keras.layers import LeakyReLU\n",
        "from tensorflow.keras.layers import Conv2D, Conv2DTranspose\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "B4kW5e9jH_3-"
      },
      "outputs": [],
      "source": [
        "img_rows = 28\n",
        "img_cols = 28\n",
        "channels = 1\n",
        "\n",
        "img_shape = (img_rows, img_cols, channels)\n",
        "z_dim = 100\n",
        "num_classes = 10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_VZQ98EH_3_"
      },
      "source": [
        "## 생성자"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1WMnav5FH_3_"
      },
      "outputs": [],
      "source": [
        "def build_generator(z_dim):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(256 * 7 * 7, input_dim=z_dim))\n",
        "    model.add(Reshape((7, 7, 256)))\n",
        "    model.add(Conv2DTranspose(128, kernel_size=3, strides=2, padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(LeakyReLU(alpha=0.01))\n",
        "    model.add(Conv2DTranspose(64, kernel_size=3, strides=1, padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(LeakyReLU(alpha=0.01))\n",
        "    model.add(Conv2DTranspose(1, kernel_size=3, strides=2, padding='same'))\n",
        "    model.add(Activation('tanh'))\n",
        "    return model\n",
        "\n",
        "def build_cgan_generator(z_dim):\n",
        "    z = Input(shape=(z_dim, ))\n",
        "    label = Input(shape=(1, ), dtype='int32')\n",
        "    label_embedding = Embedding(num_classes, z_dim, input_length=1)(label)\n",
        "    label_embedding = Flatten()(label_embedding)\n",
        "    joined_representation = Multiply()([z, label_embedding])\n",
        "    generator = build_generator(z_dim)\n",
        "    conditioned_img = generator(joined_representation)\n",
        "    return Model([z, label], conditioned_img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRuOJfryH_3_"
      },
      "source": [
        "## 판별자"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "H5wfUS6XH_4A"
      },
      "outputs": [],
      "source": [
        "def build_discriminator(img_shape):\n",
        "    model = Sequential()\n",
        "    model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=(img_shape[0], img_shape[1], img_shape[2] + 1), padding='same'))\n",
        "    model.add(LeakyReLU(alpha=0.01))\n",
        "    model.add(Conv2D(64, kernel_size=3, strides=2, padding='same'))\n",
        "    model.add(LeakyReLU(alpha=0.01))\n",
        "    model.add(Conv2D(128, kernel_size=3, strides=2, padding='same'))\n",
        "    model.add(LeakyReLU(alpha=0.01))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "    return model\n",
        "\n",
        "def build_cgan_discriminator(img_shape):\n",
        "    img = Input(shape=img_shape)\n",
        "    label = Input(shape=(1, ), dtype='int32')\n",
        "    label_embedding = Embedding(num_classes, np.prod(img_shape), input_length=1)(label)\n",
        "    label_embedding = Flatten()(label_embedding)\n",
        "    label_embedding = Reshape(img_shape)(label_embedding)\n",
        "    concatenated = Concatenate(axis=-1)([img, label_embedding])\n",
        "    discriminator = build_discriminator(img_shape)\n",
        "    classification = discriminator(concatenated)\n",
        "    return Model([img, label], classification)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBJdVw8jH_4A"
      },
      "source": [
        "## 모델 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "KI-smOh4H_4A"
      },
      "outputs": [],
      "source": [
        "# def build_cgan(generator, discriminator):\n",
        "\n",
        "#     # 랜덤 잡음 벡터 z\n",
        "#     z = Input(shape=(z_dim, ))\n",
        "\n",
        "#     # 이미지 레이블\n",
        "#     label = Input(shape=(1, ))\n",
        "\n",
        "#     # 레이블에 맞는 이미지 생성하기\n",
        "#     img = generator([z, label])\n",
        "\n",
        "#     classification = discriminator([img, label])\n",
        "\n",
        "#     # 생성자 -> 판별자 연결 모델 \n",
        "#     # G([z, label]) = x* \n",
        "#     # D(x*) = 분류\n",
        "#     model = Model([z, label], classification)\n",
        "\n",
        "#     return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "fmfU-XLNH_4B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27781de5-a3d7-4c95-9cc7-f7a69da6dfc0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_4 (InputLayer)           [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " embedding_1 (Embedding)        (None, 1, 100)       1000        ['input_4[0][0]']                \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)           [(None, 100)]        0           []                               \n",
            "                                                                                                  \n",
            " flatten_2 (Flatten)            (None, 100)          0           ['embedding_1[0][0]']            \n",
            "                                                                                                  \n",
            " multiply (Multiply)            (None, 100)          0           ['input_3[0][0]',                \n",
            "                                                                  'flatten_2[0][0]']              \n",
            "                                                                                                  \n",
            " sequential_1 (Sequential)      (None, 28, 28, 1)    1637121     ['multiply[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1,638,121\n",
            "Trainable params: 1,637,737\n",
            "Non-trainable params: 384\n",
            "__________________________________________________________________________________________________\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)           [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " embedding (Embedding)          (None, 1, 784)       7840        ['input_2[0][0]']                \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 784)          0           ['embedding[0][0]']              \n",
            "                                                                                                  \n",
            " input_1 (InputLayer)           [(None, 28, 28, 1)]  0           []                               \n",
            "                                                                                                  \n",
            " reshape (Reshape)              (None, 28, 28, 1)    0           ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 28, 28, 2)    0           ['input_1[0][0]',                \n",
            "                                                                  'reshape[0][0]']                \n",
            "                                                                                                  \n",
            " sequential (Sequential)        (None, 1)            114049      ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 121,889\n",
            "Trainable params: 121,889\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# 판별자 만들고 컴파일하기\n",
        "discriminator = build_cgan_discriminator(img_shape)\n",
        "discriminator.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.00001), metrics=['accuracy'])\n",
        "\n",
        "# 생성자 만들기\n",
        "import tensorflow as tf\n",
        "generator = build_cgan_generator(z_dim)\n",
        "optimizer_g = tf.keras.optimizers.Adam()\n",
        "train_loss = tf.keras.metrics.Mean()\n",
        "\n",
        "@tf.function\n",
        "def train_step(z, labels):\n",
        "    with tf.GradientTape() as tape:\n",
        "        #z = Input(shape=(z_dim, ))\n",
        "        #label = Input(shape=(1, ))      \n",
        "        gen_imgs = generator([z, labels])\n",
        "        pred = discriminator([gen_imgs, labels]) # or discriminator_supervised?\n",
        "        loss = tf.keras.losses.binary_crossentropy(labels, pred)\n",
        "    gradients = tape.gradient(loss, generator.trainable_variables)\n",
        "    optimizer_g.apply_gradients(zip(gradients, generator.trainable_variables))\n",
        "\n",
        "    train_loss(loss)\n",
        "    g_loss = train_loss.result()\n",
        "    \n",
        "    return gen_imgs, g_loss\n",
        "\n",
        "generator.summary()\n",
        "discriminator.summary()\n",
        "\n",
        "# generator = build_cgan_generator(z_dim)\n",
        "\n",
        "# # 생성자를 훈련하는 동안 판별자 모델 파라미터를 고정하기\n",
        "# discriminator.trainable = False\n",
        "\n",
        "# # 생성자를 훈련하기 위해 고정된 판별자로 CGAN 모델 만들고 컴파일하기\n",
        "# cgan = build_cgan(generator, discriminator)\n",
        "# cgan.compile(loss='binary_crossentropy', optimizer=Adam())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pe-xkin7H_4B"
      },
      "source": [
        "## 훈련"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "YwsOWER4H_4B"
      },
      "outputs": [],
      "source": [
        "accuracies = []\n",
        "losses = []\n",
        "\n",
        "def train(iterations, batch_size, sample_interval):\n",
        "    (X_train, y_train), (_, _) = mnist.load_data()\n",
        "    X_train = X_train / 127.5 - 1.\n",
        "    X_train = np.expand_dims(X_train, axis=3)\n",
        "\n",
        "    real = np.ones((batch_size, 1))\n",
        "    fake = np.zeros((batch_size, 1))\n",
        "\n",
        "    for iteration in range(iterations):\n",
        "        idx = np.random.randint(0, X_train.shape[0], batch_size)\n",
        "        imgs, labels = X_train[idx], y_train[idx]\n",
        "\n",
        "        z = np.random.normal(0, 1, (batch_size, z_dim))\n",
        "        # gen_imgs = generator.predict([z, labels])\n",
        "\n",
        "        labels = np.random.randint(0, num_classes, batch_size).reshape(-1, 1)\n",
        "\n",
        "        gen_imgs, g_loss = train_step(z, labels)\n",
        "\n",
        "        # 판별자를 훈련합니다.\n",
        "        d_loss_real = discriminator.train_on_batch([imgs, labels], real)\n",
        "        d_loss_fake = discriminator.train_on_batch([gen_imgs, labels], fake)\n",
        "        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "        # ---------------------\n",
        "        #  생성자를 훈련합니다.\n",
        "        # ---------------------\n",
        "\n",
        "        # 잡음 벡터의 배치를 생성합니다.\n",
        "        #z = np.random.normal(0, 1, (batch_size, z_dim))\n",
        "\n",
        "        # 랜덤한 레이블의 배치를 얻습니다.\n",
        "        # labels = np.random.randint(0, num_classes, batch_size).reshape(-1, 1)\n",
        "\n",
        "        # 생성자를 훈련합니다.\n",
        "        # g_loss = cgan.train_on_batch([z, labels], real)\n",
        "\n",
        "        if (iteration + 1) % sample_interval == 0:\n",
        "            print(\"%d [D loss: %f, acc.: %.2f%%] [G loss: %f]\" % (iteration + 1, d_loss[0], 100 * d_loss[1], g_loss))\n",
        "            losses.append((d_loss[0], g_loss))\n",
        "            accuracies.append(100 * d_loss[1])\n",
        "            sample_images()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "YHjFKlMnH_4B"
      },
      "outputs": [],
      "source": [
        "def sample_images(image_grid_rows=2, image_grid_columns=5):\n",
        "    z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, z_dim))\n",
        "    labels = np.arange(0, 10).reshape(-1, 1)\n",
        "    gen_imgs = generator.predict([z, labels])\n",
        "    gen_imgs = 0.5 * gen_imgs + 0.5\n",
        "    fig, axs = plt.subplots(image_grid_rows,\n",
        "                            image_grid_columns,\n",
        "                            figsize=(10, 4),\n",
        "                            sharey=True,\n",
        "                            sharex=True)\n",
        "\n",
        "    cnt = 0\n",
        "    for i in range(image_grid_rows):\n",
        "        for j in range(image_grid_columns):\n",
        "            axs[i, j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')\n",
        "            axs[i, j].axis('off')\n",
        "            axs[i, j].set_title(\"Digit: %d\" % labels[cnt])\n",
        "            cnt += 1\n",
        "    \n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXbNj8usH_4B"
      },
      "source": [
        "# 모델을 훈련하고 훈련 과정 확인하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "svRuBxyJH_4C",
        "outputId": "860894f9-1f2a-4664-a6f1-12edd72472cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000 [D loss: 0.641773, acc.: 85.94%] [G loss: 0.189986]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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sQUnLzWxuY96317UDdkiaVeTXvJn9iZmdZJkzlVWuPyrwMdCQQp6S7pa01MwWmNn7Gv0/FkJ4q0AftZdIljKz31A2PH9PkfehVSJ5bpJ0mZnNbOybf6ns2JE3C/RReylkaWYfNrNZZjbFzC6QtFLZQdBJSK3gWWtme5QNs10v6SZJHU9pCyE8Iunbkh6V9AtJGxpNBzq89gVlQ2uvmNmuxsFx55hZr1+GlzT63SPp+5LWhBC+N9jHqq1k8gwhrFN2xsCPlV0fYo6kzwz6wWoomSwbLlJ23ZFHB/kwSCrPv5G0X9k0yK8l/bGkpQN9qnpKKcuFkrYo+//mP0haFkLYOtjHKp81Th2rPDObK+k5SceEEMZjbw+GQ55+kKUv5OlH3bJMbYSnEDNbambHmNlJktZIWluH0LwiTz/I0hfy9KPOWVa64JH0eWXTEy8ru/gY18qpNvL0gyx9IU8/apulmyktAACAbqo+wgMAAHBEFDwAAMC9qb0aFy5c2Jzveuqpp0a/NThMCKG0ix3Onz+/mefWrcmcKVgrZeV51llnNbPcsGFDr5diRMrcNxcvXtzMc+PGjWV1iwLKynPRokXNLDdv3lxGlyioW5aM8AAAAPd6HrRsZhzRHFmZvyLJM76y8iTL+Ng3fWHf9IMRHgAAUFsUPAAAwD0KHgAA4B4FDwAAcI+CBwAAuEfBAwAA3KPgAQAA7lHwAAAA9yh4AACAexQ8AADAPQoeAADgHgUPAABwj4IHAAC4R8EDAADco+ABAADuUfAAAAD3KHgAAIB7FDwAAMA9Ch4AAOAeBQ8AAHCPggcAALhHwQMAANyj4AEAAO5R8AAAAPemxt6AqgkhtDw3s0hbgjKQpx9k6Qt5+pFKlozwAAAA9yh4AACAe0xpFcSwqi/k6QdZ+kKefqSSJSM8AADAPQoeAADgHgUPAABwj2N4Ouh1Cl0qp9ehf+TpR3teY2Pv/WabnJxsaSPL9LFv+lGFLBnhAQAA7lHwAAAA95Kd0uo1BNY+dJ0f1u4l/76TTz65pW3Hjh0d19WOYdXB9MpzYmKipW3KlCl99ZnP8/rrr29pW716dcd1tSPP4kYxdJ1/36WXXtrSdu+99/bVH1kOZhR/a/P79MKFC1vann766Y7rakeexY1631y3bl1L25IlS/rqL5UsGeEBAADuUfAAAAD3KHgAAIB71j6v19Jo1r0xojLmIouIOf8YQiht5eRZrP9RKCtPsizW/yiwb/b/vn55yJMsi/U/Ct2yZIQHAAC4R8EDAADcS/a09F7ah8ryQ279noZXpK1b/ygHefpBlr6Qpx9kyQgPAACoAQoeAADgXiWntHppvzJoXq+jzQc9Eh2jNWhm5Jmeffv2dW0rI8tFixa1PH/yySf72zAMZHx8vGsb+2a17Nmzp2ubpywZ4QEAAO5R8AAAAPcoeAAAgHsujuFJ5ZQ3lIM8/SBLX8jTjzpmyQgPAABwj4IHAAC4V5kprfzpbytXrmxpu/3220e67n6vNIn+HThwoPn4pptuamm79tprR7pu8izXvHnzmo9ffvnllrb9+/ePdN1kWb58ZsuXL29pu//++0e6bvIsV/4yLe2Xbdi8efNI151ilozwAAAA9yh4AACAexQ8AADAPet1aWgzS+a60f1ewrqMu76mJIRQ2oaRZ3xl5UmW8bFvkmcnZBlftywZ4QEAAO5R8AAAAPcqc1p6v0Z9d+2ZM2e2PN+9e3d/G4aBjDrPVIdkPRp1llOntv45m5iY6G/DMBD2TT/qkiUjPAAAwD0KHgAA4B4FDwAAcM/dMTx5veYNq3J6Hd5Dnn6QpS/k6YfnLBnhAQAA7lHwAAAA95Kd0nrxxReH7qPfU+aO9NqqDdulaMOGDUP3QZ5pmD9//tB9kGU6yrgDOnmmYcuWLUP34TlLRngAAIB7FDwAAMA9Ch4AAOBeUndLz29L+/wfd30dHnnGV9U7Mo+Pjzcft9/CgSyHd7TzzN92Y8qUKS1t5Dkc/s7Gx93SAQBAbVHwAAAA95Ka0sorcmpcGao2NDcI8oyvqsPmeWSZYd8cjPc8yTI+prQAAEBtUfAAAAD3KHgAAIB7yd5aYtDT63r1gXjI0w+y9IU8/SDL3hjhAQAA7lHwAAAA95Kd0ipDVa4Kif6Qpx9k6Qt5+uE5S0Z4AACAexQ8AADAPQoeAADgXmWO4fE0jwjy9IQsfSFPP8iyFSM8AADAPQoeAADgHgUPAABwj4IHAAC4R8EDAADco+ABAADuUfAAAAD3KHgAAIB7FDwAAMA9a78zKgAAgDeM8AAAAPcoeAAAgHsUPAAAwD0KHgAA4B4FDwAAcI+CBwAAuPf/bOiJlX2PF48AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2000 [D loss: 0.151690, acc.: 100.00%] [G loss: 1.309891]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3000 [D loss: 0.003292, acc.: 100.00%] [G loss: 6.809189]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4000 [D loss: 0.000520, acc.: 100.00%] [G loss: 12.293067]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5000 [D loss: 0.000122, acc.: 100.00%] [G loss: 17.078009]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6000 [D loss: 0.000032, acc.: 100.00%] [G loss: 21.251486]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7000 [D loss: 0.596831, acc.: 51.56%] [G loss: 23.853172]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8000 [D loss: 0.556147, acc.: 82.81%] [G loss: 20.977425]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9000 [D loss: 0.386532, acc.: 100.00%] [G loss: 18.812248]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10000 [D loss: 0.613085, acc.: 56.25%] [G loss: 17.058506]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11000 [D loss: 0.499622, acc.: 87.50%] [G loss: 15.653879]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12000 [D loss: 0.392100, acc.: 92.19%] [G loss: 14.536101]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13000 [D loss: 0.499841, acc.: 95.31%] [G loss: 13.673793]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14000 [D loss: 0.474547, acc.: 87.50%] [G loss: 12.916755]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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8hSR3ZxgG+WyS709y5Vbv6jBNKcvvSHI6w/+bv5XkqtbaDdu9rd2rxaljs1dVlyX5SJILW2tfHnt7OB559kOWfZFnPw4ty6l1eDZSVVdW1YVVdXGSNyS59hBC65U8+yHLvsizH4ec5awLniQvzTA88R8ZLj7mWjnzJs9+yLIv8uzHwWbZzZAWAMC5zL3DAwDwoBQ8AED3Llj1zaoy3jWy1trOLnYoz/HtKk9Zjs+x2RfHZj/OlaUODwDQPQUPANA9BQ8A0D0FDwDQPQUPANA9BQ8A0D0FDwDQPQUPANA9BQ8A0D0FDwDQPQUPANA9BQ8A0D0FDwDQPQUPANA9BQ8A0D0FDwDQPQUPANA9BQ8A0D0FDwDQvQvG3gAAYN5aa/d7XFUjbcm56fAAAN1T8AAA3TOktXC0Hbdsiq05VpPn9GybyaqfW0XO0+TYPDnL+3rbY2zbn1v3tU+SDg8A0D0FDwDQPQUPANC92czhWTUeuO0Y/zavfdRUxibnRp4s23fmrM+xOV/rzqnZ1Tq3XcdYWerwAADdU/AAAN2bzZDWqhbYub6nTT5d6+YpQzhZ23zWruIYPjmrhiD3cemHudHhAQC6p+ABALqn4AEAujebOTzbcBrj/MmwH+vOzZJ5XzY5tV32u7Xu/jyU/a7DAwB0T8EDAHRvskNa216ZcR+n1x1Ku2+K5NmPdbPcJHNZHt+UPms5nn3c9XxbUzw2dXgAgO4peACA7il4AIDunfgcnl3csXWKY4M80LpZy3P6ZDlvm3zu+qydNnOntqfDAwB0T8EDAHRvsqela6XOnwz7MWaWfo+Ob9u7aDM9styeDg8A0D0FDwDQvb0Mac3xjI4pbQvHJ084N0OU/djF/lz3xr77eO2TpMMDAHRPwQMAdE/BAwB0b7KnpQMA+3coV2/W4QEAuqfgAQC6t5chrVWnqq3bOlv1vLmdCgc9cWwCc6TDAwB0T8EDAHRPwQMAdO/ET0tfd4zfXACYJscmMEc6PABA9xQ8AED3FDwAQPcUPABA9xQ8AED3FDwAQPdmc7f05cvZOy0WxnP55Zff7/Hp06dH2hKA9enwAADdU/AAAN2rB7nz8Xq3Nt+Rde+kvg9THSZrre1sw046z2UnnW3vecpyfI7N7fSepyzHd64sdXgAgO4peACA7il4AIDuKXgAgO4peACA7il4AIDujXqlZafQ9W3f+crz5MiyL/LshyzXp8MDAHRPwQMAdE/BAwB0bzZ3S2d+lsd+x7xtCMcny77Isx+yXJ8ODwDQPQUPANC9UYe0Njndbd1WXU+n0PVELv2QZV/k2Q9ZrqbDAwB0T8EDAHRPwQMAdG82p6UbmwQAtqXDAwB0T8EDAHRPwQMAdE/BAwB0T8EDAHRPwQMAdE/BAwB0T8EDAHRPwQMAdK/WvQs5AMBc6fAAAN1T8AAA3VPwAADdU/AAAN1T8AAA3VPwAADd+z/5q6efKiDIuwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15000 [D loss: 0.324691, acc.: 95.31%] [G loss: 12.295760]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16000 [D loss: 0.387552, acc.: 98.44%] [G loss: 11.846272]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17000 [D loss: 0.087627, acc.: 100.00%] [G loss: 11.514059]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18000 [D loss: 0.151432, acc.: 100.00%] [G loss: 11.574058]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19000 [D loss: 0.113701, acc.: 100.00%] [G loss: 11.365358]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20000 [D loss: 0.170665, acc.: 100.00%] [G loss: 11.344275]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 하이퍼파라미터를 설정합니다.\n",
        "iterations = 20000\n",
        "batch_size = 32\n",
        "sample_interval = 1000\n",
        "\n",
        "# 지정된 반복 횟수 동안 CGAN을 훈련합니다.\n",
        "train(iterations, batch_size, sample_interval)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjneDH4nH_4D"
      },
      "source": [
        "# 훈련된 CGAN 모델의 출력"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "E1p8Kt67H_4D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "654099d5-28cc-4c54-ceb0-8530d6733dd3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1440 with 50 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 그리드 차원을 설정합니다.\n",
        "image_grid_rows = 10\n",
        "image_grid_columns = 5\n",
        "\n",
        "# 랜덤한 잡음을 샘플링합니다.\n",
        "z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, z_dim))\n",
        "\n",
        "# 생성할 이미지 레이블을 5개씩 준비합니다.\n",
        "labels_to_generate = np.array([[i for j in range(5)] for i in range(10)])\n",
        "labels_to_generate = labels_to_generate.flatten().reshape(-1, 1)\n",
        "\n",
        "# 랜덤한 잡음에서 이미지를 생성합니다.\n",
        "gen_imgs = generator.predict([z, labels_to_generate])\n",
        "\n",
        "# 이미지 픽셀 값을 [0, 1] 사이로 스케일을 변환합니다.\n",
        "gen_imgs = 0.5 * gen_imgs + 0.5\n",
        "\n",
        "# 이미지 그리드를 설정합니다.\n",
        "fig, axs = plt.subplots(image_grid_rows,\n",
        "                        image_grid_columns,\n",
        "                        figsize=(10, 20),\n",
        "                        sharey=True,\n",
        "                        sharex=True)\n",
        "\n",
        "cnt = 0\n",
        "for i in range(image_grid_rows):\n",
        "    for j in range(image_grid_columns):\n",
        "        # 이미지 그리드를 출력합니다.\n",
        "        axs[i, j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')\n",
        "        axs[i, j].axis('off')\n",
        "        axs[i, j].set_title(\"Digit: %d\" % labels_to_generate[cnt])  ## NEW\n",
        "        cnt += 1"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Chapter_8_CGAN(edited)",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "TensorFlow 2.3 on Python 3.6 (CUDA 10.1)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}