{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A subdirectory or file celeba_gan already exists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\gdown\\__main__.py:140: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
      "  warnings.warn(\n",
      "Downloading...\n",
      "From (original): https://drive.google.com/uc?id=1O7m1010EJjLE5QxLZiM9Fpjs7Oj6e684\n",
      "From (redirected): https://drive.google.com/uc?id=1O7m1010EJjLE5QxLZiM9Fpjs7Oj6e684&confirm=t&uuid=bad22561-f0e9-49bd-b347-e1cf4f5b0d52\n",
      "To: c:\\Users\\user\\Downloads\\celeba_gan\\data.zip\n",
      "\n",
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      "'unzip' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!mkdir celeba_gan\n",
    "!gdown --id 1O7m1010EJjLE5QxLZiM9Fpjs7Oj6e684 -O celeba_gan/data.zip\n",
    "!unzip -qq celeba_gan/data.zip -d celeba_gan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gdown in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (5.2.0)\n",
      "Requirement already satisfied: beautifulsoup4 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gdown) (4.12.3)\n",
      "Requirement already satisfied: filelock in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gdown) (3.16.1)\n",
      "Requirement already satisfied: requests[socks] in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gdown) (2.32.3)\n",
      "Requirement already satisfied: tqdm in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gdown) (4.67.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from beautifulsoup4->gdown) (2.6)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests[socks]->gdown) (3.4.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests[socks]->gdown) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests[socks]->gdown) (2.3.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests[socks]->gdown) (2024.12.14)\n",
      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests[socks]->gdown) (1.7.1)\n",
      "Requirement already satisfied: colorama in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from tqdm->gdown) (0.4.6)\n"
     ]
    }
   ],
   "source": [
    "!pip install gdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already extracted.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import zipfile\n",
    "\n",
    "# Path ke file ZIP\n",
    "zip_path = os.path.expanduser(\"~/Downloads/img_align_celeba.zip\")  # Path lengkap ke file ZIP\n",
    "extract_path = \"celeba_gan/img_align_celeba\"  # Path tempat ekstraksi\n",
    "\n",
    "# Ekstrak file ZIP\n",
    "if not os.path.exists(extract_path):\n",
    "    os.makedirs(extract_path, exist_ok=True)  # Membuat folder jika belum ada\n",
    "    print(\"Extracting dataset...\")\n",
    "    with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
    "        zip_ref.extractall(extract_path)\n",
    "    print(\"Dataset extracted successfully!\")\n",
    "else:\n",
    "    print(\"Dataset already extracted.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 199282 files.\n",
      "Dataset loaded successfully!\n",
      "Batch shape: (32, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "# Path ke folder gambar setelah diekstrak\n",
    "dataset_path = \"celeba_gan/img_align_celeba\"\n",
    "\n",
    "# Memuat dataset\n",
    "dataset = keras.utils.image_dataset_from_directory(\n",
    "    dataset_path,       # Path ke folder gambar\n",
    "    label_mode=None,    # Tidak ada label\n",
    "    image_size=(64, 64),  # Ukuran gambar target\n",
    "    batch_size=32,      # Ukuran batch\n",
    "    interpolation=\"nearest\"  # Metode interpolasi\n",
    ")\n",
    "\n",
    "print(\"Dataset loaded successfully!\")\n",
    "\n",
    "# Contoh menampilkan ukuran dataset\n",
    "for batch in dataset.take(1):\n",
    "    print(f\"Batch shape: {batch.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (3.10.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (4.55.3)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.4.8)\n",
      "Requirement already satisfied: numpy>=1.23 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (2.0.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (9.5.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (3.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.10.0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "print(matplotlib.__version__)  # Tampilkan versi matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Menampilkan gambar dari batch pertama\n",
    "for images in dataset.take(1):  # Ambil batch pertama\n",
    "    plt.figure(figsize=(10, 10))  # Ukuran figure\n",
    "    for i in range(9):  # Tampilkan 9 gambar pertama\n",
    "        plt.subplot(3, 3, i + 1)  # Subplot 3x3\n",
    "        plt.imshow(images[i].numpy().astype(\"uint8\"))  # Konversi gambar ke format uint8\n",
    "        plt.axis(\"off\")  # Matikan sumbu\n",
    "    plt.show()  # Tampilkan plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf # Import tensorflow\n",
    "\n",
    "dataset = dataset.map(lambda x: tf.cast(x, tf.float32) / 255.) # Apply the mapping function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "for x in dataset:\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow((x.numpy() * 255).astype(\"int32\")[0])\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\layers\\activations\\leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import layers\n",
    "\n",
    "discriminator = keras.Sequential(\n",
    "    [\n",
    "        keras.Input(shape=(64, 64, 3)),\n",
    "        layers.Conv2D(64, kernel_size=4, strides=2, padding=\"same\"),\n",
    "        layers.LeakyReLU(alpha=0.2),\n",
    "        layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
    "        layers.LeakyReLU(alpha=0.2),\n",
    "        layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
    "        layers.LeakyReLU(alpha=0.2),\n",
    "        layers.Flatten(),\n",
    "        layers.Dropout(0.2),\n",
    "        layers.Dense(1, activation=\"sigmoid\"),\n",
    "    ],\n",
    "    name=\"discriminator\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"discriminator\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"discriminator\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,136</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,193</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │         \u001b[38;5;34m3,136\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu (\u001b[38;5;33mLeakyReLU\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m131,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_1 (\u001b[38;5;33mLeakyReLU\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │       \u001b[38;5;34m262,272\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_2 (\u001b[38;5;33mLeakyReLU\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │         \u001b[38;5;34m8,193\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">404,801</span> (1.54 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m404,801\u001b[0m (1.54 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">404,801</span> (1.54 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m404,801\u001b[0m (1.54 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 128\n",
    "\n",
    "generator = keras.Sequential(\n",
    "    [\n",
    "        keras.Input(shape=(latent_dim,)),\n",
    "        layers.Dense(8 * 8 * 128),\n",
    "        layers.Reshape((8, 8, 128)),\n",
    "        layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding=\"same\"),\n",
    "        layers.LeakyReLU(alpha=0.2),\n",
    "        layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding=\"same\"),\n",
    "        layers.LeakyReLU(alpha=0.2),\n",
    "        layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding=\"same\"),\n",
    "        layers.LeakyReLU(alpha=0.2),\n",
    "        layers.Conv2D(3, kernel_size=5, padding=\"same\", activation=\"sigmoid\"),\n",
    "    ],\n",
    "    name=\"generator\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"generator\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"generator\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,056,768</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose_1              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,544</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose_2              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,097,664</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">38,403</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)           │     \u001b[38;5;34m1,056,768\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ reshape (\u001b[38;5;33mReshape\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m262,272\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_3 (\u001b[38;5;33mLeakyReLU\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose_1              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m524,544\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_4 (\u001b[38;5;33mLeakyReLU\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose_2              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,097,664\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ leaky_re_lu_5 (\u001b[38;5;33mLeakyReLU\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │        \u001b[38;5;34m38,403\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,979,651</span> (15.18 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,979,651\u001b[0m (15.18 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,979,651</span> (15.18 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,979,651\u001b[0m (15.18 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "class GAN(keras.Model):\n",
    "    def __init__(self, discriminator, generator, latent_dim):\n",
    "        super().__init__()\n",
    "        self.discriminator = discriminator\n",
    "        self.generator = generator\n",
    "        self.latent_dim = latent_dim\n",
    "        self.d_loss_metric = keras.metrics.Mean(name=\"d_loss\")\n",
    "        self.g_loss_metric = keras.metrics.Mean(name=\"g_loss\")\n",
    "\n",
    "    def compile(self, d_optimizer, g_optimizer, loss_fn):\n",
    "        super(GAN, self).compile()\n",
    "        self.d_optimizer = d_optimizer\n",
    "        self.g_optimizer = g_optimizer\n",
    "        self.loss_fn = loss_fn\n",
    "\n",
    "    @property\n",
    "    def metrics(self):\n",
    "        return [self.d_loss_metric, self.g_loss_metric]\n",
    "\n",
    "    def train_step(self, real_images):\n",
    "        batch_size = tf.shape(real_images)[0]\n",
    "        random_latent_vectors = tf.random.normal(\n",
    "            shape=(batch_size, self.latent_dim))\n",
    "        generated_images = self.generator(random_latent_vectors)\n",
    "        combined_images = tf.concat([generated_images, real_images], axis=0)\n",
    "        labels = tf.concat(\n",
    "            [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))],\n",
    "            axis=0\n",
    "        )\n",
    "        labels += 0.05 * tf.random.uniform(tf.shape(labels))\n",
    "\n",
    "        with tf.GradientTape() as tape:\n",
    "            predictions = self.discriminator(combined_images)\n",
    "            d_loss = self.loss_fn(labels, predictions)\n",
    "        grads = tape.gradient(d_loss, self.discriminator.trainable_weights)\n",
    "        self.d_optimizer.apply_gradients(\n",
    "            zip(grads, self.discriminator.trainable_weights)\n",
    "        )\n",
    "\n",
    "        random_latent_vectors = tf.random.normal(\n",
    "            shape=(batch_size, self.latent_dim))\n",
    "\n",
    "        misleading_labels = tf.zeros((batch_size, 1))\n",
    "\n",
    "        with tf.GradientTape() as tape:\n",
    "            predictions = self.discriminator(\n",
    "                self.generator(random_latent_vectors))\n",
    "            g_loss = self.loss_fn(misleading_labels, predictions)\n",
    "        grads = tape.gradient(g_loss, self.generator.trainable_weights)\n",
    "        self.g_optimizer.apply_gradients(\n",
    "            zip(grads, self.generator.trainable_weights))\n",
    "\n",
    "        self.d_loss_metric.update_state(d_loss)\n",
    "        self.g_loss_metric.update_state(g_loss)\n",
    "        return {\"d_loss\": self.d_loss_metric.result(),\n",
    "                \"g_loss\": self.g_loss_metric.result()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GANMonitor(keras.callbacks.Callback):\n",
    "    def __init__(self, num_img=3, latent_dim=128):\n",
    "        self.num_img = num_img\n",
    "        self.latent_dim = latent_dim\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))\n",
    "        generated_images = self.model.generator(random_latent_vectors)\n",
    "        generated_images *= 255\n",
    "        generated_images.numpy()\n",
    "        for i in range(self.num_img):\n",
    "            img = keras.utils.array_to_img(generated_images[i])\n",
    "            img.save(f\"generated_img_{epoch:03d}_{i}.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1034/6228\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5:06:01\u001b[0m 4s/step - d_loss: 0.5324 - g_loss: 1.4400"
     ]
    }
   ],
   "source": [
    "epochs = 1\n",
    "\n",
    "gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)\n",
    "gan.compile(\n",
    "    d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),\n",
    "    g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),\n",
    "    loss_fn=keras.losses.BinaryCrossentropy(),\n",
    ")\n",
    "\n",
    "gan.fit(\n",
    "    dataset, epochs=epochs, callbacks=[GANMonitor(num_img=10, latent_dim=latent_dim)]\n",
    ")"
   ]
  }
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