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   "metadata": {},
   "source": [
    "<div style=\"\n",
    "    border: 2px solid #4CAF50; \n",
    "    padding: 15px; \n",
    "    background-color: #f4f4f4; \n",
    "    border-radius: 10px; \n",
    "    align-items: center;\">\n",
    "\n",
    "<h1 style=\"margin: 0; color: #4CAF50;\">Neural Networks: Convolutional Neural Networks (2)</h1>\n",
    "<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
    "<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
    "\n",
    "<div style=\"flex-shrink: 0;\">\n",
    "    <img src=\"https://www.htl-grieskirchen.at/wp/wp-content/uploads/2022/11/logo_bildschirm-1024x503.png\" alt=\"Logo\" style=\"width: 250px; height: auto;\"/>\n",
    "</div>\n",
    "<p1> © 2025/26 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
    "</div>\n",
    "<div style=\"flex: 1;\">\n",
    "</div>   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640a7aba",
   "metadata": {},
   "source": [
    "Nachdem wir jetzt wissen, wie ein *Convolutional Neuronal Network* (**CNN**) funktioniert, wollen wir nun nochmal die Datasets **MNIST** und **Fashion-MNIST** ausprobieren."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8125aa",
   "metadata": {},
   "source": [
    "Erstelle somit ein Neuronales Netzwerk, welches auf der CNN Architektur basiert und auf den Datasets **MNIST** und **Fashion-MNIST** trainiert wird."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5f313f",
   "metadata": {},
   "source": [
    "# Lösung"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c231b6a",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a0a6fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, Dataset, random_split\n",
    "from torchvision import datasets, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e942400c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('mps') if torch.backends.mps.is_available() else torch.device('cpu')\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86c04c79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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