2_7_4_Semi_Supervised_GAN_(SGAN)_using_MNIST_dataset.ipynb

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      "source": [
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      "outputs": []
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      "outputs": []
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        "id": "HsugikDjsJA-"
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      "execution_count": 2,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "!pip install dash --upgrade"
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      "metadata": {
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      "cell_type": "code",
      "source": [
        "!pip install dash-daq"
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        "id": "A5yElN_oeGB5",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Zy7bXUReGEs"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tDTSE-kzeGH-"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Imports and Setup"
      ],
      "metadata": {
        "id": "NJE3dhRdeGNj"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "RpeIowRbeGRE"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random"
      ],
      "metadata": {
        "id": "v0qNRVL6eGUe"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "lPffkXIUeGXN"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "HKwxKSR4ehK1"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "K3qpB7DlehOj"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "V1haUHlwehTS"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import datasets, transforms"
      ],
      "metadata": {
        "id": "RbQuFyP8ehY4"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader, TensorDataset"
      ],
      "metadata": {
        "id": "tS7kP0g_ehde"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn"
      ],
      "metadata": {
        "id": "R7CK9gmPfEv0"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "N_tb2xIVfE06"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn.functional as F"
      ],
      "metadata": {
        "id": "hnH7ATgDfE42"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash"
      ],
      "metadata": {
        "id": "VtpgPmLkfE9T"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dash import dcc, html, Input, Output\n"
      ],
      "metadata": {
        "id": "8oH_crfSfFFG"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "k4XDwN2VfFIv"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash_daq as daq"
      ],
      "metadata": {
        "id": "uGO9moDRfQ8r"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "SMIkF8R1fRDs"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import base64"
      ],
      "metadata": {
        "id": "jAcS87OhfRIB"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from io import BytesIO"
      ],
      "metadata": {
        "id": "cXVGGn8ofRMl"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import mnist"
      ],
      "metadata": {
        "id": "SReR2OdYfRRt"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "--3kizIZfRV0"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SOUC-ZHJfRbC"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Data loading and Preprocessing"
      ],
      "metadata": {
        "id": "pTQQKO3JfRf3"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load MNIST dataset"
      ],
      "metadata": {
        "id": "dUItNJIIfRku"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transform = transforms.ToTensor()"
      ],
      "metadata": {
        "id": "Kyb7R7O_fFM0"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mnist_train = datasets.MNIST(root=\"./data\", train=True, download=True, transform=transform)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aa-0m2nDehhG",
        "outputId": "1671e7ba-4248-498b-db84-db7fe0e3e4a2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 404: Not Found\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9.91M/9.91M [00:10<00:00, 903kB/s] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 404: Not Found\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28.9k/28.9k [00:00<00:00, 134kB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 404: Not Found\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1.65M/1.65M [00:06<00:00, 244kB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 404: Not Found\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4.54k/4.54k [00:00<00:00, 11.7MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uxAP2-K9ehk-"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define labeled data size (10% labeled, 90% unlabeled)"
      ],
      "metadata": {
        "id": "8w3HQ-HOeGap"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_ratio = 0.1"
      ],
      "metadata": {
        "id": "HxYC32dhfywO"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_labeled = int(labeled_ratio * len(mnist_train))"
      ],
      "metadata": {
        "id": "_24vZ_8sfy1N"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SyJy7WpAfy6L"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Shuffle dataset indices# Shuffle dataset indices"
      ],
      "metadata": {
        "id": "YAZefUp-fy-R"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "indices = list(range(len(mnist_train)))"
      ],
      "metadata": {
        "id": "mhhMpcylfzEb"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random.shuffle(indices)"
      ],
      "metadata": {
        "id": "LgICAzxyfzIg"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DtQcwSEJgBab"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split into labeled and unlabeled datasets"
      ],
      "metadata": {
        "id": "TV-0_bidf-Yu"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_indices = indices[:num_labeled]"
      ],
      "metadata": {
        "id": "lTax0C46f-dN"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_indices = indices[num_labeled:]"
      ],
      "metadata": {
        "id": "KTl5eWyIf-iQ"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M7BUJ2xdf-o8"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract labeled and unlabeled data"
      ],
      "metadata": {
        "id": "YK_YYqjuf-uD"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_data = [(mnist_train[i][0], mnist_train[i][1]) for i in labeled_indices]\n"
      ],
      "metadata": {
        "id": "49iFZZHHf-xi"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_data = [mnist_train[i][0] for i in unlabeled_indices]  # Remove labels\n"
      ],
      "metadata": {
        "id": "KF0rI1b1gLsr"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TfmOPtd8mkOU"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U2upsJOcnAjv"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JTKHpT9_nAqJ"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save labeled & unlabeled data"
      ],
      "metadata": {
        "id": "Mxr7Je3DgL2I"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"labeled_data.pkl\", \"wb\") as f:\n",
        "    pickle.dump(labeled_data, f)"
      ],
      "metadata": {
        "id": "k6YPoROAgL8X"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u8-gCW9pgW7l"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"unlabeled_data.pkl\", \"wb\") as f:\n",
        "    pickle.dump(unlabeled_data, f)"
      ],
      "metadata": {
        "id": "I_s3bVNhgXBF"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B6UWoYjrnAxV"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bosy35dCnA3t"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to tensors"
      ],
      "metadata": {
        "id": "oUWxX5qcmkWe"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_images = torch.stack([img for img, _ in labeled_data])"
      ],
      "metadata": {
        "id": "eyQiCKJCmkeA"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_labels = torch.tensor([label for _, label in labeled_data])"
      ],
      "metadata": {
        "id": "VF7Do-nZgLxW"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_images = torch.stack(unlabeled_data)"
      ],
      "metadata": {
        "id": "DwVKpwKMmpms"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P8y1_uBsmpuL"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save cleaned & processed data"
      ],
      "metadata": {
        "id": "c1D8Nqqqmp2I"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save((labeled_images, labeled_labels), \"labeled_mnist.pt\")"
      ],
      "metadata": {
        "id": "Wkd9dQKfmp9M"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(unlabeled_images, \"unlabeled_mnist.pt\")"
      ],
      "metadata": {
        "id": "QECuoKrYmqDb"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oQzP3x3WgMDw"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BECODkhggMIV"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XmdCRyv1geWU"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Validation"
      ],
      "metadata": {
        "id": "sgLDh7JOf-2o"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check dataset size"
      ],
      "metadata": {
        "id": "x0SMzbrTf-6l"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total samples: {len(mnist_train)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPY7JJU1f--x",
        "outputId": "16c17649-3e3e-4c17-c0c2-c3ce0cdf3ea2"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total samples: 60000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HwNgna5-fzPX"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check shape of images"
      ],
      "metadata": {
        "id": "zpmB6PMufzTD"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_img, sample_label = mnist_train[0]"
      ],
      "metadata": {
        "id": "jMDgezpkfzW7"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Image Shape: {sample_img.shape} (Should be 1x28x28)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "en-0Gji2fzbK",
        "outputId": "052f2974-527f-4920-b621-8a7dff072ea9"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Shape: torch.Size([1, 28, 28]) (Should be 1x28x28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fuMQxAK8goa7"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check data type"
      ],
      "metadata": {
        "id": "9Iwd60Hhgofw"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Data Type: {sample_img.dtype}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cj8HnUQ8gok2",
        "outputId": "16b88a57-f498-4720-d6c1-5a3459ad3f90"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data Type: torch.float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ay8o8lqZgoqB"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check pixel value range"
      ],
      "metadata": {
        "id": "WBNJzlaTeGeX"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Min Pixel Value: {sample_img.min()}, Max Pixel Value: {sample_img.max()}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OEqqYAdeGjy",
        "outputId": "7a7d1b2b-b2b8-4772-989e-d8c8d3bd861e"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min Pixel Value: 0.0, Max Pixel Value: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tNWFj37TeGnf"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing or corrupt images"
      ],
      "metadata": {
        "id": "W-PQyrU-eGqc"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corrupt_count = sum(1 for img, _ in mnist_train if img is None)"
      ],
      "metadata": {
        "id": "dfJyWIm9eGtq"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Corrupt Samples: {corrupt_count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXk9XuWEg0Ju",
        "outputId": "50e36274-76ca-4830-896a-b22a685c0821"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corrupt Samples: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TMm4fJWyg0Ww"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check class distribution in labeled data"
      ],
      "metadata": {
        "id": "JRK_GMiAg0bp"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_counts = {i: 0 for i in range(10)}\n",
        "for _, label in mnist_train:\n",
        "    label_counts[label] += 1"
      ],
      "metadata": {
        "id": "L9bLOSQcg0mq"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tMzAol2thG8P"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Label Distribution:\")\n",
        "for digit, count in label_counts.items():\n",
        "    print(f\"Digit {digit}: {count} samples\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzWpJ01ug0rh",
        "outputId": "231943fa-b2e6-49a4-8752-3693f2203fa2"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label Distribution:\n",
            "Digit 0: 5923 samples\n",
            "Digit 1: 6742 samples\n",
            "Digit 2: 5958 samples\n",
            "Digit 3: 6131 samples\n",
            "Digit 4: 5842 samples\n",
            "Digit 5: 5421 samples\n",
            "Digit 6: 5918 samples\n",
            "Digit 7: 6265 samples\n",
            "Digit 8: 5851 samples\n",
            "Digit 9: 5949 samples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zd1Y7M1hg0wW"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mhI8CIBWhI6t"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize random samples"
      ],
      "metadata": {
        "id": "SNETNbwvhJGT"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 5, figsize=(10, 2))\n",
        "for i, ax in enumerate(axes):\n",
        "    img, label = mnist_train[random.randint(0, len(mnist_train) - 1)]\n",
        "    ax.imshow(img.squeeze(), cmap=\"gray\")\n",
        "    ax.set_title(f\"Label: {label}\")\n",
        "    ax.axis(\"off\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "id": "MvvVYWRBhJKp",
        "outputId": "8d114d34-f637-4373-cf97-de9431f1c156"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x200 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qeahrpCSiatS"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QASfcFXLn09K"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e4N48sPFocit"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MAIN MODEL"
      ],
      "metadata": {
        "id": "K1jeh2pYn1ER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Load processed datasets"
      ],
      "metadata": {
        "id": "YRYSqvKYn1KX"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_images, labeled_labels = torch.load(\"/content/labeled_mnist.pt\")"
      ],
      "metadata": {
        "id": "RUST3yy1n1QU"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_images = torch.load(\"/content/unlabeled_mnist.pt\")"
      ],
      "metadata": {
        "id": "HYI0VRVon1Xe"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G9Ypte6koA45"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize some labeled samples"
      ],
      "metadata": {
        "id": "lb0XiYT_oBA3"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(2, 5, figsize=(10, 5))\n",
        "for i, ax in enumerate(axes.flatten()):\n",
        "    ax.imshow(labeled_images[i].squeeze(), cmap=\"gray\")\n",
        "    ax.set_title(f\"Label: {labeled_labels[i].item()}\")\n",
        "    ax.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "vc7KJwfmoBGW",
        "outputId": "0b925e2d-ef6f-4740-a136-15a9cdc1ac7e"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 10 Axes>"
            ],
            "image/png": 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evSr79ttvVTZ//nyVOd10AznToUMHlVWsWFFlrjc+ad68eY6f85tvvlFZTEyMbfzmm2+qObVq1VLZ2LFjVfbcc8/leG2+gJ9qAQAAABhBsQEAAADACIoNAAAAAEZQbAAAAAAwIiAbxKtWraqyzz77TGX33HOPbTx16lQ156OPPvLcwhxYlqWy8+fPq2zLli1G14GccTp9tnHjxipzbRQTERk9erRt/OKLL6o5Tk2IycnJ2VkikCvt2rVTmSdPu//zzz89di34N6fG7BkzZqgsJCREZU43tujVq1eWz+n0PTgpKUllb731lm28cuVKNYe9nPfKly+vsueff15lQUFBKhsyZIiRNd2QkJCQ5RqcfsYcOnSoys6ePWsbjx8/PneLy2O8swEAAADACIoNAAAAAEZQbAAAAAAwgmIDAAAAgBEB2SDudCKtazO4iMjvv/9uG8+aNUvNOXr0qMfW5XR6qROnhiFPrgOe43S6rdM+WrNmjcpcGxppBoe3FSyovyW89NJLKnNqdPzll19U5nqzjkKFCuVidQg0hQsXto1dm7BFnJvBhw8frrI+ffqorGHDhrbxokWL1JzFixer7PPPP9eLhU9q06aNypx+3tuzZ4/KnL4vm3Ts2DGVXbt2TWVOe97150caxAEAAABAKDYAAAAAGEKxAQAAAMAIv+/Z6N69u8qceiNcD0QREYmNjbWNPd0X0aRJE9vY6bOFTpwOIIT3NWvWTGVO/UEnTpxQWXx8fJbXpz8Dea1AAfv/N02ePFnNcdr3ly9fVtnOnTtVVqtWLdvYtU9ORCQ9PT3LdSIwuX42vV69emrOmTNnVOb0WfsKFSqobMmSJbbxtGnT1BynQ/0QeC5evKiyjIyMPF2D0yGCRYoUceuxd999t4dXk7d4ZwMAAACAERQbAAAAAIyg2AAAAABgBMUGAAAAACP8vkF8+vTpbs1bvny5yjZt2uTh1diNHDnSNg4LC3PrcTSI+6YnnnhCZRERESr79NNPVUbzN3yRa0Pus88+69bjli1bprIePXqozLUpMzo6Ws1xahpH/uB66N5dd92l5ji9dl64cEFlrt9vgb/65JNP8vw5K1eubBu3bds2x9fyxvo9iXc2AAAAABhBsQEAAADACIoNAAAAAEZQbAAAAAAwwqcbxKtXr24bT5kyRc0pW7asytatW6eywYMHe25hDlxPQhURKVmypG0cFBSk5nzzzTfG1gTP2rp1q8qcTqvv16+fyiIjI1U2e/Zs23j16tW5WB1wezVq1FDZl19+meXjnG6u8dNPP6ls4MCBKvvtt99s4++++y7L50P+kZKSkuWc8PBwlYWGhppYDgKE089aY8aMUZnrz5hOTdg7d+5UWbly5VTm2gwuor/HOz3Oydq1a1U2evRotx7rq3hnAwAAAIARFBsAAAAAjKDYAAAAAGAExQYAAAAAI4Isy7LcmujQcONJlSpVUtnYsWNt48cee8yta129elVlCQkJWT5u4cKFKktNTXXrOXv16qWy+Pj4LB/ndKKk6ZPNPcnN7ZNrpvdfTr366qsqe/HFF3N0rT179qjsvffeU9msWbNydP1AlFf7T8R396C7nG6S8e6779rGTq93To3fly9fVtmGDRtU5tog7tRE6e/y+2tgbhQrVsw2/vXXX9WcK1euqKx+/foqS0tL89zC/Eh+339hYWEq+/bbb1VWr169HF3//PnzKnO6QYE7Ny1w+hru3r1bZZ06dVLZ8ePHs7y+N7i7/3hnAwAAAIARFBsAAAAAjKDYAAAAAGAExQYAAAAAI3ymQXzBggUq69OnT46u5bRWTzZRefL6To1MDz74YI6u5Q35vTktIiJCZY0bN1bZO++8o7IqVarYxsHBwWrOH3/8obJz586pbMCAASpLTEy0jZ2aev0dDeK6yVZEpHv37ip7//33Veb6Z4qNjVVznG6u4fR63bdvX5U988wztrHTDQ/8XSC/BhYpUsQ2vuuuu9ScCxcuqOz06dNuXb9kyZK2sesNBUREDhw4oLK///3vKktPT3frOQNNIO+/nIqOjlaZ0w2AihYtmuW1PPnz3r59+1TWvn17lflqM7gTGsQBAAAAeBXFBgAAAAAjKDYAAAAAGOEzPRujRo1SmevnfWfOnKnmrF27VmUFCugaKjIyUmX9+vXLcl21atVSmdOBQu58GQ8fPqyy7777TmVOh2j5Kj4vmnMPPfSQbez0efn+/furzKm3w8nq1att45EjR6o5Tp8h9Sf5rWejbt26KnvttddU5nQoVEpKisoGDRpkG7vumVtda9WqVSpz+vrccccdtrHT5/v9XSC/Bi5fvtw2dvosvNNhjk6H1Tpx7Xlz+h6ZnJyssiZNmqjszz//dOs5A00g7z9Pql69uspq1KhhG3fr1k3Ncfpzx8TEqMydQ/2cep5OnjyZ5eN8GT0bAAAAALyKYgMAAACAERQbAAAAAIyg2AAAAABghM80iIeEhKjM9cCV8+fPG12DE6dDX9LS0lTm9GV0PaDItSFYROTQoUO5WJ330ZxmVrNmzVTm1BDcokWLLK/14YcfqiwuLi5nC/MR+a1B3OmwJ9cm7FvNc7oBQVJSUpbP6bRH/uu//suta7kevnb16tUsn8/fBPJr4COPPGIbL1myRM1xuiFL5cqVVebUCFu4cGHbeOvWrWqO05554IEH9GLzqUDef76gdu3aKnM9MFdEJCwsTGVPP/20bTxnzhzPLcxH0CAOAAAAwKsoNgAAAAAYQbEBAAAAwAiKDQAAAABGFPT2Am64du2ayrzREO6qYcOGKnNqiMvMzFTZU089ZRv7ezM48t6WLVtU1q5dO5U999xzKps0aZJtHBUV5bmFwSsGDhyoMqdTuXfs2OGx53R333z22WcqC8SG8Pzk008/tY2dbk7x+uuvq2z+/Pkq69u3r8pcT7V3er0bMmSIyqpUqaIyvr/CBKe97HRa+L59+1TmdFOW/Ip3NgAAAAAYQbEBAAAAwAiKDQAAAABGUGwAAAAAMMJnGsR9VYMGDVTm1Ax+7Ngxle3evdvImuAdb731lsoaN26ssgEDBqjs6NGjOXrO4sWLq6x8+fIqi4mJyfJaa9asydEa4Du+/vpro9cPCQlRWZcuXdx67BdffOHp5cDHzJ07V2WDBg1SmdNNLCZOnKiy559/3jZu06aNmnP69GmVpaWl3XadgDuCg4Nt4zFjxqg59erVU1l6errK4uLi3JqXX/HOBgAAAAAjKDYAAAAAGEGxAQAAAMAIejY85Ny5cyo7efKkF1YCU1w/XywiYlmWyjZu3KiynPbvOB0qWaZMGbce69onkpCQkKM1IP/o1KmTyurWrauy5ORklf3yyy9G1gTf4XoIn4hzf8aGDRtU5nQgZdu2bW3ju+66S83Ztm2bW+sAsisyMtI2Hj9+vFuPGz58uMq+++47TywpYPHOBgAAAAAjKDYAAAAAGEGxAQAAAMAIig0AAAAARtAg7iELFy709hJg2KJFi1TWp08flUVFRbmVedLWrVtV9vLLL9vGTk29wF917tzZrXlOh6qlpqZ6ejnwA4cOHVJZ69atVTZ69GiVuR7it3PnTjVn8ODBuVgdcGuuN30JCgpSc86ePauypUuXGltToOKdDQAAAABGUGwAAAAAMIJiAwAAAIARFBsAAAAAjKBB3ENojgx8TieIO52afN9996msSZMmOXpOp5N4d+3apbLZs2erLD09PUfPCQC5cfjwYZUNGjRIZSVLlrSNQ0ND1ZyTJ096bmHIt5o1a6ayoUOH2saWZak5Dz30kMrOnTvnuYXlE7yzAQAAAMAIig0AAAAARlBsAAAAADCCYgMAAACAEUGWU0eM00SHkxUBN7dPrrH/4CSv9p9I/tiD/fr1U1lcXJzKnG5c8MorrxhZk6/jNRDexP7TChcurLIVK1aorH379rbxf//3f6s5PXv29NzCApC7+493NgAAAAAYQbEBAAAAwAiKDQAAAABGUGwAAAAAMIIGceQKzWnwJhrE4W28BsKb2H+a06nfn332mcpcTwKvUaOGmvP77797bmEBiAZxAAAAAF5FsQEAAADACIoNAAAAAEbQs4Fc4fOi8CZ6NuBtvAbCm9h/8CZ6NgAAAAB4FcUGAAAAACMoNgAAAAAYQbEBAAAAwAiKDQAAAABGUGwAAAAAMIJiAwAAAIARFBsAAAAAjKDYAAAAAGCE2yeIAwAAAEB28M4GAAAAACMoNgAAAAAYQbEBAAAAwAiKDQAAAABGUGwAAAAAMIJiAwAAAIARFBsAAAAAjKDYAAAAAGAExQYAAAAAIyg2AAAAABhBsQEAAADACIoNAAAAAEZQbAAAAAAwgmIDAAAAgBEUGwAAAACMoNgAAAAAYATFBgAAAAAjKDYAAAAAGEGxAQAAAMAIig0AAAAARlBsAAAAADCCYgMAAACAERQbAAAAAIyg2AAAAABgBMUGAAAAACMoNgAAAAAYQbEBAAAAwAiKDQAAAABGUGwAAAAAMIJiAwAAAIARFBsAAAAAjKDYAAAAAGAExQYAAAAAIyg2AAAAABhBsQEAAADACIoNAAAAAEZQbAAAAAAwgmIDAAAAgBEUGwAAAACMoNgAAAAAYATFBgAAAAAjKDYAAAAAGEGxAQAAAMAIig0AAAAARlBsAAAAADCCYgMAAACAERQbAAAAAIyg2AAAAABgBMUGAAAAACMoNgAAAAAYQbEBAAAAwAiKDQAAAABGUGwAAAAAMIJiAwAAAIAR+b7YOHr0qAQFBcnkyZM9ds1NmzZJUFCQbNq0yWPXRGBi/8Gb2H/wNvYgvIn9lzf8stiYP3++BAUFyY4dO7y9FGNOnDghPXr0kBIlSkjx4sXlH//4hxw+fNjby4Lkj/23dOlSqVevnoSGhkpkZKTExcVJamqqt5cFyR/7b/369dK6dWspXbq0lChRQho1aiQfffSRt5eF/xXoe3D58uUSGxsrlStXliJFikiNGjVkxIgRcv78eW8vDRL4+2/8+PESFBSkfoWGhnp7aTlW0NsLgHbp0iVp3bq1XLhwQcaMGSMhISEydepUadmypezatUtKlSrl7SUigM2ePVuGDBkibdq0kbfffluSk5Nl+vTpsmPHDklMTPTrFzz4vk8//VSio6Pl73//+81vusuWLZP+/ftLamqqDB8+3NtLRIB78sknpXz58tK3b1+pVKmS/PzzzzJz5kxZt26d/PjjjxIWFubtJSIfmD17thQrVuzmODg42IuryR2KDR/07rvvyoEDB2T79u3SsGFDERHp2LGj1K5dW6ZMmSITJkzw8goRqNLT02XMmDHSokUL+eqrryQoKEhERJo2bSpdunSR999/X5599lkvrxKBbObMmVKuXDnZsGGDFC5cWEREBg8eLDVr1pT58+dTbMC4hIQEadWqlS2rX7++DBgwQBYvXixPPPGEdxaGfCUmJkZKly7t7WV4hF9+jMod6enp8vLLL0v9+vUlIiJCihYtKs2bN5eNGzfe8jFTp06VqKgoCQsLk5YtW8ru3bvVnL1790pMTIzccccdEhoaKg0aNJBPP/00y/VcvnxZ9u7d69ZHURISEqRhw4Y3Cw0RkZo1a0qbNm1k2bJlWT4e3uev+2/37t1y/vx5iY2NvVloiIh07txZihUrJkuXLs3yueB9/rr/RETS0tKkZMmSNwsNEZGCBQtK6dKl+R9lP+LPe9C10BAR6dq1q4iI/Prrr1k+Ht7nz/vvBsuyJC0tTSzLcvsxvipgi420tDSZO3eutGrVSiZOnCjjx4+XlJQUad++vezatUvNX7hwobzzzjvyzDPPyOjRo2X37t3y4IMPyunTp2/O2bNnjzRp0kR+/fVXGTVqlEyZMkWKFi0q0dHRsmLFituuZ/v27XLvvffKzJkzbzsvMzNT/ud//kcaNGigfq9Ro0Zy6NAhuXjxontfBHiNv+6/q1eviog4/lAXFhYmP/30k2RmZrrxFYA3+ev+E/n3D3p79uyRsWPHysGDB+XQoUPy6quvyo4dO2TkyJHZ/lrAO/x5Dzo5deqUiEjA/E9zoAuE/Ve5cmWJiIiQ8PBw6du3r20tfsfyQx9++KElItYPP/xwyzkZGRnW1atXbdm5c+essmXLWo8//vjN7MiRI5aIWGFhYVZycvLNPDEx0RIRa/jw4TezNm3aWHXq1LGuXLlyM8vMzLSaNm1qVatW7Wa2ceNGS0SsjRs3qmzcuHG3/bOlpKRYImK98sor6vdmzZpliYi1d+/e214DZgX6/gsKCrLi4uJs+d69ey0RsUTESk1Nve01YFYg7z/LsqxLly5ZPXr0sIKCgm7uuSJFilgrV67M8rHIG4G+B53ExcVZwcHB1v79+3P0eHhOoO+/adOmWUOHDrUWL15sJSQkWMOGDbMKFixoVatWzbpw4UKWj/dFAfvORnBwsBQqVEhE/v1uwdmzZyUjI0MaNGggP/74o5ofHR0tFSpUuDlu1KiRNG7cWNatWyciImfPnpUNGzZIjx495OLFi5Kamiqpqany+++/S/v27eXAgQNy4sSJW66nVatWYlmWjB8//rbr/vPPP0VEbB8huOFGY+6NOfBd/rr/SpcuLT169JAFCxbIlClT5PDhw7J582aJjY2VkJAQEWH/+QN/3X8i/37tq169usTExMiSJUtk0aJF0qBBA+nbt698//332fxKwFv8eQ+6+uc//ynz5s2TESNGSLVq1bL9eOQ9f95/w4YNkxkzZkjv3r2lW7duMm3aNFmwYIEcOHBA3n333Wx+JXxDwBYbIiILFiyQunXrSmhoqJQqVUoiIyNl7dq1cuHCBTXX6QWkevXqcvToUREROXjwoFiWJWPHjpXIyEjbr3HjxomIyJkzZ3K95hsfX7nxcZa/unLlim0OfJs/7j8RkTlz5sjDDz8s8fHxUqVKFWnRooXUqVNHunTpIiJiuzsGfJe/7r+hQ4fK6tWrZenSpdKzZ0/p06ePrF+/XsqVKyfDhg3zyHMgb/jrHvyrzZs3S1xcnLRv315ef/11j18f5gTC/ruhd+/ecuedd8r69euNPYdJAXs3qkWLFsnAgQMlOjpann/+eSlTpowEBwfLG2+8IYcOHcr29W58Tj0+Pl7at2/vOKdq1aq5WrOIyB133CGFCxeWkydPqt+7kZUvXz7XzwOz/HX/iYhERETIqlWr5NixY3L06FGJioqSqKgoadq0qURGRkqJEiU88jwwx1/3X3p6usybN09GjhwpBQr83/+FhYSESMeOHWXmzJmSnp5+838s4bv8dQ/+VVJSkjzyyCNSu3ZtSUhIkIIFA/ZHpoATCPvPVcWKFeXs2bNGn8OUgP2Xk5CQIJUrV5bly5fb7qpzowJ1deDAAZXt379f7r77bhH5d6OOyL+/6bVt29bzC/5fBQoUkDp16jgeVpOYmCiVK1eW8PBwY88Pz/DX/fdXlSpVkkqVKomIyPnz52Xnzp3SrVu3PHlu5I6/7r/ff/9dMjIy5Pr16+r3rl27JpmZmY6/B9/jr3vwhkOHDkmHDh2kTJkysm7dOt7R9TP+vv9cWZYlR48elfvvvz/Pn9sTAvZjVDcOP7H+csuwxMRE2bZtm+P8lStX2j5vt337dklMTJSOHTuKiEiZMmWkVatWMmfOHMd3HVJSUm67nuzc9iwmJkZ++OEHW8Gxb98+2bBhg3Tv3j3Lx8P7/Hn/ORk9erRkZGRwxoGf8Nf9V6ZMGSlRooSsWLFC0tPTb+aXLl2S1atXS82aNfkYqZ/w1z0o8u87Tz300ENSoEAB+eKLLyQyMjLLx8C3+PP+c7rW7NmzJSUlRTp06JDl432RX7+z8cEHH8jnn3+u8mHDhknnzp1l+fLl0rVrV+nUqZMcOXJE3nvvPalVq5ZcunRJPaZq1arSrFkzefrpp+Xq1asybdo0KVWqlO1Wi7NmzZJmzZpJnTp1ZNCgQVK5cmU5ffq0bNu2TZKTkyUpKemWa92+fbu0bt1axo0bl2WD0JAhQ+T999+XTp06SXx8vISEhMjbb78tZcuWlREjRrj/BYJRgbr/3nzzTdm9e7c0btxYChYsKCtXrpQvv/xSXnvtNdvZL/CuQNx/wcHBEh8fLy+99JI0adJE+vfvL9evX5d58+ZJcnKyLFq0KHtfJBgViHtQRKRDhw5y+PBhGTlypGzZskW2bNly8/fKli0r7dq1c+OrA9MCdf9FRUVJbGys1KlTR0JDQ2XLli2ydOlSue+++2Tw4MHuf4F8Sd7fACv3btz27Fa/jh8/bmVmZloTJkywoqKirMKFC1v333+/tWbNGmvAgAFWVFTUzWvduO3ZpEmTrClTplgVK1a0ChcubDVv3txKSkpSz33o0CGrf//+1p133mmFhIRYFSpUsDp37mwlJCTcnOOJ2+4dP37ciomJsYoXL24VK1bM6ty5s3XgwIGcfsngQYG+/9asWWM1atTICg8Pt4oUKWI1adLEWrZsWW6+ZPCgQN9/lmVZixcvtho1amSVKFHCCgsLsxo3bmx7DnhXoO/B2/3ZWrZsmYuvHDwh0PffE088YdWqVcsKDw+3QkJCrKpVq1ovvPCClZaWlpsvm1cFWVYAHE0IAAAAwOcEbM8GAAAAAO+i2AAAAABgBMUGAAAAACMoNgAAAAAYQbEBAAAAwAiKDQAAAABGuH2o31+PewduyKs7J7P/4CQv79zNHoQTXgPhTew/eJO7+493NgAAAAAYQbEBAAAAwAiKDQAAAABGUGwAAAAAMIJiAwAAAIARFBsAAAAAjKDYAAAAAGAExQYAAAAAIyg2AAAAABhBsQEAAADACIoNAAAAAEZQbAAAAAAwgmIDAAAAgBEUGwAAAACMoNgAAAAAYATFBgAAAAAjKDYAAAAAGEGxAQAAAMCIgt5egD+qWLGiykqVKqWyBQsW2MZFihRRc4YOHaqyL774IherAwAgMBQooP9PNCQkRGVVq1ZV2fjx423jmJgYj61r+/btWT6fiMhXX32lsoyMDI+tA/AHvLMBAAAAwAiKDQAAAABGUGwAAAAAMIJiAwAAAIARQZZlWW5NDAoyvRaf1KNHD5XNmDFDZZGRkTm6/p49e1TWoUMHlZ04cSJH1zfNze2Ta/l1/+H28mr/ibAH/2rJkiUq69mzp8q2bNliG+/du1fNGTRokOcW5gW8BnrOY489prJ27dqpzGmv+aq33npLZaNGjfLY9dl/8CZ39x/vbAAAAAAwgmIDAAAAgBEUGwAAAACMoNgAAAAAYAQN4n8RFhamsp9++kll1atXV1laWprKRowYYRtHR0erOZ06dVLZq6++qrJx48apzBfQnAZvokHcvOeff15lEyZMUFlwcLDKXP9+Lly4oOY0bNhQZb/99pvKfPXUZV4D3VOwYEGVud4cwGmv3X333W5d3+l78M8//2wbJyQkuHUtd7z22msqK1q0qFuPdToVPafYf57Tq1cvlY0cOVJldevWVZnT32lmZqZnFiYimzdvto07d+6s5ly6dMljz+cuGsQBAAAAeBXFBgAAAAAjKDYAAAAAGKE/RJmPvfLKKypz6s84d+6cyrp27aqyb7/91jbet2+fmtO+fXuV/e1vf7vtOuF/ypQpo7LHH3/cNn700UfVHKfPsy9atEhly5YtU9nq1auzs0RA+vbtqzKnz6Y7fT7Z6bO7//rXv2zj0qVLqzn79+9XWf/+/VW2ePFilcF/1KxZU2WzZs3K8nEHDx5U2dSpU1W2fPlylZ0+fdrN1WVfgwYNVNanTx9jz4fccfpZrl+/fraxa5+tiEihQoVU5vRa59Sf4cl+mubNm9vGERERao43ejbcxTsbAAAAAIyg2AAAAABgBMUGAAAAACMoNgAAAAAYka8bxMuXL28bP/vss249bunSpSpzbQZ3smXLFpVdvnzZreeE/3A6vPG9995TmVPTuCunBjOnJsRu3bqpzLWhbOfOnVk+H/IX1xtbzJ07V81xOoxt3bp1Kvvggw9UtmLFCtu4fv36as7KlStVNmrUKJXRIO7frl27prI//vjDNnY6FM/1JgMiIrNnz/bcwnLI9ecH+A6ngyA///xzlVWqVCkPVpN9R48eVdnHH39sG6ekpOTRajyDdzYAAAAAGEGxAQAAAMAIig0AAAAARlBsAAAAADAiXzeIv/TSS7ax00mR169fV9nEiRNz9HwtWrRQWZEiRXJ0LfiGRx55RGUfffSRypwaH105nfh94cIFt9bRqVMnlbk28VarVk3NSUtLc+v6CEyup9inp6erOU6nhQ8aNEhlp06dyvL5nG5S4NQMafLkZ3jHvn37VNalSxfb2OlkcF9RokQJ27hevXpuPe7s2bMGVoMbhg8frrInn3xSZVFRUSrz5AnfTj777DPbeMGCBW49bteuXSo7ePCgJ5bkNbyzAQAAAMAIig0AAAAARlBsAAAAADCCYgMAAACAEfmmQbxKlSoq69mzZ5aPc2poPHbsWI7W4HStK1eu5Oha8A2vv/66ypyawVNTU1U2ZMgQ2/iTTz5Rc9xtYHM6mdn1xNS4uDg1x5cbMuFZMTExKmvXrp1tPG7cODVn+/btKjtz5kyO1hAdHa2y++67T2VODegIPJs2bbKNhw0bpuYEBwfn0Wpuz7UROSIiQs1xep13/TcGz2rSpInKnG6GYtqIESNUNnfuXNv40qVLebUcn8M7GwAAAACMoNgAAAAAYATFBgAAAAAjKDYAAAAAGJFvGsRdm3FF9ImgThITEz22BqdGyMKFC3vs+jCrdu3aKrv33ntVlpKSojKnE7537NjhmYWJ880Hvv32W9v4b3/7m8eeD/7H6e/f9QYV33zzjZrz/fff5/g577rrLtt42rRpas6JEydUtmHDhhw/J/yX62uWtzg1dY8cOTLLx33wwQcqS0pK8sia4Cw2NlZlLVu2VJnrzQhERDIzM3N0/YSEBPcWh5t4ZwMAAACAERQbAAAAAIyg2AAAAABgRED2bBQrVkxlTp/BDAoKso2dDlD7+OOPPbauSpUqqSwkJMRj14dZTj03BQroet31IB8Rz/ZnuOurr76yjSdOnKjmTJgwQWX79+83tiZ4j1PPxkcffWQb56Y/o0KFCipz3YNlypRRc5o1a6aynB4aCGRXZGSkyl5++WWVudNfuXTpUo+sCbmza9culTn1Z7hzaG7dunVVRs9G9vHOBgAAAAAjKDYAAAAAGEGxAQAAAMAIig0AAAAARgRkg3hERITKnA5kc20OcmqM/eWXXzy2rlatWnnsWsh7ffv2dWuea9Ott7Ru3do2Dg8PV3Nee+01lc2bN09lX3zxhecWBq+oUqWKyjZu3Jija915550qW7Rokcruuece27hPnz5qzo8//pijNQCe4LRvH3jggSwfN3XqVJX9/PPPHlkTcufChQsqGzNmjMoGDBhgG1evXl3NiY+PV9kff/yhMqcbsOD/8M4GAAAAACMoNgAAAAAYQbEBAAAAwAiKDQAAAABGBGSDuFPzzrFjx1TmeqL3qVOn1Jzz5897bF01a9b02LWQ99asWaOyhx56SGX169dX2eHDh1WWnp7umYWJSIkSJVRWpEiRLB8XExOjsqJFi6qMBnH/Urx4cZU53SAgpzZs2KCyatWqqaxHjx628YoVKzy2BiC7nnjiCZU1b97crccmJyfbxuPHj1dzrl+/nqN1wTynBu49e/bYxsuWLVNzChUqpLLXX39dZU4/d86ePds2zs/7g3c2AAAAABhBsQEAAADACIoNAAAAAEZQbAAAAAAwIiAbxJ2aupOSklTm2iC+efNmU0vKljlz5nh7CXCwcuVKlf2///f/VOZ0gvjQoUNV5noC7bVr19xah9MpzG3atFFZRESEW9dD4ElLS1OZO6fqLlmyRM154403VObUDN69e3eVOf2bQWBxuhFF69atjT7ntm3bVHb27FmVuTaET58+Xc0JDQ1V2fHjx1XWqVMn2/jixYtZrhO+zfWmL//5n/+p5jg1gztx2luuDeGuDeP5Ce9sAAAAADCCYgMAAACAERQbAAAAAIyg2AAAAABgREA2iOeU62mSueXaoBsZGanmOJ06+a9//cuj64BnuJ4gKyIyePBglc2YMUNljRs3VtnSpUs9s7BbOHLkiG28fv16Nad9+/ZG1wDf4bTfXE9BPnHihFvXOnXqlMpKliypspCQENvY3ZsgwDfUrVvXNn722WfVnCZNmqjsP/7jP4ytSUTkwIEDKnP6XlqjRg3bOCwsTM1xpxlcRGT37t3ZWSL8kNMp4/Xq1VNZTEyMygoU0P9337JlS9uYBnEAAAAA8DCKDQAAAABGUGwAAAAAMCLIsizLrYlBQabXYtSqVatU1qVLF9u4d+/eak5uPlfvejjfoEGD1JyPP/5YZb169crxc+Y1N7dPrvnT/itbtqzK+vfvr7Ly5cvbxtHR0WpOiRIlVLZo0SKV/fzzzypbtmyZbex02OX8+fNV1rBhQ5U1atTINnb6fLQ35NX+E/GvPejEtX9CRGTTpk22sdPn7532jROnno0ffvjBNnY6kPLgwYNuXd9XBcproNP+cP2+2aFDhxxf3+lQyUuXLtnGFSpUyPH1c6pp06Yq+/777/N8HTkVKPvPV1WuXFllrocBiuj+IBGRy5cv28bPPfecmvPhhx/mYnXe5+7+450NAAAAAEZQbAAAAAAwgmIDAAAAgBEUGwAAAACM4FA/gx577LEs5zg1rsO/nT59WmWTJk3K8nHDhw83sZzbSkxMVJlTM3t4eLht7CsN4nDfCy+8oDLXhvBXX31VzZk5c6Zb13/wwQdV9uKLL9rGX3zxhZrTvHlzlXGwqVmFChVS2S+//KIyp+ZYV9u2bVPZ5MmTVbZz506VuR7y+PTTT6s5L730UpZryI1y5coZvT782+HDh1XWsWNHt+a5HiL5+OOPqzn+3iDuLt7ZAAAAAGAExQYAAAAAIyg2AAAAABhBsQEAAADAiHzdIO7JEzHj4uJUVrCg/cu7Y8cONWflypUeWwNgylNPPWUbjx8/3jsLgVtKly6tsqFDh6rM9ZT5119/Xc1xbeK9FddriYhs3rzZNt6yZYuaExMTo7J33nnHredEzjidZOxOM/jYsWNVNn36dJW5ngwuItK6dWuVjRo1yjZ2ulmAaQsXLlTZ119/rTLXU6M/+ugjNefq1aueWxh81oULF7y9BL/DOxsAAAAAjKDYAAAAAGAExQYAAAAAIyg2AAAAABiRrxvELcvK0eOqVq2qMndOiI6Pj1fZlStXcrQGwBOuX7+usoyMDJX16tXLNnY6adrpWvCO4sWLq8z1FHgRkf3799vG7jaDu+vkyZO28eLFi9Ucp9fFuXPnquzy5cueW1g+16ZNmxw9rnbt2irr16+fyqKjo1Xm1PwdGhqa5XOeOHFCZdOmTVOZ095yPel53rx5ak7RokVV9sgjj6jMtYF+yZIlag4N4vlDTk+1X716tYdX4j94ZwMAAACAERQbAAAAAIyg2AAAAABgRJDlZuOCJw/A84ZPPvlEZV27drWN165dq+a88cYbKlu6dKnK7rrrLpXt3bvXNq5fv76a8+eff+rF+pGc9r1kl7/vP3/itL979OhhGzdt2lTN+f77742t6Vbyav+J+P8efPHFF1Xmelij02ftd+7c6bE1lCtXTmXJyckqe+yxx1TmdPiaL/DH10CnNeflv6VbWbVqlcqcDhBNSkpy63qFChWyjRs1aqTmPProoyrbtWuXylx/hvjjjz/cWoNp/rj//Enfvn1VtmDBApUVKKD/7z4zM9M2fuCBB9Qcb3zf9CR39x/vbAAAAAAwgmIDAAAAgBEUGwAAAACMoNgAAAAAYES+aRBv0qSJyjZt2mQbuzaT5VadOnVs4z179nj0+r6A5rTAM3LkSJW9+eabtvFzzz2n5sycOdPYmm6FBnH3RUZGqsz1NbBYsWJqTrdu3VS2Y8eOHK3B9ZA1EZE1a9aozJ8aKf3xNfAf//iHylasWOGx6zvdbMV1r4mIzJkzxzZ2OuTW6ZBR/B9/3H++qlSpUipbuXKlyv7+97+rzOnr43qIn+uNVkRE0tPTs7FC30ODOAAAAACvotgAAAAAYATFBgAAAAAjKDYAAAAAGJFvGsSdvPrqq7Zxz5491ZyqVauq7MyZMyrr3bu3yjZs2GAb+8IJrZ5Gc1rgKVmypMoOHDhgG//8889qTuvWrY2t6VZoEM+dunXr2sZOzZBOJ+N+9dVXKnN6bEhIiG08depUNcfphPLY2FiVXb9+XWW+wB9fAwsWLKiyl19+OcvHXbp0SWVOf6dOf1eupynDM/xx//kK14bwDz/8UM15+OGH3brWyZMnVda1a1fbOKc31vBlNIgDAAAA8CqKDQAAAABGUGwAAAAAMIJiAwAAAIAR+bpBHLlHc1r+4NrEW6dOHTWnUqVKKjt16pSxNYnQIO5p5cuXV9nw4cNV1r17d5U5/f27/v0cOnRIzRkyZIjK1q9ff9t1+hJeA+FN7D+tRIkSbs1zfR2bPXt2jp+zfv36KktKSsrx9fwFDeIAAAAAvIpiAwAAAIARFBsAAAAAjKDYAAAAAGAEDeLIFZrT8od69erZxk4nofbr109lixcvNrYmERrE4X28BsKb2H/a5MmTVdayZUuVVatWzTYuVqyYW9dftWqVyrp16+bm6gILDeIAAAAAvIpiAwAAAIARFBsAAAAAjCjo7QUA8H179+61jffs2eOllQAAcGvx8fHeXgJc8M4GAAAAACMoNgAAAAAYQbEBAAAAwAiKDQAAAABGcKgfcoUDheBNHOoHb+M1EN7E/oM3cagfAAAAAK+i2AAAAABgBMUGAAAAACMoNgAAAAAY4XaDOAAAAABkB+9sAAAAADCCYgMAAACAERQbAAAAAIyg2AAAAABgBMUGAAAAACMoNgAAAAAYQbEBAAAAwAiKDQAAAABGUGwAAAAAMOL/A90Z+dVmWLhOAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "90Od0k-PoBNi"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zswa2w5Bod1Q"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure batch sizes match"
      ],
      "metadata": {
        "id": "hCDOaBp4od70"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64"
      ],
      "metadata": {
        "id": "QqBttwj6oeEy"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_loader = DataLoader(TensorDataset(labeled_images, labeled_labels), batch_size=batch_size, shuffle=True, drop_last=True)\n"
      ],
      "metadata": {
        "id": "5IdlbrZeoeKh"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_loader = DataLoader(TensorDataset(unlabeled_images), batch_size=batch_size, shuffle=True, drop_last=True)\n"
      ],
      "metadata": {
        "id": "nz09HEmpoeQa"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XeG75IcdonOE"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Generator"
      ],
      "metadata": {
        "id": "BuJzYDiQonT3"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Generator(nn.Module):\n",
        "    def __init__(self, latent_dim=100):\n",
        "        super(Generator, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(latent_dim, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(256, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 28 * 28),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, z):\n",
        "        return self.model(z).view(-1, 1, 28, 28)"
      ],
      "metadata": {
        "id": "7-XobY6Fonah"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vOR6kVTRossh"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Discriminator"
      ],
      "metadata": {
        "id": "2EGsCiploszR"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Discriminator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.feature_extractor = nn.Sequential(\n",
        "            nn.Linear(28 * 28, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 256),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        self.classifier = nn.Linear(256, 11)  # 10 classes + 1 for fake data\n",
        "\n",
        "    def forward(self, x):\n",
        "        features = self.feature_extractor(x.view(-1, 28 * 28))\n",
        "        return self.classifier(features)"
      ],
      "metadata": {
        "id": "GCv2CPpbongs"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iC0r_sYxonn6"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FQ2YfyH1onuF"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DLexXwZXonzk"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialize models"
      ],
      "metadata": {
        "id": "0Fh02N6kon5p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "generator = Generator()"
      ],
      "metadata": {
        "id": "VJ3eJAxxoeXq"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator = Discriminator()"
      ],
      "metadata": {
        "id": "hynTc6pnoegu"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4aQpNIJTo7-K"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "HdT77ysXo8FL"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGEcf5o7o8LY",
        "outputId": "6413ad78-de8d-4f7e-c0ec-d2ad85b37b81"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Generator(\n",
              "  (model): Sequential(\n",
              "    (0): Linear(in_features=100, out_features=256, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=256, out_features=512, bias=True)\n",
              "    (3): ReLU()\n",
              "    (4): Linear(in_features=512, out_features=784, bias=True)\n",
              "    (5): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwjJp_bIiays",
        "outputId": "21ed377e-f1be-472b-a0e9-b51011bd8c75"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Discriminator(\n",
              "  (feature_extractor): Sequential(\n",
              "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=512, out_features=256, bias=True)\n",
              "    (3): ReLU()\n",
              "  )\n",
              "  (classifier): Linear(in_features=256, out_features=11, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XPcqhJmxpECe"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KDWm1V5XpELI"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimizers"
      ],
      "metadata": {
        "id": "udqzBjU1pETG"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g_optimizer = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n"
      ],
      "metadata": {
        "id": "f3hXA8NipEa8"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n"
      ],
      "metadata": {
        "id": "46PFVHtXpEi2"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3P8DcKempOg7"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss function"
      ],
      "metadata": {
        "id": "HyivUiBUpOoI"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gan_loss = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "VrS8RGdYpOv9"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8FDp2iMFpO38"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create results directory"
      ],
      "metadata": {
        "id": "IID1l3ijpTXc"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.makedirs(\"results\", exist_ok=True)"
      ],
      "metadata": {
        "id": "KhH3LggJpTgU"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def save_generated_images(epoch):\n",
        "    \"\"\"Generate and save a batch of fake images for visualization.\"\"\"\n",
        "    generator.eval()\n",
        "    with torch.no_grad():\n",
        "        z = torch.randn(16, 100, device=device)\n",
        "        fake_images = generator(z).cpu()\n",
        "\n",
        "    fig, axes = plt.subplots(4, 4, figsize=(4, 4))\n",
        "    for i, ax in enumerate(axes.flat):\n",
        "        ax.imshow(fake_images[i].squeeze(), cmap=\"gray\")\n",
        "        ax.axis(\"off\")\n",
        "\n",
        "    plt.savefig(f\"results/generated_epoch_{epoch}.png\")\n",
        "    plt.close()\n",
        "    generator.train()"
      ],
      "metadata": {
        "id": "TMIzMQQzpToI"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_sgan(epochs=10):\n",
        "    generator.train()\n",
        "    discriminator.train()\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        for (real_images, real_labels), (unlabeled_images,) in zip(labeled_loader, unlabeled_loader):\n",
        "            real_images, real_labels = real_images.to(device), real_labels.to(device)\n",
        "            unlabeled_images = unlabeled_images.to(device)\n",
        "            batch_size = real_images.size(0)\n",
        "\n",
        "            # Generate fake images\n",
        "            z = torch.randn(batch_size, 100, device=device)\n",
        "            fake_images = generator(z)\n",
        "\n",
        "            # Train Discriminator\n",
        "            d_optimizer.zero_grad()\n",
        "\n",
        "            real_preds = discriminator(real_images)\n",
        "            fake_preds = discriminator(fake_images.detach())\n",
        "            unlabeled_preds = discriminator(unlabeled_images)\n",
        "\n",
        "            # Compute discriminator losses\n",
        "            real_loss = gan_loss(real_preds[:, :-1], F.one_hot(real_labels, num_classes=10).float())\n",
        "            fake_loss = gan_loss(fake_preds[:, -1].unsqueeze(1), torch.zeros((batch_size, 1), device=device))\n",
        "            unlabeled_loss = gan_loss(unlabeled_preds[:, -1].unsqueeze(1), torch.zeros((batch_size, 1), device=device))\n",
        "\n",
        "            d_loss = real_loss + fake_loss + unlabeled_loss\n",
        "            d_loss.backward()\n",
        "            d_optimizer.step()\n",
        "\n",
        "            # Train Generator\n",
        "            g_optimizer.zero_grad()\n",
        "            fake_preds = discriminator(fake_images)\n",
        "\n",
        "            # Instead of training the generator to minimize \"fake class,\"\n",
        "            # we push it towards being classified as real classes\n",
        "            g_loss = gan_loss(fake_preds[:, :-1], torch.ones_like(fake_preds[:, :-1]) / 10)\n",
        "            g_loss.backward()\n",
        "            g_optimizer.step()\n",
        "\n",
        "        print(f\"Epoch {epoch+1}/{epochs}, D Loss: {d_loss.item():.4f}, G Loss: {g_loss.item():.4f}\")\n",
        "        save_generated_images(epoch+1)\n",
        "\n",
        "    # Save models\n",
        "    torch.save(generator.state_dict(), \"results/generator.pth\")\n",
        "    torch.save(discriminator.state_dict(), \"results/discriminator.pth\")\n",
        "    print(\"Training complete! Models saved.\")"
      ],
      "metadata": {
        "id": "7-Nt2ZnapTul"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dCMjWXZ8pT5O"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Start Training"
      ],
      "metadata": {
        "id": "FoCkJ1_KpT_z"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_sgan(epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qc_4jkBpUHy",
        "outputId": "65865ce2-1eb5-417d-94ad-a585119e20cc"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10, D Loss: 0.6812, G Loss: 2.3026\n",
            "Epoch 2/10, D Loss: 0.3689, G Loss: 2.3026\n",
            "Epoch 3/10, D Loss: 0.3223, G Loss: 2.3026\n",
            "Epoch 4/10, D Loss: 0.3476, G Loss: 2.3026\n",
            "Epoch 5/10, D Loss: 0.3572, G Loss: 2.3026\n",
            "Epoch 6/10, D Loss: 0.2376, G Loss: 2.3026\n",
            "Epoch 7/10, D Loss: 0.2452, G Loss: 2.3026\n",
            "Epoch 8/10, D Loss: 0.1042, G Loss: 2.3026\n",
            "Epoch 9/10, D Loss: 0.1540, G Loss: 2.3026\n",
            "Epoch 10/10, D Loss: 0.2011, G Loss: 2.3026\n",
            "Training complete! Models saved.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b8EPunXfppu_"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DvhzXed9pp1d"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U2cHHbjjpw54"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evalution of the model"
      ],
      "metadata": {
        "id": "HBVfjt6npxCY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load trained models"
      ],
      "metadata": {
        "id": "pvxXLCTjpxK9"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "pLo98KZrpxRz"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u5vOZtB7pxfr"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = torch.load(\"results/generator.pth\", map_location=device)"
      ],
      "metadata": {
        "id": "tLjEnVBTqHLa"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator = torch.load(\"results/discriminator.pth\", map_location=device)\n"
      ],
      "metadata": {
        "id": "VQrfcQtSqHUj"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iUwo7LqMqHdc"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize models before loading state_dict"
      ],
      "metadata": {
        "id": "3JQGrKPDpO_p"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator_model = Generator().to(device)"
      ],
      "metadata": {
        "id": "1jCncezSqQVP"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator_model = Discriminator().to(device)"
      ],
      "metadata": {
        "id": "VIKpiVRtqQdx"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ds7f1yVQqQk1"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator_model.load_state_dict(torch.load(\"results/generator.pth\", map_location=device))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahYUgUAQqQsg",
        "outputId": "f6c71ac6-15d9-4661-edd4-02177d684a41"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator_model.load_state_dict(torch.load(\"results/discriminator.pth\", map_location=device))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDlXN0qVqQ0K",
        "outputId": "7fc36458-7363-4012-8bb5-8f881fa52bbf"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pb70U6KeqQ8q"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator_model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5o_MhPKYqRLt",
        "outputId": "997cf4bf-d159-49c0-df8e-29bb5c7d0c30"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Generator(\n",
              "  (model): Sequential(\n",
              "    (0): Linear(in_features=100, out_features=256, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=256, out_features=512, bias=True)\n",
              "    (3): ReLU()\n",
              "    (4): Linear(in_features=512, out_features=784, bias=True)\n",
              "    (5): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator_model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrr5GAMipEuF",
        "outputId": "f782564e-4491-485d-d6f5-766e0bc249d0"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Discriminator(\n",
              "  (feature_extractor): Sequential(\n",
              "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=512, out_features=256, bias=True)\n",
              "    (3): ReLU()\n",
              "  )\n",
              "  (classifier): Linear(in_features=256, out_features=11, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O6i0liHVqdyV"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q85R58QJqd5P"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate synthetic images"
      ],
      "metadata": {
        "id": "I3p0O1nwqeC1"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z = torch.randn(16, 100, device=device)"
      ],
      "metadata": {
        "id": "dXZRKkATqeLA"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_images = generator_model(z).cpu().detach()"
      ],
      "metadata": {
        "id": "0wAYYD5WqeTf"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fvt2cgybqk9u"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load real images"
      ],
      "metadata": {
        "id": "fyQEesieqlFj"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_images, labeled_labels = torch.load(\"/content/labeled_mnist.pt\")"
      ],
      "metadata": {
        "id": "imevsDoKqlOg"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "real_images = labeled_images[:16].cpu().detach()"
      ],
      "metadata": {
        "id": "jnfQj_2Yqea1"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yL28exu_qsCB"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare real and generated images"
      ],
      "metadata": {
        "id": "I6jf1_LOqsIi"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(4, 8, figsize=(10, 5))\n",
        "for i in range(16):\n",
        "    axes[i//4, 2*(i%4)].imshow(real_images[i].squeeze(), cmap='gray')\n",
        "    axes[i//4, 2*(i%4)].axis('off')\n",
        "    axes[i//4, 2*(i%4)+1].imshow(fake_images[i].squeeze(), cmap='gray')\n",
        "    axes[i//4, 2*(i%4)+1].axis('off')\n",
        "\n",
        "plt.suptitle(\"Left: Real | Right: Generated\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "I7YLBGjgqsQf",
        "outputId": "eb3633db-097e-4a95-8827-83e9d369d515"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 32 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SwRxzttwqw7L"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FRKHSuM4qxDJ"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Discriminator"
      ],
      "metadata": {
        "id": "Q38PU8vaqxKy"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "real_preds = discriminator_model(real_images.to(device))"
      ],
      "metadata": {
        "id": "LVYcpG1VqxTT"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_preds = discriminator_model(fake_images.to(device))"
      ],
      "metadata": {
        "id": "kcXVeGGwqxa_"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0OaOoJ-Fq4DV"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Real Image Predictions:\\n\", real_preds.softmax(dim=1)[:, :-1].mean(dim=0))\n",
        "print(\"\\nFake Image Predictions:\\n\", fake_preds.softmax(dim=1)[:, -1].mean().item())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5JlWmdKq4Os",
        "outputId": "ac3cfd97-b54d-4ed0-8faf-1cdd9b02d0dc"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Real Image Predictions:\n",
            " tensor([0.1197, 0.1031, 0.0685, 0.0658, 0.1015, 0.2012, 0.0657, 0.0654, 0.0657,\n",
            "        0.1248], device='cuda:0', grad_fn=<MeanBackward1>)\n",
            "\n",
            "Fake Image Predictions:\n",
            " 0.10253884643316269\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yz06O6S3q4Xw"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save generated images and evaluation results"
      ],
      "metadata": {
        "id": "-bre9HPXqsZU"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(fake_images, \"generated_images.pth\")"
      ],
      "metadata": {
        "id": "-JOQv-_tqshL"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = {\"real_preds\": real_preds.cpu().detach(), \"fake_preds\": fake_preds.cpu().detach()}\n"
      ],
      "metadata": {
        "id": "HYIn6MyMqso-"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"discriminator_results.pkl\", \"wb\") as f:\n",
        "    pickle.dump(results, f)"
      ],
      "metadata": {
        "id": "A3bEVGHbrC3I"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zpYIuHFYqekD"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ETALBwC4pE1U"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zxqJZhjeia5N"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dash App for Visualization"
      ],
      "metadata": {
        "id": "5qDukRj1ia-9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load MNIST dataset"
      ],
      "metadata": {
        "id": "i0MkgtPdjMHm"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n"
      ],
      "metadata": {
        "id": "HtwapwPDjMRG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44d60735-616e-409c-c7ba-17d242c38a5b"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pCFEdLGTjS3a"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_sample_image(index):\n",
        "    \"\"\"Convert a sample image to base64 for displaying in Dash.\"\"\"\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.imshow(train_images[index], cmap='gray')\n",
        "    ax.axis('off')\n",
        "    buf = BytesIO()\n",
        "    plt.savefig(buf, format='png')\n",
        "    buf.seek(0)\n",
        "    encoded_image = base64.b64encode(buf.getvalue()).decode('utf-8')\n",
        "    plt.close()\n",
        "    return f'data:image/png;base64,{encoded_image}'"
      ],
      "metadata": {
        "id": "acZQ6XS-jPo4"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HdchMdPRjVVC"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate a DataFrame for visualization"
      ],
      "metadata": {
        "id": "09jtPX1jjVdH"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels_df = pd.DataFrame({'Label': train_labels})"
      ],
      "metadata": {
        "id": "G0ow7IE9jWfG"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PbGB6eVvjMYL"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Dash app"
      ],
      "metadata": {
        "id": "_DFdhDeliefk"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = dash.Dash(__name__)"
      ],
      "metadata": {
        "id": "ppEqzSZdibFf"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X5hEdHnDibLA"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app.layout = html.Div([\n",
        "    html.H1(\"MNIST Dataset Dashboard\"),\n",
        "\n",
        "    html.Div([\n",
        "        html.Label(\"Select Sample Image Index:\"),\n",
        "        dcc.Slider(0, len(train_images) - 1, step=1, value=random.randint(0, len(train_images) - 1),\n",
        "                   id='image-slider')\n",
        "    ]),\n",
        "\n",
        "    html.Div([html.Img(id='image-display', style={'width': '200px', 'height': '200px'})]),\n",
        "\n",
        "    html.H3(\"Label Distribution\"),\n",
        "    dcc.Graph(id='label-distribution'),\n",
        "\n",
        "    html.Label(\"Select Number of GAN-Generated Images:\"),\n",
        "    dcc.Slider(1, 10, step=1, value=5, id='gan-slider'),\n",
        "\n",
        "    html.Div(id='gan-images', style={'display': 'flex', 'flex-wrap': 'wrap'})\n",
        "])\n",
        "\n",
        "@app.callback(\n",
        "    Output('image-display', 'src'),\n",
        "    Input('image-slider', 'value')\n",
        ")\n",
        "def update_image(index):\n",
        "    return get_sample_image(index)\n",
        "\n",
        "@app.callback(\n",
        "    Output('label-distribution', 'figure'),\n",
        "    Input('image-slider', 'value')\n",
        ")\n",
        "def update_distribution(_):\n",
        "    fig = px.histogram(labels_df, x='Label', nbins=10, title='Label Distribution')\n",
        "    return fig\n",
        "\n",
        "@app.callback(\n",
        "    Output('gan-images', 'children'),\n",
        "    Input('gan-slider', 'value')\n",
        ")\n",
        "def generate_gan_images(n):\n",
        "    return [html.Img(src=get_sample_image(random.randint(0, len(train_images) - 1)),\n",
        "                     style={'width': '100px', 'height': '100px', 'margin': '5px'})\n",
        "            for _ in range(n)]"
      ],
      "metadata": {
        "id": "JSjR_5kfibQr"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zZML1NeVsxTb"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gQbAPLuDiWJO"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "OOv5ERESiWPF",
        "outputId": "e7621f8f-eb13-4e44-99fd-b5685a891814"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "    const iframe = document.createElement('iframe');\n",
              "    iframe.src = new URL(path, url).toString();\n",
              "    iframe.height = height;\n",
              "    iframe.width = width;\n",
              "    iframe.style.border = 0;\n",
              "    iframe.allow = [\n",
              "        'accelerometer',\n",
              "        'autoplay',\n",
              "        'camera',\n",
              "        'clipboard-read',\n",
              "        'clipboard-write',\n",
              "        'gyroscope',\n",
              "        'magnetometer',\n",
              "        'microphone',\n",
              "        'serial',\n",
              "        'usb',\n",
              "        'xr-spatial-tracking',\n",
              "    ].join('; ');\n",
              "    element.appendChild(iframe);\n",
              "  })(8050, \"/\", \"100%\", 650, false, window.element)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}