Conditional_GAN_(cGAN)_for_Controlled_Generation.ipynb

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      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "# Define the Generator class\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self, noise_dim, label_dim, output_dim):\n",
        "        super(Generator, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(noise_dim + label_dim, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128, output_dim),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, noise, labels):\n",
        "        x = torch.cat([noise, labels], dim=1)\n",
        "        return self.model(x)\n",
        "\n",
        "# Define dimensions\n",
        "noise_dim = 100\n",
        "label_dim = 10\n",
        "output_dim = 28 * 28  # Example for generating 28x28 images\n",
        "\n",
        "# Initialize the generator\n",
        "generator = Generator(noise_dim, label_dim, output_dim)\n",
        "\n",
        "# Generate random noise and labels\n",
        "noise = torch.randn(1, noise_dim)\n",
        "labels = torch.randn(1, label_dim)\n",
        "\n",
        "# Generate a sample\n",
        "generated_sample = generator(noise, labels)\n",
        "print(generated_sample.shape)"
      ]
    }
  ]
}