Data_Augmentation_with_GANs_(Generative_Adversarial_Networks).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):\n",
        "        super().__init__()\n",
        "        # Define generator layers\n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Linear(100, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(256, 784),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.fc(x)\n",
        "\n",
        "# Generate synthetic samples\n",
        "batch_size = 64  # Define batch size\n",
        "noise = torch.randn(batch_size, 100)  # Create random noise\n",
        "\n",
        "# Initialize the generator and generate synthetic data\n",
        "generator = Generator()\n",
        "synthetic_data = generator(noise)\n",
        "\n",
        "# Reshape synthetic data to the image format (e.g., for MNIST dataset)\n",
        "synthetic_images = synthetic_data.view(batch_size, 1, 28, 28)  # Reshape to (batch_size, channels, height, width)\n",
        "\n",
        "# Print the shape of the synthetic images\n",
        "print(\"Shape of synthetic images:\", synthetic_images.shape)"
      ]
    }
  ]
}