Contrastive_Learning_for_Representation_Learning.ipynb

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      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "def contrastive_loss(z_i, z_j, temperature=0.5):\n",
        "    # Normalize embeddings to unit vectors\n",
        "    z_i = F.normalize(z_i, p=2, dim=1)\n",
        "    z_j = F.normalize(z_j, p=2, dim=1)\n",
        "\n",
        "    # Concatenate embeddings\n",
        "    embeddings = torch.cat([z_i, z_j], dim=0)\n",
        "\n",
        "    # Compute similarity matrix\n",
        "    sim_matrix = torch.matmul(embeddings, embeddings.T) / temperature\n",
        "\n",
        "    # Create labels\n",
        "    batch_size = z_i.size(0)\n",
        "    labels = torch.cat([torch.arange(batch_size), torch.arange(batch_size)], dim=0).to(embeddings.device)\n",
        "\n",
        "    # Mask to avoid similarity of embeddings with themselves\n",
        "    mask = torch.eye(labels.size(0), device=embeddings.device).bool()\n",
        "    sim_matrix = sim_matrix.masked_fill(mask, float('-inf'))\n",
        "\n",
        "    # Cross entropy loss\n",
        "    loss = F.cross_entropy(sim_matrix, labels)\n",
        "\n",
        "    return loss\n",
        "\n",
        "# Example usage\n",
        "z_i = torch.randn(32, 128)  # Example embeddings for 32 samples, 128-dimensional\n",
        "z_j = torch.randn(32, 128)\n",
        "\n",
        "loss = contrastive_loss(z_i, z_j)\n",
        "print(f\"Contrastive Loss: {loss.item()}\")"
      ]
    }
  ]
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