Point_cloud.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#DATA"
      ],
      "metadata": {
        "id": "QKjXQOijGwOL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import glob"
      ],
      "metadata": {
        "id": "pDsbAmWgGbHz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import glob \n",
        "import numpy as np # Define the path to the main folder \n",
        "main_folder = '/content/drive/MyDrive/DGM/ShapeNet_pointclouds/02691156'\n",
        "# Get the names of all subfolders in the main folder \n",
        "subfolders = glob.glob(main_folder + '/*/') \n",
        "# Initialize the NumPy array to store the point clouds \n",
        "point_clouds = [] \n",
        "# Loop over the subfolders \n",
        "for folder in subfolders: \n",
        "# Get the name of the pointcloud_1024.npy file in the subfolder \n",
        "  file = folder + '/pointcloud_1024.npy'\n",
        "  # Load the .npy file and append it to the NumPy array \n",
        "  point_cloud = np.load(file) \n",
        "  point_clouds.append(point_cloud) \n",
        "# Convert the list of point clouds to a NumPy array \n",
        "point_clouds = np.array(point_clouds)"
      ],
      "metadata": {
        "id": "PQFueh8LFs1i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n"
      ],
      "metadata": {
        "id": "SnKH3RVSIHAn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "point_clouds = np.load(\"/content/drive/MyDrive/DGM/ShapeNet_pointclouds/train.npy\")"
      ],
      "metadata": {
        "id": "Xxmf0X8sH9H8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(point_clouds)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "EWGJqSbCILPK",
        "outputId": "a13fc04e-3406-4241-ea0b-8896159be646"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4032"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "\n",
        "\n",
        "# Convert the NumPy array to a PyTorch tensor\n",
        "point_clouds = torch.from_numpy(point_clouds[:4032])\n",
        "\n",
        "# Create a TensorDataset from the tensor\n",
        "dataset = TensorDataset(point_clouds)\n",
        "\n",
        "# Create a DataLoader from the dataset\n",
        "dataloader = DataLoader(dataset, batch_size=16, shuffle=True)"
      ],
      "metadata": {
        "id": "gNzgUCrZGiuE"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "point_clouds = torch.from_numpy(point_clouds[:4032])"
      ],
      "metadata": {
        "id": "NSyH3MukfRSE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = TensorDataset(normalized)\n",
        "\n",
        "# Create a DataLoader from the dataset\n",
        "dataloader = DataLoader(dataset, batch_size=32, shuffle=True)"
      ],
      "metadata": {
        "id": "3vyknGYndyqs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "asdf=np.asarray(point_clouds)"
      ],
      "metadata": {
        "id": "lelzBXzBTMn-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('train.npy', asdf)"
      ],
      "metadata": {
        "id": "mSHT7o6QTdpN"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "f=np.load('train.npy') "
      ],
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "f.shape"
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      "outputs": [
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            "text/plain": [
              "(4032, 1024, 3)"
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          "metadata": {},
          "execution_count": 235
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model"
      ],
      "metadata": {
        "id": "2thS4loMGx1E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Define the model\n",
        "class PointCloudAutoEncoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    \n",
        "    # Encoder layers\n",
        "    self.conv1 = nn.Conv1d(3, 16, kernel_size=3, stride=2, padding=1)\n",
        "    self.conv2 = nn.Conv1d(16, 32, kernel_size=3, stride=2, padding=1)\n",
        "    self.conv3 = nn.Conv1d(32, 8, kernel_size=3, stride=2, padding=1)\n",
        "    \n",
        "    # Latent space\n",
        "    self.fc1 = nn.Linear(1024, 128)\n",
        "    \n",
        "    # Decoder layers\n",
        "    self.fc2 = nn.Linear(128, 1024)\n",
        "    self.deconv1 = nn.ConvTranspose1d(64, 32, kernel_size=4, stride=4)\n",
        "    self.deconv2 = nn.ConvTranspose1d(32, 16,kernel_size=4, stride=4)\n",
        "    self.deconv3 = nn.ConvTranspose1d(16, 3, kernel_size=4, stride=4)\n",
        "    \n",
        "  def forward(self, x):\n",
        "  # Encoder\n",
        "    x = self.conv1(x)\n",
        "    x = self.conv2(x)\n",
        "    x = self.conv3(x)\n",
        "    \n",
        "    # Latent space\n",
        "    x = x.view(x.size(0), -1)\n",
        "    x = self.fc1(x)\n",
        "    \n",
        "    # Decoder\n",
        "    x = self.fc2(x)\n",
        "    x = x.view(x.size(0), 64, 16)\n",
        "    x = self.deconv1(x)\n",
        "    x = self.deconv2(x)\n",
        "    x = self.deconv3(x)\n",
        "    \n",
        "    return x\n",
        "\n"
      ],
      "metadata": {
        "id": "8FW8hRSJGyi9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training"
      ],
      "metadata": {
        "id": "ZOPDWyrsG5q0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs=100"
      ],
      "metadata": {
        "id": "vgZgGilQNX6X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import optim\n",
        "\n",
        "# Define the model and move it to the GPU\n",
        "model = PointCloudAutoEncoder()\n",
        "if torch.cuda.is_available():\n",
        "    model = model.cuda()\n",
        "\n",
        "# Define the optimizer and loss function\n",
        "optimizer = optim.Adam(model.parameters())\n",
        "loss_fn = torch.nn.MSELoss()\n",
        "\n",
        "\n",
        "# Train the model\n",
        "for epoch in range(num_epochs):\n",
        "  for data in dataloader:\n",
        "      # Move the data to the GPU\n",
        "      if torch.cuda.is_available():\n",
        "          data = data[0].reshape(-1,3,1024).to(\"cuda\", dtype=torch.float)\n",
        "\n",
        "      # Forward pass\n",
        "      output = model(data)\n",
        "\n",
        "      # Calculate the loss\n",
        "      loss = loss_fn(output, data)\n",
        "      print(loss)\n",
        "      # Backward pass and optimize\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n"
      ],
      "metadata": {
        "id": "6MBQREfNG7Cb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=point_clouds[1000].reshape(-1,3,1024)\n",
        "x.shape"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 1024])"
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=x.to(\"cuda\", dtype=torch.float)\n",
        "output = model(x)"
      ],
      "metadata": {
        "id": "7k85L6eKJU9_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z=np.asarray(output.reshape(1024,3).to(\"cpu\").detach())"
      ],
      "metadata": {
        "id": "G5z5Cf1WJaXT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(z)"
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          "data": {
            "text/plain": [
              "1024"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('r2.npy', z)"
      ],
      "metadata": {
        "id": "T1i9NiWiO2ES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.conv1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6x1-bg_iPFnQ",
        "outputId": "de8d8406-2fec-474c-dbeb-f5ac045f2d55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Conv1d(3, 16, kernel_size=(3,), stride=(2,), padding=(1,))"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "Ai5TZ9MVb3pj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z=point_clouds[1000]"
      ],
      "metadata": {
        "id": "EDxqYt1bcZRj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(z, columns = ['A','B','C'])\n"
      ],
      "metadata": {
        "id": "bLNch1sCb0Dh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TaomjyYatxbu",
        "outputId": "e2da645f-c0c2-4aa4-9120-dfd3fc104f00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1024, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df"
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        "id": "bzsEjXyhcDXp",
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            "text/plain": [
              "                                         A  \\\n",
              "0     tensor(-0.0029, dtype=torch.float64)   \n",
              "1      tensor(0.0057, dtype=torch.float64)   \n",
              "2      tensor(0.0060, dtype=torch.float64)   \n",
              "3      tensor(0.0057, dtype=torch.float64)   \n",
              "4     tensor(-0.0119, dtype=torch.float64)   \n",
              "...                                    ...   \n",
              "1019   tensor(0.0298, dtype=torch.float64)   \n",
              "1020  tensor(-0.0194, dtype=torch.float64)   \n",
              "1021  tensor(-0.0444, dtype=torch.float64)   \n",
              "1022   tensor(0.0061, dtype=torch.float64)   \n",
              "1023  tensor(-0.0263, dtype=torch.float64)   \n",
              "\n",
              "                                         B  \\\n",
              "0      tensor(0.0230, dtype=torch.float64)   \n",
              "1      tensor(0.0037, dtype=torch.float64)   \n",
              "2     tensor(-0.1527, dtype=torch.float64)   \n",
              "3      tensor(0.1571, dtype=torch.float64)   \n",
              "4     tensor(-0.2798, dtype=torch.float64)   \n",
              "...                                    ...   \n",
              "1019  tensor(-0.0623, dtype=torch.float64)   \n",
              "1020  tensor(-0.0734, dtype=torch.float64)   \n",
              "1021  tensor(-0.0176, dtype=torch.float64)   \n",
              "1022  tensor(-0.1220, dtype=torch.float64)   \n",
              "1023   tensor(0.0566, dtype=torch.float64)   \n",
              "\n",
              "                                         C  \n",
              "0      tensor(0.0137, dtype=torch.float64)  \n",
              "1      tensor(0.4825, dtype=torch.float64)  \n",
              "2     tensor(-0.2984, dtype=torch.float64)  \n",
              "3     tensor(-0.2750, dtype=torch.float64)  \n",
              "4      tensor(0.0319, dtype=torch.float64)  \n",
              "...                                    ...  \n",
              "1019  tensor(-0.0842, dtype=torch.float64)  \n",
              "1020   tensor(0.0311, dtype=torch.float64)  \n",
              "1021   tensor(0.0612, dtype=torch.float64)  \n",
              "1022  tensor(-0.2405, dtype=torch.float64)  \n",
              "1023  tensor(-0.0473, dtype=torch.float64)  \n",
              "\n",
              "[1024 rows x 3 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tensor(-0.0029, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.0230, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.0137, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tensor(0.0057, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.0037, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.4825, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tensor(0.0060, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.1527, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.2984, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tensor(0.0057, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.1571, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.2750, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tensor(-0.0119, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.2798, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.0319, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019</th>\n",
              "      <td>tensor(0.0298, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.0623, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.0842, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1020</th>\n",
              "      <td>tensor(-0.0194, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.0734, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.0311, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>tensor(-0.0444, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.0176, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.0612, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1022</th>\n",
              "      <td>tensor(0.0061, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.1220, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.2405, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1023</th>\n",
              "      <td>tensor(-0.0263, dtype=torch.float64)</td>\n",
              "      <td>tensor(0.0566, dtype=torch.float64)</td>\n",
              "      <td>tensor(-0.0473, dtype=torch.float64)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024 rows × 3 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter_3d(df ,x='A', y='B', z='C')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "JVYIemfyYyv8",
        "outputId": "88112b81-e1e3-45bf-891e-bcdf47665cdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "data=point_clouds"
      ],
      "metadata": {
        "id": "gSYAtFuAbhQa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "ILzRhWWcdXwV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean = torch.mean(data, dim=1, keepdim=True)\n",
        "\n",
        "# Subtract the mean from each point\n",
        "normalized = data - mean\n"
      ],
      "metadata": {
        "id": "4wKQ3Dg3dDjX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(data)):\n",
        "  data[i]=torch.Tensor(data[i])"
      ],
      "metadata": {
        "id": "A7D7sr8cdaRK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(torch.Tensor(data[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ww0NVV0QKMUY",
        "outputId": "716262de-8065-48b1-c3e8-62dcbf009e10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Generate dummy data with the shape [batch_size, 1024, 3]\n",
        "data = torch.randn(32, 1024, 3)\n",
        "\n",
        "# Calculate the mean of the point coordinates\n",
        "mean = torch.mean(data, dim=1, keepdim=True)\n",
        "\n",
        "# Subtract the mean from each point\n",
        "output = data - mean\n",
        "\n",
        "# Print the shape of the output tensor\n",
        "print(output.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yy7NUOqKdShT",
        "outputId": "d8dc6d1c-3d5f-4a9c-ee76-70c843a7e030"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([32, 1024, 3])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GMyJ_C4XdSx-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#projection loss"
      ],
      "metadata": {
        "id": "VbIFJCG3yJgu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_projection(point_cloud):\n",
        "    # Generate the x, y, and z coordinates of the points\n",
        "    x = point_cloud[:, 0]\n",
        "    y = point_cloud[:, 1]\n",
        "    z = point_cloud[:, 2]\n",
        "\n",
        "    # Calculate the 2D projection of the points\n",
        "    projection = torch.stack([x, y], dim=1)\n",
        "\n",
        "    return projection\n",
        "\n",
        "# Generate a batch of dummy point cloud data with the shape [batch_size, num_points, 3]\n",
        "generated = torch.randn(32, 1024, 3)\n",
        "ground_truth = torch.randn(32, 1024, 3)\n",
        "\n",
        "# Create 2D projections of the point clouds\n",
        "generated_projection = create_projection(generated)\n",
        "ground_truth_projection = create_projection(ground_truth)\n",
        "\n",
        "# Calculate the loss between the generated and ground truth projections\n",
        "loss = torch.nn.MSELoss()(generated_projection, ground_truth_projection)\n",
        "\n",
        "# Print the calculated loss\n",
        "print(loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jl43-yMGyK_5",
        "outputId": "6fd4fe4f-3e88-4216-8730-7c8a88f721f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(2.0317)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the loss function\n",
        "loss_fn = torch.nn.MSELoss()\n",
        "\n",
        "# Define the optimizer\n",
        "optimizer = torch.optim.Adam(model.parameters())\n",
        "\n",
        "# Train the model for a number of epochs\n",
        "for epoch in range(num_epochs):\n",
        "    # Loop over the point cloud data\n",
        "    for point_cloud, ground_truth in data_loader:\n",
        "        # Create projections of the point cloud and ground truth\n",
        "        projections = []\n",
        "        for viewpoint in viewpoints:\n",
        "            projections.append(create_projection(point_cloud, viewpoint))\n",
        "            projections.append(create_projection(ground_truth, viewpoint))\n",
        "\n",
        "        # Concatenate the projections\n",
        "        projections = torch.cat(projections, dim=1)\n",
        "\n",
        "        # Move the data to the GPU, if available\n",
        "        if torch.cuda.is_available():\n",
        "            point_cloud = point_cloud.cuda()\n",
        "            ground_truth = ground_truth.cuda()\n",
        "            projections = projections.cuda()\n",
        "\n",
        "        # Pass the data through the model\n",
        "        decoded = model(point_cloud)\n",
        "\n",
        "        # Calculate the reconstruction loss\n",
        "        reconstruction_loss = loss_fn(decoded, ground_truth)\n",
        "\n",
        "        # Calculate the projection loss\n",
        "        projection_loss = loss_fn(projections, decoded)\n",
        "\n",
        "        # Calculate the total loss\n",
        "        loss = reconstruction_loss + projection_loss\n",
        "\n",
        "        # Backpropagate the error and update the model parameters\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    # Print the loss for the current epoch\n",
        "    print(f\"Epoch {epoch}: Loss = {loss.item()}\")"
      ],
      "metadata": {
        "id": "MrVwoiDVyMQ7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}