A_Convolutional_Neural_Network_(CNN)_for_image_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "authorship_tag": "ABX9TyO3wf2Y0Ou8CnN1TlQBxWX0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OneFineStarstuff/OneFineStardust/blob/main/A_Convolutional_Neural_Network_(CNN)_for_image_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SHl-EeBO3ZZX"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as datasets\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# Define CNN model\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        self.fc1 = nn.Linear(16 * 16 * 16, 10)  # Adjust dimensions for your dataset\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = x.view(-1, 16 * 16 * 16)\n",
        "        x = self.fc1(x)\n",
        "        return x\n",
        "\n",
        "# Load data, define criterion and optimizer\n",
        "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n",
        "trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
        "trainloader = DataLoader(trainset, batch_size=64, shuffle=True)\n",
        "\n",
        "model = CNN()\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(5):\n",
        "    for images, labels in trainloader:\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()"
      ]
    }
  ]
}