classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a3607bd-fb87-4578-8537-b4396156752d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importamos las librerias necesarioas, para crear nuewtra red neuronal, transformar las imagenes a una matriz, y descargar el dataset\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7826616b-6582-48e6-9333-163003b45224",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fae0229-4bcc-4da0-8bf7-03e58f05c052",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████| 170498071/170498071 [00:10<00:00, 17017885.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/cifar-10-python.tar.gz to ./data\n"
     ]
    }
   ],
   "source": [
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
    "                                          shuffle=True, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "409d90f9-50f8-4ffe-add8-5f928f904297",
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2bb465fb-8b9e-4179-9c7b-2198d6b89604",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RedClasificadora(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(RedClasificadora, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)\n",
    "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
    "        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
    "        self.fc1 = nn.Linear(64 * 8 * 8, 512) \n",
    "        self.fc2 = nn.Linear(512, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self,x):\n",
    "        x = self.pool(self.relu(self.conv1(x)))\n",
    "        x = self.pool(self.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 64 * 8 * 8) \n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        x = self.softmax(x)\n",
    "        return x\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d84d08b-851a-49a5-9f87-77c0f530dea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = RedClasificadora()\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ee42c74-d2f5-4b96-aa89-bebae9474e3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Epoch [1/10], Step [100], Loss: 2.3027\n",
      "Epoch [1/10], Step [200], Loss: 2.3024\n",
      "Epoch [1/10], Step [300], Loss: 2.3021\n",
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      "Epoch [1/10], Step [1100], Loss: 2.2980\n",
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      "Epoch [10/10], Step [11900], Loss: 1.7204\n",
      "Epoch [10/10], Step [12000], Loss: 1.7447\n",
      "Epoch [10/10], Step [12100], Loss: 1.7286\n",
      "Epoch [10/10], Step [12200], Loss: 1.7503\n",
      "Epoch [10/10], Step [12300], Loss: 1.7108\n",
      "Epoch [10/10], Step [12400], Loss: 1.7636\n",
      "Epoch [10/10], Step [12500], Loss: 1.7643\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model.train()\n",
    "\n",
    "num_epochs = 10\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        outputs = model(inputs)\n",
    "\n",
    "        loss = criterion(outputs, labels)\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        if i % 100 == 99: \n",
    "            print(f'Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}], Loss: {running_loss / 100:.4f}')\n",
    "            running_loss = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0984b68f-5449-41b9-adea-4e34f5dbe21b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
    "                                         shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b405b45-7231-4121-9a10-cb0ad5a06806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 66.64%\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images,labels = data\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data,1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "print(f'Accuracy of the network on the 10000 test images: {100 * correct / total:.2f}%')\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f01ca1d6-22bb-4ceb-a41d-bb70b22806d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4c45612d-bf23-49ee-a974-ed324bef49cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted class is: dog\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from io import BytesIO\n",
    "from PIL import Image\n",
    "import torchvision.transforms as transforms\n",
    "import torch\n",
    "\n",
    "def load_image(image_path):\n",
    "    if image_path.startswith('http://') or image_path.startswith('https://'):\n",
    "        response = requests.get(image_path)\n",
    "        image = Image.open(BytesIO(response.content))\n",
    "    else:\n",
    "        image = Image.open(image_path)\n",
    "\n",
    "    transform = transforms.Compose(\n",
    "        [transforms.Resize((32, 32)),\n",
    "         transforms.ToTensor(),\n",
    "         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "    image = transform(image)\n",
    "    image = image.unsqueeze(0)\n",
    "    return image\n",
    "\n",
    "def predict(image_path):\n",
    "    image = load_image(image_path)\n",
    "    with torch.no_grad():\n",
    "        out = model(image)\n",
    "        _, predicted = torch.max(out.data, 1)\n",
    "    return predicted.item()\n",
    "\n",
    "\n",
    "image_path = 'https://www.princeton.edu/sites/default/files/styles/half_16_9_1440/public/images/2022/02/KOA_Nassau_2697x1517.jpg?itok=Nd5wwOFu'\n",
    "predicted_class = predict(image_path)\n",
    "\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "print(f'clase predicha: {classes[predicted_class]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f9831c-2ef9-4868-8648-b0d121e41a06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd08cd88-bd98-4758-ad42-d7a8c3f70670",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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