03.cnn.ipynb

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    "<a href=\"https://colab.research.google.com/github/guanidine/Machine-Learning-Basics/blob/main/03.cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "# Imports\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch import optim, nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "from tqdm.contrib import tenumerate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# Create CNN\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self, in_channels=1, num_classes=10):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "        self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))\n",
    "        self.fc1 = nn.Linear(16 * 7 * 7, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.pool(x)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = self.pool(x)\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        x = self.fc1(x)\n",
    "        return x"
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   "outputs": [
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     "text": [
      "torch.Size([64, 10])\n"
     ]
    }
   ],
   "source": [
    "# Test\n",
    "model = CNN()\n",
    "x = torch.randn(64, 1, 28, 28)\n",
    "print(model(x).shape)"
   ],
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   "source": [
    "# Set device\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ],
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   "cell_type": "code",
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   "source": [
    "# Hyperparameters\n",
    "in_channel = 1\n",
    "num_classes = 10\n",
    "learning_rate = 0.001\n",
    "batch_size = 64\n",
    "num_epochs = 5"
   ],
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   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# Load Data\n",
    "train_dateset = datasets.MNIST(root='dataset/', train=True, transform=transforms.ToTensor(), download=True)\n",
    "train_loader = DataLoader(dataset=train_dateset, batch_size=batch_size, shuffle=True)\n",
    "test_dateset = datasets.MNIST(root='dataset/', train=False, transform=transforms.ToTensor(), download=True)\n",
    "test_loader = DataLoader(dataset=test_dateset, batch_size=batch_size, shuffle=True)"
   ],
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   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# Initialize network\n",
    "model = CNN(in_channels=in_channel, num_classes=num_classes).to(device)"
   ],
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   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "# Loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)"
   ],
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    "# Train network\n",
    "for epoch in range(num_epochs):\n",
    "    for batch_idx, (data, targets) in tenumerate(train_loader):\n",
    "        # Get data to cuda if possible\n",
    "        data = data.to(device)  # 64 x 1 x 28 x 28\n",
    "        targets = targets.to(device)\n",
    "\n",
    "        # Forward\n",
    "        scores = model(data)\n",
    "        loss = criterion(scores, targets)\n",
    "\n",
    "        # Backward\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "\n",
    "        # Gradient descent or adam step\n",
    "        optimizer.step()"
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     "text": [
      "Checking accuracy on training data\n",
      "Got 59196 / 60000 with accuracy 98.66\n",
      "Checking accuracy on test data\n",
      "Got 9848 / 10000 with accuracy 98.48\n"
     ]
    },
    {
     "output_type": "execute_result",
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       "tensor(0.9848, device='cuda:0')"
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   ],
   "source": [
    "# Check accuracy on training & test\n",
    "def check_accuracy(loader, model):\n",
    "    if loader.dataset.train:\n",
    "        print(\"Checking accuracy on training data\")\n",
    "    else:\n",
    "        print(\"Checking accuracy on test data\")\n",
    "\n",
    "    num_correct = 0\n",
    "    num_samples = 0\n",
    "    model.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for x, y in loader:\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "\n",
    "            scores = model(x)\n",
    "            _, predictions = scores.max(1)\n",
    "            num_correct += (predictions == y).sum()\n",
    "            num_samples += predictions.size(0)\n",
    "\n",
    "        print(f\"Got {num_correct} / {num_samples} with accuracy {float(num_correct) / float(num_samples) * 100:.2f}\")\n",
    "\n",
    "    model.train()\n",
    "    return num_correct / num_samples\n",
    "\n",
    "\n",
    "check_accuracy(train_loader, model)\n",
    "check_accuracy(test_loader, model)"
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