CIFAR10-Convo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in ./.venv/lib/python3.11/site-packages (23.3.1)\n",
      "Requirement already satisfied: tqdm in ./.venv/lib/python3.11/site-packages (4.66.1)\n",
      "Requirement already satisfied: numpy in ./.venv/lib/python3.11/site-packages (1.26.1)\n",
      "Requirement already satisfied: torch in ./.venv/lib/python3.11/site-packages (2.1.0)\n",
      "Requirement already satisfied: torchvision in ./.venv/lib/python3.11/site-packages (0.16.0)\n",
      "Requirement already satisfied: matplotlib in ./.venv/lib/python3.11/site-packages (3.8.1)\n",
      "Requirement already satisfied: scikit-learn in ./.venv/lib/python3.11/site-packages (1.3.2)\n",
      "Collecting torchinfo\n",
      "  Downloading torchinfo-1.8.0-py3-none-any.whl.metadata (21 kB)\n",
      "Requirement already satisfied: filelock in ./.venv/lib/python3.11/site-packages (from torch) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions in ./.venv/lib/python3.11/site-packages (from torch) (4.8.0)\n",
      "Requirement already satisfied: sympy in ./.venv/lib/python3.11/site-packages (from torch) (1.12)\n",
      "Requirement already satisfied: networkx in ./.venv/lib/python3.11/site-packages (from torch) (3.2.1)\n",
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      "Requirement already satisfied: fsspec in ./.venv/lib/python3.11/site-packages (from torch) (2023.10.0)\n",
      "Requirement already satisfied: requests in ./.venv/lib/python3.11/site-packages (from torchvision) (2.31.0)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in ./.venv/lib/python3.11/site-packages (from torchvision) (10.1.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in ./.venv/lib/python3.11/site-packages (from matplotlib) (1.1.1)\n",
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      "Requirement already satisfied: python-dateutil>=2.7 in ./.venv/lib/python3.11/site-packages (from matplotlib) (2.8.2)\n",
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      "Requirement already satisfied: urllib3<3,>=1.21.1 in ./.venv/lib/python3.11/site-packages (from requests->torchvision) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in ./.venv/lib/python3.11/site-packages (from requests->torchvision) (2023.7.22)\n",
      "Requirement already satisfied: mpmath>=0.19 in ./.venv/lib/python3.11/site-packages (from sympy->torch) (1.3.0)\n",
      "Downloading torchinfo-1.8.0-py3-none-any.whl (23 kB)\n",
      "Installing collected packages: torchinfo\n",
      "Successfully installed torchinfo-1.8.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pip tqdm numpy torch torchvision matplotlib scikit-learn torchinfo --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torch.utils.data import DataLoader\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchinfo import summary\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import random as rd\n",
    "from tqdm import tqdm\n",
    "\n",
    "# if torch.backends.mps.is_available():\n",
    "#     torch.set_default_device(\"mps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "t = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(\n",
    "            mean=(0.49186864, 0.48265335, 0.44717798),\n",
    "            std=(0.24697131, 0.24338901, 0.26159254),\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "\n",
    "train_dataset = datasets.CIFAR10(root=\"./data\", download=True, train=True, transform=t)\n",
    "test_dataset = datasets.CIFAR10(root=\"./data\", download=True, train=False, transform=t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset + test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(dataset=train_dataset, batch_size=4, shuffle=True)\n",
    "test_loader = DataLoader(dataset=test_dataset, batch_size=4, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird  plane deer  car  \n"
     ]
    }
   ],
   "source": [
    "# functions to show an image\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(train_loader)\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
    "# print labels\n",
    "print(' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 torch.Size([4, 3, 32, 32]) torch.Size([4])\n"
     ]
    }
   ],
   "source": [
    "for i, (X_train_tensor, y_train_tensor) in enumerate(train_loader):\n",
    "    print(i, X_train_tensor.shape, y_train_tensor.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the MLP architecture\n",
    "class CIFAR_Convo(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CIFAR_Convo, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(\n",
    "            in_channels=3,\n",
    "            out_channels=6,\n",
    "            kernel_size=5,\n",
    "            stride=(1, 1),\n",
    "            padding=(1, 1),\n",
    "        )\n",
    "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            in_channels=6,\n",
    "            out_channels=16,\n",
    "            kernel_size=5,\n",
    "            stride=(1, 1),\n",
    "            padding=(1, 1),\n",
    "        )\n",
    "        self.fc_lyr1 = nn.Linear(16 * 6 * 6, 128)\n",
    "        self.fc_lyr2 = nn.Linear(128, 64)\n",
    "        self.fc_lyr3 = nn.Linear(64, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.conv1(x))\n",
    "        out = self.pool(out)\n",
    "        out = F.relu(self.conv2(out))\n",
    "        out = self.pool(out)\n",
    "        # out = out.reshape(out.size(0), -1)\n",
    "        out = torch.flatten(out, 1)\n",
    "        out = F.relu(self.fc_lyr1(out))\n",
    "        out = F.relu(self.fc_lyr2(out))\n",
    "        out = self.fc_lyr3(out)\n",
    "\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of the MLP model\n",
    "model = CIFAR_Convo()\n",
    "# model = torch.load(\"./models/cifar10_1699471134_99E_model.pt\")\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=5e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "CIFAR_Convo                              [4, 10]                   --\n",
       "├─Conv2d: 1-1                            [4, 6, 30, 30]            456\n",
       "├─MaxPool2d: 1-2                         [4, 6, 15, 15]            --\n",
       "├─Conv2d: 1-3                            [4, 16, 13, 13]           2,416\n",
       "├─MaxPool2d: 1-4                         [4, 16, 6, 6]             --\n",
       "├─Linear: 1-5                            [4, 128]                  73,856\n",
       "├─Linear: 1-6                            [4, 64]                   8,256\n",
       "├─Linear: 1-7                            [4, 10]                   650\n",
       "==========================================================================================\n",
       "Total params: 85,634\n",
       "Trainable params: 85,634\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 3.61\n",
       "==========================================================================================\n",
       "Input size (MB): 0.05\n",
       "Forward/backward pass size (MB): 0.27\n",
       "Params size (MB): 0.34\n",
       "Estimated Total Size (MB): 0.66\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(model, input_size=(4, 3, 32, 32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_ar = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [01:13<00:00, 36.90s/it, Loss: 1.119035005569458]  \n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "model.train()\n",
    "epoch = 2\n",
    "\n",
    "for epoch in (pbar:= tqdm(range(epoch))):\n",
    "    for i, (X_train_tensor, y_train_tensor) in enumerate(train_loader):\n",
    "        # Forward pass: Get predictions\n",
    "        y_pred = model(X_train_tensor)\n",
    "        # Compute Loss\n",
    "        loss = criterion(y_pred.squeeze(), y_train_tensor)\n",
    "        loss_ar.append(loss.item())\n",
    "\n",
    "        pbar.set_postfix_str(f\"Loss: {loss.item()}\")\n",
    "        \n",
    "        # Zero grad\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Backward pass\n",
    "        loss.backward()\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the loss history\n",
    "plt.plot(loss_ar)\n",
    "plt.title('Loss History')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, 'model.pt')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run a test using predefined values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('cat', 21.33033275604248),\n",
       " ('ship', 15.16420841217041),\n",
       " ('car', 12.748712301254272),\n",
       " ('frog', 12.246301025152206),\n",
       " ('truck', 9.90241914987564),\n",
       " ('plane', 8.464040607213974),\n",
       " ('dog', 7.756631821393967),\n",
       " ('horse', 4.847782850265503),\n",
       " ('deer', 3.830518200993538),\n",
       " ('bird', 3.7090495228767395)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = test_dataset\n",
    "X_input_indx = rd.randint(0, len(d))\n",
    "\n",
    "image, label = d[X_input_indx]\n",
    "imshow(image) # from previous cell\n",
    "print(classes[label]) # from previous cell\n",
    "\n",
    "X_final = image.unsqueeze(0)\n",
    "\n",
    "model.eval()\n",
    "output = (model(X_final) ** 2).sqrt().squeeze()\n",
    "\n",
    "o = {}\n",
    "for i in range(10):\n",
    "    o[classes[i]] = (output / output.sum())[i].item() * 100\n",
    "\n",
    "sorted(o.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 6246 / 10000 with accuracy 62.46\n",
      "Got 45133 / 50000 with accuracy 90.27\n"
     ]
    }
   ],
   "source": [
    "def check_accuracy(loader, model):\n",
    "    num_correct = 0\n",
    "    num_samples = 0\n",
    "    model.eval()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for x, y in loader:\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",
    "check_accuracy(loader=train_loader, model=model)\n",
    "check_accuracy(loader=test_loader, model=model)"
   ]
  }
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