cnn_scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "fef524f43db6461a96fded3cbfe24e3b",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d83d0ed1748f47f48ef4958f0bff198c",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82b75994c7d14ad58f7fdb02d135177c",
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      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sharoz/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc30e2c90d6a41b9bb7d7fe475a8795e",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "23481fe4834545f99cc2312c1665455e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sharoz/anaconda3/lib/python3.9/site-packages/datasets/features/image.py:332: UserWarning: Downcasting array dtype uint8 to uint8 to be compatible with 'Pillow'\n",
      "  warnings.warn(f\"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'\")\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "066f8e3b370342b690b1715e0715e508",
       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"cifar10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datasets.dataset_dict.DatasetDict"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'test': Dataset({\n",
      "    features: ['img', 'label'],\n",
      "    num_rows: 10000\n",
      "}),\n",
      " 'train': Dataset({\n",
      "    features: ['img', 'label'],\n",
      "    num_rows: 50000\n",
      "})}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint \n",
    "\n",
    "pprint(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset['test']['img'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset['test']['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x7F9B0100F7C0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(dataset['test']['img'][0])\n",
    "type(dataset['test']['label'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from jinja2->torch) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from sympy->torch) (1.2.1)\n",
      "Installing collected packages: nvidia-nvjitlink-cu12, nvidia-cusparse-cu12, nvidia-cublas-cu12, triton, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusolver-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, torch\n",
      "  Attempting uninstall: torch\n",
      "    Found existing installation: torch 1.13.1\n",
      "    Uninstalling torch-1.13.1:\n",
      "      Successfully uninstalled torch-1.13.1\n",
      "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.3.52 nvidia-nvtx-cu12-12.1.105 torch-2.1.0 triton-2.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install torch --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'torch' has no attribute 'Tensor'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb Cell 11\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb#X11sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb#X11sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mnn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnn\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb#X11sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39moptim\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39moptim\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb#X11sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mnn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mfunctional\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mF\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mmodules\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m  \u001b[39m# noqa: F403\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mparameter\u001b[39;00m \u001b[39mimport\u001b[39;00m (\n\u001b[1;32m      3\u001b[0m     Parameter \u001b[39mas\u001b[39;00m Parameter,\n\u001b[1;32m      4\u001b[0m     UninitializedParameter \u001b[39mas\u001b[39;00m UninitializedParameter,\n\u001b[1;32m      5\u001b[0m     UninitializedBuffer \u001b[39mas\u001b[39;00m UninitializedBuffer,\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mparallel\u001b[39;00m \u001b[39mimport\u001b[39;00m DataParallel \u001b[39mas\u001b[39;00m DataParallel\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mmodule\u001b[39;00m \u001b[39mimport\u001b[39;00m Module\n\u001b[1;32m      2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mlinear\u001b[39;00m \u001b[39mimport\u001b[39;00m Identity, Linear, Bilinear, LazyLinear\n\u001b[1;32m      3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mconv\u001b[39;00m \u001b[39mimport\u001b[39;00m Conv1d, Conv2d, Conv3d, \\\n\u001b[1;32m      4\u001b[0m     ConvTranspose1d, ConvTranspose2d, ConvTranspose3d, \\\n\u001b[1;32m      5\u001b[0m     LazyConv1d, LazyConv2d, LazyConv3d, LazyConvTranspose1d, LazyConvTranspose2d, LazyConvTranspose3d\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py:8\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mweakref\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mparameter\u001b[39;00m \u001b[39mimport\u001b[39;00m Parameter\n\u001b[1;32m      9\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mhooks\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mhooks\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorch\u001b[39;00m \u001b[39mimport\u001b[39;00m Tensor, device, dtype\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/parameter.py:13\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39m__instancecheck__\u001b[39m(\u001b[39mself\u001b[39m, instance):\n\u001b[1;32m      9\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__instancecheck__\u001b[39m(instance) \u001b[39mor\u001b[39;00m (\n\u001b[1;32m     10\u001b[0m             \u001b[39misinstance\u001b[39m(instance, torch\u001b[39m.\u001b[39mTensor) \u001b[39mand\u001b[39;00m \u001b[39mgetattr\u001b[39m(instance, \u001b[39m'\u001b[39m\u001b[39m_is_param\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m))\n\u001b[0;32m---> 13\u001b[0m \u001b[39mclass\u001b[39;00m \u001b[39mParameter\u001b[39;00m(torch\u001b[39m.\u001b[39;49mTensor, metaclass\u001b[39m=\u001b[39m_ParameterMeta):\n\u001b[1;32m     14\u001b[0m \u001b[39m    \u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\"\"A kind of Tensor that is to be considered a module parameter.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \n\u001b[1;32m     16\u001b[0m \u001b[39m    Parameters are :class:`~torch.Tensor` subclasses, that have a\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[39m            :ref:`locally-disable-grad-doc` for more details. Default: `True`\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m     30\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39m__new__\u001b[39m(\u001b[39mcls\u001b[39m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, requires_grad\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'torch' has no attribute 'Tensor'"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "class CNNClassifier:\n",
    "    def __init__(self, input_channels, num_classes, conv1_out_channels, conv2_out_channels, fc1_out_features, fc2_out_features, learning_rate, batch_size, num_epochs):\n",
    "        self.input_channels = input_channels\n",
    "        self.num_classes = num_classes\n",
    "        self.conv1_out_channels = conv1_out_channels\n",
    "        self.conv2_out_channels = conv2_out_channels\n",
    "        self.fc1_out_features = fc1_out_features\n",
    "        self.fc2_out_features = fc2_out_features\n",
    "        self.learning_rate = learning_rate\n",
    "        self.batch_size = batch_size\n",
    "        self.num_epochs = num_epochs\n",
    "\n",
    "        # Define the CNN architecture\n",
    "        self.model = self.build_model()\n",
    "        self.loss_fn = nn.CrossEntropyLoss()\n",
    "        self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n",
    "\n",
    "    def build_model(self):\n",
    "        model = nn.Sequential(\n",
    "            nn.Conv2d(self.input_channels, self.conv1_out_channels, kernel_size=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.Conv2d(self.conv1_out_channels, self.conv2_out_channels, kernel_size=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(self.conv2_out_channels * 6 * 6, self.fc1_out_features),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(self.fc1_out_features, self.fc2_out_features),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(self.fc2_out_features, self.num_classes)\n",
    "        )\n",
    "        return model\n",
    "\n",
    "    def train(self, train_loader, val_loader):\n",
    "        train_losses = []\n",
    "        val_losses = []\n",
    "        train_accuracies = []\n",
    "        val_accuracies = []\n",
    "\n",
    "        for epoch in range(self.num_epochs):\n",
    "            self.model.train()\n",
    "            running_loss = 0.0\n",
    "            correct = 0\n",
    "            total = 0\n",
    "\n",
    "            for images, labels in train_loader:\n",
    "                self.optimizer.zero_grad()\n",
    "                outputs = self.model(images)\n",
    "                loss = self.loss_fn(outputs, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                running_loss += loss.item()\n",
    "                _, predicted = torch.max(outputs, 1)\n",
    "                total += labels.size(0)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "\n",
    "            train_loss = running_loss / len(train_loader)\n",
    "            train_accuracy = 100 * correct / total\n",
    "\n",
    "            self.model.eval()\n",
    "            val_loss = 0.0\n",
    "            correct = 0\n",
    "            total = 0\n",
    "\n",
    "            with torch.no_grad():\n",
    "                for images, labels in val_loader:\n",
    "                    outputs = self.model(images)\n",
    "                    loss = self.loss_fn(outputs, labels)\n",
    "                    val_loss += loss.item()\n",
    "                    _, predicted = torch.max(outputs, 1)\n",
    "                    total += labels.size(0)\n",
    "                    correct += (predicted == labels).sum().item()\n",
    "\n",
    "            val_loss /= len(val_loader)\n",
    "            val_accuracy = 100 * correct / total\n",
    "\n",
    "            print(f\"Epoch {epoch + 1}/{self.num_epochs}, \"\n",
    "                  f\"Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.2f}%, \"\n",
    "                  f\"Validation Loss: {val_loss:.4f}, Validation Accuracy: {val_accuracy:.2f}%\")\n",
    "\n",
    "            train_losses.append(train_loss)\n",
    "            val_losses.append(val_loss)\n",
    "            train_accuracies.append(train_accuracy)\n",
    "            val_accuracies.append(val_accuracy)\n",
    "\n",
    "        return train_losses, val_losses, train_accuracies, val_accuracies\n",
    "\n",
    "    def evaluate(self, test_loader):\n",
    "        self.model.eval()\n",
    "        correct = 0\n",
    "        total = 0\n",
    "\n",
    "        all_predictions = []\n",
    "        all_labels = []\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for images, labels in test_loader:\n",
    "                outputs = self.model(images)\n",
    "                _, predicted = torch.max(outputs, 1)\n",
    "                total += labels.size(0)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "\n",
    "                all_predictions.extend(predicted.cpu().numpy())\n",
    "                all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "        accuracy = 100 * correct / total\n",
    "\n",
    "        return accuracy, all_predictions, all_labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torchvision\n",
      "  Downloading torchvision-0.16.0-cp39-cp39-manylinux1_x86_64.whl (6.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 6.9 MB 276 kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: torch==2.1.0 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torchvision) (2.1.0)\n",
      "Requirement already satisfied: numpy in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torchvision) (1.24.3)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torchvision) (9.0.1)\n",
      "Requirement already satisfied: requests in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torchvision) (2.31.0)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (12.1.0.106)\n",
      "Requirement already satisfied: filelock in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (3.6.0)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (12.1.105)\n",
      "Requirement already satisfied: networkx in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (2.7.1)\n",
      "Requirement already satisfied: jinja2 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (3.1.2)\n",
      "Requirement already satisfied: fsspec in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (2023.9.2)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (12.1.105)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (2.18.1)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (12.1.105)\n",
      "Requirement already satisfied: triton==2.1.0 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (2.1.0)\n",
      "Requirement already satisfied: typing-extensions in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (4.5.0)\n",
      "Requirement already satisfied: sympy in /home/sharoz/anaconda3/lib/python3.9/site-packages (from torch==2.1.0->torchvision) (1.10.1)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.1.0->torchvision) (12.3.52)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from jinja2->torch==2.1.0->torchvision) (2.1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from requests->torchvision) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from requests->torchvision) (2023.7.22)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from requests->torchvision) (1.26.16)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from requests->torchvision) (2.10)\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/sharoz/anaconda3/lib/python3.9/site-packages (from sympy->torch==2.1.0->torchvision) (1.2.1)\n",
      "Installing collected packages: torchvision\n",
      "Successfully installed torchvision-0.16.0\n"
     ]
    }
   ],
   "source": [
    "!pip install torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'torch' has no attribute '_running_with_deploy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb Cell 13\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb#X15sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorchvision\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtransforms\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mtransforms\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb#X15sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mPIL\u001b[39;00m \u001b[39mimport\u001b[39;00m Image\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/sharoz/Documents/NeuralNetPlayground/cnn_scratch.ipynb#X15sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torchvision/__init__.py:6\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmodulefinder\u001b[39;00m \u001b[39mimport\u001b[39;00m Module\n\u001b[1;32m      5\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorchvision\u001b[39;00m \u001b[39mimport\u001b[39;00m _meta_registrations, datasets, io, models, ops, transforms, utils\n\u001b[1;32m      8\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mextension\u001b[39;00m \u001b[39mimport\u001b[39;00m _HAS_OPS\n\u001b[1;32m     10\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torchvision/_meta_registrations.py:4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mfunctools\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlibrary\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[39m# Ensure that torch.ops.torchvision is visible\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorchvision\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mextension\u001b[39;00m  \u001b[39m# noqa: F401\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/library.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_ops\u001b[39;00m \u001b[39mimport\u001b[39;00m OpOverload\n\u001b[1;32m      2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtyping\u001b[39;00m \u001b[39mimport\u001b[39;00m Set\n\u001b[1;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtraceback\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/_ops.py:10\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtyping\u001b[39;00m \u001b[39mimport\u001b[39;00m Any, Callable, Dict, List, Type, Union\n\u001b[1;32m      8\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39m_C\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorch\u001b[39;00m \u001b[39mimport\u001b[39;00m _utils_internal\n\u001b[1;32m     11\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39m_functorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyfunctorch\u001b[39;00m \u001b[39mimport\u001b[39;00m dispatch_functorch\n\u001b[1;32m     13\u001b[0m \u001b[39m# Query `hasattr` only once.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/_utils_internal.py:16\u001b[0m\n\u001b[1;32m      8\u001b[0m log \u001b[39m=\u001b[39m logging\u001b[39m.\u001b[39mgetLogger(\u001b[39m__name__\u001b[39m)\n\u001b[1;32m     11\u001b[0m \u001b[39m# this arbitrary-looking assortment of functionality is provided here\u001b[39;00m\n\u001b[1;32m     12\u001b[0m \u001b[39m# to have a central place for overrideable behavior. The motivating\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[39m# use is the FB build environment, where this source file is replaced\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[39m# by an equivalent.\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39;49m_running_with_deploy():\n\u001b[1;32m     17\u001b[0m     \u001b[39m# __file__ is meaningless in the context of frozen torch used in torch deploy.\u001b[39;00m\n\u001b[1;32m     18\u001b[0m     \u001b[39m# setting empty torch_parent should allow below functions to operate without crashing,\u001b[39;00m\n\u001b[1;32m     19\u001b[0m     \u001b[39m# but it's unclear if there is a valid use case for them in the context of deploy.\u001b[39;00m\n\u001b[1;32m     20\u001b[0m     torch_parent \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     21\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'torch' has no attribute '_running_with_deploy'"
     ]
    }
   ],
   "source": [
    "import torchvision.transforms as transforms\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "class CIFARDataset(Dataset):\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        self.transform = transforms.ToTensor()\n",
    "\n",
    "    def __len__(self):\n",
    "       return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img = Image.fromarray(np.array(self.data['img'][idx])) # convert to PIL image\n",
    "        img_t = self.transform(img) # apply transform\n",
    "        label = self.data['label'][idx]\n",
    "        return img_t, label\n",
    "\n",
    "train_dataset = CIFARDataset(dataset['train'])\n",
    "test_dataset = CIFARDataset(dataset['test'])\n",
    "\n",
    "batch_size = 50\n",
    "\n",
    "#initialize the dataloaders\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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