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BERT-ImageClassifier

Image Classification
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BERT-ImageClassifier
File size: 14,810 Bytes 
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1efa9df0-5f50-415c-b574-fae1236cb2b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision.datasets import MNIST, CIFAR10\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "\n",
    "dataset = CIFAR10(root='./data/', train=True, download=True, transform=\n",
    "transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomCrop(32, padding=4),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "]))\n",
    "batch_size = 192 # 96\n",
    "train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9254b0b-0b70-4d87-b9eb-62a37212ba5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 32, 32])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = next(iter(train_loader))[0]\n",
    "print(img[0].shape)\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.imshow(img[0].permute(1,2 , 0), interpolation='nearest')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}