data_exploration.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract and transform the data\n",
    "train_set = torchvision.datasets.MNIST(\n",
    "    root='./data/',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform = transforms.ToTensor()\n",
    ")\n",
    "test_set = torchvision.datasets.MNIST(\n",
    "    root='./data/',\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform = transforms.ToTensor()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data\n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# obtain one batch of training images\n",
    "images, labels = next(iter(train_loader))\n",
    "images = images.numpy()\n",
    "\n",
    "# plot the images in the batch, along with corresponding labels\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "for i in np.arange(24):\n",
    "    ax = fig.add_subplot(2, 12, i+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(images[i]), cmap='gray')\n",
    "    ax.set_title(train_set.classes[labels[i].item()])\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('visualizations/train_samples.png', bbox_inches='tight')\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "len(train_set)  # length of the entire train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "len(test_set)  # length of entire test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['0 - zero',\n",
       " '1 - one',\n",
       " '2 - two',\n",
       " '3 - three',\n",
       " '4 - four',\n",
       " '5 - five',\n",
       " '6 - six',\n",
       " '7 - seven',\n",
       " '8 - eight',\n",
       " '9 - nine']"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# display the classes\n",
    "train_set.classes  # or test_set.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([5, 0, 4,  ..., 5, 6, 8])"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "train_set.targets  # tensor with correct class labels for train_set\n",
    "# >> train_set.targets.shape\n",
    "#    torch.Size([60000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([7, 2, 1,  ..., 4, 5, 6])"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "test_set.targets  # tensor with correct class labels for test_set\n",
    "# >> test_set.targets.shape\n",
    "#    torch.Size([10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([5923, 6742, 5958, 6131, 5842, 5421, 5918, 6265, 5851, 5949])"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "train_set.targets.bincount()  # frequency of each class in train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([ 980, 1135, 1032, 1010,  982,  892,  958, 1028,  974, 1009])"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "test_set.targets.bincount()  # frequency of each class in test set"
   ]
  }
 ]
}