3.5.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import d2lzh as d2l\n",
    "from torch.utils import data\n",
    "import sys\n",
    "import time\n",
    "from torchvision.datasets import FashionMNIST\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from torchvision import transforms\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train = FashionMNIST('~/datasets', train=True, transform=transforms.ToTensor())\n",
    "mnist_test = FashionMNIST('~/datasets', train=False, transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mnist_train), len(mnist_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature, label = mnist_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1, 28, 28]), 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.shape, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(i)] for i in labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_fashion_mnist(images, labels):\n",
    "    d2l.use_svg_display()\n",
    "    _, figs = d2l.plt.subplots(1, len(images), figsize=(12, 12))\n",
    "    for f, img, lbl in zip(figs, images, labels):\n",
    "        f.imshow(img.view(28, 28).numpy())\n",
    "        f.set_title(lbl)\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 864x864 with 10 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
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   ],
   "source": [
    "X, y = [], []\n",
    "for i in range(10):\n",
    "    X.append(mnist_train[i][0])\n",
    "    y.append(mnist_train[i][1])\n",
    "show_fashion_mnist(X, get_fashion_mnist_labels(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "if sys.platform.startswith('win'):\n",
    "    num_workers = 0\n",
    "else:\n",
    "    num_workers = 4\n",
    "    \n",
    "train_iter = data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "test_iter = data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.93 sec'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = time.time()\n",
    "for X, y in train_iter:\n",
    "    continue\n",
    "'%.2f sec' % (time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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