notebooks-1/5-fashion-mnist.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Image Classification Data (Fashion-MNIST)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:01:31.154647Z",
     "start_time": "2019-07-03T22:01:29.325928Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "7"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import d2l\n",
    "from mxnet import gluon \n",
    "d2l.use_svg_display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Use the provided `FashionMNIST` class to download and load a dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:01:31.761507Z",
     "start_time": "2019-07-03T22:01:31.157805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_train = gluon.data.vision.FashionMNIST(train=True)\n",
    "mnist_test = gluon.data.vision.FashionMNIST(train=False)\n",
    "\n",
    "len(mnist_train), len(mnist_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Visualize the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:01:54.004645Z",
     "start_time": "2019-07-03T22:01:53.213167Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "25"
    }
   },
   "outputs": [
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      "text/plain": [
       "<Figure size 972x216 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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]\n",
    "X, y = mnist_train[:18]\n",
    "d2l.show_images(X.squeeze(axis=-1), 2, 9, titles=get_fashion_mnist_labels(y));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Read a mini-batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:01:32.689503Z",
     "start_time": "2019-07-03T22:01:32.582475Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "28"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 1, 28, 28) (256,)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 256\n",
    "transformer = gluon.data.vision.transforms.ToTensor()\n",
    "train_iter = gluon.data.DataLoader(mnist_train.transform_first(transformer),\n",
    "                                   batch_size, shuffle=True, num_workers=4)\n",
    "\n",
    "for X, y in train_iter:\n",
    "    print(X.shape, y.shape)\n",
    "    break"
   ]
  }
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