notebooks-2/6-vgg.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   },
   "source": [
    "# Networks Using Blocks (VGG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "end_time": "2019-07-03T22:21:55.309728Z",
     "start_time": "2019-07-03T22:21:51.994279Z"
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   "source": [
    "import d2l\n",
    "from mxnet import gluon, np, npx\n",
    "from mxnet.gluon import nn\n",
    "npx.set_np()\n",
    "\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(\n",
    "    batch_size=128, resize=224)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "VGG block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "end_time": "2019-07-03T22:21:55.320094Z",
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   "source": [
    "def vgg_block(num_convs, num_channels):\n",
    "    blk = nn.Sequential()\n",
    "    for _ in range(num_convs):\n",
    "        blk.add(nn.Conv2D(num_channels, kernel_size=3,\n",
    "                          padding=1, activation='relu'))\n",
    "    blk.add(nn.MaxPool2D(pool_size=2, strides=2))\n",
    "    return blk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:21:55.336383Z",
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   "outputs": [],
   "source": [
    "conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))\n",
    "\n",
    "def vgg(conv_arch):\n",
    "    net = nn.Sequential()\n",
    "    # The convolutional layer part\n",
    "    for (num_convs, num_channels) in conv_arch:\n",
    "        net.add(vgg_block(num_convs, num_channels))\n",
    "    # The fully connected layer part\n",
    "    net.add(nn.Dense(4096, activation='relu'), nn.Dropout(0.5),\n",
    "            nn.Dense(4096, activation='relu'), nn.Dropout(0.5),\n",
    "            nn.Dense(10))\n",
    "    return net\n",
    "\n",
    "net = vgg(conv_arch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "Training with narrow version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:28:14.142303Z",
     "start_time": "2019-07-03T22:21:55.338438Z"
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     "text": [
      "loss 0.177, train acc 0.935, test acc 0.925\n",
      "9350.4 exampes/sec on gpu(0)\n"
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    "ratio = 4\n",
    "small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]\n",
    "net = vgg(small_conv_arch)\n",
    "d2l.train_ch5(net, train_iter, test_iter, num_epochs=10, lr=0.05)"
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