notebooks-2/5-alexnet.ipynb

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   "metadata": {
    "slideshow": {
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   },
   "source": [
    "# Deep Convolutional Neural Networks (AlexNet)"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:16:27.779005Z",
     "start_time": "2019-07-03T22:16:24.966374Z"
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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"
    }
   },
   "source": [
    "The model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:16:27.796186Z",
     "start_time": "2019-07-03T22:16:27.782555Z"
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   "source": [
    "net = nn.Sequential()\n",
    "net.add(# A larger 11 x 11 window to capture large objects.\n",
    "        nn.Conv2D(96, kernel_size=11, strides=4, activation='relu'),\n",
    "        nn.MaxPool2D(pool_size=3, strides=2),\n",
    "        # Make the convolution window smaller, and increase the\n",
    "        # number of output channels\n",
    "        nn.Conv2D(256, kernel_size=5, padding=2, activation='relu'),\n",
    "        nn.MaxPool2D(pool_size=3, strides=2),\n",
    "        # Use three successive convolutional layers and a smaller convolution\n",
    "        # window. \n",
    "        nn.Conv2D(384, kernel_size=3, padding=1, activation='relu'),\n",
    "        nn.Conv2D(384, kernel_size=3, padding=1, activation='relu'),\n",
    "        nn.Conv2D(256, kernel_size=3, padding=1, activation='relu'),\n",
    "        nn.MaxPool2D(pool_size=3, strides=2),\n",
    "        # Several times larger fully connected layers with dropout\n",
    "        nn.Dense(4096, activation=\"relu\"), nn.Dropout(0.5),\n",
    "        nn.Dense(4096, activation=\"relu\"), nn.Dropout(0.5),\n",
    "        nn.Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Check the outputs layer by layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:16:27.847831Z",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv0 output shape:\t (1, 96, 54, 54)\n",
      "pool0 output shape:\t (1, 96, 26, 26)\n",
      "conv1 output shape:\t (1, 256, 26, 26)\n",
      "pool1 output shape:\t (1, 256, 12, 12)\n",
      "conv2 output shape:\t (1, 384, 12, 12)\n",
      "conv3 output shape:\t (1, 384, 12, 12)\n",
      "conv4 output shape:\t (1, 256, 12, 12)\n",
      "pool2 output shape:\t (1, 256, 5, 5)\n",
      "dense0 output shape:\t (1, 4096)\n",
      "dropout0 output shape:\t (1, 4096)\n",
      "dense1 output shape:\t (1, 4096)\n",
      "dropout1 output shape:\t (1, 4096)\n",
      "dense2 output shape:\t (1, 10)\n"
     ]
    }
   ],
   "source": [
    "X = np.random.uniform(size=(1, 1, 224, 224))\n",
    "net.initialize()\n",
    "for layer in net:\n",
    "    X = layer(X)\n",
    "    print(layer.name, 'output shape:\\t', X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:20:58.845230Z",
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     "n": "5"
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     "text": [
      "loss 0.329, train acc 0.880, test acc 0.891\n",
      "14578.6 exampes/sec on gpu(0)\n"
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