notebooks-2/8-resnet.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Residual Networks (ResNet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:37:50.067206Z",
     "start_time": "2019-07-03T22:37:46.213755Z"
    }
   },
   "outputs": [],
   "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(batch_size=256, resize=96)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Residual block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:37:50.083212Z",
     "start_time": "2019-07-03T22:37:50.070948Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "1"
    }
   },
   "outputs": [],
   "source": [
    "class Residual(nn.Block):\n",
    "    def __init__(self, num_channels, use_1x1conv=False, strides=1, **kwargs):\n",
    "        super(Residual, self).__init__(**kwargs)\n",
    "        self.conv1 = nn.Conv2D(num_channels, kernel_size=3, padding=1, strides=strides)\n",
    "        self.conv2 = nn.Conv2D(num_channels, kernel_size=3, padding=1)\n",
    "        self.conv3 = None\n",
    "        if use_1x1conv:\n",
    "            self.conv3 = nn.Conv2D(num_channels, kernel_size=1, strides=strides)\n",
    "        self.bn1 = nn.BatchNorm()\n",
    "        self.bn2 = nn.BatchNorm()\n",
    "\n",
    "    def forward(self, X):\n",
    "        Y = npx.relu(self.bn1(self.conv1(X)))\n",
    "        Y = self.bn2(self.conv2(Y))\n",
    "        if self.conv3:\n",
    "            X = self.conv3(X)\n",
    "        return npx.relu(Y + X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "A situation where the input and output are of the same shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:37:50.119721Z",
     "start_time": "2019-07-03T22:37:50.085475Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "2"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3, 6, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blk = Residual(3)\n",
    "blk.initialize()\n",
    "X = np.random.uniform(size=(4, 3, 6, 6))\n",
    "blk(X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Halve the output height and width while increasing the number of output channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:37:50.171045Z",
     "start_time": "2019-07-03T22:37:50.122531Z"
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    "attributes": {
     "classes": [],
     "id": "",
     "n": "3"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 6, 3, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blk = Residual(6, use_1x1conv=True, strides=2)\n",
    "blk.initialize()\n",
    "blk(X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The ResNet block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:37:50.187029Z",
     "start_time": "2019-07-03T22:37:50.175512Z"
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   "outputs": [],
   "source": [
    "def resnet_block(num_channels, num_residuals, first_block=False):\n",
    "    blk = nn.Sequential()\n",
    "    for i in range(num_residuals):\n",
    "        if i == 0 and not first_block:\n",
    "            blk.add(Residual(num_channels, use_1x1conv=True, strides=2))\n",
    "        else:\n",
    "            blk.add(Residual(num_channels))\n",
    "    return blk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:37:50.213229Z",
     "start_time": "2019-07-03T22:37:50.194294Z"
    }
   },
   "outputs": [],
   "source": [
    "net = nn.Sequential()\n",
    "net.add(nn.Conv2D(64, kernel_size=7, strides=2, padding=3),\n",
    "        nn.BatchNorm(), nn.Activation('relu'),\n",
    "        nn.MaxPool2D(pool_size=3, strides=2, padding=1),\n",
    "        resnet_block(64, 2, first_block=True),\n",
    "        resnet_block(128, 2),\n",
    "        resnet_block(256, 2),\n",
    "        resnet_block(512, 2),\n",
    "        nn.GlobalAvgPool2D(), \n",
    "        nn.Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:40:13.823984Z",
     "start_time": "2019-07-03T22:37:50.216572Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.011, train acc 0.997, test acc 0.894\n",
      "12520.9 exampes/sec on gpu(0)\n"
     ]
    },
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