{
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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 池化层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:14:08.041117Z",
     "start_time": "2019-07-03T22:14:06.519244Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "3"
    }
   },
   "outputs": [],
   "source": [
    "from mxnet import np, npx\n",
    "from mxnet.gluon import nn\n",
    "npx.set_np()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "实现二维池化层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:14:08.174881Z",
     "start_time": "2019-07-03T22:14:08.043310Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "4"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4., 5.],\n",
       "       [7., 8.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pool2d(X, pool_size, mode='max'):\n",
    "    p_h, p_w = pool_size\n",
    "    Y = np.zeros((X.shape[0] - p_h + 1, X.shape[1] - p_w + 1))\n",
    "    for i in range(Y.shape[0]):\n",
    "        for j in range(Y.shape[1]):\n",
    "            if mode == 'max':\n",
    "                Y[i, j] = np.max(X[i: i + p_h, j: j + p_w])\n",
    "            elif mode == 'avg':\n",
    "                Y[i, j] = X[i: i + p_h, j: j + p_w].mean()\n",
    "    return Y\n",
    "\n",
    "X = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])\n",
    "pool2d(X, (2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "默认填充和步幅。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:14:08.188017Z",
     "start_time": "2019-07-03T22:14:08.176923Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "15"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[ 0.  1.  2.  3.]\n",
      "   [ 4.  5.  6.  7.]\n",
      "   [ 8.  9. 10. 11.]\n",
      "   [12. 13. 14. 15.]]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[[10.]]]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.arange(16).reshape((1, 1, 4, 4))\n",
    "print(X)\n",
    "\n",
    "pool2d = nn.MaxPool2D(3)\n",
    "pool2d(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "指定填充和步幅。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:14:08.195734Z",
     "start_time": "2019-07-03T22:14:08.189887Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "7"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[ 0.  1.  2.  3.]\n",
      "   [ 4.  5.  6.  7.]\n",
      "   [ 8.  9. 10. 11.]\n",
      "   [12. 13. 14. 15.]]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[[ 5.,  7.],\n",
       "         [13., 15.]]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X)\n",
    "pool2d = nn.MaxPool2D(3, padding=1, strides=2)\n",
    "pool2d(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "多通道。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:14:08.217621Z",
     "start_time": "2019-07-03T22:14:08.197280Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "9"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[ 0.  1.  2.  3.]\n",
      "   [ 4.  5.  6.  7.]\n",
      "   [ 8.  9. 10. 11.]\n",
      "   [12. 13. 14. 15.]]\n",
      "\n",
      "  [[ 1.  2.  3.  4.]\n",
      "   [ 5.  6.  7.  8.]\n",
      "   [ 9. 10. 11. 12.]\n",
      "   [13. 14. 15. 16.]]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[[ 5.,  7.],\n",
       "         [13., 15.]],\n",
       "\n",
       "        [[ 6.,  8.],\n",
       "         [14., 16.]]]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.concatenate((X, X + 1), axis=1)\n",
    "print(X)\n",
    "pool2d = nn.MaxPool2D(3, padding=1, strides=2)\n",
    "pool2d(X)"
   ]
  }
 ],
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  "toc": {
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   "title_cell": "Table of Contents",
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