code/20-填充和步幅.ipynb

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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### 填充和步幅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
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   "source": [
    "导入包，定义comp_conv2d函数(进行卷积操作, 输出后两维，便于观察高宽的维度变化)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "def comp_conv2d(conv2d, X):\n",
    "    X = X.reshape((1, 1) + X.shape) #X的维度之前加入批量大小数(batch_size)和输入通道数(channel_in)\n",
    "    Y = conv2d(X)                    \n",
    "    return Y.reshape(Y.shape[2:])  #去掉前面的两维后(原来四维) 进行输出\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### padding\n",
    "在所有侧边填充1个像素(padding=1(即(1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 8])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv2d = nn.Conv2d(1, 1, kernel_size=3, padding=1) #输入输出通道数为1, 卷积核大小3x3, 填充为1(上下左右各填充一行)\n",
    "X = torch.rand(size=(8, 8))         \n",
    "comp_conv2d(conv2d, X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "填充不同的高度和宽度(padding=(2,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 8])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv2d = nn.Conv2d(1, 1, kernel_size=(5, 3), padding=(2, 1))\n",
    "comp_conv2d(conv2d, X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### stride\n",
    "将高度和宽度的步幅设置为2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 4])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv2d = nn.Conv2d(1, 1, kernel_size=3, padding=1, stride=2)\n",
    "comp_conv2d(conv2d, X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "一个稍微复杂的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv2d = nn.Conv2d(1, 1, kernel_size=(3, 5), padding=(0, 1), stride=(3, 4))\n",
    "comp_conv2d(conv2d, X).shape"
   ]
  }
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