06_Autoencoder.ipynb

{
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  "metadata": {
    "colab": {
      "name": "06_Autoencoder.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNkwE7UxIeORjrHb0VPVdNK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OUCTheoryGroup/colab_demo/blob/master/06_Autoencoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FX3VZGDlpgQx",
        "colab_type": "text"
      },
      "source": [
        "## 自编码器 Autoencoder\n",
        "\n",
        "大家都学习过理论知识，应该对于自编码器比较了解了\n",
        "\n",
        "下面我们还是结合代码来学习。首先引入基本的库，然后定义了两个后面要使用的函数"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uJgIKftoQFC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import some libraries\n",
        "import torch\n",
        "import torchvision\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import transforms\n",
        "from torchvision.datasets import MNIST\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# 把输入数据由向量转化为图像\n",
        "def to_img(x):\n",
        "    x = 0.5 * (x + 1)\n",
        "    x = x.view(x.size(0), 28, 28)\n",
        "    return x\n",
        "\n",
        "# 显示图像\n",
        "def display_images(in_, out, n=1):\n",
        "    for N in range(n):\n",
        "        if in_ is not None:\n",
        "            in_pic = to_img(in_.cpu().data)\n",
        "            plt.figure(figsize=(6, 2))\n",
        "            for i in range(4):\n",
        "                plt.subplot(1,4,i+1)\n",
        "                plt.imshow(in_pic[i+4*N], 'gray')\n",
        "                plt.axis('off')\n",
        "        out_pic = to_img(out.cpu().data)\n",
        "        plt.figure(figsize=(6, 2))\n",
        "        for i in range(4):\n",
        "            plt.subplot(1,4,i+1)\n",
        "            plt.imshow(out_pic[i+4*N], 'gray')\n",
        "            plt.axis('off')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mr-lr5SFtAcq",
        "colab_type": "text"
      },
      "source": [
        "## 1. 加载数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIqpDH75ocz4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 每次加载数据的 batch_size，即每次加载256张图像\n",
        "batch_size = 256\n",
        "\n",
        "# 使用GPU训练，可以在菜单 \"代码执行工具\" -> \"更改运行时类型\" 里进行设置\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "img_transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5,), (0.5,))\n",
        "])\n",
        "\n",
        "dataset = MNIST('./data', transform=img_transform, download=True)\n",
        "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PCJkuZBstFIx",
        "colab_type": "text"
      },
      "source": [
        "## 2. 标准自编码器\n",
        "\n",
        "下面代码非常简单，可以看到 Standard AE 输入是 784 维向量，然后中间层有30个节点，然一输入 784 维向量。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bE8ymMv5oq_0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d = 30 \n",
        "\n",
        "class Autoencoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Linear(28 * 28, d),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.Linear(d, 28 * 28),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.encoder(x)\n",
        "        x = self.decoder(x)\n",
        "        return x\n",
        "\n",
        "#模型放到GPU上，同时定义损失函数和优化器\n",
        "model = Autoencoder().to(device)\n",
        "criterion = nn.MSELoss()\n",
        "learning_rate = 1e-3\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JsSupSBnuJ-U",
        "colab_type": "text"
      },
      "source": [
        "下面我们进入模型训练步骤，这里我们跑 3 个 epoch，每轮训练结束，输出一下原图像，以及重建结果。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAHSuwkHo34_",
        "colab_type": "code",
        "outputId": "efdefd11-8e62-4217-a41c-93092cf2834d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "num_epochs = 3\n",
        "for epoch in range(num_epochs):\n",
        "    for data in dataloader:\n",
        "        img, _ = data\n",
        "        img = img.to(device)\n",
        "        img.requires_grad_()\n",
        "        img = img.view(img.size(0), -1)\n",
        "        # ===================forward=====================\n",
        "        output = model(img)\n",
        "        loss = criterion(output, img.data)\n",
        "        # ===================backward====================\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    print(f'epoch [{epoch + 1}/{num_epochs}], loss:{loss.item():.4f}')\n",
        "    display_images(img, output) "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch [1/3], loss:0.2129\n",
            "epoch [2/3], loss:0.1656\n",
            "epoch [3/3], loss:0.1378\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y48gjOlaRbVaSHdG9p8hYhHWHXGYWfi9MzOyIe26//fZW5n780EMPRcQgE8IKchDRgTas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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABXCAYAAACnZJZlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npEZXRCQhNboiIgmp0RURSUiNrohIQv8CrE3ZXbumIOYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pNGmh5TEpR0+KG03JbSi/lDiEsY12hpHOsY82L1B+7g8dGxwfmxe45JWONo8/TUKWZcqZ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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABXCAYAAACnZJZlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAALkElEQVR4nO3dV4xV1RfH8e+IUiw4AiIW0DiiGAmC\nJbFhicbB2B8QsKKiDyYGDXbsNWoUFVuihhKiaMQG9oglalARUEERBCVgiyUW7Oj48M9ad90/4+XW\nfcv8Pk8ry7n3HI/jnn12Wbupra0NERFJY71q34CISEeiRldEJCE1uiIiCanRFRFJSI2uiEhCanRF\nRBJaP9c/bGpq0nqydWhra2sq9rN6vutW7PPVs103PdvKyfVs1dMVEUlIja6ISEJqdEVEElKjKyKS\nUM6JNBGRQm2yySYA/Pzzz8mvfcstt3h8zjnnALDvvvt6bs6cOcnv6f+ppysikpAaXRGRhDS8ICJl\nNXLkSCB7eGH69OlJrn344Yd7XKtla9XTFRFJqCnXX4Na2XkyePBgAObPn++5df0Va2rKbAixn500\naZLnJkyYAMDChQtLujftSMttu+2283jGjBkef/PNNwAMGzYs5+e1a6pyGuXZjho1yuNp06Z5/MEH\nHwAwdOhQz6Wa3NOONBGRGqFGV0QkobqaSPvnn3/y/tnPPvvM45dffhmAQw45xHMHHXQQANtvv315\nbk6y9OrVC4BXX311rRzA9ddfn/yepDH16dOn3bz97lVjvXAu6umKiCSkRldEJKG6GF746aefAJg1\na5bnBg4c6LHNkK9YscJzRx11lMe2QmHmzJmea21tBeDUU0/1XFzdIIWz7Z8ATz75JABbb7215666\n6iqP40oGkWLst99+AFx55ZWes7YC4K677kp9S3lRT1dEJKG66OkuX74cgKOPPtpzLS0tHm+55ZYA\nfPXVV5775JNPPO7evTsAzc3NnuvUqRMAPXv2rMAdd0ynn366x3vvvTeQvYby4YcfTn5P9ahLly4e\n2+9pIdasWQPAn3/+WbZ7qkU33ngjABtttJHn5s6d6/GSJUuS31M+1NMVEUlIja6ISEJ1MbzQnmXL\nlrUbm1j44vzzzwdgn3328dynn34KwFNPPVWpW+wwzj77bACuueYaz02cOBHInvzsyPr27evxrrvu\n6rENk8VJ4DvuuMPjbbbZpuBrvf766wDsv//+BX+21sUJ9F122QXIHkY49thjk99TodTTFRFJqG57\nul27dvW4c+fOAFx99dWeGz16tMe2lCkuIbG4Vgfba1EsIrThhht6PGbMGAC+/fZbz40dOzbdjdUI\nK8wUf8922mknIPM7CtlL69or3BR3Uy5YsACARYsWee6HH34AMhPMAL/88ovHDz74YFH3X8vsmd19\n992e23jjjQF4+umnPffll1+mvbEiqKcrIpKQGl0RkYTqanhh00039fj555/3eM8998z5OXtdixMU\ncR1vo4s1a20y8fLLLy/4e+x1DuDdd9/1eIcddgCy1+l2FHHttx2EaGuUAT7//HMAJk+e7Lmvv/7a\n4xdeeGGt74yTajaU0NHZGv14yOSvv/4KwK233lqVeyqWeroiIgmp0RURSajmhhfsFTW+ytrseP/+\n/T23wQYbrPVZO54DMutEAaZOnQrAX3/9Vd6brRMXXHCBx/YM33rrLc/F2d9cunXr5rENKUBmzXMj\nzpq3J9ZgtsI+kFk3Gp/3PffcA2SvLpD8bLXVVh5feumlQPZqj7POOguojxULkXq6IiIJVa2nG3fl\nxN6C/XVbV6GPeIrEmWeeCcDjjz/uuY46AWFraePkYpx8+P777wF46aWX8v5OW2Mae8ex52ZFbf74\n448i7rh+bLbZZkD24YfWu4XMhO2cOXM8N2DAACB74lHyY71byLyhxbfZJ554oqTvt7Xmcffq7Nmz\nAfjuu+9K+u5c1NMVEUlIja6ISEJVG14YMmSIx7GoR9xqmkv8OSu48sgjj5Tp7urX+uv/7z9pPCXj\nvffe8/jAAw8E4Pfff8/7O6+77joAevfu7bmhQ4d6PG/evKLutR7ECVsrjrTXXnu1+7N2gsmjjz7q\nORuSiMM5w4cP91gTbP8tvvab2267zeNiDpzcfffdPbYTJw477DDPvfHGGwAcccQRJV0nF/V0RUQS\nqlpPN+7Q2XbbbT22s81uv/12z33xxRdA9o608ePHe2yTcpdcconnYvGbRp/giS677LK1cnFJmO3i\nKYQVbYm947hrqpHZvztkJiRtMhLgpptu8vidd94BMkVqAE4++WQgUwwHssuJnnvuuQC8//775bzt\nuhWXesY34I8//hjIbjfyFYsvxXbBTpSJk/J27toVV1zhufPOO6/ga+ainq6ISEJqdEVEEqqJHWnx\naO4Y5xLX6B133HEA3H///Z6Lr9E2EdSo4qurTSrGHX2vvfZaXt+z3nqZv8EHH3ywxzbREId8KrmO\nsZYsXLjQ4/h88hWfmbHCOADPPfcckH3KQ0cqxmR23HFHAEaMGOG5uPssDgvky2rwnnbaaZ6Lh1iu\nWrUKyBxsG7VX57hc1NMVEUlIja6ISEI1MbxQjL///tvjhx56CIB7773Xc7vttpvH9loYZykbgW2Z\nfvbZZz1nKzzi1upY6MdmZyPbYnnkkUd6Lh7wZ69a2spaHvH31Fbe3HzzzZ6rh8MVy82K1/To0cNz\n9v81wPTp0wv+TtuiHbdqx++0oZ0pU6Z4zgoU3XnnnQVfL1/q6YqIJNSUa8C4qampcqPJFTBjxgyP\njznmGI+tkv/bb79d9mu2tbXlt4WuHaU+X1tD+tFHH5XyNetkvbC4Djq+aVRSsc+3mGdru/kA1qxZ\nU8xlC2aTv62trZ6Lu/3iIZXllvLZtieWbrR1uCtXrvTcoYce6rFNehXCdg7akfQAq1ev9th2A8aS\npNbDLXUdeq5nq56uiEhCanRFRBKq24m09rz44osex+GFRrV06VIATjjhBM/ZNmBbowiwbNkyj+Ok\nmznxxBOB7AmHuXPnemzDCqmGFKrFThiBTGGVSgxJ9erVy2N75rHwTSWHFGpJrJlttW3jNuBihhT6\n9u3rsRUWigeBxvXrVvDmvvvuK/g6pVBPV0QkITW6IiIJlTS8YLPn8SC+Bx54wOM333yzlK8v2IUX\nXpj0etVm647j2sMY5xKHH2x1R5y9j/9NG31YwcQazRdddBEAJ510kueKqX27xRZbANnro2OFvJ13\n3hmAa6+9tuDvbiS2iiquKe/SpctaPxfrcJszzjjD43g0lR2eGrdyW8kAyNTOTU09XRGRhEpap2u9\noRtuuMFzsXdrxVdifdFKsHq88S9aLHhzwAEHALB48eKyX7ua63SLYSchPPbYY56zCv0jR470XK2c\nwpFyLWm/fv08tl1jLS0tnluyZAmQObzwv2y++eYejx49GoA+ffp4LvaYrbZunAxNpdrrdHv27Omx\n7XaMNXTnz58frwlkF3dqT6xLPGHCBCC7OFa5T4H4L1qnKyJSI9ToiogkVNLwgr16xW10gwYN8the\n8eMhifF4ExPX0dnEw/Lly9fKjRkzxnPxNcMGzONr3ahRozyO63fLrd6GF2wiIa5JteGXuCa1kvVE\nC1GtV2Bbz2nPBjLDL4UMBSxatAiADz/80HPjxo3zuJi1qOVS7eGFaODAgQCccsopnosTZDbxG38v\nbdL4mWee8dysWbM8TjWU0B4NL4iI1IiyF7y5+OKLPbYD3Zqbm3N+Ju5+2mOPPYDs3rEdmxwLZMTC\nFb/99huQPRH0yiuvFHrrRamHnm58LpMmTQIyZe2gtksJ1lJvzHZQde7cOe/PWOGcuBSqVtTSs200\n6umKiNQINboiIglVtJ6uDQccf/zxnuvatSuQ/wGUUZywszV4APPmzSv2FktWD8MLcV1pt27dABg2\nbJjnfvzxxxS3URS9AleOnm3laHhBRKRGqNEVEUmooY7rqYZ6GF6oZ3oFrhw928rR8IKISI1Qoysi\nkpAaXRGRhNToiogkpEZXRCQhNboiIgmp0RURSUiNrohIQmp0RUQSyrkjTUREyks9XRGRhNToiogk\npEZXRCQhNboiIgmp0RURSUiNrohIQv8CrE3ZXbumIOYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NVk6OMo6Dx5t1nse8J80TKWnUvCcizIPkFKMz1+8W5+2s3NHui7dST15vmf7nP/9pdZ7L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\nhUJhQNSiWygUCgOiFt1CoVAYEP8FksYAdmZ7G7AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T8nkbSFwyXnj",
        "colab_type": "text"
      },
      "source": [
        "从上面的输出中，我们可以看出，重建的 loss 在下降，因此，从重建的结果来看，基本的轮廓重建较好，但细节有所损失。3 个 epoch 已经看到明显的效果改进，感兴趣的同学可以把epoch改大，多跑几轮，看看效果。\n",
        "\n",
        "## 3. 降噪自编码器\n",
        "\n",
        "下面我们看看降噪自编码器的效果如何。它的特点是，输入的数据是 bad_img，即损坏了的图像；另外，中间层的节点数量会更多一些。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTAGYZEJzFTX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d = 500  # d 的值由30涨到了500\n",
        "\n",
        "class Denoising_Autoencoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Linear(28 * 28, d),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.Linear(d, 28 * 28),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.encoder(x)\n",
        "        x = self.decoder(x)\n",
        "        return x\n",
        "    \n",
        "DenosingAE_model = Denoising_Autoencoder().to(device)\n",
        "criterion = nn.MSELoss()\n",
        "learning_rate = 1e-3\n",
        "DenosingAE_optimizer = torch.optim.Adam(DenosingAE_model.parameters(), lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FTV1hZXzqRM",
        "colab_type": "text"
      },
      "source": [
        "模型训练步骤，仍然是跑 3 个 epoch，每轮训练结束，输出原图像，以及重建结果。\n",
        "\n",
        "和上面的代码不同的是，使用 nn.Dropout 函数，随机丢弃原图像中的像素，增加重建的难度。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2maUlVfz0Ck",
        "colab_type": "code",
        "outputId": "0eb27860-ecf0-4e8d-a6a7-46814776bb18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "num_epochs = 3\n",
        "# 用来丢弃图像中的像素\n",
        "do = nn.Dropout()  \n",
        "for epoch in range(num_epochs):\n",
        "    for data in dataloader:\n",
        "        img = img.to(device)\n",
        "        img.requires_grad_()\n",
        "        img = img.view(img.size(0), -1)\n",
        "        # 随机丢弃图像中的一些像素\n",
        "        noise = do(torch.ones(img.shape)).to(device)\n",
        "        img_bad = (img * noise).to(device)\n",
        "        # ===================forward=====================\n",
        "        output = DenosingAE_model(img_bad) # 这里输入的是 img_bad \n",
        "        loss = criterion(output, img.data)\n",
        "        # ===================backward====================\n",
        "        DenosingAE_optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        DenosingAE_optimizer.step()\n",
        "    # ===================log========================\n",
        "    print(f'epoch [{epoch + 1}/{num_epochs}], loss:{loss.item():.4f}')\n",
        "    display_images(img_bad, output)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch [1/3], loss:0.0250\n",
            "epoch [2/3], loss:0.0107\n",
            "epoch [3/3], loss:0.0078\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9prRfMuBNiBSzXSqbs5xX1WfM8gDj6RJCyH8MfnQJIaRB1LsXJFnI5Nm7d29nh33+7dJc\nFqnpM4TWnIsZe90eYE1/oX61ZXOfVTsXKX3XRc48uOk62wnVick/2uR69UPLB769v6G4vaE+Q3Xd\nciWWOajpEkJIg0Q70iRKamI5v+K5zogUjTDHkaYNypKioYfyczW4kFz7dw71mTq/oWBNpbT3lOdM\nmYcYtM9bMrRjHc6oUJ2YoDS+vnK1dKk+QzsSQkiXwI8uIYQ0iNqRpjVLtaZVjKmq7UfbTkybTTkw\ncsx+X3sl6ofaTDHV65zLVFLe3TqeKaXNUv8PGnJvVSjdd9V4cpcfYutolySo6RJCSIMUd6SVckaE\n6tc9Dl9ZN6/UFewSKdp2jkaTqqnmaLohWd03R5Sc25T3NJTvK5trwdR1Iq0O7Ve7vSu3Te02tJBW\nyxNphBDSJfCjSwghDeJdXiCEEFIWarqEENIg/OgSQkiD8KNLCCENwo8uIYQ0CD+6hBDSIPzoEkJI\ng/wPpRUG0snAdIAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZPaGuxZQpU2Kc2hKlHvzOKoQfah0DXS0NHzz66KMd3pNy+3UJJqsP7r777pBdeeWVHd6v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\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JuQ9GGTTToKZLCCE9wpcuIYT0iNm9oOV5zJcrrrjin4Gn60N7zLpcudVEy/U5SnOtxhz2\nfE+e8XLmbEmbVN0+3AseE93jCqiVs7RtiRxe15jnAJgc1pUGqbZT6+XO3q1pUwM1XUII6ZGqQJoH\nq7ZQUtZKg+oj8NA1XkrGWm27VsPPjd1KKxz1/NfObY2mW2Il1GrcpbK1JqcterRW62WXuT5HiRyH\nV7ATQsiYwJcuIYT0SNXNEbXBilZtcrJZTTTPODU3R1i3qnrImbOjcGN09VUjh3erqvXZzbkKPG08\nrhlL3RSeNi22WKe20nq210py7gWtrbVNydi5uhqpuaWmSwghPVKs6Xq0Vo1WmmppX6k+S9sC7TVd\nDyVBFut3Uqv11rRpoel65raVFVaCVdMtGcf6bLe4I82jTVoPr2kVfJN4gnOepWc88IYQQsYEvnQJ\nIaRHku4FQgghbaGmSwghPcKXLiGE9AhfuoQQ0iN86RJCSI/wpUsIIT3Cly4hhPTI/wA8Swr6yJzA\nigAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XXFL13UB6B44c2NI1xpiMeNI1xpiMNNVPl4mthQsXhkyD3zTb33rrrZAxQfPtt9+GTJNm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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5zSFbyM6Sa0m1NjXZoklrSs8dasf81rQhjbbCC7WewdxQQ0o52t8c1+kSQkiPED2RVspD\nyn1Lt/GWdKWHrmtzw5sUb9x1XRseXEr7pdzTtg5PzPGScts25nnP8RpDeWqeHNFkl0+XS4odWnty\n+gV6uoQQUhF2uoQQUpFdtRe2EZT25U8JH+QOj3P+xzYpZXdMPSnX1rIzlVo254YCtGXG/K4Gg5L2\npYRutOXk9hvacgz0dAkhpCJZJ0do0mLz5HgGTW+kNj2DnIm00GRPDiVHFykTEjlepUxre8lYimdU\n4lptOdp2Sim7rbbN9SZj88Q8tyllptjGL9IIIaRHYKdLCCEV8YYXCCGElIWeLiGEVISdLiGEVISd\nLiGEVISdLiGEVISdLiGEVISdLiGEVOQ/3nPA8E1AtyUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TQLHRit7vzB3n5p5AeeOgKpLaqUXvO/3Oqd8er4dW6+mOJqy+03zmVC61WtWTJk0CUNts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\n0TXGmIx40TXGmIz8B8ZcasPforrUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x144 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtjtGe2LD7p8",
        "colab_type": "text"
      },
      "source": [
        "从 denosing Autoencoder 的示例可以看出，即使输入的是损坏的图像，重建的效果仍然非常好。具体原因大家可以自己思考。"
      ]
    }
  ]
}