09_CGAN_DCGAN_mnist.ipynb

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      "name": "09_CGAN_DCGAN_mnist.ipynb",
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      "authorship_tag": "ABX9TyNO4pUhf/YzuloSe6XWcUsy",
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      "name": "python3",
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    "accelerator": "GPU"
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  "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/09_CGAN_DCGAN_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8UXs83cHPUfv",
        "colab_type": "text"
      },
      "source": [
        "## CGAN and DCGAN\n",
        "\n",
        "Conditional Generative Adversarial Nets，简单来说就是条件生成-对抗网络。在生成器以及判别器上它都多了一个标签作为输入。\n",
        "\n",
        "所以，生成器的输入是噪声和标签，输出还是生成图；判别器的输入是生成图，真实图以及标签，输出还是真和假。\n",
        "\n",
        "之前的GAN教程，实验数据用的是简单的散点，在本教程中，我们使用MNIST，结果更加形象。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBuJ2JyHQAbq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 基本参数\n",
        "z_dim = 100\n",
        "batch_size = 128\n",
        "learning_rate = 0.0002\n",
        "total_epochs = 30\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",
        "# 加载MNIST数据集\n",
        "dataloader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('./data', train=True, download=True,\n",
        "        transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])\n",
        "        ), batch_size, shuffle=False, drop_last=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwTIGHx4R9FQ",
        "colab_type": "text"
      },
      "source": [
        "## CGAN\n",
        "\n",
        "首先实现CGAN。下面分别是 **判别器** 和 **生成器** 的网络结构，可以看出网络结构非常简单，具体如下：\n",
        "- 生成器：(784 + 10) ==> 512 ==> 256 ==> 1\n",
        "- 判别器：(100 + 10) ==> 128 ==> 256 ==> 512 ==> 784\n",
        "\n",
        "可以看出，去掉生成器和判别器那 10 维的标签信息，和普通的GAN是完全一样的。下面是网络的具体实现代码："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IFxiPkmQQ9V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Discriminator(nn.Module):\n",
        "\t'''全连接判别器，用于1x28x28的MNIST数据,输出是数据和类别'''\n",
        "\tdef __init__(self):\n",
        "\t\tsuper(Discriminator, self).__init__()\n",
        "\t\tself.model = nn.Sequential(\n",
        "\t\t\t  nn.Linear(28*28+10, 512),\n",
        "\t\t\t  nn.LeakyReLU(0.2, inplace=True),\n",
        "\t\t\t  nn.Linear(512, 256),\n",
        "\t\t\t  nn.LeakyReLU(0.2, inplace=True),\n",
        "\t\t\t  nn.Linear(256, 1),\n",
        "\t\t\t  nn.Sigmoid()\n",
        "\t\t)\n",
        "  \n",
        "\tdef forward(self, x, c):\n",
        "\t\tx = x.view(x.size(0), -1)\n",
        "\t\tvalidity = self.model(torch.cat([x, c], -1))\n",
        "\t\treturn validity\n",
        "\n",
        "class Generator(nn.Module):\n",
        "\t'''全连接生成器，用于1x28x28的MNIST数据，输入是噪声和类别'''\n",
        "\tdef __init__(self, z_dim):\n",
        "\t\tsuper(Generator, self).__init__()\n",
        "\t\tself.model = nn.Sequential(\n",
        "\t\t\t  nn.Linear(z_dim+10, 128),\n",
        "\t\t\t  nn.LeakyReLU(0.2, inplace=True),\n",
        "\t\t\t  nn.Linear(128, 256),\n",
        "\t\t\t  nn.BatchNorm1d(256, 0.8),\n",
        "\t\t\t  nn.LeakyReLU(0.2, inplace=True),\n",
        "\t\t\t  nn.Linear(256, 512),\n",
        "\t\t\t  nn.BatchNorm1d(512, 0.8),\n",
        "\t\t\t  nn.LeakyReLU(0.2, inplace=True),\n",
        "\t\t\t  nn.Linear(in_features=512, out_features=28*28),\n",
        "\t\t\t  nn.Tanh()\n",
        "\t \t)\n",
        "\n",
        "\tdef forward(self, z, c):\n",
        "\t\tx = self.model(torch.cat([z, c], dim=1))\n",
        "\t\tx = x.view(-1, 1, 28, 28)\n",
        "\t\treturn x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLHEyK5Rcwk2",
        "colab_type": "text"
      },
      "source": [
        "下面定义相关的模型："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPs7HWjlcv5I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 初始化构建判别器和生成器\n",
        "discriminator = Discriminator().to(device)\n",
        "generator = Generator(z_dim=z_dim).to(device)\n",
        "\n",
        "# 初始化二值交叉熵损失\n",
        "bce = torch.nn.BCELoss().to(device)\n",
        "ones = torch.ones(batch_size).to(device)\n",
        "zeros = torch.zeros(batch_size).to(device)\n",
        "\n",
        "# 初始化优化器，使用Adam优化器\n",
        "g_optimizer = optim.Adam(generator.parameters(), lr=learning_rate)\n",
        "d_optimizer = optim.Adam(discriminator.parameters(), lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOfx0PVTh3ai",
        "colab_type": "text"
      },
      "source": [
        "开始训练："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfPlJ_H8h5N0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 开始训练，一共训练total_epochs\n",
        "for epoch in range(total_epochs):\n",
        "\n",
        "\t# torch.nn.Module.train() 指的是模型启用 BatchNormalization 和 Dropout\n",
        "\t# torch.nn.Module.eval() 指的是模型不启用 BatchNormalization 和 Dropout\n",
        "\t# 因此，train()一般在训练时用到， eval() 一般在测试时用到\n",
        "\tgenerator = generator.train()\n",
        "\n",
        "\t# 训练一个epoch\n",
        "\tfor i, data in enumerate(dataloader):\n",
        "\n",
        "\t\t# 加载真实数据\n",
        "\t\treal_images, real_labels = data\n",
        "\t\treal_images = real_images.to(device)\n",
        "\t\t# 把对应的标签转化成 one-hot 类型\n",
        "\t\ttmp = torch.FloatTensor(real_labels.size(0), 10).zero_()\n",
        "\t\treal_labels = tmp.scatter_(dim=1, index=torch.LongTensor(real_labels.view(-1, 1)), value=1)\n",
        "\t\treal_labels = real_labels.to(device)\n",
        "\n",
        "\t\t# 生成数据\n",
        "\t\t# 用正态分布中采样batch_size个随机噪声\n",
        "\t\tz = torch.randn([batch_size, z_dim]).to(device)\n",
        "\t\t# 生成 batch_size 个 ont-hot 标签\n",
        "\t\tc = torch.FloatTensor(batch_size, 10).zero_()\n",
        "\t\tc = c.scatter_(dim=1, index=torch.LongTensor(np.random.choice(10, batch_size).reshape([batch_size, 1])), value=1)\n",
        "\t\tc = c.to(device)\n",
        "\t\t# 生成数据\n",
        "\t\tfake_images = generator(z,c)\n",
        "\n",
        "\t\t# 计算判别器损失，并优化判别器\n",
        "\t\treal_loss = bce(discriminator(real_images, real_labels), ones)\n",
        "\t\tfake_loss = bce(discriminator(fake_images.detach(), c), zeros)\n",
        "\t\td_loss = real_loss + fake_loss\n",
        "\n",
        "\t\td_optimizer.zero_grad()\n",
        "\t\td_loss.backward()\n",
        "\t\td_optimizer.step()\n",
        "\n",
        "\t\t# 计算生成器损失，并优化生成器\n",
        "\t\tg_loss = bce(discriminator(fake_images, c), ones)\n",
        "\n",
        "\t\tg_optimizer.zero_grad()\n",
        "\t\tg_loss.backward()\n",
        "\t\tg_optimizer.step()\n",
        "\n",
        "\t# 输出损失\n",
        "\tprint(\"[Epoch %d/%d] [D loss: %f] [G loss: %f]\" % (epoch, total_epochs, d_loss.item(), g_loss.item()))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8EGEUZFo_NO",
        "colab_type": "text"
      },
      "source": [
        "下面我们用随机噪声生成一组图像，看看CGAN的效果："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXZTRCsMpFP_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#用于生成效果图\n",
        "# 生成100个随机噪声向量\n",
        "fixed_z = torch.randn([100, z_dim]).to(device)\n",
        "# 生成100个one_hot向量，每类10个\n",
        "fixed_c = torch.FloatTensor(100, 10).zero_()\n",
        "fixed_c = fixed_c.scatter_(dim=1, index=torch.LongTensor(np.array(np.arange(0, 10).tolist()*10).reshape([100, 1])), value=1)\n",
        "fixed_c = fixed_c.to(device)\n",
        "\n",
        "generator = generator.eval()\n",
        "fixed_fake_images = generator(fixed_z, fixed_c)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "for j in range(10):\n",
        "    for i in range(10):\n",
        "        img = fixed_fake_images[j*10+i, 0, :, :].detach().cpu().numpy()\n",
        "        img = img.reshape([28, 28])\n",
        "        plt.subplot(10, 10, j*10+i+1)\n",
        "        plt.imshow(img, 'gray')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHAI73-t61v5",
        "colab_type": "text"
      },
      "source": [
        "我曾经把epoch设置成200，CGAN生成的效果还是非常不错的 ~~~ 如下所示：\n",
        "\n",
        "![CGAN](https://gaopursuit.oss-cn-beijing.aliyuncs.com/202003/FsAc9HE6vdT_TOnZzOeXd6Kq0U1b.jpg)\n",
        "\n",
        "但后来很长时间都跑不出来这个结果了，不清楚是哪里的设置出问题了，很忧伤 ~~~ \n",
        "\n",
        "考虑到上面代码是把图像直接拉成一个向量来处理，没有考虑空间上的特性，因此，效果理论上会不如使用卷积操作的 DCGAN。两者代码也非常类似，我们下面比较一下。\n",
        "\n",
        "## DCGAN\n",
        "\n",
        "下面我们实现DCGAN。下面分别是 **判别器** 和 **生成器** 的网络结构，和之前类似，只是使用了卷积结构。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATjcL98j9Uyd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class D_dcgan(nn.Module):\n",
        "\t'''滑动卷积判别器'''\n",
        "\tdef __init__(self):\n",
        "\t\tsuper(D_dcgan, self).__init__()\n",
        "\t\tself.conv = nn.Sequential(\n",
        "            # 第一个滑动卷积层，不使用BN，LRelu激活函数\n",
        "            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=2, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            # 第二个滑动卷积层，包含BN，LRelu激活函数\n",
        "            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            # 第三个滑动卷积层，包含BN，LRelu激活函数\n",
        "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            # 第四个滑动卷积层，包含BN，LRelu激活函数\n",
        "            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=1),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.LeakyReLU(0.2, inplace=True)\n",
        "        )\n",
        "\n",
        "\t\t# 全连接层+Sigmoid激活函数\n",
        "\t\tself.linear = nn.Sequential(nn.Linear(in_features=128, out_features=1), nn.Sigmoid())\n",
        "\n",
        "\tdef forward(self, x):\n",
        "\t\tx = self.conv(x)\n",
        "\t\tx = x.view(x.size(0), -1)\n",
        "\t\tvalidity = self.linear(x)\n",
        "\t\treturn validity\n",
        "\n",
        "class G_dcgan(nn.Module):\n",
        "\t'''反滑动卷积生成器'''\n",
        "\n",
        "\tdef __init__(self, z_dim):\n",
        "\t\tsuper(G_dcgan, self).__init__()\n",
        "\t\tself.z_dim = z_dim\n",
        "\t\t# 第一层：把输入线性变换成256x4x4的矩阵，并在这个基础上做反卷机操作\n",
        "\t\tself.linear = nn.Linear(self.z_dim, 4*4*256)\n",
        "\t\tself.model = nn.Sequential(\n",
        "            # 第二层：bn+relu\n",
        "            nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=3, stride=2, padding=0),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.ReLU(inplace=True),\n",
        "            # 第三层：bn+relu\n",
        "            nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=3, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(inplace=True),\n",
        "            # 第四层:不使用BN，使用tanh激活函数\n",
        "            nn.ConvTranspose2d(in_channels=64, out_channels=1, kernel_size=4, stride=2, padding=2),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "\tdef forward(self, z):\n",
        "\t\t# 把随机噪声经过线性变换，resize成256x4x4的大小\n",
        "\t\tx = self.linear(z)\n",
        "\t\tx = x.view([x.size(0), 256, 4, 4])\n",
        "\t\t# 生成图片\n",
        "\t\tx = self.model(x)\n",
        "\t\treturn x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yrgxJZs8-kO1",
        "colab_type": "text"
      },
      "source": [
        "定义相关的模型："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wr6nrPQC-mOk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 构建判别器和生成器\n",
        "d_dcgan = D_dcgan().to(device)\n",
        "g_dcgan = G_dcgan(z_dim=z_dim).to(device)\n",
        "\n",
        "def weights_init_normal(m):\n",
        "    classname = m.__class__.__name__\n",
        "    if classname.find('Conv') != -1:\n",
        "        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "    elif classname.find('BatchNorm2d') != -1:\n",
        "        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
        "        torch.nn.init.constant_(m.bias.data, 0.0)\n",
        "\n",
        "# 使用均值为0，方差为0.02的正态分布初始化神经网络\n",
        "d_dcgan.apply(weights_init_normal)\n",
        "g_dcgan.apply(weights_init_normal)\n",
        "\n",
        "# 初始化优化器，使用Adam优化器\n",
        "g_dcgan_optim = optim.Adam(g_dcgan.parameters(), lr=learning_rate)\n",
        "d_dcgan_optim = optim.Adam(d_dcgan.parameters(), lr=learning_rate)\n",
        "\n",
        "# 加载MNIST数据集，和之前不同的是，DCGAN输入的图像被 resize 成 32*32 像素\n",
        "dcgan_dataloader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('./data', train=True, download=True,\n",
        "        transform=transforms.Compose([transforms.Resize(32), transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))])\n",
        "        ), batch_size, shuffle=True, drop_last=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZvCYQQa_p-_",
        "colab_type": "text"
      },
      "source": [
        "开始训练模型："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAUJUMe0_sGy",
        "colab_type": "code",
        "outputId": "1b236bd4-755d-4f86-8790-3123757731b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        }
      },
      "source": [
        "# 开始训练，一共训练 total_epochs\n",
        "\n",
        "for e in range(total_epochs):\n",
        "\n",
        "\t# 给generator启用 BatchNormalization\n",
        "\tg_dcgan = g_dcgan.train()\n",
        "\t# 训练一个epoch\n",
        "\tfor i, data in enumerate(dcgan_dataloader):\n",
        "\n",
        "\t\t# 加载真实数据，不加载标签\n",
        "\t\treal_images, _ = data\n",
        "\t\treal_images = real_images.to(device)\n",
        "\n",
        "\t\t# 用正态分布中采样batch_size个噪声，然后生成对应的图片\n",
        "\t\tz = torch.randn([batch_size, z_dim]).to(device)\n",
        "\t\tfake_images = g_dcgan(z)\n",
        "\n",
        "\t\t# 计算判别器损失，并优化判别器\n",
        "\t\treal_loss = bce(d_dcgan(real_images), ones)\n",
        "\t\tfake_loss = bce(d_dcgan(fake_images.detach()), zeros)\n",
        "\t\td_loss = real_loss + fake_loss\n",
        "\n",
        "\t\td_dcgan_optim.zero_grad()\n",
        "\t\td_loss.backward()\n",
        "\t\td_dcgan_optim.step()\n",
        "\n",
        "\t\t# 计算生成器损失，并优化生成器\n",
        "\t\tg_loss = bce(d_dcgan(fake_images), ones)\n",
        "\n",
        "\t\tg_dcgan_optim.zero_grad()\n",
        "\t\tg_loss.backward()\n",
        "\t\tg_dcgan_optim.step()\n",
        "\t\t\n",
        "    # 输出损失\n",
        "\tprint (\"[Epoch %d/%d] [D loss: %f] [G loss: %f]\" % (e, total_epochs, d_loss.item(), g_loss.item()))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:498: UserWarning: Using a target size (torch.Size([128])) that is different to the input size (torch.Size([128, 1])) is deprecated. Please ensure they have the same size.\n",
            "  return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 0/30] [D loss: 1.045440] [G loss: 0.970043]\n",
            "[Epoch 1/30] [D loss: 0.982702] [G loss: 1.100872]\n",
            "[Epoch 2/30] [D loss: 0.977530] [G loss: 1.125384]\n",
            "[Epoch 3/30] [D loss: 0.848266] [G loss: 1.203098]\n",
            "[Epoch 4/30] [D loss: 0.867079] [G loss: 1.215819]\n",
            "[Epoch 5/30] [D loss: 0.731456] [G loss: 1.605735]\n",
            "[Epoch 6/30] [D loss: 0.370109] [G loss: 2.159085]\n",
            "[Epoch 7/30] [D loss: 0.177716] [G loss: 2.797460]\n",
            "[Epoch 8/30] [D loss: 0.122290] [G loss: 3.515564]\n",
            "[Epoch 9/30] [D loss: 0.067713] [G loss: 4.166862]\n",
            "[Epoch 10/30] [D loss: 0.049966] [G loss: 4.166396]\n",
            "[Epoch 11/30] [D loss: 0.071364] [G loss: 4.487647]\n",
            "[Epoch 12/30] [D loss: 0.032918] [G loss: 4.669094]\n",
            "[Epoch 13/30] [D loss: 0.067831] [G loss: 4.622688]\n",
            "[Epoch 14/30] [D loss: 0.016590] [G loss: 5.150414]\n",
            "[Epoch 15/30] [D loss: 0.039516] [G loss: 4.939036]\n",
            "[Epoch 16/30] [D loss: 0.013571] [G loss: 6.400079]\n",
            "[Epoch 17/30] [D loss: 0.064513] [G loss: 4.190432]\n",
            "[Epoch 18/30] [D loss: 0.036726] [G loss: 4.953189]\n",
            "[Epoch 19/30] [D loss: 0.090487] [G loss: 4.276911]\n",
            "[Epoch 20/30] [D loss: 0.017509] [G loss: 5.403644]\n",
            "[Epoch 21/30] [D loss: 0.021693] [G loss: 5.649231]\n",
            "[Epoch 22/30] [D loss: 0.008464] [G loss: 6.226363]\n",
            "[Epoch 23/30] [D loss: 0.011662] [G loss: 6.579892]\n",
            "[Epoch 24/30] [D loss: 0.057066] [G loss: 5.253453]\n",
            "[Epoch 25/30] [D loss: 0.015664] [G loss: 5.425864]\n",
            "[Epoch 26/30] [D loss: 0.009977] [G loss: 5.983655]\n",
            "[Epoch 27/30] [D loss: 0.011179] [G loss: 5.815823]\n",
            "[Epoch 28/30] [D loss: 0.006860] [G loss: 6.122502]\n",
            "[Epoch 29/30] [D loss: 0.009682] [G loss: 6.439797]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5JimBg_CBNv_",
        "colab_type": "text"
      },
      "source": [
        "下面我们用一组随机噪声输出图像，看看DCGAN的效果："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BXqZmRjBTz4",
        "colab_type": "code",
        "outputId": "1bbb942d-d40f-42c3-f973-9335eb9a0138",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        }
      },
      "source": [
        "#用于生成效果图\n",
        "# 生成100个随机噪声向量\n",
        "fixed_z = torch.randn([100, z_dim]).to(device)\n",
        "g_dcgan = g_dcgan.eval()\n",
        "fixed_fake_images = g_dcgan(fixed_z)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "for j in range(10):\n",
        "    for i in range(10):\n",
        "        img = fixed_fake_images[j*10+i, 0, :, :].detach().cpu().numpy()\n",
        "        img = img.reshape([32, 32])\n",
        "        plt.subplot(10, 10, j*10+i+1)\n",
        "        plt.imshow(img, 'gray')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TWLBgAampqZhMJgKBgHTXlpWVYTKZmD59OvHx8RQWFlJYWAgwYLEEgUCAxYsXM3v2bGJj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NVqbrnlFhwOB7/61a9EZSWz2YxarWbv3r289tprHD58uEdtigcOHIjJZCIgIECY+B0OBw0NDSLl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ca3V69e9O7dWxAlXC4XW7ZsYf58fzOO4uJirFYrNpsNp9PZ6Q8RID4+nujoaBGPLi0tPe4C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\nDHTGmHwNDAwMDAx05v8Bw5IcYFlWUM8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 100 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgXYPpoESPly",
        "colab_type": "text"
      },
      "source": [
        "这里只用了30个 epoch，效果还可以，如果增大 epoch 的数量，效果可能会更好，大家可以自己试一试。"
      ]
    }
  ]
}