Example_04_MIE_CGAN_MNIST.ipynb

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      "name": "MNIST_CGAN_with_MIE.ipynb",
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PAGaH2_Vcno",
        "colab_type": "text"
      },
      "source": [
        "# MIE-CGAN on image data\n",
        "\n",
        "Here we show an example implementation of the MIE selection criterion for image data (with one channel). The code is an expansion of Yangyangii's [implementation](https://github.com/Yangyangii/GAN-Tutorial)of a basic CGAN for the MNIST dataset.\n",
        "\n",
        "First, we install all needed packages:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otVyqag5Vcn1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%capture \n",
        "\n",
        "!pip install torch torchvision\n",
        "!pip install esda\n",
        "!pip install pysal\n",
        "!pip install urbanaccess \n",
        "!pip install pandana"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFDzSgPRcMO7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkYn1e8ocOTu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "\n",
        "!apt-get install -qq curl g++ make\n",
        "!curl -L http://download.osgeo.org/libspatialindex/spatialindex-src-1.8.5.tar.gz | tar xz\n",
        "os.chdir('spatialindex-src-1.8.5')\n",
        "!./configure\n",
        "!make\n",
        "!make install\n",
        "!pip install rtree\n",
        "!ldconfig\n",
        "os.chdir(\"..\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMftvgGCcZuE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "import esda\n",
        "import pysal\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zE18PRoCVcoU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.utils import save_image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzDv4D-9Vcox",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from matplotlib.pyplot import imshow, imsave\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Wk0xcTysU85",
        "colab_type": "text"
      },
      "source": [
        "## Codebase\n",
        "\n",
        "Let's first define the model and needed utility functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGQfLCZdVco5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MODEL_NAME = 'ConditionalGAN'\n",
        "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoR2eCeMVco9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def to_onehot(x, num_classes=10):\n",
        "    assert isinstance(x, int) or isinstance(x, (torch.LongTensor, torch.cuda.LongTensor))\n",
        "    if isinstance(x, int):\n",
        "        c = torch.zeros(1, num_classes).long()\n",
        "        c[0][x] = 1\n",
        "    else:\n",
        "        x = x.cpu()\n",
        "        c = torch.LongTensor(x.size(0), num_classes)\n",
        "        c.zero_()\n",
        "        c.scatter_(1, x, 1) # dim, index, src value\n",
        "    return c"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stA2TP06VcpH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_sample_image(G, n_noise=100):\n",
        "    \"\"\"\n",
        "        save sample 100 images\n",
        "    \"\"\"\n",
        "    img = np.zeros([280, 280])\n",
        "    for j in range(10):\n",
        "        c = torch.zeros([10, 10]).to(DEVICE)\n",
        "        c[:, j] = 1\n",
        "        z = torch.randn(10, n_noise).to(DEVICE)\n",
        "        y_hat = G(z,c).view(10, 28, 28)\n",
        "        result = y_hat.cpu().data.numpy()\n",
        "        img[j*28:(j+1)*28] = np.concatenate([x for x in result], axis=-1)\n",
        "    return img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ODpDpBHVcpN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Discriminator(nn.Module):\n",
        "    \"\"\"\n",
        "        Simple Discriminator w/ MLP\n",
        "    \"\"\"\n",
        "    def __init__(self, input_size=784, condition_size=10, num_classes=1):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.layer = nn.Sequential(\n",
        "            nn.Linear(input_size+condition_size, 512),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Linear(512, 256),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Linear(256, num_classes),\n",
        "            nn.Sigmoid(),\n",
        "        )\n",
        "    \n",
        "    def forward(self, x, c):        \n",
        "        x, c = x.view(x.size(0), -1), c.view(c.size(0), -1).float()\n",
        "        v = torch.cat((x, c), 1) # v: [input, label] concatenated vector\n",
        "        y_ = self.layer(v)\n",
        "        return y_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ph4UV26wVcpV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Generator(nn.Module):\n",
        "    \"\"\"\n",
        "        Simple Generator w/ MLP\n",
        "    \"\"\"\n",
        "    def __init__(self, input_size=100, condition_size=10, num_classes=784):\n",
        "        super(Generator, self).__init__()\n",
        "        self.layer = nn.Sequential(\n",
        "            nn.Linear(input_size+condition_size, 128),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Linear(128, 256),\n",
        "            nn.BatchNorm1d(256),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Linear(256, 512),\n",
        "            nn.BatchNorm1d(512),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Linear(512, 1024),\n",
        "            nn.BatchNorm1d(1024),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Linear(1024, num_classes),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "        \n",
        "    def forward(self, x, c):\n",
        "        x, c = x.view(x.size(0), -1), c.view(c.size(0), -1).float()\n",
        "        v = torch.cat((x, c), 1) # v: [input, label] concatenated vector\n",
        "        y_ = self.layer(v)\n",
        "        y_ = y_.view(x.size(0), 1, 28, 28)\n",
        "        return y_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlvQVECGVcpe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "D = Discriminator().to(DEVICE)\n",
        "G = Generator().to(DEVICE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_7G_yyCVcpl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize(mean=[0.5],\n",
        "                                std=[0.5])]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCQEdbdZtYV5",
        "colab_type": "text"
      },
      "source": [
        "## Model training\n",
        "\n",
        "We can now train the model. First, we access the MNIST dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y47HLfreVcpq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = datasets.MNIST(root='../data/', train=True, transform=transform, download=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmwEQnAntZjl",
        "colab_type": "text"
      },
      "source": [
        "Let's prepare the training settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKj1wfk3Vcpx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 64\n",
        "condition_size = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjImrOfTVcp5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_loader = DataLoader(dataset=mnist, batch_size=batch_size, shuffle=True, drop_last=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uiun5rH9Vcp-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.BCELoss()\n",
        "D_opt = torch.optim.Adam(D.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
        "G_opt = torch.optim.Adam(G.parameters(), lr=0.0002, betas=(0.5, 0.999))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RC2dMcw3VcqF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max_epoch = 100\n",
        "step = 0\n",
        "n_critic = 1 # for training more k steps about Discriminator\n",
        "n_noise = 100\n",
        "check_interval = 300"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xws4GfySVcqK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "D_labels = torch.ones([batch_size, 1]).to(DEVICE) # Discriminator Label to real\n",
        "D_fakes = torch.zeros([batch_size, 1]).to(DEVICE) # Discriminator Label to fake"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9-RmZq7af64",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w = pysal.lib.weights.lat2W(28,28,rook=False) #Define neighbourhood matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "de0KPnUKVcqP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if not os.path.exists('samples'):\n",
        "    os.makedirs('samples')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_mOOOkGtit1",
        "colab_type": "text"
      },
      "source": [
        "And we are ready to train the model!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "jXFwZtwxVcqV",
        "colab_type": "code",
        "outputId": "58c92e37-0615-4212-ed18-9a3115a8b488",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "intervals = []\n",
        "mies = []\n",
        "mies_batch = []\n",
        "rmses = []\n",
        "rmses_batch = []\n",
        "disc_loss = []\n",
        "gen_loss = []\n",
        "for epoch in range(max_epoch):\n",
        "    for idx, (images, labels) in enumerate(data_loader):\n",
        "        # Training Discriminator\n",
        "        x = images.to(DEVICE)\n",
        "        y = labels.view(batch_size, 1)\n",
        "        y = to_onehot(y).to(DEVICE)\n",
        "        x_outputs = D(x, y)\n",
        "        D_x_loss = criterion(x_outputs, D_labels)\n",
        "\n",
        "        z = torch.randn(batch_size, n_noise).to(DEVICE)\n",
        "        z_trained = G(z, y)\n",
        "        z_outputs = D(z_trained, y)\n",
        "        D_z_loss = criterion(z_outputs, D_fakes)\n",
        "        D_loss = D_x_loss + D_z_loss\n",
        "        \n",
        "        D.zero_grad()\n",
        "        D_loss.backward()\n",
        "        D_opt.step()\n",
        "        \n",
        "        if step % n_critic == 0:\n",
        "            # Training Generator\n",
        "            z = torch.randn(batch_size, n_noise).to(DEVICE)\n",
        "            z_outputs = D(G(z, y), y)\n",
        "            G_loss = criterion(z_outputs, D_labels)\n",
        "\n",
        "            G.zero_grad()\n",
        "            G_loss.backward()\n",
        "            G_opt.step()\n",
        "        \n",
        "        if step % check_interval ==0:\n",
        "          #calculate metrics\n",
        "          for i in range(batch_size):\n",
        "            #Calculate Moran's I values of input and generated data\n",
        "            mi_true = pysal.explore.esda.moran.Moran_Local(x[i][0].cpu(),w,permutations=1).Is.reshape(28,28)\n",
        "            mi_gen = pysal.explore.esda.moran.Moran_Local(z_trained[i][0].cpu().detach().numpy(),w,permutations=1).Is.reshape(28,28)\n",
        "            #Calculate selection metrics: MIE and RMSE\n",
        "            mie = np.mean(abs(np.concatenate(mi_true)-np.concatenate(mi_gen)))\n",
        "            rmse = np.sqrt(np.mean((np.concatenate(np.array(x[i][0].cpu())) - np.concatenate(np.array(z_trained[i][0].cpu().detach().numpy())))**2))\n",
        "            mies.append(mie)\n",
        "            rmses.append(rmse)\n",
        "          #Save metrics\n",
        "          mies_batch.append(np.mean(mies))\n",
        "          rmses_batch.append(np.mean(rmses))\n",
        "          disc_loss.append(D_loss.item())\n",
        "          gen_loss.append(G_loss.item())\n",
        "          intervals.append(step)\n",
        "          #Save models\n",
        "          torch.save(G, \"gen_iter %d.pkl.gz\" % step)\n",
        "          torch.save(D, \"disc_iter %d.pkl.gz\" % step)\n",
        "\n",
        "        if step % 500 == 0:\n",
        "          print('Epoch: {}/{}, Step: {}, D Loss: {}, G Loss: {}'.format(epoch, max_epoch, step, D_loss.item(), G_loss.item()))\n",
        "        \n",
        "        if step % 1000 == 0:\n",
        "            G.eval()\n",
        "            img = get_sample_image(G, n_noise)\n",
        "            imsave('samples/{}_step{}.jpg'.format(MODEL_NAME, str(step).zfill(3)), img, cmap='gray')\n",
        "            G.train()\n",
        "        step += 1"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Generator. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Sequential. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Linear. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type LeakyReLU. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type BatchNorm1d. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Tanh. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Discriminator. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/serialization.py:292: UserWarning: Couldn't retrieve source code for container of type Sigmoid. It won't be checked for correctness upon loading.\n",
            "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0/100, Step: 0, D Loss: 1.4082860946655273, G Loss: 0.699702799320221\n",
            "Epoch: 0/100, Step: 500, D Loss: 1.2467727661132812, G Loss: 0.8995413780212402\n",
            "Epoch: 1/100, Step: 1000, D Loss: 1.1910319328308105, G Loss: 1.2778873443603516\n",
            "Epoch: 1/100, Step: 1500, D Loss: 1.123782753944397, G Loss: 1.1247559785842896\n",
            "Epoch: 2/100, Step: 2000, D Loss: 1.1824380159378052, G Loss: 1.247546911239624\n",
            "Epoch: 2/100, Step: 2500, D Loss: 1.2414945363998413, G Loss: 1.2496607303619385\n",
            "Epoch: 3/100, Step: 3000, D Loss: 1.1879792213439941, G Loss: 1.3296630382537842\n",
            "Epoch: 3/100, Step: 3500, D Loss: 1.18966543674469, G Loss: 0.9751136302947998\n",
            "Epoch: 4/100, Step: 4000, D Loss: 1.4371533393859863, G Loss: 1.9905483722686768\n",
            "Epoch: 4/100, Step: 4500, D Loss: 1.2650792598724365, G Loss: 0.607941746711731\n",
            "Epoch: 5/100, Step: 5000, D Loss: 1.267794132232666, G Loss: 1.1237871646881104\n",
            "Epoch: 5/100, Step: 5500, D Loss: 1.3036775588989258, G Loss: 0.8374974727630615\n",
            "Epoch: 6/100, Step: 6000, D Loss: 1.3152716159820557, G Loss: 1.1401865482330322\n",
            "Epoch: 6/100, Step: 6500, D Loss: 1.3624536991119385, G Loss: 0.6276642680168152\n",
            "Epoch: 7/100, Step: 7000, D Loss: 1.2888288497924805, G Loss: 0.7121606469154358\n",
            "Epoch: 8/100, Step: 7500, D Loss: 1.3249831199645996, G Loss: 0.9952735900878906\n",
            "Epoch: 8/100, Step: 8000, D Loss: 1.3105850219726562, G Loss: 0.7703834772109985\n",
            "Epoch: 9/100, Step: 8500, D Loss: 1.353317379951477, G Loss: 1.0858988761901855\n",
            "Epoch: 9/100, Step: 9000, D Loss: 1.2605557441711426, G Loss: 0.89365553855896\n",
            "Epoch: 10/100, Step: 9500, D Loss: 1.3223994970321655, G Loss: 0.7045646905899048\n",
            "Epoch: 10/100, Step: 10000, D Loss: 1.3369253873825073, G Loss: 0.8019403219223022\n",
            "Epoch: 11/100, Step: 10500, D Loss: 1.3586924076080322, G Loss: 0.7324196696281433\n",
            "Epoch: 11/100, Step: 11000, D Loss: 1.3266987800598145, G Loss: 0.8221948146820068\n",
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            "Epoch: 98/100, Step: 92000, D Loss: 1.242476224899292, G Loss: 0.9350374937057495\n",
            "Epoch: 98/100, Step: 92500, D Loss: 1.249589443206787, G Loss: 0.8867231607437134\n",
            "Epoch: 99/100, Step: 93000, D Loss: 1.2321012020111084, G Loss: 1.2030620574951172\n",
            "Epoch: 99/100, Step: 93500, D Loss: 1.310749888420105, G Loss: 0.7699154615402222\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KC3GkR5tpw_",
        "colab_type": "text"
      },
      "source": [
        "We now save the training results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl1uDGEOH1XK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd \n",
        "# intialise data of lists. \n",
        "data = {'Step': intervals, \n",
        "        'MIE': mies_batch,\n",
        "        'RMSE': rmses_batch,\n",
        "        'G_Loss': gen_loss,\n",
        "        'D_Loss': disc_loss\n",
        "        } \n",
        "# Create DataFrame \n",
        "df = pd.DataFrame(data) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDhhkbhwtyvV",
        "colab_type": "text"
      },
      "source": [
        "Let's plot the training steps, looking at the losses of Generator and Discriminator, as well as the model selection criteria we are testing (MIE and RMSE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJYWqzoF_J5m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "d00727c4-9753-4366-89a9-0a6744754348"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, (ax1,ax2,ax3,ax4) = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))\n",
        "ax1.plot(df[\"Step\"], df[\"MIE\"])\n",
        "ax2.plot(df[\"Step\"], df[\"RMSE\"])\n",
        "ax3.plot(df[\"Step\"], df[\"G_Loss\"])\n",
        "ax4.plot(df[\"Step\"], df[\"D_Loss\"])\n",
        "ax1.set_title(\"MIE throughout training\")\n",
        "ax2.set_title(\"RMSE throughout training\")\n",
        "ax3.set_title(\"Generator loss throughout training\")\n",
        "ax4.set_title(\"Discriminator loss throughout training\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Discriminator loss throughout training')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5TjDA2AWTmuGLyPNF5G+De5YxVI1Mk2jW/dlk75MuB/B/g+9hPoxxal6m0uduBnCAiOyb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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-H3ghbhXVcqe",
        "colab_type": "text"
      },
      "source": [
        "## Data Sampling\n",
        "\n",
        "Let's see how the generators look. First, how does data from the final generator look like?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgeleW3PVcqf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "3c826274-479c-4234-a390-f37b0c925e67"
      },
      "source": [
        "# generation to image\n",
        "G.eval()\n",
        "imshow(get_sample_image(G, n_noise), cmap='gray')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbed9e40cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RZBuSfDt3sI7eGQ0AUXMWobw5SCa5rWQDcLvD1N82RkDJssylS5dIkiSy2Wzk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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2EWjMb0QzN-",
        "colab_type": "text"
      },
      "source": [
        "Let us now check the training steps at which we had the lowest values of MIE / RMSE respectively:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnxSV9rKQqfE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "step_MIE = df[\"Step\"][df[\"MIE\"] == min(df[\"MIE\"])]\n",
        "step_RMSE = df[\"Step\"][df[\"RMSE\"] == min(df[\"RMSE\"])]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_IPl7BITC2g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "75bb0ddc-eee7-4134-fc94-880890979357"
      },
      "source": [
        "step_MIE"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "312    93600\n",
              "Name: Step, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzNhnhKNJfBl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "90778b4d-0b60-4290-b590-ea1e5b8af8f2"
      },
      "source": [
        "step_RMSE"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "312    93600\n",
              "Name: Step, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqS3gnOQvcjz",
        "colab_type": "text"
      },
      "source": [
        "Let's check data from the generator with the lowest MIE:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKW-jSPLSob4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "b8b006a3-0c56-4cdc-e9bd-702e708f755f"
      },
      "source": [
        "G_opt = torch.load(\"gen_iter \" + str(step_MIE.values[0]) + \".pkl.gz\")\n",
        "G_opt.eval()\n",
        "imshow(get_sample_image(G_opt, n_noise), cmap='gray')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbed9e2cc18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u3DgUFBSgtLQUhw8fxqZNm7B8+XI0NTXBZrPBarVKPpAD4YSEBMyfPx8nT56k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\n5ZcxY8YMWK3WgGHMRI6KipI2UkVFRQUcYHz99ddht9tbHKv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pmNHR0UhLS5OQWD3PFysf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\nH5w6jEAHddAfnDqMQAd10B+c/h+mUNr6+NdXDwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukzKn_B5vhf_",
        "colab_type": "text"
      },
      "source": [
        "And now data from the generator with the lowest RMSE:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DEfYyYwkchn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "702d392f-5c3e-4c42-fa14-f55641d877cf"
      },
      "source": [
        "G_opt = torch.load(\"gen_iter \" + str(step_RMSE.values[0]) + \".pkl.gz\")\n",
        "G_opt.eval()\n",
        "imshow(get_sample_image(G_opt, n_noise), cmap='gray')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbed9d8dd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HHnkESUlJfp/0YxgGPXv2REFBAQwGA0wmEx577DGcPHkSHMfhzTffRGRkZEDf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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S643_IKjwX6B",
        "colab_type": "text"
      },
      "source": [
        "## Moran's I embedding\n",
        "\n",
        "To visualize how the Moran's I embedds spatial patterns, here is a random image from the last batch, together with its corresponding Moran's I values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYiuNFD0joD9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "597c5322-5bd8-4ced-959d-eb6dbf9fc56e"
      },
      "source": [
        "plt.gray()\n",
        "\n",
        "fig, (ax1,ax2) = plt.subplots(nrows=1, ncols=2, figsize=(9, 6))\n",
        "ax2.imshow(pysal.explore.esda.moran.Moran_Local(x[42][0].cpu(),w,permutations=1).Is.reshape(28,28))\n",
        "ax1.imshow(x[42][0].cpu())\n",
        "ax1.set_title(\"Image (grayscale values)\")\n",
        "ax2.set_title(\"Local Moran's I of grayscale values\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, \"Local Moran's I of grayscale values\")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2crYjXV1dMWXKlGHKFMBg9PT0qLe3t2E7MrFu4YhYI2lN8bjb9n2SZuVNEcBY\nRjsCjD+DugfD9lxJr5e0pJh0ou27bJ9je5smyyy0vdT20pYyBTAm0I4A40PtJZIXZrS3kPQrSV+I\niMtsT5f0qKSQdIpS9+eHa9bBJRKgg4zUJZI+OdoRLpEAnaPqEsmAejBsT5J0qaQLIuIySYqIRyJi\nY0T0SvqOpH1yJQxg7KEdAcaXgXyLxJLOlnRfRHy1NH1GabZ3S7o7f3oAxgLaEWD8Gci3SPaX9GtJ\nyyT1FpNPlnSUpHlKXZsrJR1f3MhVtS4ukQAdZAS/RZKtHeESCdA5qi6RDPgejBwoMIDOMtL3YORA\ngQF0jpbvwQAAABgMCgwAAJAdBQYAAMiOAgMAAGRHgQEAALKjwAAAANlRYAAAgOwoMAAAQHYUGAAA\nIDsKDAAAkB0FBgAAyI4CAwAAZEeBAQAAsqPAAAAA2VFgAACA7CaO8PYelfRQ6fl2xbRORo6t6/T8\npPGZ4w4Z1zViIuLRDRs20I7k1en5SeSYy4i1I46IjNsZHNtLI2KvtiUwAOTYuk7PTyLH0Ww0HJdO\nz7HT85PIMZeRzJFLJAAAIDsKDAAAkF27C4yz2rz9gSDH1nV6fhI5jmaj4bh0eo6dnp9EjrmMWI5t\nvQcDAACMTe3uwQAAAGNQ2woM22+3/TvbK2x/pl15NGN7pe1ltu+wvbTd+UiS7XNsr7N9d2naK2wv\ntr28+HebDsxxke3VxbG8w/Yhbc5xju1f2r7X9j22Tyqmd8SxrMivo45ju3V6GyLRjmTOsWPO/05v\nQ2pyHLHj2JZLJLYnSPq9pIMkrZJ0i6SjIuLeEU+mCdsrJe0VER3znWbb8yU9Jen8iHhdMe3Lkh6L\niC8Wjew2EfHpDstxkaSnIuIr7cqrzPYMSTMi4jbbUyXdKulwScepA45lRX5HqIOOYzuNhjZEoh3J\nnOMidcj53+ltSE2OI9aOtKsHYx9JKyLigYh4VtLFkg5rUy6jRkTcIOmxfpMPk3Re8fg8pROobZrk\n2FEiYk1E3FY87pZ0n6RZ6pBjWZEfXkQbMkS0I63r9DakyKvt7Ui7CoxZkh4uPV+lzmtAQ9LPbd9q\ne2G7k6kwPSLWFI/XSprezmQqnGj7rqLrs63dr2W250p6vaQl6sBj2S8/qUOPYxuMhjZEoh3JrePO\n/05vQ6T2tSPc5Nnc/hHxBkkHSzqh6LLraJGud3Xi14K+JWknSfMkrZF0WnvTSWxvIelSSZ+IiCfL\nsU44lg3y68jjiEq0I/l03Pnf6W2I1N52pF0FxmpJc0rPZxfTOkZErC7+XSfpcqUu2U70SHGtre+a\n27o25/MyEfFIRGyMiF5J3y+NIegAAAFJSURBVFEHHEvbk5RedBdExGXF5I45lo3y68Tj2EYd34ZI\ntCM5ddr53+ltSJFDW9uRdhUYt0jaxfaOtjeRdKSkK9uUy8vY3ry4KUa2N5f0Vkl3Vy/VNldKOrZ4\nfKykK9qYS0N9L7jCu9XmY2nbks6WdF9EfLUU6ohj2Sy/TjuObdbRbYhEO5JbJ53/nd6GSJ3RjrRt\noK3iqzFfkzRB0jkR8YW2JNKA7b9S+rQhpV+cvbAT8rN9kaQFSr+G94ikz0n6saRLJL1K6Zdqj4iI\ntt0c1STHBUrdcSFppaTjS9cpR5zt/SX9WtIySb3F5JOVrk+2/VhW5HeUOug4tlsntyES7cgw5LhA\nHXL+d3obUpPjiLUjjOQJAACy4yZPAACQHQUGAADIjgIDAABkR4EBAACyo8AAAADZUWAAAIDsKDAA\nAEB2FBgAACC7/w+neLNIKs4+lwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vKPuTZXkJqO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}