03_CE_BCE_iCarl.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "03_CE-BCE_iCarl.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8AlIcK8iKdP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d0e80fe3-2854-4c8d-818e-9d27f0f6fa99"
      },
      "source": [
        "pip install 'import_ipynb'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: import_ipynb in /usr/local/lib/python3.6/dist-packages (0.1.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNfj-qcxiQPm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "2798ac58-cbc5-44a8-d657-f4dae19f4f79"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "  \n",
        "%cd /content/drive/My\\ Drive/MLDLProjects/Lande_Napolitano_Pipoli/Project_root\n",
        " \n",
        " \n",
        "import import_ipynb\n",
        "from cifar100_Rseed import cifar_100\n",
        "from net import resnet32"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/My Drive/MLDLProjects/Lande_Napolitano_Pipoli/Project_root\n",
            "importing Jupyter notebook from cifar100_Rseed.ipynb\n",
            "IMPORT CIFAR DONE Rseed\n",
            "importing Jupyter notebook from net.ipynb\n",
            "IMPORT NET DONE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ndwgma5sBg0Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d8c38d5a-240f-44db-caf2-0e53d19bce30"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics import confusion_matrix as s_cm\n",
        "import seaborn as sn\n",
        "import matplotlib.patches as mpatches"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvfpM-WHD5cP",
        "colab_type": "text"
      },
      "source": [
        "iCarl Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeBTHHFrffBg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.autograd import Variable\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from net import resnet32\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "import math\n",
        "from sklearn.preprocessing import normalize\n",
        "import copy\n",
        "\n",
        "\n",
        "\n",
        "class iCaRLNet(nn.Module):\n",
        "    def __init__(self, feature_size, n_classes, lr=0.2, gamma=0.2, weight_decay=1e-5, milestone=[49, 63], batch_size=128, num_epochs=70, momentum=0.9):\n",
        "        # Network architecture\n",
        "        super(iCaRLNet, self).__init__()\n",
        "        self.net = resnet32(num_classes=n_classes)\n",
        "        self.feature_extractor = self.net.get_fm_out\n",
        "        self.lr = lr\n",
        "        self.gamma = gamma\n",
        "        self.weight_decay = weight_decay\n",
        "        self.milestone = milestone\n",
        "        self.batch_size = batch_size\n",
        "        self.num_epochs = num_epochs\n",
        "        self.n_classes = 0\n",
        "        self.n_known = 0\n",
        "        self.momentum = momentum\n",
        "        self.classificationLoss = []\n",
        "        self.distillationLoss = []\n",
        "        # List containing exemplar_sets\n",
        "        # Each exemplar_set is a np.array of N images\n",
        "        # with shape (N, C, H, W)\n",
        "        self.exemplar_sets = []\n",
        "\n",
        "        # Learning method\n",
        "        self.MSE= nn.MSELoss() \n",
        "        self.BCEwithL = nn.BCEWithLogitsLoss()  #reduction \n",
        "        self.cls_loss = nn.CrossEntropyLoss()\n",
        "        self.dist_loss = nn.BCELoss()   \n",
        "\n",
        "        # Means of exemplars\n",
        "        self.compute_means = True\n",
        "        self.exemplar_means = []\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "      self.net = self.net.cuda()\n",
        "      return self.net.forward(x)\n",
        "\n",
        "\n",
        "    def increment_classes(self, n):\n",
        "        \"\"\"Add n classes in the final fc layer\"\"\"\n",
        "        in_features = self.fc.in_features\n",
        "        out_features = self.fc.out_features\n",
        "        weight = self.fc.weight.data\n",
        "\n",
        "        self.fc = nn.Linear(in_features, out_features + n, bias=False)\n",
        "        self.fc.weight.data[:out_features] = weight\n",
        "        self.n_classes += n\n",
        "\n",
        "    def classify(self, x, cif):\n",
        "        \n",
        "\n",
        "        if self.compute_means:\n",
        "            print(\"Computing mean of exemplars...\")\n",
        "            exemplar_means = []\n",
        "            for P_y in self.exemplar_sets:  #P_y list of indices\n",
        "                features = np.zeros((0,64))\n",
        "                sub = Subset(cif, P_y)\n",
        "                dl = torch.utils.data.DataLoader(sub, batch_size=self.batch_size,\n",
        "                                               shuffle=False, num_workers=4)\n",
        "                # Extract feature for each exemplar in P_y\n",
        "                with torch.no_grad():\n",
        "                  for ind, ex, lab in dl:\n",
        "                    ex = Variable(ex).cuda()\n",
        "                    feature = self.feature_extractor(ex).data.cpu().numpy()\n",
        "                    feature = normalize(feature, axis=1, norm='l2')\n",
        "                    features = np.concatenate((features,feature), axis=0)\n",
        "\n",
        "                features = torch.tensor(features)\n",
        "                mu_y = features.mean(0).squeeze()\n",
        "                mu_y.data = mu_y.data / torch.norm(mu_y, p=2)  # L2 Normalize\n",
        "                exemplar_means.append(mu_y)\n",
        "\n",
        "            self.exemplar_means = exemplar_means\n",
        "            self.compute_means = False\n",
        "            print(\"Done\")\n",
        "\n",
        "\n",
        "        exemplar_means = self.exemplar_means\n",
        "        means = torch.stack(exemplar_means)  # (n_classes, feature_size)\n",
        "        means = torch.stack([means] * self.batch_size)  # (batch_size, n_classes, feature_size)\n",
        "        means = means.transpose(1, 2)  # (batch_size, feature_size, n_classes)\n",
        "        feature = self.feature_extractor(x)  # (batch_size, feature_size)\n",
        "        for i in range(feature.size(0)):  # Normalize\n",
        "            feature.data[i] = feature.data[i] / torch.norm(feature.data[i], p=2)\n",
        "        feature = feature.unsqueeze(2)  # (batch_size, feature_size, 1)\n",
        "        feature = feature.expand_as(means)  # (batch_size, feature_size, n_classes)\n",
        "        feature = feature.cuda()\n",
        "        means = means.cuda()\n",
        "        dists = torch.sqrt((feature - means).pow(2).sum(1)).squeeze()  # (batch_size, n_classes)\n",
        "        _, preds = dists.min(1) \n",
        "        return preds\n",
        "\n",
        "    def construct_exemplar_set(self, images, m):\n",
        "        \n",
        "        # Compute and cache features for each example\n",
        "        features = np.zeros((0,64))\n",
        "        indices = np.zeros((0), dtype=int)\n",
        "        dl = torch.utils.data.DataLoader(images, batch_size=self.batch_size,\n",
        "                                               shuffle=False, num_workers=4)\n",
        "        with torch.no_grad():\n",
        "          for ind, img, lab in dl:\n",
        "            x = Variable(img).cuda()\n",
        "            feature = self.feature_extractor(x).data.cpu().numpy()\n",
        "            feature = normalize(feature, axis=1, norm='l2')\n",
        "            features = np.concatenate((features,feature), axis=0)\n",
        "            indices = np.concatenate((indices,ind), axis=0)\n",
        "\n",
        "       \n",
        "        class_mean = np.mean(features, axis=0)\n",
        "        class_mean = class_mean / np.linalg.norm(class_mean)  # Normalize\n",
        "        exemplar_set = []\n",
        "        exemplar_features = np.zeros((0,64))\n",
        "\n",
        "        for k in range(1, int(m)+1):\n",
        "            S = np.sum(exemplar_features, axis=0)\n",
        "            phi = features\n",
        "            mu = class_mean\n",
        "            mu_p = 1.0 / k * (phi + S)\n",
        "            mu_p = normalize(mu_p, axis=1, norm='l2')\n",
        "            i = np.argmin(np.sqrt(np.sum((mu - mu_p) ** 2, axis=1)))\n",
        "            exemplar_set.append(indices[i])\n",
        "            addfeature =  np.expand_dims(features[i], axis=0)\n",
        "            exemplar_features = np.concatenate((exemplar_features,addfeature), axis=0)\n",
        "            #remove duplicates\n",
        "            features = np.delete(features, i, 0)\n",
        "            indices = np.delete(indices, i, 0)\n",
        "        self.exemplar_sets.append(exemplar_set)\n",
        "        \n",
        "\n",
        "\n",
        "    def reduce_exemplar_sets(self, m):\n",
        "        for y, P_y in enumerate(self.exemplar_sets):\n",
        "            self.exemplar_sets[y] = P_y[:int(m)]\n",
        "\n",
        "    def combine_dataset_with_exemplars(self, cifar):\n",
        "        newindexes = []\n",
        "        for y, P_y in enumerate(self.exemplar_sets):\n",
        "            exemplar_images = P_y\n",
        "            exemplar_labels = [y] * len(P_y)\n",
        "            print(exemplar_images[0].shape)\n",
        "            newindexes+=cifar.data_append(exemplar_images, exemplar_labels)\n",
        "        return newindexes\n",
        "\n",
        "    def exemplarIndexes(self):\n",
        "      Indexes = []\n",
        "      for P_y in self.exemplar_sets:\n",
        "        Indexes += P_y\n",
        "      return Indexes\n",
        "\n",
        "\n",
        "    def update_representation(self, cifar, batchindexes, diz, file):\n",
        "        prev_model = copy.deepcopy(self)\n",
        "        prev_model = prev_model.eval().cuda()\n",
        "        self.compute_means = True\n",
        "        self.n_classes += 10\n",
        "        print(len(batchindexes))\n",
        "        # Form combined training set\n",
        "        newindexes = []\n",
        "        if self.n_classes > 10:\n",
        "          newindexes = self.exemplarIndexes() \n",
        "        newindexes += list(batchindexes)\n",
        "        print(len(newindexes))\n",
        "        reprdata = Subset(cifar, newindexes)\n",
        "\n",
        "        loader = torch.utils.data.DataLoader(reprdata, batch_size=self.batch_size,\n",
        "                                             shuffle=True, num_workers=4, drop_last=True)\n",
        "\n",
        "        # Run network training\n",
        "\n",
        "        optimizer = optim.SGD(self.net.parameters(), lr=self.lr, weight_decay=self.weight_decay, momentum=self.momentum)\n",
        "        scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=self.milestone, gamma=self.gamma)\n",
        "\n",
        "        \n",
        "        print(\"TRAIN\")\n",
        "        for epoch in range(self.num_epochs):\n",
        "            losses = []\n",
        "            classlosses = []\n",
        "            distlosses = []\n",
        "            for indices, images, labels in loader:\n",
        "              labels = torch.tensor([torch.tensor(diz[c.item()]) for c in labels])\n",
        "              images = Variable(torch.FloatTensor(images)).cuda()\n",
        "              labels = Variable(labels).cuda()\n",
        "              optimizer.zero_grad()\n",
        "              g = self.forward(images)\n",
        "              y_hot = F.one_hot(labels, self.n_classes).float().cuda()\n",
        "              # cause hot are long and sigmoid is float/double\n",
        "\n",
        "              if self.n_known > 0:\n",
        "                q = prev_model.forward(images)\n",
        "                q = torch.sigmoid(q)\n",
        "                distloss = self.BCEwithL(g[:,:self.n_known],q[:,:self.n_known])\n",
        "                distlosses.append(distloss.item())\n",
        "                indexes = []\n",
        "\n",
        "                for i, li in enumerate(labels):\n",
        "                  if li > self.n_known:\n",
        "                    indexes.append(i)\n",
        "\n",
        "                loss = self.cls_loss(g[indexes,:], labels[indexes]) \n",
        "                classlosses.append(loss.item())\n",
        "                loss += distloss\n",
        "\n",
        "              else:\n",
        "                loss = self.cls_loss(g, labels) \n",
        "                classlosses.append(loss.item())\n",
        "\n",
        "              losses.append(loss.item())\n",
        "              loss.backward()\n",
        "              optimizer.step()\n",
        "\n",
        "            #save data for plot\n",
        "            self.classificationLoss.append(np.mean(np.array(classlosses)))\n",
        "            if len(distlosses) == 0:\n",
        "              self.distillationLoss.append(0)\n",
        "            else:\n",
        "              self.distillationLoss.append(np.mean(np.array(distlosses)))\n",
        "\n",
        "\n",
        "            if(epoch + 1) % 5 == 0:\n",
        "              if len(distlosses) == 0:\n",
        "                mean_dist = 0\n",
        "              else:\n",
        "                mean_dist = np.mean(np.array(distlosses))\n",
        "              print('Epoch [%d/%d], Average_Loss(tot, dist, class): %.4f, %.4f, %.4f' \\\n",
        "                    % (epoch + 1, self.num_epochs, np.mean(np.array(losses)), mean_dist, np.mean(np.array(classlosses))), file=file)\n",
        "              print('Epoch [%d/%d], Average_Loss(tot, dist, class): %.4f, %.4f, %.4f' \\\n",
        "                    % (epoch + 1, self.num_epochs, np.mean(np.array(losses)), mean_dist, np.mean(np.array(classlosses))))\n",
        "            scheduler.step()\n",
        "\n",
        "    def compute_confusion_matrix(self, cif, plot=False, title=\"Title\"):\n",
        "      \n",
        "        cifar_test = cifar_100(100, 'test')\n",
        "        dl = torch.utils.data.DataLoader(cifar_test, batch_size=128,shuffle=True, num_workers=4, drop_last=True)\n",
        "        mapper = cif.get_dictionary()\n",
        "        y_true = []\n",
        "        y_pred = []\n",
        "        for _, images, labels in dl:\n",
        "            images = Variable(images).cuda()\n",
        "            y_true.extend(labels)\n",
        "            y_pred_tmp = self.classify(images, cif)\n",
        "            y_pred_tmp = [p.item() for p in y_pred_tmp]\n",
        "            y_pred.extend(y_pred_tmp)\n",
        "        \n",
        "        y_true = [mapper[l.item()] for l in y_true]\n",
        "        acc_matrix = s_cm(y_true, y_pred)\n",
        "\n",
        "        if plot:\n",
        "            plt.figure(figsize=(11,11))\n",
        "            df_cm = pd.DataFrame(acc_matrix)\n",
        "            sn.heatmap(df_cm, square=True, xticklabels=20, yticklabels=20)\n",
        "            plt.title(f\"{title} confusion matrix\")\n",
        "            plt.show()\n",
        "\n",
        "        return acc_matrix\n",
        "\n",
        "    def plotLosses(self, title = \"Title\"):\n",
        "      plt.rcParams['font.size'] = 22\n",
        "      x=np.arange(1, self.num_epochs*self.n_classes/10+1, 1)\n",
        "      plt.figure(figsize=(30, 15))\n",
        "      b_patch = mpatches.Patch(color='blue', label='Classification Loss')\n",
        "      r_patch = mpatches.Patch(color='red', label='Distillation Loss')\n",
        "      plt.legend(handles=[b_patch,r_patch])\n",
        "      plt.plot(x ,self.distillationLoss,'r', x, self.classificationLoss,'b')\n",
        "      plt.title(f\"{title} Loss\")\n",
        "      xT=np.arange(0, self.num_epochs*self.n_classes/10+1, self.num_epochs)\n",
        "      plt.xticks(xT)\n",
        "      plt.savefig(f'1_accStats.png')\n",
        "      plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bx4wSwCsD8TC",
        "colab_type": "text"
      },
      "source": [
        "Main"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qv4ZVuYviDK1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7875a424-36dc-4b6c-a01b-01274609a500"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.datasets as dsets\n",
        "import torchvision.models as models\n",
        "import torchvision.transforms as transforms\n",
        "from torch.autograd import Variable\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import Subset, DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import random\n",
        "\n",
        "torch.cuda.current_device()\n",
        "torch.cuda._initialized = True\n",
        "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "print(DEVICE)\n",
        "\n",
        "\n",
        "# Hyper Parameters\n",
        "total_classes = 100\n",
        "num_classes = 10\n",
        "\n",
        "def accuracy_on(dl, model, cif):\n",
        "  total = 0\n",
        "  correct = 0\n",
        "  \n",
        "  # it iterates over a data loader so it returns batches of data\n",
        "  for ind, images, labels in dl: \n",
        "    labels = torch.tensor([torch.tensor(diz[c.item()]) for c in labels])\n",
        "    images = Variable(images).cuda()\n",
        "    pred = model.classify(images, cif)#.cuda()\n",
        "    total += len(labels)\n",
        "\n",
        "    n = 0\n",
        "    for p, l in zip(pred, labels):\n",
        "      if p == l:\n",
        "        n += 1\n",
        "    correct += n\n",
        "\n",
        "  return correct / total\n",
        "\n",
        "# Initialize CNN\n",
        "K = 2000  # total number of exemplars\n",
        "icarl = iCaRLNet(64, 100, num_epochs=70, lr=0.2)\n",
        "icarl = icarl.to(DEVICE)\n",
        "BATCH_SIZE = 128\n",
        "acc_vect = []\n",
        "\n",
        "randomseed = 981\n",
        "cifarTrain = cifar_100(num_classes, 'train', randomseed)\n",
        "cifarTest = cifar_100(num_classes, 'test', randomseed)\n",
        "randomlist = cifarTrain.get_classes_list()\n",
        "diz = cifarTrain.get_dictionary()\n",
        "\n",
        "with open(\"03 - Print CE BCE.txt\", 'w') as file:\n",
        "  for s in range(10):\n",
        "      # Load Datasets\n",
        "      print(\"Loading training examples for classes\", randomlist[s*num_classes:s*num_classes + num_classes], file=file)\n",
        "      print(\"Loading training examples for classes\", randomlist[s*num_classes:s*num_classes + num_classes])\n",
        "      batchindexes = cifarTrain.getClassIndexes(randomlist[s*num_classes:s*num_classes + num_classes])\n",
        "      batch = Subset(cifarTrain, batchindexes)\n",
        "\n",
        "      print(\"Batch size: {}\".format(len(batch)))\n",
        "      testindexes = cifarTest.getClassIndexes(randomlist[0:s*num_classes + num_classes])\n",
        "      test_set = Subset(cifarTest, testindexes)\n",
        "\n",
        "\n",
        "      train_loader = torch.utils.data.DataLoader(batch, batch_size=BATCH_SIZE,\n",
        "                                                shuffle=True, num_workers=4, drop_last=True)\n",
        "\n",
        "      test_loader = torch.utils.data.DataLoader(test_set, batch_size=BATCH_SIZE,\n",
        "                                                shuffle=False, num_workers=4, drop_last=True)\n",
        "\n",
        "\n",
        "      # Update representation via BackProp\n",
        "      icarl.train()\n",
        "      icarl.update_representation(cifarTrain, batchindexes, diz, file)\n",
        "      icarl.eval()\n",
        "      m = K / icarl.n_classes\n",
        "\n",
        "      # Reduce exemplar sets for known classes\n",
        "      icarl.reduce_exemplar_sets(m)\n",
        "\n",
        "      # Construct exemplar sets for new classes\n",
        "      for y in randomlist[s*num_classes:s*num_classes + num_classes]:\n",
        "          print(\"Constructing exemplar set for class-%d...\" % (y))\n",
        "          imagesInd = cifarTrain.getClassIndexes([y])\n",
        "          images = Subset(cifarTrain, imagesInd)\n",
        "          icarl.construct_exemplar_set(images, m)\n",
        "          print(\"Done exemplars\")\n",
        "\n",
        "      icarl.n_known = icarl.n_classes\n",
        "      print(\"iCaRL classes: %d\" % icarl.n_known)\n",
        "\n",
        "      total = 0.0\n",
        "      correct = 0.0\n",
        "\n",
        "      print(len(train_loader))\n",
        "      for indices, images, labels in train_loader:\n",
        "          labels = [torch.tensor(diz[c.item()]) for c in labels]\n",
        "          labels = torch.tensor(labels)\n",
        "          images = Variable(images).cuda()\n",
        "          preds = icarl.classify(images, cifarTrain)\n",
        "          total = total + len(labels)\n",
        "          correct += (preds.data.cpu() == labels).sum()\n",
        "\n",
        "      print('Train Accuracy: %.4f %%' % (100.0 * correct / total), file=file)\n",
        "      print('Train Accuracy: %.4f %%' % (100.0 * correct / total))\n",
        "      print(f'Train Accuracy: {accuracy_on(train_loader, icarl, cifarTrain)}')\n",
        "      total = 0.0\n",
        "      correct = 0.0\n",
        "\n",
        "      for indices, images, labels in test_loader:\n",
        "          labels = [torch.tensor(diz[c.item()]) for c in labels]\n",
        "          labels = torch.tensor(labels)\n",
        "          images = Variable(images).cuda()\n",
        "          preds = icarl.classify(images, cifarTrain)\n",
        "          total = total + len(labels)\n",
        "          correct += (preds.data.cpu() == labels).sum()\n",
        "\n",
        "      print('Test Accuracy: %.4f %%' % (100.0 * correct / total), file=file)\n",
        "      print('Test Accuracy: %.4f %%' % (100.0 * correct / total))\n",
        "      print(f'Train Accuracy: {accuracy_on(test_loader, icarl, cifarTrain)}')\n",
        "      acc_vect.append(100.0 * correct.item() / total)\n",
        "      \n",
        "\n",
        "  print(acc_vect)\n",
        "  print(acc_vect, file=file)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Loading training examples for classes [28, 7, 17, 45, 46, 91, 63, 13, 95, 59]\n",
            "Batch size: 5000\n",
            "5000\n",
            "5000\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 1.5858, 0.0000, 1.5858\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 1.2590, 0.0000, 1.2590\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 1.0312, 0.0000, 1.0312\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.8117, 0.0000, 0.8117\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.6378, 0.0000, 0.6378\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.5160, 0.0000, 0.5160\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.4335, 0.0000, 0.4335\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.3080, 0.0000, 0.3080\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.2641, 0.0000, 0.2641\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1404, 0.0000, 0.1404\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.0708, 0.0000, 0.0708\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.0503, 0.0000, 0.0503\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.0291, 0.0000, 0.0291\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.0281, 0.0000, 0.0281\n",
            "Constructing exemplar set for class-28...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-7...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-17...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-45...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-46...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-91...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-63...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-13...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-95...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-59...\n",
            "Done exemplars\n",
            "iCaRL classes: 10\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 98.6779 %\n",
            "Train Accuracy: 0.9865785256410257\n",
            "Test Accuracy: 83.9286 %\n",
            "Train Accuracy: 0.8392857142857143\n",
            "Loading training examples for classes [90, 69, 93, 22, 20, 39, 6, 61, 19, 81]\n",
            "Batch size: 5000\n",
            "5000\n",
            "7000\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 1.4206, 0.0229, 1.3977\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.8703, 0.0137, 0.8566\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.6356, 0.0120, 0.6235\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.5018, 0.0109, 0.4909\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.3950, 0.0100, 0.3850\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.3444, 0.0101, 0.3343\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.2976, 0.0100, 0.2876\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.2672, 0.0100, 0.2572\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.1982, 0.0096, 0.1886\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1199, 0.0097, 0.1103\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.0642, 0.0095, 0.0547\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.0367, 0.0093, 0.0274\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.0429, 0.0093, 0.0336\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.0312, 0.0093, 0.0219\n",
            "Constructing exemplar set for class-90...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-69...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-93...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-22...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-20...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-39...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-6...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-61...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-19...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-81...\n",
            "Done exemplars\n",
            "iCaRL classes: 20\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 80.3686 %\n",
            "Train Accuracy: 0.7982772435897436\n",
            "Test Accuracy: 49.3750 %\n",
            "Train Accuracy: 0.49375\n",
            "Loading training examples for classes [96, 37, 76, 40, 43, 8, 1, 83, 2, 49]\n",
            "Batch size: 5000\n",
            "5000\n",
            "7000\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 1.0193, 0.0441, 0.9752\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.5581, 0.0402, 0.5179\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.4480, 0.0395, 0.4085\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.3708, 0.0392, 0.3316\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.2714, 0.0385, 0.2329\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.2360, 0.0384, 0.1976\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.1791, 0.0381, 0.1410\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.1647, 0.0383, 0.1264\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.1829, 0.0381, 0.1448\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1095, 0.0380, 0.0715\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.0679, 0.0376, 0.0303\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.0611, 0.0377, 0.0234\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.0507, 0.0377, 0.0130\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.0603, 0.0379, 0.0224\n",
            "Constructing exemplar set for class-96...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-37...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-76...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-40...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-43...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-8...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-1...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-83...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-2...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-49...\n",
            "Done exemplars\n",
            "iCaRL classes: 30\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 83.0128 %\n",
            "Train Accuracy: 0.8243189102564102\n",
            "Test Accuracy: 38.1114 %\n",
            "Train Accuracy: 0.3811141304347826\n",
            "Loading training examples for classes [12, 27, 68, 29, 80, 86, 24, 11, 26, 51]\n",
            "Batch size: 5000\n",
            "5000\n",
            "6980\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 1.2780, 0.0681, 1.2099\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.8242, 0.0587, 0.7655\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.6476, 0.0568, 0.5908\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.5360, 0.0558, 0.4803\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.4689, 0.0553, 0.4137\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.4128, 0.0551, 0.3577\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.3629, 0.0549, 0.3080\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.3268, 0.0545, 0.2723\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.2937, 0.0542, 0.2396\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1824, 0.0540, 0.1284\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.1111, 0.0536, 0.0575\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.0957, 0.0536, 0.0421\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.0905, 0.0536, 0.0368\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.0775, 0.0534, 0.0242\n",
            "Constructing exemplar set for class-12...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-27...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-68...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-29...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-80...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-86...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-24...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-11...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-26...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-51...\n",
            "Done exemplars\n",
            "iCaRL classes: 40\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 76.2420 %\n",
            "Train Accuracy: 0.7632211538461539\n",
            "Test Accuracy: 29.7127 %\n",
            "Train Accuracy: 0.29712701612903225\n",
            "Loading training examples for classes [82, 16, 72, 52, 60, 36, 14, 41, 73, 38]\n",
            "Batch size: 5000\n",
            "5000\n",
            "7000\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 0.7861, 0.0709, 0.7151\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.5627, 0.0653, 0.4973\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.4284, 0.0637, 0.3647\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.3670, 0.0630, 0.3040\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.3370, 0.0627, 0.2743\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.2681, 0.0626, 0.2055\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.2196, 0.0624, 0.1572\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.2054, 0.0621, 0.1433\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.1737, 0.0621, 0.1115\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1377, 0.0619, 0.0758\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.0962, 0.0618, 0.0343\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.0901, 0.0618, 0.0283\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.0805, 0.0619, 0.0187\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.0808, 0.0620, 0.0189\n",
            "Constructing exemplar set for class-82...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-16...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-72...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-52...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-60...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-36...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-14...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-41...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-73...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-38...\n",
            "Done exemplars\n",
            "iCaRL classes: 50\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 76.5024 %\n",
            "Train Accuracy: 0.7580128205128205\n",
            "Test Accuracy: 25.2404 %\n",
            "Train Accuracy: 0.25240384615384615\n",
            "Loading training examples for classes [79, 50, 94, 54, 42, 15, 87, 5, 75, 78]\n",
            "Batch size: 5000\n",
            "5000\n",
            "7000\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 0.7950, 0.0786, 0.7164\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.5356, 0.0736, 0.4620\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.4177, 0.0717, 0.3460\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.4030, 0.0717, 0.3313\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.3438, 0.0712, 0.2727\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.2894, 0.0706, 0.2188\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.2605, 0.0702, 0.1903\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.2248, 0.0698, 0.1550\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.2178, 0.0701, 0.1477\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1549, 0.0699, 0.0849\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.1065, 0.0697, 0.0368\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.0934, 0.0695, 0.0238\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.0919, 0.0695, 0.0223\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.0872, 0.0695, 0.0178\n",
            "Constructing exemplar set for class-79...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-50...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-94...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-54...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-42...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-15...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-87...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-5...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-75...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-78...\n",
            "Done exemplars\n",
            "iCaRL classes: 60\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 75.7212 %\n",
            "Train Accuracy: 0.7574118589743589\n",
            "Test Accuracy: 21.0768 %\n",
            "Train Accuracy: 0.21076766304347827\n",
            "Loading training examples for classes [35, 66, 99, 98, 3, 58, 77, 71, 23, 47]\n",
            "Batch size: 5000\n",
            "5000\n",
            "6980\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 0.8781, 0.0910, 0.7871\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.5169, 0.0841, 0.4329\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.4290, 0.0822, 0.3468\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.3255, 0.0816, 0.2439\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.2864, 0.0813, 0.2051\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.2511, 0.0808, 0.1703\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.2286, 0.0806, 0.1480\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.2168, 0.0805, 0.1363\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.1849, 0.0804, 0.1045\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1554, 0.0806, 0.0748\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.1130, 0.0802, 0.0327\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.1037, 0.0803, 0.0234\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.0986, 0.0801, 0.0184\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.0983, 0.0803, 0.0181\n",
            "Constructing exemplar set for class-35...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-66...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-99...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-98...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-3...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-58...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-77...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-71...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-23...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-47...\n",
            "Done exemplars\n",
            "iCaRL classes: 70\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 67.6683 %\n",
            "Train Accuracy: 0.6784855769230769\n",
            "Test Accuracy: 17.5203 %\n",
            "Train Accuracy: 0.1752025462962963\n",
            "Loading training examples for classes [18, 64, 97, 34, 56, 89, 85, 30, 9, 25]\n",
            "Batch size: 5000\n",
            "5000\n",
            "6960\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 1.1980, 0.0959, 1.1021\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.7552, 0.0870, 0.6681\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.5759, 0.0840, 0.4919\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.4826, 0.0828, 0.3998\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.4025, 0.0822, 0.3204\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.3182, 0.0816, 0.2365\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.3073, 0.0813, 0.2260\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.2549, 0.0813, 0.1736\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.2465, 0.0808, 0.1657\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1799, 0.0807, 0.0992\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.1182, 0.0804, 0.0377\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.1129, 0.0808, 0.0321\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.1065, 0.0806, 0.0259\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.1034, 0.0806, 0.0228\n",
            "Constructing exemplar set for class-18...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-64...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-97...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-34...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-56...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-89...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-85...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-30...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-9...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-25...\n",
            "Done exemplars\n",
            "iCaRL classes: 80\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 72.2155 %\n",
            "Train Accuracy: 0.7191506410256411\n",
            "Test Accuracy: 15.1462 %\n",
            "Train Accuracy: 0.1514616935483871\n",
            "Loading training examples for classes [70, 48, 57, 84, 92, 53, 67, 65, 10, 31]\n",
            "Batch size: 5000\n",
            "5000\n",
            "7000\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 0.9518, 0.0958, 0.8561\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.6829, 0.0875, 0.5954\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.5511, 0.0855, 0.4656\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.4720, 0.0844, 0.3875\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.3968, 0.0842, 0.3126\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.3564, 0.0840, 0.2724\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.3365, 0.0835, 0.2530\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.3296, 0.0834, 0.2462\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.2523, 0.0834, 0.1690\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.1839, 0.0832, 0.1007\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.1332, 0.0830, 0.0502\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.1207, 0.0828, 0.0379\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.1115, 0.0829, 0.0286\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.1106, 0.0831, 0.0275\n",
            "Constructing exemplar set for class-70...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-48...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-57...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-84...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-92...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-53...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-67...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-65...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-10...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-31...\n",
            "Done exemplars\n",
            "iCaRL classes: 90\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 67.8686 %\n",
            "Train Accuracy: 0.6784855769230769\n",
            "Test Accuracy: 13.5379 %\n",
            "Train Accuracy: 0.1353794642857143\n",
            "Loading training examples for classes [88, 33, 4, 21, 74, 32, 62, 0, 55, 44]\n",
            "Batch size: 5000\n",
            "5000\n",
            "6980\n",
            "TRAIN\n",
            "Epoch [5/70], Average_Loss(tot, dist, class): 1.1269, 0.0985, 1.0283\n",
            "Epoch [10/70], Average_Loss(tot, dist, class): 0.8981, 0.0885, 0.8096\n",
            "Epoch [15/70], Average_Loss(tot, dist, class): 0.7367, 0.0865, 0.6502\n",
            "Epoch [20/70], Average_Loss(tot, dist, class): 0.6493, 0.0849, 0.5644\n",
            "Epoch [25/70], Average_Loss(tot, dist, class): 0.5517, 0.0843, 0.4674\n",
            "Epoch [30/70], Average_Loss(tot, dist, class): 0.4957, 0.0836, 0.4121\n",
            "Epoch [35/70], Average_Loss(tot, dist, class): 0.4197, 0.0833, 0.3363\n",
            "Epoch [40/70], Average_Loss(tot, dist, class): 0.3900, 0.0830, 0.3070\n",
            "Epoch [45/70], Average_Loss(tot, dist, class): 0.3582, 0.0831, 0.2751\n",
            "Epoch [50/70], Average_Loss(tot, dist, class): 0.2839, 0.0829, 0.2010\n",
            "Epoch [55/70], Average_Loss(tot, dist, class): 0.1831, 0.0825, 0.1006\n",
            "Epoch [60/70], Average_Loss(tot, dist, class): 0.1645, 0.0826, 0.0818\n",
            "Epoch [65/70], Average_Loss(tot, dist, class): 0.1507, 0.0828, 0.0679\n",
            "Epoch [70/70], Average_Loss(tot, dist, class): 0.1414, 0.0826, 0.0589\n",
            "Constructing exemplar set for class-88...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-33...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-4...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-21...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-74...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-32...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-62...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-0...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-55...\n",
            "Done exemplars\n",
            "Constructing exemplar set for class-44...\n",
            "Done exemplars\n",
            "iCaRL classes: 100\n",
            "39\n",
            "Computing mean of exemplars...\n",
            "Done\n",
            "Train Accuracy: 67.4279 %\n",
            "Train Accuracy: 0.6742788461538461\n",
            "Test Accuracy: 11.2280 %\n",
            "Train Accuracy: 0.11227964743589744\n",
            "[83.92857142857143, 49.375, 38.11141304347826, 29.712701612903224, 25.240384615384617, 21.076766304347824, 17.52025462962963, 15.14616935483871, 13.537946428571429, 11.227964743589743]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdonwgNRC_lg",
        "colab_type": "text"
      },
      "source": [
        "Plots\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZBmGAG4C_uh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "78db10ca-11cc-40e9-eebf-3a796cb6f933"
      },
      "source": [
        "plt.rcParams['font.size'] = 20\n",
        "icarl.compute_confusion_matrix(plot=True, title = \"03 - CE BCE\", cif = cifarTrain)\n",
        "icarl.plotLosses(title = \"03 - CE BCE\")\n",
        "plt.rcParams['font.size'] = 22\n",
        "icarl.compute_confusion_matrix(plot=True, title = \"03 - CE BCE\", cif = cifarTrain)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x792 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x792 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5,  0,  0, ...,  0,  2, 12],\n",
              "       [ 2,  0,  0, ...,  1,  1, 27],\n",
              "       [ 0,  0,  5, ...,  0,  0,  0],\n",
              "       ...,\n",
              "       [ 0,  0,  0, ..., 76,  0,  0],\n",
              "       [ 0,  0,  0, ...,  0, 26,  0],\n",
              "       [ 2,  0,  0, ...,  0,  2, 40]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwhwwPv-phbQ",
        "colab_type": "text"
      },
      "source": [
        "log 1 + x"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6ZXU7pQphiI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "de52a5c8-021e-4915-e8f7-497c1fb5c67a"
      },
      "source": [
        "title= \"03 - CE BCE\"\n",
        "cifarTest = cifar_100(100, 'test')\n",
        "dl = torch.utils.data.DataLoader(cifarTest, batch_size=128,shuffle=True, num_workers=4, drop_last=True)\n",
        "mapper = cifarTrain.get_dictionary()\n",
        "y_true = []\n",
        "y_pred = []\n",
        "for _, images, labels in dl:\n",
        "  images = Variable(images).cuda()\n",
        "  y_true.extend(labels)\n",
        "  y_pred_tmp = icarl.classify(images, cifarTrain)\n",
        "  y_pred_tmp = [p.item() for p in y_pred_tmp]\n",
        "  y_pred.extend(y_pred_tmp)\n",
        "\n",
        "y_true = [mapper[l.item()] for l in y_true]\n",
        "acc_matrix = s_cm(y_true, y_pred)\n",
        "acc_matrix = np.log(1+acc_matrix)\n",
        "\n",
        "plt.figure(figsize=(11,11))\n",
        "df_cm = pd.DataFrame(acc_matrix)\n",
        "sn.heatmap(df_cm, square=True, xticklabels=20, yticklabels=20)\n",
        "plt.title(f\"{title} confusion matrix\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x792 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RCwWTyS_YUji",
        "colab_type": "text"
      },
      "source": [
        "Acc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10cg_TnulPfO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6fb4f47a-4d81-4206-e4f3-8cc86c269064"
      },
      "source": [
        "title = \"03 - CE BCE\"\n",
        "plt.rcParams['font.size'] = 22\n",
        "x=np.arange(10, 110, 10)\n",
        "y=np.arange(0, 110, 10)\n",
        "print(x)\n",
        "plt.figure(figsize=(30, 15))\n",
        "b_patch = mpatches.Patch(color='green', label='Test Accuracy')\n",
        "plt.legend(handles=[b_patch])\n",
        "plt.plot(x ,acc_vect,'g', x ,acc_vect,'gs')\n",
        "plt.title(f\"{title} Test Accuracy\")\n",
        "plt.xticks(x)\n",
        "plt.yticks(y)\n",
        "# plt.savefig(f'1_accStats.png')\n",
        "plt.show()\n",
        "\n",
        "title =  \"03 - CE BCE\"\n",
        "plt.rcParams['font.size'] = 22\n",
        "x=np.arange(10, 110, 10)\n",
        "y=np.arange(0, 110, 10)\n",
        "print(x)\n",
        "plt.figure(figsize=(30, 15))\n",
        "b_patch = mpatches.Patch(color='green', label='Test Accuracy')\n",
        "plt.legend(handles=[b_patch])\n",
        "plt.plot(x ,acc_vect,'g')\n",
        "plt.title(f\"{title} Test Accuracy\")\n",
        "plt.xticks(x)\n",
        "plt.yticks(y)\n",
        "# plt.savefig(f'1_accStats.png')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 10  20  30  40  50  60  70  80  90 100]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nAAAAAABAt9LZI9kOHce9k0xJMjNJY5LBSY5I8vskRyb58jPu2a7j+OdNfO6jHceXbLFS6GUG9BuQ1vGtWfbgssxYsLm/dgoAAAAAAH1DZ49kT3/+Nkm+V1XV5KqqVlZV9XBVVT/OUwNZleQDpZSGLfWlpZTjSykLSykL77///i31sdDjjG0cm6N3PzrT503P6rWr//YNAAAAAADQR3T2SPanZ/z/6xuerKpqYZ76CcaS5KCOt59+SmzbDa9/hqefNvvTxk5WVfW1qqpGVVU1aqeddnpuxdDLtIxtSZI0z2muuQQAAAAAALqPzh7J7nmW/2/smld2HH/XcdxlE5+78wbXAs9iyPZDctqY03LF4isyZ8WcunMAAAAAAKBb6OyR7NfP+P+Oz3LNyzqOTz9B9quO4z4bu7iU8uIke27k84FnMWX0lAzfYXiaZjelbX1b3TkAAAAAAFC7Th3Jqqr6fZLbOl6+bcPzpZTBSd7Q8XJhx3FBkvuTvKaUMmYjH3tcnvobZ7d3fD7wNwzoNyCt41uz/KHlmbFgRt05AAAAAABQu85+kixJzu44nlpKGfX0m6WUgUkuSrJ9nvq7ZAuSpKqq9iT/3HHZRaWUlz/jnuFJvrTB5wKbYWzj2By9+9GZPm96Vq9dXXcOAAAAAADUqtNHsqqqfpLkvCQ7JLmllDKvlHJVkpVJ3pXk90neU1VV9YzbWpL8JMkeSZaXUq4spfwkyX/mqb9d1lpV1Y86ux16m5axLUmS5jnNNZcAAAAAAEC9uuJJslRVNTXJMUn+I8leSd6e5LEkM5LsXVXV8g2ub09yZJKmJCuSjE1yUJ564ux9VVVN6opu6G2GbD8kp405LVcsviJzVsypOwcAAAAAAGpT/voBrt5n1KhR1cKFC//2hdBHtK1vy14X7ZUkufOEOzOg34CaiwAAAAAAoHOUUn5ZVdWojZ3rkifJgO5jQL8BaR3fmuUPLc+MBTPqzgEAAAAAgFoYyaAPGts4NkfvfnSmz5ue1WtX150DAAAAAABdzkgGfVTL2JYkyeQ5k2suAQAAAACArmckgz5qyPZDcvqY03Pl4iszZ8WcunMAAAAAAKBLGcmgD2se3ZzhOwxP0+ymtK1vqzsHAAAAAAC6jJEM+rAB/QakdXxrlj+0POctOK/uHAAAAAAA6DJGMujjxjaOzTG7H5Oz5p2V1WtX150DAAAAAABdwkgGZMbYGSmlZPKcyXWnAAAAAABAlzCSARmy/ZCcduBpuXLxlZmzYk7dOQAAAAAA0OmMZECSpHl0c4bvMDxNs5vStr6t7hwAAAAAAOhURjIgSTKg34C0jm/N8oeW57wF59WdAwAAAAAAncpIBvyvsY1jc8zux+SseWdl9drVdecAAAAAAECnMZIBf2XG2BkppWTynMl1pwAAAAAAQKcxkgF/Zcj2Q3LagaflysVXZs6KOXXnAAAAAABApzCSAf9H8+jmjNhxRJpmN6VtfVvdOQAAAAAAsMUZyYD/Y0C/AWkd35rlDy3PeQvOqzsHAAAAAAC2OCMZsFGHNhyaY3Y/JmfNOyurHl5Vdw4AAAAAAGxRRjLgWc0YOyOllDTPba47BQAAAAAAtigjGfCshmw/JKcdeFquXHxl5qyYU3cOAAAAAABsMUYyYJOaRzdnxI4j0jS7KW3r2+rOAQAAAACALcJIBmzSgH4D0jq+NcsfWp7zFpxXdw4AAAAAAGwRRjLgbzq04dAcs/sxOWveWVn18Kq6cwAAAAAA4AUzkgGbZcbYGSmlpHluc90pAAAAAADwghnJgM0yZPshOe3A03Ll4itz3Yrr6s4BAAAAAIAXxEgGbLbm0c0ZseOINM1uStv6trpzAAAAAADgeTOSAZttQL8BaR3fmhUPrch5C86rOwcAAAAAAJ43IxnwnBzacGiO2f2YnDXvrKx6eFXdOQAAAAAA8LwYyYDnbMbYGSmlZPKcyXWnAAAAAADA82IkA56zIdsPyeljTs9VS67KdSuuqzsHAAAAAACeMyMZ8Lw0j27OiB1HpGl2U9rWt9WdAwAAAAAAz4mRDHhe+m/dP63jW7PioRU5b8F5decAAAAAAMBzYiQDnrdDGw7NMbsfk7PmnZVVD6+qOwcAAAAAADabkQx4QWaMnZFSSibPmVx3CgAAAAAAbDYjGfCCDNl+SE4fc3quWnJVrltxXd05AAAAAACwWYxkwAvWPLo5I3YckabZTWlb31Z3DgAAAAAA/E1GMuAF6791/7SOb82Kh1bk3FvOrTsHAAAAAAD+JiMZsEUc2nBojt3j2Jw9/+ysenhV3TkAAAAAALBJRjJgi5lx6IyUUjJ5zuS6UwAAAAAAYJOMZMAWs/P2O+f0MafnqiVX5boV19WdAwAAAAAAz8pIBmxRzaObM3LHkWma3ZS29W115wAAAAAAwEYZyYAtqv/W/dM6vjUrHlqRc285t+4cAAAAAADYKCMZsMUd0nBIjt3j2Jw9/+ysenhV3TkAAAAAAPB/GMmATjHj0BkppWTynMl1pwAAAAAAwP9hJAM6xc7b75zTx5yeq5ZcletWXFd3DgAAAAAA/BUjGdBpmkc3Z+SOI9M0uylt69vqzgEAAAAAgP9lJAM6Tf+t+6d1fGtWPLQi595ybt05AAAAAADwv4xkQKc6pOGQHLvHsTl7/tlZ9fCqunMAAAAAACCJkQzoAjMOnZFSSibPmVx3CgAAAAAAJDGSAV1g5+13zuljTs9VS67K7OWz684BAAAAAAAjGdA1mkc3Z+SOIzPpuklpW99Wdw4AAAAAAH2ckQzoEv237p/W8a1Z8dCKnHvLuXXnAAAAAADQxxnJgC5zSMMhOXaPY3P2/LOz6uFVdecAAAAAANCHGcmALjXj0BkppWTynMl1pwAAAAAA0IcZyYAutfP2O+f0MafnqiVXZfby2XXnAAAAAADQRxnJgC7XPLo5I3ccmUnXTUrb+ra6cwAAAAAA6IOMZECX6791/7SOb82Kh1bk3FvOrTsHAAAAAIA+yEgG1OKQhkNy7B7H5uz5Z+d3D/+u7hwAAAAAAPoYIxlQmxmHzkgpJZPnTK47BQAAAACAPsZIBtRm5+13zufHfD5XL7k6s5fPrjsHAAAAAIA+xEgG1Gry6MkZuePITLpuUtrWt9WdAwAAAABAH2EkA2rVf+v+aR3fmhUPrci5t5xbdw4AAAAAAH2EkQyo3SENh+S4PY7L2fPPzu8e/l3dOQAAAAAA9AFGMqBbOO/Q81JKyeQ5k+tOAQAAAACgDzCSAd3CztvvnM+P+XyuXnJ1Zi+fXXcOAAAAAAC9nJEM6DYmj56ckTuOTNPspjy+/vG6cwAAAAAA6MU6fSQrpVxcSqk28W/Js9y3VSnlpFLKwlLKo6WUtaWU+aWU93R2M1CP/lv3T+v41qz848qce8u5decAAAAAANCL9evC7/p5khUbef+/N3yjlLJ1kiuTHJ7kkSRzkwxI8rYk3y+lvLmqqk91YitQk0MaDslxexyXc+afk/e/7v0ZOmho3UkAAAAAAPRCXTmSfaOqqos389pP56mBbFGSf6iq6r4kKaUMTzI/yaRSyk1VVf2oU0qBWp136Hm5Zvk1mTxncq5611V15wAAAAAA0At1u79J1vEU2ckdL094eiBLkqqqlif5bMfLaV3dBnSNnbffOZ8f8/lcveTqzF4+u+4cAAAAAAB6oW43kiUZneTlSdZUVTVvI+cvS/JEkn1KKa/u0jKgy0wePTkjdxyZptlNeXz943XnAAAAAADQy3TlSPbWUsqMUsrXSinTSyljSykb+/69O463b+xDqqp6LMldHS//vjNCgfr137p/Lnj7BVn5x5U595Zz684BAAAAAKCX6cq/SfbBjby3qJTy7qqq7nzGe7t2HFdt4rNW56mBbNdNXAP0cAcPOzjH7XFczpl/Tt7/uvdn6KChdScBAAAAANBLdMWTZL9JMinJHkm2S/J3Sd6R5I6O927Y4GcTt+s4/nkTn/lox/ElGztZSjm+lLKwlLLw/vvvfyHtQM1mjJ2RrcpWmTxnct0pAAAAAAD0Ip0+klVVNbOqqtaqqhZXVfXnqqr+u6qqa5Lsm+TWPPX3x07Zwt/5taqqRlVVNWqnnXbakh8NdLHXvPQ1OX3M6bl6ydWZvXx23TkAAAAAAPQSXflzi3+lqqp1pZR/TPKjJG9/xqmnnxLbdhO3P/202Z86ow3oXiaPnpxpN03L27//9v9z7hXbviJ/mPqHGqoAAAAAAOjJuuLnFjdlScfxmT+3+LuO4y6buG/nDa4FerH+W/dPe9W+0XP3/fm+Lq4BAAAAAKA3qHsk27Hj+Ogz3vtVx3Gfjd1QSnlxkj07Xv66k7oAAAAAAADoxeoeyd7Zcbz9Ge8tSHJ/kteUUsZs5J7jkmyT5Paqqn7fyX0AAAAAAAD0Qp06kpVS/r6U8o5SytYbvN+vlDIlyaSOt1qePldVVXuSf+54eVEp5eXPuG94ki91vDy788qBnuTBxx6sOwEAAAAAgB6mXyd//tAkVyV5qJTyqyT/k6d+YnGvJH+X5MkkJ1dVNWeD+1qSjElyWJLlpZQb89TTYwcnGZiktaqqH3VyO9BDNMxqyOcO+Fw+9aZP5UXbvKjuHAAAAAAAeoDO/rnFO5Kcn2Rpkj2SHJPkoCSPJfl2kn2rqvqXDW/qeJrsyCRNSVYkGdtx3y+TvK+qqkkb3gP0bq/Y9hUbfX/HF+2YMbuMySk3npLhrcPzrV9/K+1PtndxHQAAAAAAPU2pqqruhk41atSoauHChXVnAJ1s/qr5+cz1n8ltv78tr93ptfnSwV/KhOETUkqpOw0AAAAAgJqUUn5ZVdWojZ3r7CfJALrEgbscmAUfXZDLj7s869rX5bAfHJa3fOctuW3NbXWnAQAAAADQDRnJgF6jlJJj9jgmd514V7789i9nyQNL8uZvvjnHXXZclj+4vO48AAAAAAC6ESMZ0Otss/U2OWGfE7KiaUW+eNAXM3v57Ozx5T1y0jUn5b5H76s7DwA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            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[ 10  20  30  40  50  60  70  80  90 100]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}