GKT-unlearn.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Zero Shot Unlearning - Gated Knowledge Transfer\n",
        "#### Dataset - CIFAR-10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Importing Libraries:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SinEhOvN17G",
        "outputId": "e85bc222-9a73-499f-a45e-efa7a1961a2a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7e28635df2f0>"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Necessary Imports\n",
        "import os\n",
        "import torch\n",
        "import requests\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision\n",
        "from torchvision.models import resnet18\n",
        "import torch.nn.functional as F\n",
        "import pandas as pd\n",
        "from torchvision.utils import make_grid\n",
        "import math\n",
        "\n",
        "torch.manual_seed(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ZOPNX8FyKv7X"
      },
      "outputs": [],
      "source": [
        "# Define the device\n",
        "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Data Pre-processing:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNESboNYN3-7",
        "outputId": "4083c8b0-f184-4543-9c48-2750f167e2b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "# Define transformations\n",
        "normalize = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),])\n",
        "\n",
        "# Load CIFAR-10 dataset:\n",
        "\n",
        "# Loading train set: 50000 images\n",
        "train_set = torchvision.datasets.CIFAR10(root=\"./data\", train=True, download=True, transform=normalize)\n",
        "\n",
        "train_loader = DataLoader(train_set, batch_size=128, shuffle=True, num_workers=2)\n",
        "\n",
        "# Loading test set: 10000 images\n",
        "held_out = torchvision.datasets.CIFAR10(root=\"./data\", train=False, download=True, transform=normalize)\n",
        "\n",
        "# Splitting test data into test and validation set: test = 5000 images, val = 5000 images\n",
        "test_set, val_set = torch.utils.data.random_split(held_out, [5000, 5000])\n",
        "\n",
        "test_loader = DataLoader(test_set, batch_size=128, shuffle=False, num_workers=2)\n",
        "\n",
        "val_loader = DataLoader(val_set, batch_size=128, shuffle=False, num_workers=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Importing ResNet18 Model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "B-Il__6oN7ee",
        "outputId": "7a5c889b-7aeb-447b-87bb-e1b010ed2fea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=512, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ResNet18\n",
        "# Download pre-trained weights:\n",
        "local_path = \"weights_resnet18_cifar10.pth\"\n",
        "if not os.path.exists(local_path):\n",
        "    response = requests.get(\n",
        "        \"https://storage.googleapis.com/unlearning-challenge/weights_resnet18_cifar10.pth\"\n",
        "    )\n",
        "    open(local_path, \"wb\").write(response.content)\n",
        "\n",
        "weights_pretrained = torch.load(local_path, map_location=DEVICE)\n",
        "\n",
        "# Load model with pre-trained weights\n",
        "model = resnet18(weights=None, num_classes=10)\n",
        "model.load_state_dict(weights_pretrained)\n",
        "model.to(DEVICE)\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### Class-wise split of data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "PYg9wgjQN8kQ"
      },
      "outputs": [],
      "source": [
        "# Split the dataset by class\n",
        "num_classes = 10 # total classes in CIFAR-10\n",
        "classwise_train = {i: [] for i in range(num_classes)}\n",
        "classwise_test = {i: [] for i in range(num_classes)}\n",
        "\n",
        "for img, label in train_set:\n",
        "    classwise_train[label].append((img, label))\n",
        "\n",
        "for img, label in held_out:\n",
        "    classwise_test[label].append((img, label))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KIypStuKv7Z"
      },
      "source": [
        "#### Defining Functions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "KMTKaMM4N9ix"
      },
      "outputs": [],
      "source": [
        "# Define additional necessary functions and classes\n",
        "class View(nn.Module):\n",
        "    def __init__(self, shape):\n",
        "        super(View, self).__init__()\n",
        "        self.shape = shape\n",
        "\n",
        "    def forward(self, x):\n",
        "        return x.view(self.shape)\n",
        "\n",
        "def KT_loss_generator(student_logits, teacher_logits, KL_temperature):\n",
        "    if student_logits.shape != teacher_logits.shape:\n",
        "        raise ValueError(f\"Shape mismatch: {student_logits.shape} vs {teacher_logits.shape}\")\n",
        "\n",
        "    divergence_loss = divergence(student_logits, teacher_logits, KL_temperature)\n",
        "    total_loss = -divergence_loss\n",
        "    return total_loss\n",
        "\n",
        "def KT_loss_student(student_logits, student_activations, teacher_logits, teacher_activations, KL_temperature=1, AT_beta=250):\n",
        "    if student_logits.shape != teacher_logits.shape:\n",
        "        raise ValueError(f\"Shape mismatch: {student_logits.shape} vs {teacher_logits.shape}\")\n",
        "\n",
        "    divergence_loss = divergence(student_logits, teacher_logits, KL_temperature)\n",
        "    if AT_beta > 0:\n",
        "        at_loss = 0\n",
        "        for i in range(len(student_activations)):\n",
        "            at_loss = at_loss + AT_beta * attention_diff(student_activations[i], teacher_activations[i])\n",
        "    else:\n",
        "        at_loss = 0\n",
        "    total_loss = divergence_loss + at_loss\n",
        "    return total_loss\n",
        "\n",
        "class LearnableLoader(nn.Module):\n",
        "    def __init__(self, n_repeat_batch, num_channels=3, device='cuda'):\n",
        "        super(LearnableLoader, self).__init__()\n",
        "        self.batch_size = 256\n",
        "        self.n_repeat_batch = n_repeat_batch\n",
        "        self.z_dim = 128\n",
        "        self.generator = Generator(self.z_dim, num_channels=num_channels).to(device=device)\n",
        "        self.device = device\n",
        "        self._running_repeat_batch_idx = 0\n",
        "        self.z = torch.randn((self.batch_size, self.z_dim)).to(device=self.device)\n",
        "\n",
        "    def __next__(self):\n",
        "        if self._running_repeat_batch_idx == self.n_repeat_batch:\n",
        "            self.z = torch.randn((self.batch_size, self.z_dim)).to(device=self.device)\n",
        "            self._running_repeat_batch_idx = 0\n",
        "        images = self.generator(self.z)\n",
        "        self._running_repeat_batch_idx += 1\n",
        "        return images\n",
        "\n",
        "    def samples(self, n, grid=True):\n",
        "        self.generator.eval()\n",
        "        with torch.no_grad():\n",
        "            z = torch.randn((n, self.z_dim)).to(device=self.device)\n",
        "            images = self.generator(z).cpu()\n",
        "            if grid:\n",
        "                images = make_grid(images, nrow=round(math.sqrt(n)), normalize=True)\n",
        "        self.generator.train()\n",
        "        return images\n",
        "\n",
        "    def __iter__(self):\n",
        "        return self\n",
        "\n",
        "def attention(x):\n",
        "    return F.normalize(x.pow(2).mean(1).view(x.size(0), -1))\n",
        "\n",
        "def attention_diff(x, y):\n",
        "    return (attention(x) - attention(y)).pow(2).mean()\n",
        "\n",
        "def divergence(student_logits, teacher_logits, KL_temperature):\n",
        "    return F.kl_div(F.log_softmax(student_logits / KL_temperature, dim=1), F.softmax(teacher_logits / KL_temperature, dim=1))\n",
        "\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self, z_dim, num_channels=3):\n",
        "        super(Generator, self).__init__()\n",
        "        prefinal_layer = nn.Conv2d(64, num_channels, 3, stride=1, padding=1)\n",
        "        final_layer = nn.BatchNorm2d(num_channels, affine=True)\n",
        "        self.layers = nn.Sequential(\n",
        "            nn.Linear(z_dim, 128 * 8**2),\n",
        "            View((-1, 128, 8, 8)),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.Upsample(scale_factor=2),\n",
        "            nn.Conv2d(128, 128, 3, stride=1, padding=1),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Upsample(scale_factor=2),\n",
        "            nn.Conv2d(128, 64, 3, stride=1, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            prefinal_layer,\n",
        "            final_layer\n",
        "        )\n",
        "\n",
        "    def forward(self, z):\n",
        "        return self.layers(z)\n",
        "\n",
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
        "\n",
        "def training_step(model, batch, device):\n",
        "    images, labels = batch\n",
        "    images, labels = images.to(device), labels.to(device)\n",
        "    out, *_ = model(images)                  # Generate predictions\n",
        "    loss = F.cross_entropy(out, labels) # Calculate loss\n",
        "    return loss\n",
        "\n",
        "def validation_step(model, batch, device):\n",
        "    images, labels = batch\n",
        "    images, labels = images.to(device), labels.to(device)\n",
        "    out = model(images)\n",
        "    loss = F.cross_entropy(out, labels)\n",
        "    acc = accuracy(out, labels)\n",
        "    return {'Loss': loss.detach(), 'Acc': acc}\n",
        "\n",
        "def validation_epoch_end(outputs):\n",
        "    batch_losses = [x['Loss'] for x in outputs]\n",
        "    epoch_loss = torch.stack(batch_losses).mean()   # Combine losses\n",
        "    batch_accs = [x['Acc'] for x in outputs]\n",
        "    epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies\n",
        "    return {'Loss': epoch_loss.item(), 'Acc': epoch_acc.item()}\n",
        "\n",
        "def epoch_end(model, epoch, result):\n",
        "    print(\"Epoch [{}], last_lr: {:.5f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n",
        "        epoch, result['lrs'][-1], result['train_loss'], result['Loss'], result['Acc']))\n",
        "\n",
        "@torch.no_grad()\n",
        "def evaluate(model, val_loader, device='cuda'):\n",
        "    model.eval()\n",
        "    outputs = []\n",
        "    for batch in val_loader:\n",
        "        images, labels = batch\n",
        "        output = validation_step(model, batch, device)\n",
        "        outputs.append(output)\n",
        "    return validation_epoch_end(outputs)\n",
        "\n",
        "def get_lr(optimizer):\n",
        "    for param_group in optimizer.param_groups:\n",
        "        return param_group['lr']\n",
        "\n",
        "def fit_one_cycle(epochs, max_lr, model, train_loader, val_loader,\n",
        "                  weight_decay=0, grad_clip=None, opt_func=torch.optim.SGD, device='cuda'):\n",
        "    torch.cuda.empty_cache()\n",
        "    history = []\n",
        "\n",
        "    optimizer = opt_func(model.parameters(), max_lr, weight_decay=weight_decay)\n",
        "\n",
        "    sched = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=3, verbose=True)\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        model.train()\n",
        "        train_losses = []\n",
        "        lrs = []\n",
        "        for batch in train_loader:\n",
        "            loss = training_step(model, batch, device)\n",
        "            train_losses.append(loss)\n",
        "            loss.backward()\n",
        "\n",
        "            if grad_clip:\n",
        "                nn.utils.clip_grad_value_(model.parameters(), grad_clip)\n",
        "\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            lrs.append(get_lr(optimizer))\n",
        "\n",
        "\n",
        "        # Validation phase\n",
        "        result = evaluate(model, val_loader, device)\n",
        "        result['train_loss'] = torch.stack(train_losses).mean().item()\n",
        "        result['lrs'] = lrs\n",
        "        epoch_end(model, epoch, result)\n",
        "        history.append(result)\n",
        "        sched.step(result['Loss'])\n",
        "    return history"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BizzAQo2Kv7a"
      },
      "source": [
        "#### Forgetting Class 0 using GKT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25onBU7rN_b4",
        "outputId": "3927f510-a2c6-4fa6-bc04-3359e9d18c43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Forget DataLoader settings: 128 True\n",
            "Retain DataLoader settings: 128 True\n"
          ]
        }
      ],
      "source": [
        "# Define training process\n",
        "forget_classes = [0]\n",
        "forget_valid = [(img, label) for cls in forget_classes for img, label in classwise_test[cls]]\n",
        "retain_valid = [(img, label) for cls in range(num_classes) if cls not in forget_classes for img, label in classwise_test[cls]]\n",
        "\n",
        "forget_valid_dl = DataLoader(forget_valid, batch_size=128, num_workers=2, pin_memory=True, drop_last=True)\n",
        "retain_valid_dl = DataLoader(retain_valid, batch_size=128, num_workers=2, pin_memory=True, drop_last=True)\n",
        "\n",
        "print(\"Forget DataLoader settings:\", forget_valid_dl.batch_size, forget_valid_dl.drop_last)\n",
        "print(\"Retain DataLoader settings:\", retain_valid_dl.batch_size, retain_valid_dl.drop_last)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "W643oPSysve3"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Evaluating the Teacher Model's performance on retain and forget class:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "ZFx7a5OxOAGO",
        "outputId": "5d3c3f65-15ec-44fe-cf8d-8d07bbd917f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Performance of Fully Trained Model on Forget Class\n",
            "Accuracy: 90.51339030265808\n",
            "Loss: 0.34194526076316833\n",
            "Performance of Fully Trained Model on Retain Class\n",
            "Accuracy: 88.33705186843872\n",
            "Loss: 0.4593777358531952\n"
          ]
        }
      ],
      "source": [
        "n_generator_iter = 1\n",
        "n_student_iter = 10\n",
        "n_repeat_batch = n_generator_iter + n_student_iter\n",
        "\n",
        "student = resnet18(weights=None, num_classes=num_classes).to(DEVICE)\n",
        "generator = LearnableLoader(n_repeat_batch=n_repeat_batch, num_channels=3, device=DEVICE).to(DEVICE)\n",
        "\n",
        "optimizer_generator = torch.optim.Adam(generator.parameters(), lr=0.001)\n",
        "scheduler_generator = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_generator, mode='min', factor=0.5, patience=2, verbose=True)\n",
        "\n",
        "optimizer_student = torch.optim.Adam(student.parameters(), lr=0.001)\n",
        "scheduler_student = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_student, mode='min', factor=0.5, patience=2, verbose=True)\n",
        "\n",
        "print(\"Performance of Fully Trained Model on Forget Class\")\n",
        "history = [evaluate(model, forget_valid_dl, device=DEVICE)]\n",
        "print(\"Accuracy: {}\".format(history[0][\"Acc\"]*100))\n",
        "print(\"Loss: {}\".format(history[0][\"Loss\"]))\n",
        "\n",
        "print(\"Performance of Fully Trained Model on Retain Class\")\n",
        "history = [evaluate(model, retain_valid_dl, device=DEVICE)]\n",
        "print(\"Accuracy: {}\".format(history[0][\"Acc\"]*100))\n",
        "print(\"Loss: {}\".format(history[0][\"Loss\"]))\n",
        "\n",
        "history = [evaluate(student, forget_valid_dl, device=DEVICE)]\n",
        "AccForget = history[0][\"Acc\"]*100\n",
        "ErrForget = history[0][\"Loss\"]\n",
        "\n",
        "history = [evaluate(student, retain_valid_dl, device=DEVICE)]\n",
        "AccRetain = history[0][\"Acc\"]*100\n",
        "ErrRetain = history[0][\"Loss\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "btH6fhvnKv7b"
      },
      "source": [
        "#### Training the unlearning model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "vIM3AnAQKv7b"
      },
      "outputs": [],
      "source": [
        "# Defining a function to extract activations:\n",
        "def get_activation(name, activations):\n",
        "    def hook(model, input, output):\n",
        "        activations[name] = output\n",
        "    return hook\n",
        "\n",
        "# Layers to hook\n",
        "layers_to_hook = ['layer1', 'layer2', 'layer3', 'layer4']\n",
        "\n",
        "# Register hooks for student model\n",
        "student_activations = {}\n",
        "for layer_name in layers_to_hook:\n",
        "    layer = dict(student.named_modules())[layer_name]\n",
        "    layer.register_forward_hook(get_activation(layer_name, student_activations))\n",
        "\n",
        "# Register hooks for teacher model\n",
        "teacher_activations = {}\n",
        "for layer_name in layers_to_hook:\n",
        "    layer = dict(model.named_modules())[layer_name]\n",
        "    layer.register_forward_hook(get_activation(layer_name, teacher_activations))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "z3cEbDPSeGCz",
        "outputId": "db3015c9-b141-4ac6-d0e4-c710587352ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "1      0        0.0  11.160714  13.584733  12.847672          -0.182322   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "1         8.972163  \n",
            "Epoch 0 completed in 0.61 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "2     50        0.0  14.408483   3.804337   3.045884          -0.080513   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "2         1.209489  \n",
            "Epoch 50 completed in 0.61 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "3    100        0.0  15.078124   4.455238   3.179249           -0.09022   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "3         1.147852  \n",
            "Epoch 100 completed in 0.61 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "4    150        0.0   12.03125   5.244913   3.330179          -0.097826   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "4         1.269762  \n",
            "Epoch 150 completed in 0.67 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "5    200        0.0  13.660714   6.355075   2.904944          -0.118442   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "5         1.653931  \n",
            "Epoch 200 completed in 0.69 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "6    250        0.0  18.515626   6.208791   2.363091          -0.131854   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "6         1.617379  \n",
            "Epoch 250 completed in 0.68 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "7    300        0.0  22.142857   5.968507   2.091605          -0.134174   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "7         1.624376  \n",
            "Epoch 300 completed in 0.71 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "8    350        0.0  21.651785   6.184915   2.191012           -0.13674   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "8         1.662109  \n",
            "Epoch 350 completed in 0.68 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "9    400        0.0  26.941964   5.079465   1.976727          -0.130162   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "9         1.551872  \n",
            "Epoch 400 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "10    450        0.0  21.573661   5.479585   2.230855          -0.137415   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "10         1.549016  \n",
            "Epoch 450 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "11    500        0.0  22.299106   6.046007    2.24831          -0.135228   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "11         1.549804  \n",
            "Epoch 500 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "12    550        0.0  22.466518   5.834889   2.246028          -0.133457   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "12         1.489703  \n",
            "Epoch 550 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "13    600        0.0   25.30134   5.351861   2.197591          -0.133833   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "13         1.598228  \n",
            "Epoch 600 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "14    650        0.0  26.763391   5.403475   2.198246          -0.130894   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "14         1.541116  \n",
            "Epoch 650 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "15    700        0.0  23.035714    5.96301    2.45287          -0.130454   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "15         1.526533  \n",
            "Epoch 700 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "16    750        0.0  23.917411   6.006292    2.51624          -0.125302   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "16         1.543341  \n",
            "Epoch 750 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "17    800        0.0  24.375001   5.597422   2.324398          -0.124627   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "17         1.517396  \n",
            "Epoch 800 completed in 0.74 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "18    850        0.0  21.953125   5.922908   2.609281          -0.125589   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "18         1.505073  \n",
            "Epoch 850 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "19    900        0.0  24.408482   5.791292   2.583036          -0.120953   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "19         1.506377  \n",
            "Epoch 900 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "20    950        0.0  27.098215   5.779437    2.36104          -0.118801   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "20         1.473137  \n",
            "Epoch 950 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "21   1000        0.0  25.267857   5.871187   2.447884          -0.119632   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "21         1.468299  \n",
            "Epoch 1000 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "22   1050        0.0  27.388394   5.829272   2.530891          -0.116695   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "22         1.458227  \n",
            "Epoch 1050 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "23   1100        0.0  26.305804   5.735573   2.457644          -0.118635   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "23         1.467573  \n",
            "Epoch 1100 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "24   1150        0.0  28.191963   5.711496   2.423496          -0.116675   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "24         1.456158  \n",
            "Epoch 1150 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "25   1200        0.0  27.868304   5.712955   2.392061          -0.116879   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "25         1.471679  \n",
            "Epoch 1200 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "26   1250        0.0  28.225446   5.641488   2.364207          -0.115826   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "26         1.454979  \n",
            "Epoch 1250 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "27   1300        0.0  26.629463   5.749638   2.538389          -0.114514   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "27         1.441489  \n",
            "Epoch 1300 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "28   1350        0.0  26.361609     5.8478   2.618382            -0.1152   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "28          1.44077  \n",
            "Epoch 1350 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "29   1400        0.0  27.232143   5.767734   2.555157          -0.114471   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "29         1.440423  \n",
            "Epoch 1400 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "30   1450        0.0  27.388394   5.856303   2.494637          -0.112939   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "30         1.453417  \n",
            "Epoch 1450 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "31   1500        0.0  27.522323   5.717623   2.486239          -0.114585   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "31         1.428638  \n",
            "Epoch 1500 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "32   1550        0.0  26.640624   5.809634    2.56368           -0.11297   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "32         1.420872  \n",
            "Epoch 1550 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "33   1600        0.0   27.04241   5.799901   2.551446          -0.113156   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "33         1.447331  \n",
            "Epoch 1600 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "34   1650        0.0  27.154016   5.769088   2.526629          -0.114585   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "34         1.428302  \n",
            "Epoch 1650 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "35   1700        0.0  27.399555   5.781899   2.510291          -0.112615   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "35         1.447369  \n",
            "Epoch 1700 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "36   1750        0.0  27.053571   5.781881    2.51748          -0.112063   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "36         1.438321  \n",
            "Epoch 1750 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "37   1800        0.0  27.321428    5.77064   2.505796          -0.113143   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "37         1.438686  \n",
            "Epoch 1800 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "38   1850        0.0  27.410713   5.769244   2.507071          -0.113323   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "38         1.435253  \n",
            "Epoch 1850 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "39   1900        0.0  27.511162   5.725572   2.480568          -0.112061   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "39         1.443953  \n",
            "Epoch 1900 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "40   1950        0.0  27.611607   5.734571    2.49166          -0.113118   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "40         1.441294  \n",
            "Epoch 1950 completed in 0.67 seconds\n"
          ]
        }
      ],
      "source": [
        "# Loop 1:\n",
        "# total_pseudo_batches = 2000\n",
        "# threshold = 0.02\n",
        "\n",
        "import time\n",
        "\n",
        "idx_pseudo = 0\n",
        "total_n_pseudo_batches = 2000\n",
        "n_pseudo_batches = 0\n",
        "running_gen_loss = []\n",
        "running_stu_loss = []\n",
        "\n",
        "threshold = 0.02\n",
        "KL_temperature = 1\n",
        "AT_beta = 250\n",
        "\n",
        "df = pd.DataFrame(columns=[\"Epochs\", \"AccForget\", \"AccRetain\", \"ErrForget\", \"ErrRetain\", \"MeanGeneratorLoss\", \"MeanStudentLoss\"])\n",
        "\n",
        "df = pd.concat([df, pd.DataFrame([{\"Epochs\": 0, \"AccForget\": AccForget, \"AccRetain\": AccRetain, \"ErrForget\": ErrForget,\n",
        "                                   \"ErrRetain\": ErrRetain, \"MeanGeneratorLoss\": None, \"MeanStudentLoss\": None}])], ignore_index=True)\n",
        "\n",
        "zero_count = 0\n",
        "\n",
        "while n_pseudo_batches < total_n_pseudo_batches:\n",
        "    epoch_start_time = time.time()  # Record the start time of the epoch\n",
        "\n",
        "    x_pseudo = generator.__next__()\n",
        "\n",
        "    preds = model(x_pseudo)\n",
        "    mask = (torch.softmax(preds.detach(), dim=1)[:, 0] <= threshold)\n",
        "    x_pseudo = x_pseudo[mask]\n",
        "\n",
        "    if x_pseudo.size(0) == 0:\n",
        "        zero_count += 1\n",
        "        if zero_count > 100:\n",
        "            print(\"Generator Stopped Producing datapoints corresponding to retain classes.\")\n",
        "        continue\n",
        "    else:\n",
        "        zero_count = 0\n",
        "\n",
        "    ## Take n_generator_iter steps on generator\n",
        "    if idx_pseudo % n_repeat_batch < n_generator_iter:\n",
        "        student_logits = student(x_pseudo)\n",
        "\n",
        "        teacher_logits = model(x_pseudo)\n",
        "\n",
        "        generator_total_loss = KT_loss_generator(student_logits, teacher_logits, KL_temperature=KL_temperature)\n",
        "\n",
        "        optimizer_generator.zero_grad()\n",
        "        generator_total_loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(generator.parameters(), 5)\n",
        "        optimizer_generator.step()\n",
        "        running_gen_loss.append(generator_total_loss.cpu().detach())\n",
        "\n",
        "    elif idx_pseudo % n_repeat_batch < (n_generator_iter + n_student_iter):\n",
        "        with torch.no_grad():\n",
        "            teacher_logits = model(x_pseudo)\n",
        "\n",
        "        student_logits = student(x_pseudo)\n",
        "\n",
        "        student_activations_list = list(student_activations.values())\n",
        "        teacher_activations_list = list(teacher_activations.values())\n",
        "\n",
        "        student_total_loss = KT_loss_student(student_logits, student_activations_list,\n",
        "                                             teacher_logits, teacher_activations_list,\n",
        "                                             KL_temperature=KL_temperature, AT_beta=AT_beta)\n",
        "\n",
        "        optimizer_student.zero_grad()\n",
        "        student_total_loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(student.parameters(), 5)\n",
        "        optimizer_student.step()\n",
        "        running_stu_loss.append(student_total_loss.cpu().detach())\n",
        "\n",
        "    if (idx_pseudo + 1) % n_repeat_batch == 0:\n",
        "        if (n_pseudo_batches % 50 == 0):\n",
        "            MeanGLoss = np.mean(running_gen_loss)\n",
        "            running_gen_loss = []\n",
        "            MeanSLoss = np.mean(running_stu_loss)\n",
        "            running_stu_loss = []\n",
        "\n",
        "            history_forget = [evaluate(student, forget_valid_dl, device=DEVICE)]\n",
        "            AccForget = history_forget[0][\"Acc\"]*100\n",
        "            ErrForget = history_forget[0][\"Loss\"]\n",
        "\n",
        "            history_retain = [evaluate(student, retain_valid_dl, device=DEVICE)]\n",
        "            AccRetain = history_retain[0][\"Acc\"]*100\n",
        "            ErrRetain = history_retain[0][\"Loss\"]\n",
        "\n",
        "            df = pd.concat([df, pd.DataFrame([{\"Epochs\": n_pseudo_batches, \"AccForget\": AccForget, \"AccRetain\": AccRetain, \"ErrForget\": ErrForget,\n",
        "                                               \"ErrRetain\": ErrRetain, \"MeanGeneratorLoss\": MeanGLoss, \"MeanStudentLoss\": MeanSLoss}])], ignore_index=True)\n",
        "            print(df.iloc[-1:])\n",
        "            scheduler_student.step(history_retain[0][\"Loss\"])\n",
        "            scheduler_generator.step(history_forget[0][\"Loss\"])\n",
        "\n",
        "            epoch_end_time = time.time()  # Record the end time of the epoch\n",
        "            epoch_duration = epoch_end_time - epoch_start_time  # Calculate the duration\n",
        "            print(f\"Epoch {n_pseudo_batches} completed in {epoch_duration:.2f} seconds\")\n",
        "\n",
        "\n",
        "        n_pseudo_batches += 1\n",
        "\n",
        "    idx_pseudo += 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "5WHc-a4kvWP0",
        "outputId": "11b84d26-6d05-483f-d013-a063eddfa065"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Loop 1 Plot: Accuracy VS Epochs\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot Acc Retain and Acc Forget over epochs\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(df[\"Epochs\"], df[\"AccRetain\"], label='Acc Retain', marker='o')\n",
        "plt.plot(df[\"Epochs\"], df[\"AccForget\"], label='Acc Forget', marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Accuracy over Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "lWrfQdVvvj9x",
        "outputId": "f726027e-ac12-4ba4-b235-344628422c5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "1      0        0.0  11.160714   6.495994   4.471582          -0.135477   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "1         9.880075  \n",
            "Epoch 0 completed in 0.60 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "2     50        0.0  12.254465   5.669247    2.66957          -0.105935   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "2         1.735884  \n",
            "Epoch 50 completed in 0.74 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "3    100        0.0  13.649553   5.798112   2.333625          -0.139983   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "3         1.777456  \n",
            "Epoch 100 completed in 0.68 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "4    150        0.0   13.91741   7.420841   2.338364          -0.144064   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "4         1.900177  \n",
            "Epoch 150 completed in 0.71 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "5    200        0.0  15.212053   5.715537   2.314633          -0.151248   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "5         1.845084  \n",
            "Epoch 200 completed in 0.67 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "6    250        0.0  20.558035   6.157657   2.166144          -0.146932   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "6         1.780253  \n",
            "Epoch 250 completed in 0.68 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "7    300        0.0  19.720982   6.307709   2.161086          -0.149791   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "7         1.772309  \n",
            "Epoch 300 completed in 0.69 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "8    350        0.0  20.535715   6.471193   2.213301          -0.146319   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "8         1.730115  \n",
            "Epoch 350 completed in 0.69 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "9    400        0.0  17.857143   5.939686   2.207381          -0.143932   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "9          1.73058  \n",
            "Epoch 400 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "10    450        0.0   19.21875   6.428049    2.36298          -0.143539   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "10         1.690535  \n",
            "Epoch 450 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "11    500        0.0  18.861607   6.659581   2.483023          -0.136245   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "11         1.731481  \n",
            "Epoch 500 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "12    550        0.0  16.439731   6.397254   2.627689          -0.135831   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "12         1.654096  \n",
            "Epoch 550 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "13    600        0.0  23.337054    6.36715   2.373215          -0.134618   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "13         1.622384  \n",
            "Epoch 600 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "14    650        0.0  25.669643   6.454659   2.178399          -0.130635   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "14         1.599417  \n",
            "Epoch 650 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "15    700        0.0  25.892857   6.366969   2.300489           -0.13165   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "15         1.607661  \n",
            "Epoch 700 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "16    750        0.0  22.734375   6.697106   2.443468          -0.130849   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "16         1.572221  \n",
            "Epoch 750 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "17    800        0.0  22.600447   6.599177   2.627483          -0.126383   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "17         1.559169  \n",
            "Epoch 800 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "18    850        0.0  20.848215   6.788154   2.764304          -0.124803   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "18         1.548177  \n",
            "Epoch 850 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "19    900        0.0  23.180804   6.893436   2.565936          -0.125834   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "19         1.557125  \n",
            "Epoch 900 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "20    950        0.0  22.924107   6.863921   2.634628          -0.121535   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "20         1.531097  \n",
            "Epoch 950 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "21   1000        0.0  24.921875   6.746505    2.63326          -0.121125   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "21         1.510004  \n",
            "Epoch 1000 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "22   1050        0.0  22.377232   6.946874   2.753915          -0.121078   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "22          1.51112  \n",
            "Epoch 1050 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "23   1100        0.0  24.207589   6.661272   2.674929          -0.117942   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "23         1.501616  \n",
            "Epoch 1100 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "24   1150        0.0  23.593751   6.624204    2.62278          -0.118004   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "24         1.507979  \n",
            "Epoch 1150 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "25   1200        0.0  22.444196   7.001673   2.812449           -0.11699   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "25          1.49612  \n",
            "Epoch 1200 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "26   1250        0.0  24.129464   6.680002   2.685225          -0.117336   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "26         1.500756  \n",
            "Epoch 1250 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "27   1300        0.0  23.459822   6.960112   2.808205          -0.114586   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "27         1.493743  \n",
            "Epoch 1300 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "28   1350        0.0  23.571429   7.024869   2.818936          -0.115942   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "28          1.48668  \n",
            "Epoch 1350 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "29   1400        0.0  24.486607   6.947431   2.782231          -0.115193   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "29          1.49499  \n",
            "Epoch 1400 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "30   1450        0.0  24.732143   6.752859   2.748925          -0.114722   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "30         1.495378  \n",
            "Epoch 1450 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "31   1500        0.0  24.408482   6.828664   2.794988          -0.115192   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "31         1.474187  \n",
            "Epoch 1500 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "32   1550        0.0  24.899554   6.834694    2.75122          -0.114088   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "32         1.495734  \n",
            "Epoch 1550 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "33   1600        0.0  24.319196   6.880038   2.767741          -0.114282   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "33         1.475113  \n",
            "Epoch 1600 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "34   1650        0.0  24.787946   6.848103   2.746315          -0.114854   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "34         1.496153  \n",
            "Epoch 1650 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "35   1700        0.0  24.419643   6.885731   2.780595          -0.113582   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "35         1.484572  \n",
            "Epoch 1700 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "36   1750        0.0  24.341518   6.917052   2.821842          -0.112437   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "36         1.476359  \n",
            "Epoch 1750 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "37   1800        0.0  24.464285   6.888506   2.804953          -0.114085   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "37          1.48236  \n",
            "Epoch 1800 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "38   1850        0.0  24.419643   6.846807    2.79619           -0.11361   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "38         1.476781  \n",
            "Epoch 1850 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "39   1900        0.0  24.319196   6.868055   2.794943          -0.114836   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "39         1.484511  \n",
            "Epoch 1900 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "40   1950        0.0  24.564733   6.843368   2.794039          -0.114507   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "40         1.479573  \n",
            "Epoch 1950 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "41   2000        0.0   24.53125   6.830317   2.777838          -0.114976   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "41         1.481267  \n",
            "Epoch 2000 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "42   2050        0.0    24.6875   6.797675   2.745766          -0.114692   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "42         1.477099  \n",
            "Epoch 2050 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "43   2100        0.0  24.553572   6.818099   2.764673          -0.115012   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "43         1.487379  \n",
            "Epoch 2100 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "44   2150        0.0  24.553572   6.829156   2.765939           -0.11409   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "44         1.496129  \n",
            "Epoch 2150 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "45   2200        0.0  24.631697   6.822757   2.762483          -0.112902   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "45         1.468297  \n",
            "Epoch 2200 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "46   2250        0.0  24.542411   6.835663   2.770442          -0.114066   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "46         1.464951  \n",
            "Epoch 2250 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "47   2300        0.0  24.508929    6.84097   2.772009          -0.114348   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "47         1.469324  \n",
            "Epoch 2300 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "48   2350        0.0  24.464285   6.842081   2.770444          -0.111942   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "48         1.471224  \n",
            "Epoch 2350 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "49   2400        0.0  24.508929   6.832842   2.767742          -0.114587   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "49         1.472859  \n",
            "Epoch 2400 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "50   2450        0.0  24.575894   6.827525    2.76447          -0.113803   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "50         1.479373  \n",
            "Epoch 2450 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "51   2500        0.0  24.575894     6.8259   2.763067          -0.112453   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "51         1.482311  \n",
            "Epoch 2500 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "52   2550        0.0  24.609375   6.829627   2.765626          -0.113806   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "52         1.466893  \n",
            "Epoch 2550 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "53   2600        0.0  24.609375    6.82999   2.765575          -0.112387   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "53          1.46295  \n",
            "Epoch 2600 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "54   2650        0.0  24.609375   6.826363   2.763441          -0.112741   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "54         1.488497  \n",
            "Epoch 2650 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "55   2700        0.0  24.598214   6.826615   2.763155          -0.114484   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "55         1.488981  \n",
            "Epoch 2700 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "56   2750        0.0  24.598214   6.827222   2.763158          -0.114535   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "56           1.4658  \n",
            "Epoch 2750 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "57   2800        0.0  24.587053   6.826287   2.762777          -0.113035   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "57         1.470628  \n",
            "Epoch 2800 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "58   2850        0.0  24.587053    6.82564   2.761837          -0.112002   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "58         1.473116  \n",
            "Epoch 2850 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "59   2900        0.0  24.609375   6.825311   2.761534          -0.113684   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "59         1.473962  \n",
            "Epoch 2900 completed in 0.76 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "60   2950        0.0  24.564733   6.826251   2.761999          -0.114198   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "60         1.482259  \n",
            "Epoch 2950 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "61   3000        0.0  24.598214   6.825536   2.761461          -0.113666   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "61         1.481641  \n",
            "Epoch 3000 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "62   3050        0.0  24.575894    6.82595   2.761409          -0.115506   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "62         1.485717  \n",
            "Epoch 3050 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "63   3100        0.0  24.575894   6.826501   2.761637          -0.112115   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "63         1.475318  \n",
            "Epoch 3100 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "64   3150        0.0  24.598214    6.82607   2.761358          -0.113299   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "64         1.488475  \n",
            "Epoch 3150 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "65   3200        0.0  24.575894   6.826993   2.761772          -0.113772   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "65         1.472521  \n",
            "Epoch 3200 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "66   3250        0.0  24.575894   6.827147   2.761568          -0.114682   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "66         1.475318  \n",
            "Epoch 3250 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "67   3300        0.0  24.575894   6.827866   2.762167          -0.113964   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "67         1.478269  \n",
            "Epoch 3300 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "68   3350        0.0  24.575894   6.827491   2.761924          -0.114234   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "68         1.469291  \n",
            "Epoch 3350 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "69   3400        0.0  24.587053   6.828555   2.761704          -0.114332   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "69         1.492053  \n",
            "Epoch 3400 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "70   3450        0.0  24.575894   6.827571   2.761021          -0.113721   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "70         1.493444  \n",
            "Epoch 3450 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "71   3500        0.0  24.587053   6.828207   2.761667          -0.114725   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "71         1.467626  \n",
            "Epoch 3500 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "72   3550        0.0  24.575894   6.829701   2.762367          -0.113679   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "72         1.486717  \n",
            "Epoch 3550 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "73   3600        0.0  24.598214   6.829036   2.761532          -0.113626   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "73         1.476048  \n",
            "Epoch 3600 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "74   3650        0.0  24.564733   6.828952   2.761564          -0.114195   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "74         1.466581  \n",
            "Epoch 3650 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "75   3700        0.0  24.542411    6.82878   2.761559          -0.113164   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "75         1.456589  \n",
            "Epoch 3700 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "76   3750        0.0  24.564733   6.828491   2.760516          -0.113298   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "76          1.47719  \n",
            "Epoch 3750 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "77   3800        0.0  24.542411   6.827756   2.759655          -0.113979   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "77         1.480652  \n",
            "Epoch 3800 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "78   3850        0.0  24.598214   6.829697     2.7608          -0.113473   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "78         1.480668  \n",
            "Epoch 3850 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "79   3900        0.0  24.587053   6.828646   2.760121          -0.113477   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "79         1.473827  \n",
            "Epoch 3900 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "80   3950        0.0  24.609375    6.82902   2.760179          -0.112737   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "80         1.479906  \n",
            "Epoch 3950 completed in 0.74 seconds\n"
          ]
        }
      ],
      "source": [
        "# Loop 2:\n",
        "# total_pseudo_batches = 4000\n",
        "# threshold = 0.01\n",
        "\n",
        "import time\n",
        "\n",
        "idx_pseudo = 0\n",
        "total_n_pseudo_batches = 4000\n",
        "n_pseudo_batches = 0\n",
        "running_gen_loss = []\n",
        "running_stu_loss = []\n",
        "\n",
        "threshold = 0.01\n",
        "KL_temperature = 1\n",
        "AT_beta = 250\n",
        "\n",
        "df = pd.DataFrame(columns=[\"Epochs\", \"AccForget\", \"AccRetain\", \"ErrForget\", \"ErrRetain\", \"MeanGeneratorLoss\", \"MeanStudentLoss\"])\n",
        "\n",
        "df = pd.concat([df, pd.DataFrame([{\"Epochs\": 0, \"AccForget\": AccForget, \"AccRetain\": AccRetain, \"ErrForget\": ErrForget,\n",
        "                                   \"ErrRetain\": ErrRetain, \"MeanGeneratorLoss\": None, \"MeanStudentLoss\": None}])], ignore_index=True)\n",
        "\n",
        "zero_count = 0\n",
        "\n",
        "while n_pseudo_batches < total_n_pseudo_batches:\n",
        "    epoch_start_time = time.time()  # Record the start time of the epoch\n",
        "\n",
        "    x_pseudo = generator.__next__()\n",
        "\n",
        "    preds = model(x_pseudo)\n",
        "    mask = (torch.softmax(preds.detach(), dim=1)[:, 0] <= threshold)\n",
        "    x_pseudo = x_pseudo[mask]\n",
        "\n",
        "    if x_pseudo.size(0) == 0:\n",
        "        zero_count += 1\n",
        "        if zero_count > 100:\n",
        "            print(\"Generator Stopped Producing datapoints corresponding to retain classes.\")\n",
        "        continue\n",
        "    else:\n",
        "        zero_count = 0\n",
        "\n",
        "    ## Take n_generator_iter steps on generator\n",
        "    if idx_pseudo % n_repeat_batch < n_generator_iter:\n",
        "        student_logits = student(x_pseudo)\n",
        "\n",
        "        teacher_logits = model(x_pseudo)\n",
        "\n",
        "        generator_total_loss = KT_loss_generator(student_logits, teacher_logits, KL_temperature=KL_temperature)\n",
        "\n",
        "        optimizer_generator.zero_grad()\n",
        "        generator_total_loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(generator.parameters(), 5)\n",
        "        optimizer_generator.step()\n",
        "        running_gen_loss.append(generator_total_loss.cpu().detach())\n",
        "\n",
        "    elif idx_pseudo % n_repeat_batch < (n_generator_iter + n_student_iter):\n",
        "        with torch.no_grad():\n",
        "            teacher_logits = model(x_pseudo)\n",
        "\n",
        "        student_logits = student(x_pseudo)\n",
        "\n",
        "        student_activations_list = list(student_activations.values())\n",
        "        teacher_activations_list = list(teacher_activations.values())\n",
        "\n",
        "        student_total_loss = KT_loss_student(student_logits, student_activations_list,\n",
        "                                             teacher_logits, teacher_activations_list,\n",
        "                                             KL_temperature=KL_temperature, AT_beta=AT_beta)\n",
        "\n",
        "        optimizer_student.zero_grad()\n",
        "        student_total_loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(student.parameters(), 5)\n",
        "        optimizer_student.step()\n",
        "        running_stu_loss.append(student_total_loss.cpu().detach())\n",
        "\n",
        "    if (idx_pseudo + 1) % n_repeat_batch == 0:\n",
        "        if (n_pseudo_batches % 50 == 0):\n",
        "            MeanGLoss = np.mean(running_gen_loss)\n",
        "            running_gen_loss = []\n",
        "            MeanSLoss = np.mean(running_stu_loss)\n",
        "            running_stu_loss = []\n",
        "\n",
        "            history_forget = [evaluate(student, forget_valid_dl, device=DEVICE)]\n",
        "            AccForget = history_forget[0][\"Acc\"]*100\n",
        "            ErrForget = history_forget[0][\"Loss\"]\n",
        "\n",
        "            history_retain = [evaluate(student, retain_valid_dl, device=DEVICE)]\n",
        "            AccRetain = history_retain[0][\"Acc\"]*100\n",
        "            ErrRetain = history_retain[0][\"Loss\"]\n",
        "\n",
        "            df = pd.concat([df, pd.DataFrame([{\"Epochs\": n_pseudo_batches, \"AccForget\": AccForget, \"AccRetain\": AccRetain, \"ErrForget\": ErrForget,\n",
        "                                               \"ErrRetain\": ErrRetain, \"MeanGeneratorLoss\": MeanGLoss, \"MeanStudentLoss\": MeanSLoss}])], ignore_index=True)\n",
        "            print(df.iloc[-1:])\n",
        "            scheduler_student.step(history_retain[0][\"Loss\"])\n",
        "            scheduler_generator.step(history_forget[0][\"Loss\"])\n",
        "\n",
        "            epoch_end_time = time.time()  # Record the end time of the epoch\n",
        "            epoch_duration = epoch_end_time - epoch_start_time  # Calculate the duration\n",
        "            print(f\"Epoch {n_pseudo_batches} completed in {epoch_duration:.2f} seconds\")\n",
        "\n",
        "\n",
        "        n_pseudo_batches += 1\n",
        "\n",
        "    idx_pseudo += 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "fvS2hNOj2Xtw",
        "outputId": "bca23e20-ccde-4043-f8fe-451a56d9e3d3"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0kAAAHWCAYAAACi1sL/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACBwUlEQVR4nO3dd3yT1f4H8E+SpuneG0opLcNStlBAljJaVJClgPID1IuKgAsXeqUU9OLWiwOvXkS9LEEFQaHKEBAtVMACtcgoZRdKKd20TZPz+yMkJM1oClltP+/Xq/eS5zl5npNvQs2Xc873SIQQAkRERERERAQAkDq7A0RERERERK6ESRIREREREZEeJklERERERER6mCQRERERERHpYZJERERERESkh0kSERERERGRHiZJREREREREepgkERERERER6WGSREREREREpIdJEhEREem0bt0ad999t7O7QUTkVEySiIhcyMcffwyJRIKkpCRnd4XspHXr1pBIJCZ/UlJSnN09IiIC4ObsDhAR0XXLly9H69atkZmZiePHjyM+Pt7ZXSI76Nq1K2bPnm10PCoqygm9ISKiupgkERG5iLy8PPz+++/47rvv8Oijj2L58uVITU11drdMqqiogLe3t7O74ZJqa2uhVqvh7u5utk2LFi0wadIkB/aKiIgagtPtiIhcxPLlyxEYGIi77roL48aNw/Lly022Ky4uxtNPP43WrVtDoVCgZcuWmDx5MgoLC3VtqqqqMG/ePLRr1w4eHh6IjIzEmDFjkJubCwDYvn07JBIJtm/fbnDtkydPQiKR4IsvvtAdmzp1Knx8fJCbm4s777wTvr6+eOCBBwAAv/76K+699160atUKCoUC0dHRePrpp3H16lWjfv/999+47777EBoaCk9PT7Rv3x4vv/wyAOCXX36BRCLB2rVrjZ63YsUKSCQSZGRkWIzfiRMncO+99yIoKAheXl7o3bs3fvzxR935ixcvws3NDWlpaUbPPXLkCCQSCT788EODOD/11FOIjo6GQqFAfHw83njjDajVaqN4vf3223j//fcRFxcHhUKBnJwci321hjbuJ06cQHJyMry9vREVFYX58+dDCGHQtqKiArNnz9b1tX379nj77beN2gHAsmXL0KtXL3h5eSEwMBADBgzAzz//bNRu165d6NWrFzw8PNCmTRt89dVXBueVSiXS0tLQtm1beHh4IDg4GP369cPmzZtv+rUTETkbR5KIiFzE8uXLMWbMGLi7u2PixIlYvHgx/vjjD/Ts2VPXpry8HP3798fhw4fx0EMPoXv37igsLMT69etx9uxZhISEQKVS4e6778bWrVsxYcIEPPnkkygrK8PmzZuRnZ2NuLi4BvettrYWycnJ6NevH95++214eXkBANasWYPKykpMnz4dwcHByMzMxAcffICzZ89izZo1uucfPHgQ/fv3h1wuxyOPPILWrVsjNzcXGzZswGuvvYZBgwYhOjoay5cvx+jRo43iEhcXhz59+pjt38WLF9G3b19UVlbiiSeeQHBwML788kuMHDkS33zzDUaPHo3w8HAMHDgQq1evNhqh+/rrryGTyXDvvfcCACorKzFw4ECcO3cOjz76KFq1aoXff/8dc+bMQX5+Pt5//32D5y9duhRVVVV45JFHoFAoEBQUZDGeSqXSIKnV8vb2hqenp+6xSqVCSkoKevfujTfffBPp6elITU1FbW0t5s+fDwAQQmDkyJH45Zdf8PDDD6Nr16746aef8Nxzz+HcuXN47733dNdLS0vDvHnz0LdvX8yfPx/u7u7Ys2cPtm3bhmHDhunaHT9+HOPGjcPDDz+MKVOm4PPPP8fUqVPRo0cPdOzYEQAwb948LFy4EP/4xz/Qq1cvlJaWYu/evdi/fz+GDh1q8fUTEbk8QURETrd3714BQGzevFkIIYRarRYtW7YUTz75pEG7uXPnCgDiu+++M7qGWq0WQgjx+eefCwDi3XffNdvml19+EQDEL7/8YnA+Ly9PABBLly7VHZsyZYoAIF588UWj61VWVhodW7hwoZBIJOLUqVO6YwMGDBC+vr4Gx/T7I4QQc+bMEQqFQhQXF+uOFRQUCDc3N5Gammp0H31PPfWUACB+/fVX3bGysjIRGxsrWrduLVQqlRBCiP/85z8CgDh06JDB8xMSEsQdd9yhe7xgwQLh7e0tjh49atDuxRdfFDKZTJw+fVoIcT1efn5+oqCgwGIftWJiYgQAkz8LFy7UtdPGfdasWbpjarVa3HXXXcLd3V1cunRJCCHEunXrBADx6quvGtxn3LhxQiKRiOPHjwshhDh27JiQSqVi9OjRunjoX7du/3bu3Kk7VlBQIBQKhZg9e7buWJcuXcRdd91l1WsmImpsON2OiMgFLF++HOHh4bj99tsBABKJBOPHj8eqVaugUql07b799lt06dLFaLRF+xxtm5CQEMyaNctsmxsxffp0o2P6ox4VFRUoLCxE3759IYTAn3/+CQC4dOkSdu7ciYceegitWrUy25/Jkyejuroa33zzje7Y119/jdra2nrX72zcuBG9evVCv379dMd8fHzwyCOP4OTJk7rpb2PGjIGbmxu+/vprXbvs7Gzk5ORg/PjxumNr1qxB//79ERgYiMLCQt3PkCFDoFKpsHPnToP7jx07FqGhoRb7qC8pKQmbN282+pk4caJR25kzZ+r+LJFIMHPmTNTU1GDLli261y6TyfDEE08YPG/27NkQQmDTpk0AgHXr1kGtVmPu3LmQSg3/81/3c5GQkID+/fvrHoeGhqJ9+/Y4ceKE7lhAQAD++usvHDt2zOrXTUTUWDBJIiJyMpVKhVWrVuH2229HXl4ejh8/juPHjyMpKQkXL17E1q1bdW1zc3ORmJho8Xq5ublo37493NxsN6Pazc0NLVu2NDp++vRpTJ06FUFBQfDx8UFoaCgGDhwIACgpKQEA3Rfr+vrdoUMH9OzZ02At1vLly9G7d+96q/ydOnUK7du3Nzp+yy236M4DQEhICAYPHozVq1fr2nz99ddwc3PDmDFjdMeOHTuG9PR0hIaGGvwMGTIEAFBQUGBwn9jYWIv9qyskJARDhgwx+omJiTFoJ5VK0aZNG4Nj7dq1A6BZD6V9bVFRUfD19bX42nNzcyGVSpGQkFBv/+omswAQGBiIK1eu6B7Pnz8fxcXFaNeuHTp16oTnnnsOBw8erPfaRESNAdckERE52bZt25Cfn49Vq1Zh1apVRueXL19usF7EFsyNKOmPWulTKBRGow8qlQpDhw5FUVERXnjhBXTo0AHe3t44d+4cpk6dalDgwFqTJ0/Gk08+ibNnz6K6uhq7d+82KKZgCxMmTMCDDz6IrKwsdO3aFatXr8bgwYMREhKia6NWqzF06FA8//zzJq+hTVS09EfUmgKZTGbyuNArBDFgwADk5ubi+++/x88//4z//ve/eO+99/DJJ5/gH//4h6O6SkRkF0ySiIicbPny5QgLC8NHH31kdO67777D2rVr8cknn8DT0xNxcXHIzs62eL24uDjs2bMHSqUScrncZJvAwEAAmgpu+rSjDtY4dOgQjh49ii+//BKTJ0/WHa9b3Uw7ElJfvwFNAvPMM89g5cqVuHr1KuRyucE0OHNiYmJw5MgRo+N///237rzWqFGj8Oijj+qm3B09ehRz5swxeF5cXBzKy8t1I0fOolarceLECYOk7OjRowA0m9ICmte2ZcsWlJWVGYwm1X3tcXFxUKvVyMnJQdeuXW3Sv6CgIDz44IN48MEHUV5ejgEDBmDevHlMkoio0eN0OyIiJ7p69Sq+++473H333Rg3bpzRz8yZM1FWVob169cD0Kx9OXDggMlS2dp/5R87diwKCwtNjsBo28TExEAmkxmtrfn444+t7rt2tEF/dEEIgX//+98G7UJDQzFgwAB8/vnnOH36tMn+aIWEhGD48OFYtmwZli9fjpSUFIMRHnPuvPNOZGZmGpQJr6iowKefforWrVsbTDELCAhAcnIyVq9ejVWrVsHd3R2jRo0yuN59992HjIwM/PTTT0b3Ki4uRm1tbb19shX991EIgQ8//BByuRyDBw8GoHntKpXK6P1+7733IJFIMHz4cACa5FAqlWL+/PlGo3x13wdrXL582eCxj48P4uPjUV1d3eBrERG5Go4kERE50fr161FWVoaRI0eaPN+7d2+EhoZi+fLlGD9+PJ577jl88803uPfee/HQQw+hR48eKCoqwvr16/HJJ5+gS5cumDx5Mr766is888wzyMzMRP/+/VFRUYEtW7bg8ccfxz333AN/f3/ce++9+OCDDyCRSBAXF4cffvjBaK2NJR06dEBcXByeffZZnDt3Dn5+fvj2228N1q1oLVq0CP369UP37t3xyCOPIDY2FidPnsSPP/6IrKwsg7aTJ0/GuHHjAAALFiywqi8vvvgiVq5cieHDh+OJJ55AUFAQvvzyS+Tl5eHbb781mio4fvx4TJo0CR9//DGSk5MREBBgcP65557D+vXrcffdd+tKX1dUVODQoUP45ptvcPLkSauSN3POnTuHZcuWGR338fExSNg8PDyQnp6OKVOmICkpCZs2bcKPP/6Il156SVcoYsSIEbj99tvx8ssv4+TJk+jSpQt+/vlnfP/993jqqad0Jd/j4+Px8ssvY8GCBejfvz/GjBkDhUKBP/74A1FRUVi4cGGDXkNCQgIGDRqEHj16ICgoCHv37sU333xjUGiCiKjRclZZPSIiEmLEiBHCw8NDVFRUmG0zdepUIZfLRWFhoRBCiMuXL4uZM2eKFi1aCHd3d9GyZUsxZcoU3XkhNKW5X375ZREbGyvkcrmIiIgQ48aNE7m5ubo2ly5dEmPHjhVeXl4iMDBQPProoyI7O9tkCXBvb2+TfcvJyRFDhgwRPj4+IiQkREybNk0cOHDA6BpCCJGdnS1Gjx4tAgIChIeHh2jfvr145ZVXjK5ZXV0tAgMDhb+/v7h69ao1YRRCCJGbmyvGjRunu36vXr3EDz/8YLJtaWmp8PT0FADEsmXLTLYpKysTc+bMEfHx8cLd3V2EhISIvn37irffflvU1NQIIa6XAH/rrbes7qelEuAxMTG6dtq45+bmimHDhgkvLy8RHh4uUlNTjUp4l5WViaefflpERUUJuVwu2rZtK9566y2D0t5an3/+uejWrZtQKBQiMDBQDBw4UFd6Xts/U6W9Bw4cKAYOHKh7/Oqrr4pevXqJgIAA4enpKTp06CBee+01XWyIiBoziRA3MMZORERkJ7W1tYiKisKIESOwZMkSZ3fHaaZOnYpvvvkG5eXlzu4KEVGzwzVJRETkUtatW4dLly4ZFIMgIiJyJK5JIiIil7Bnzx4cPHgQCxYsQLdu3XT7LRERETkaR5KIiMglLF68GNOnT0dYWBi++uorZ3eHiIiaMa5JIiIiIiIi0sORJCIiIiIiIj1MkoiIiIiIiPQ0+cINarUa58+fh6+vLyQSibO7Q0RERERETiKEQFlZGaKioow2GtfX5JOk8+fPIzo62tndICIiIiIiF3HmzBm0bNnS7PkmnyT5+voC0ATCz8/PqX1RKpX4+eefMWzYMMjlcqf2palijO2PMXYMxtn+GGP7Y4ztjzG2P8bYMRwV59LSUkRHR+tyBHOafJKknWLn5+fnEkmSl5cX/Pz8+JfMThhj+2OMHYNxtj/G2P4YY/tjjO2PMXYMR8e5vmU4LNxARERERESkh0kSERERERGRHiZJREREREREepr8miRrCCFQW1sLlUpl1/solUq4ubmhqqrK7vdqrkzFWCaTwc3NjSXgiYiIiMgqzT5JqqmpQX5+PiorK+1+LyEEIiIicObMGX5htxNzMfby8kJkZCTc3d2d2DsiIiIiagyadZKkVquRl5cHmUyGqKgouLu72zV5UavVKC8vh4+Pj8XNq+jG1Y2xEAI1NTW4dOkS8vLy0LZtW8aeiIiIiCxq1klSTU0N1Go1oqOj4eXlZff7qdVq1NTUwMPDg1/U7cRUjD09PSGXy3Hq1CndOSIiIiIic/hNHWDC0gzwPSYiIiIia/GbIxERERERkZ5mPd2OyJ5UaoHMvCIUlFUhzNcDvWKDIJOyYAcRERGRq2OSZAP8Mmxf27dvx+23344rV64gICDA2d2xSnp2PtI25CC/pEp3LNLfA6kjEpCSGOnEnhERERFRfTjd7ialZ+ej3xvbMPGz3XhyVRYmfrYb/d7YhvTsfLvfOyMjAzKZDHfddZfd7zVo0CBIJBJIJBJ4eHigXbt2WLhwIYQQVl/jiy++uKEkp2/fvsjPz4e/v3+Dn+sM6dn5mL5sv0GCBAAXSqowfdl+h3w2iIiIiOjGMUm6Cc7+MrxkyRLMmjULO3fuxPnz5+16LwCYNm0a8vPzceTIEcyZMwdz587FJ598Yvf7uru7IyIiwqX2llKpBTJyL+P7rHPIyL0MlVrojqdtyIGp1FF7LG1Djq49EREREbkeJkl6hBCorKm16qesSonU9X9Z/DI8b30OyqqUBs+7WqMyeb2GjMgAQHl5Ob7++mtMnz4dd911F7744gujNhs2bEDPnj3h4eGBkJAQjB49WneuuroaL7zwAqKjo6FQKBAfH48lS5ZYvKeXlxciIiIQExODBx98EJ07d8bmzZsNrvnss8+iRYsW8Pb2RlJSErZv3w5AM2XuwQcfRElJiW5Eat68eQCA//3vf7j11lvh6+uLiIgI3H///SgoKNBdd/v27ZBIJCguLgZwfUTqp59+wi233AIfHx+kpKQgP98xIzSWRg8z84qMkmZ9AkB+SRUy84oc0lciIiIiajiuSdJzValCwtyfbHItAeBCaRU6zfvZqvY585Ph5W7927F69Wp06NAB7du3x6RJk/DUU09hzpw5utGWH3/8EaNHj8bLL7+Mr776CjU1Ndi4caPu+ZMnT0ZGRgYWLVqELl26IC8vD4WFhda9NiGwa9cu/P3332jbtq3u+MyZM5GTk4NVq1YhKioKa9euRUpKCg4dOoS+ffvi/fffx9y5c3HkyBEAgI+PDwBAqVRiwYIFaN++PQoKCvDMM89g6tSpBv2tq7KyEm+//Tb+97//QSqVYtKkSXj22Wfxv//9z+oY3gjt6GHdlFY7eti/bYhV1ykoM59IEREREZFzMUlqpJYsWYJJkyYBAFJSUlBSUoIdO3Zg0KBBAIDXXnsNEyZMQFpamu45Xbp0AQAcPXoUq1evxubNmzFkyBAAQJs2beq958cff4z//ve/qKmpgVKphIeHB5544gkAwOnTp7F06VKcPn0aUVFRAIBnn30W6enpWLp0Kf71r3/B398fEokEERERBtd96KGHdH9u06YNFi1ahJ49e6K8vFyXSNWlVCrxySefIC4uDoAmQZs/f369r+FmWDOVbucx6xLNMF9uaEtERETkqpgk6fGUy5AzP9mqtpl5RZi69I96233xYE/0ig0CAKjVapSVlsHXz9doc1NPuczqfh45cgSZmZlYu3YtAMDNzQ3jx4/HkiVLdElSVlYWpk2bZvL5WVlZkMlkGDhwoNX3BIAHHngAL7/8Mq5cuYLU1FT07dsXffv2BQAcOnQIKpUK7dq1M3hOdXU1goODLV533759mDdvHg4cOIArV65ArVYD0CReCQkJJp/j5eWlS5AAIDIy0mCKnj3UN5VOy1MuxVWl2uQ5CYAIfw/dZ4KIiIiIXA+TJD0SicTqKW/924Yi0t8DF0qqTI4saL8M928bqisHrlarUesug5e7m1GS1BBLlixBbW2tbsQG0EyBUygU+PDDD+Hv7w9PT0+zz7d0zhJ/f3/Ex8cD0Ez3i4+PR+/evTFkyBCUl5dDJpNh3759kMkMEz5zo0EAUFFRgeTkZCQnJ2P58uUIDQ3F6dOnkZycjJqaGrPPk8vlBo8lEkmD13U1lLVT5Cb2aoWlv50EAIPPhrbsROqIBJaIJyIiInJhLNxwg2RSCVJHaEY56n7dteeX4draWnz11Vd45513kJWVpfs5cOAAoqKisHLlSgBA586dsXXrVpPX6NSpE9RqNXbs2HHD/fDx8cGTTz6JZ599FkIIdOvWDSqVCgUFBYiPjzf40U6vc3d3h0qlMrjO33//jcuXL+P1119H//790aFDB7uPCN0oa6fIDU2IwOJJ3RHhb9g+wt8Diyd15z5JRERERC6OSdJNSEmMdPiX4R9++AFXrlzBww8/jMTERIOfsWPH6irUpaamYuXKlUhNTcXhw4dx6NAhvPHGGwCA1q1bY8qUKXjooYewbt065OXlYfv27Vi9enWD+vLoo4/i6NGj+Pbbb9GuXTs88MADmDx5Mr777jvk5eUhMzMTCxcuxI8//qi7b3l5ObZu3YrCwkJUVlaiVatWcHd3xwcffIATJ05g/fr1WLBggW2DZiO9YoMQ6e9hlBRrSaDZMLZXbBBSEiOx64U7sOzhXpDLNM/48qFeTJCIiIiIGgEmSTdJ+2V45bTe+PeErlg5rTd2vXCH3b4ML1myBEOGDDG5serYsWOxd+9eHDx4EIMGDcKaNWuwfv16dO3aFXfccQcyMzN1bRcvXoxx48bh8ccfR4cOHTBt2jRUVFQ0qC9BQUGYPHky5s2bB7VajaVLl2Ly5MmYPXs22rdvj1GjRuGPP/5Aq1atAGg2hX3ssccwfvx4hIaG4s0330RoaCi++OILrFmzBgkJCXj99dfx9ttv31yQ7ER/9LAuU6OHMqkE/dqGonPLAABA9rkSB/SSiIiIiG6WRNh7IYeTlZaWwt/fHyUlJfDz8zM4V1VVhby8PMTGxsLDw/7VxtRqNUpLS+Hn53dTa5LIPHMxtuV7/dEvx/HWT0cMjkX6eyB1RILJ5Hj+hhx8/lsepvZtjXkjO97UvV2BUqnExo0bceeddxqtDSPbYZztjzG2P8bY/hhj+2OMHcNRcbaUG+hz6jf1hQsXomfPnvD19UVYWBhGjRql20NHa9CgQbrNR7U/jz32mJN6TARU12oq1/VuE2TV6GGXaM2oX9aZYkd10W5UaoE9eUXYVyjBnrwiqNRN+t9YiIiIqJlyanW7HTt2YMaMGejZsydqa2vx0ksvYdiwYcjJyYG3t7eu3bRp0wz2wPHy8nJGd4kAADuOaApLjOneEvd0bVFv+y7Xptvl5JeiplYNd7fGOYqYnp2PtA0518qgy/DVsb0WR9DIvlRqgcy8IhSUVSHMV7MWjlUTiYiIbMOpSVJ6errB4y+++AJhYWHYt28fBgwYoDvu5eVltAEpkTNcLq/GwWtriwa2C7XqOTHBXvD3lKPkqhJHLpShU0vj9WSuLj07H9OX7Tcqd3+hpArTl+1n1T4HM0xYNW4kYWWiRUREZJpL7ZNUUqL58hkUZLjR5vLly7Fs2TJERERgxIgReOWVV8yOJlVXV6O6ulr3uLS0FIBmnqNSqTRoq1QqIYSAWq3WbWBqT9rlX9p7ku2Zi7FarYYQAkql0mgfp4bY/vdFCAF0iPBFkKfM6DNlTqcWfth1/DL2n7qMDuGNayRUpRaYt/4vk/uBCWiKVqRt+AuD2gbzC7YVVGqBvaeuoKCsGmG+CtwaE2gybtrPVt3P2E9/XcSsVQfMJqwfTOiC5I7h9fbjp78u4tWNf+NC6fXflxF+Cvzzzg5WPb8pMBdjsh3G2P4YY/tjjB3DUXG29vouU7hBrVZj5MiRKC4uxq5du3THP/30U8TExCAqKgoHDx7ECy+8gF69euG7774zeZ158+YhLS3N6PiKFSuMEis3NzdEREQgOjoa7u7utn1B5FJqampw5swZXLhwAbW1tTd8nf8dk2JvoRSDo9QYGWN9ovvjaSl+PidFUqga98c3rgT5WIkEH+bUn1jOTFChrb9L/DpxWQcuS/DdSSmKa64nRQHuAmNaq9EluP7YqQWQtl+G4hrAeIc2ABAIcAdSu6tgKV89cFmCz49qp33qN9T04aF21vWnqVMLILdUglIl4CcH4vyExbg2lnsRETVnlZWVuP/+++st3OAySdL06dOxadMm7Nq1Cy1btjTbbtu2bRg8eDCOHz+OuLg4o/OmRpKio6NRWFhosrrdmTNn0Lp1a4dUtxNCoKysDL6+vpBI+F8/ezAX46qqKpw8eRLR0dE3/F6r1QJ93tyOogollj10K5Jig+p/0jVbDhdg+oostAvzwY+z+t7Q/Z1lw8F8PLPmUL3t3r23E0Z05pQ7c8yNAGk/pXVHgJRKJTZv3oyhQ4fqqvzsySvCpM/31nsvS59PlVpg0Ds7DUaQ6vYnwl+BX54Z0GhHBhsyWlc3xlrWjrRZc6/62lhzL2tfk6NY+7p3517Ctox9uKNPD/SOC21wbBpjG0f3pTnG2NHvFWNs/zaA5d/JtlRaWoqQkJB6kySXmG43c+ZM/PDDD9i5c6fFBAkAkpKSAMBskqRQKKBQKIyOy+Vyo4CrVCpIJBJIpVKHlOTWTv/S3pNsz1yMpVIpJBKJyc+BtQ6eLUZRhRI+CjckxYVCLrP+PezROhgAcPxSOWrUEngrXOKvnlUiA7zrb3StXXMvjWpujY9KLfDapiMWpyy+tukIhnduYfQfDv3P7OVK60ZBL1fWmn0v9uZeNpsgafuTX1KNP8+WoU9csFX3cyXWrtdSqQX2X6vUGHy2DH3iw3SxT8/ON5nQXiytxqxVB3Rr8Ky5V31trLkXAKtfky3WmNV3nYa/bhm+OpbV4Ng0xjbO60vzibHz3ivG2F5t6rqZ72rWsPbaTh1JEkJg1qxZWLt2LbZv3462bdvW+5zffvsN/fr1w4EDB9C5c+d623OfpObFnvskfbD1GN7ZfBTJHcPxn/+7tcHP7/2vrbhQWoWvH+mNpDaN58unSi3Q741tuFBSZfJLvmbkwQO7Xrij0Y482IKl/xD4e7pj4me7673Gymm90ScuGCq1QMbxAvz86x4M65+EPvFhqKlV4/Hl+/DLkUtWX8eU77PO4clVWfVe498TuuKeri0c9sXb2jaWmCswor2CNcnN0IQI9Htjm8G5uteK8PfAK3clYMYKy/cCYLE/H93fDQt+PGzxXv5ecpRUKm/qNTUkkbLmi2V9Ma7vdTfVNo8MiMWnO/Ncoi9sw/eqsbTR//3kavskOTVJevzxx7FixQp8//33aN++ve64v78/PD09kZubixUrVuDOO+9EcHAwDh48iKeffhotW7bEjh07rLoHk6TmxZ5J0rjFv2PvqSv41+hOuD+pVYOf/+j/9uKnvy7ipTs74JEBxqOgrszcFyNA88uuuVe3s/TFUQBoG+aDYwXl9V7n3xO6QuEmNfqSGuLjDneZFOfNfJnW5+fhhj/nDtONYNX9QpyZV2RVwrbgno4I9VU45Iu3tW0s3UubzN9MciMAdG8VgP2ni+uNj7e7DBU1KrP3CvdTAJDgQqn598xHIUN5telrWKMhCZs1iVR9CZA1SZ3mdcPiaGWQlxwCwJVK84unfRQyABKUV5sfPbWmjZ+HGyQSCUqumr9XgKcbIJGg2EJ/grzkkEgkuFxRY7aNVKJZW2bxPpCg2EJfgr3lkEqkuFRufjpsqK87hAAulZvvS4i3OyABCs20sfa9CvF2hwAsvu4Qb3dIJOb7Y829bNUGAMJ83SGExGwMAcvvlS7GkOBSmeX7ABIUWGhjTfxCfdwhlUpwsd7Xbf73ia3bCMBsfwDr/j4EeLpBwPzfPVP/wMokSf/mZtblLF26FFOnTsWZM2cwadIkZGdno6KiAtHR0Rg9ejT++c9/WnxR+hySJKlVwKnfgfKLgE84ENMXkBovdGeSZH/2SpJKKpXotuBnqAXw24t3oEWAZ4Ov8dEvx/HWT0dwV+dIfHR/9wY/39nWXxuB0P+F4SGX4v3xXZt1glTfl/OGGNguFDuPXjKZjAKAr4cbHr4tFv/eegwAzLab1j8W3aIDseBHwy/E4X4KxAZ7Y3de0Q330dZfvBvyr422GK1TuEl1G0I3FT4KN7OJgrWJVH0JEAB4uElR1cRiR0TOpT/zwdWSJKcujKgvP4uOjrZ6xMhpctYD6S8ApeevH/OLAlLeABJG2vXWGRkZ6NevH1JSUvDjjz/a9V6DBg0y+V4olUq4uTl3fY1EIsHatWsxatQou91j1/FCqIVmROBGEiQA6BodAAA4cKbYdh1zIE93NwgAgV5y9Aysxs/npPB2d0Nyx4bvYdaU9ufJzCuyKkHy83BDWVWt2cQGAHYctTyVzstdhlmD26JDpK/JZCEpNgjrss7js1/zAOQZPf9iabXBvw5qR0/qPu4dG2Q2kbpe9j0HajVMfvHWliPXfvE2txYLAF5aewhSiaSeEvP132tIQpjJ/tbV1BIkABZHUjRrzKrwxKo/Lb4Ps1b9CVU9oWGCRES2VlB28//AaC+NZ/W4K8pZD6yeDKN/zy3N1xy/7yu7JkpLlizBrFmzsGTJEpw/fx5RUVF2uxcATJs2DfPnzzc4dqMJUk1NTaMqu779SAEA6zeQNSWxhWYT2bNXruJyeTWCfYwLjLiyjYfyAQD3dIlEJ/UJ7LjohssVNTheUI624b5WX8dWG6Fay94JmbW/4Mf1aImlv500mZgAwB0dwrD17wKL17hYWo3MvCKkJEZiaEKEydc1oG0onllzwOJ1gr3dseCeRKORpgi9UZndFkZltF+8n//2oMUv3k9+nQWlyvI/hhVVWN6vQnuvl9YdsnivzTmWY9cQAdc2fzY3vTTQW15vv60V5O2OKxU1FpNnW6i1NA8MqDdBIiKyhzBf+y93uVGc86VPCKCmwrqfqlJg0/MwPeHl2rH0FzTt9J+nrDR9vQbOeiwvL8fXX3+N6dOn46677sIXX3xh1GbDhg3o2bMnPDw8EBISgtGjR+vOVVdX44UXXkB0dDQUCgXi4+OxZMkSi/f08vJCRESEwY/Wt99+i44dO0KhUKB169Z45513DJ7bunVrLFiwAJMnT4afnx8eeeQRAMBnn32G6OhoeHl5YfTo0Xj33XcREBBg8Nzvv/8e3bt3h4eHB9q0aYO0tDTdXketW7cGAIwePRoSiQRt2rSxNoRWE0Lo/oV/UHvr/rXaFH9POdqEairFHTxXYpO+OUp1rQpbci4CAFI6hsNNCvSICQAA/Ha80OrraKde1R150Y4GpGfn26zP2vv1e2MbJn62G0+uysLEz3aj3xvbbHofa3/BD02IwOJJ3RHhb9g+wt8Diyd1x8iu1v0jhzYpk0kl6BMXjHu6tkCfuOsb+UZaMdJ5uaIGgd7u2PXCHVg5rTf+PaErVk7rjV0v3IGUxEirEz9LIxgA6k2QGsLSehEthZv5/6RJAAR5Wzd948HbYnXPqXsNAHj1nkRE+nuY3KlK2y7CT4EIP8ttIv098Oo9iRbvFeAlt3gNa1+TrQR5u9/0626KbQDNOhdX6Avb8L1qLG0i/TX/wOeqOJKkT1kJ/MtWozFCMwXv9WjdESmAAHPNXzoPuFtXahkAVq9ejQ4dOqB9+/aYNGkSnnrqKcyZM0e3zuvHH3/E6NGj8fLLL+Orr75CTU0NNm7cqHv+5MmTkZGRgUWLFqFLly7Iy8tDYaH1X3b17du3D/fddx/mzZuH8ePH4/fff8fjjz+O4OBgTJ06Vdfu7bffxty5c5GamgpAU6nwsccewxtvvIGRI0diy5YteOWVVwyu/euvv2Ly5MlYtGgR+vfvj9zcXF2ClZqaij/++ANhYWFYunQpUlJS7LL/1N8XylBQVg1PuQw9YwNv6lpdWgbgxKUKHDhTjNtvIuFytF3HClFWXYtwPwW6RQcg/S+gb5tg/J5bhN9zL2PqtS+VlqjUAmkbcuqdVjU0IcImIz3m1sJoEzJbFZvoFRuEUB+FxYXWEf7XR3rMjQBl5F626n71JWXWJjgFZVW6RKuh93BVDyS1wtLfTgIwPVr36j2JWPDj4XorNc68Ix7tI3yMRjwj9EY8pVIJpi/bb3ZkcN7IjgBgsY32Woul3c3eq75rWPOarB35sjSqVXdt08287qbaZlp/TcU0V+gL2/C9agxtUkckuPRUe44kNVJLlizBpEmTAAApKSkoKSkxWDP02muvYcKECUhLS8Mtt9yCLl26YM6cOQCAo0ePYvXq1fj8888xevRotGnTBoMHD8b48eMt3vPjjz+Gj4+P7mf27NkAgHfffReDBw/GK6+8gnbt2mHq1KmYOXMm3nrrLYPn33HHHZg9ezbi4uIQFxeHDz74AMOHD8ezzz6Ldu3a4fHHH8fw4cMNnpOWloYXX3wRU6ZMQZs2bTB06FAsWLAA//nPfwAAoaGa6W8BAQGIiIjQPbal7ddKLveNC4bCzbggR0N0aamZcnfwbMlN98uRNh66AAAYfu3LIQD0bqP515/dJy5DVc9UHqD+tTvaaVWZN1FUQKu+hAzQJGTW9Ls+aiHgpTD9uTD1HwJzI0C9YoPqHZ2w5l/drE1wLLWzpi/WjmDYYuTB2nvVN1p3Z+coXeJR935136uUxEizI20ANMmNhXulJEZa1UZ7LXP3qu8a1rwma0a+rBnVSh2RgDs72+Z1N8U2c+5McJm+sA3fq8bSxpU5tbqdIzSoup0QmtEka5z6HVg+rv52D3yjqXaHa5XXysrg5+trXN1O7gVYOQpy5MgRJCYm4ty5cwgL04xGzJw5EyUlJfjf//4HQDM17qOPPsKDDz5o9PzVq1fj/vvvx9WrV62uHjJo0CDExsbi5Zdf1h0LCAhASEgIunfvjnvuuUc3QgRopsjde++9uHr1KmQyGVq3bo1p06YZPL9bt24YPXo05s6dqzu2aNEizJ07F8XFxQA0SVB5eTlksutfQlUqFaqqqlBRUQEvLy+Dwg32qG434dMM7D5RhPn3dMTkPq0b9Ny69p++gjEf/45gb3fs/ecQu4x82VpNrRq3vroZpVW1WP1oH3Rr6YuNGzdiWHIKer2+HWVVtVg/8zZ0bhlg8ToN3Z/nZmTkXm7QvkQ34830v/Hx9lx4yqXw8ZAblIxt6For7egXYPpf3az5j4qt9rWqry/aogz13Uc78mDpNQGwyb20r8kWJcmt5Yj9n6y5hrVVBgHLny1b7bekbVN3v68biU1ja+PovjTHGDv6vWKM7d8GYHU71yaRWD/lLe4OTRW70nyYXpck0ZyPu+N6OXC1GpCrNPe4iRLgS5YsQW1trUGhBiEEFAoFPvzwQ90+U+ZYOmeJv78/4uPjb+i5AODtbf10Qq3y8nKkpaVhzJgxRuccsbdVWZUSe09eAQAManfz0+MSIv3gJtXsLXCu+CpaBnrd9DXt7bfcQpRW1SLUV4EeMYFQqzTrUNxkUiTFBmPL4Yv4PfdyvUmSLUY4rNWQKWc34/fjhVi8IxcA8M59XZHc0fRUOmtp/9XN0jSv+sikEqSOSLjpKQ7W9KW+KWfWTCfTviZb3KvuaJ2l1zY0IaLeLz7WqO9e1ra52ftYKuahPW/NZ6u+6zTkNcmkEiTFBuHyYYEkM38XbBU/V2rj6L40xxjbqg1j7DptXBGTpBsllWnKfK+eDNOFdAGkvG5yv6SbUVtbi6+++grvvPMOhg0bZnBu1KhRWLlyJR577DF07twZW7duNTmS1KlTJ6jVauzYsQNDhgy56T7dcsst+O233wyO/fbbb2jXrp3BCFBd7du3xx9//GFwrO7j7t2748iRIxaTM7lcDpXqxjdktOT33MuoVQvEhnijVfDNJzQechk6RPoi+1wpDpwpaRRJ0saDmiIHwxM1a4XUeqHuG6dJkn47XojHBlreIFc7hcvcaAAAeCtk6Nk68Kb77IiE7EpFDZ5enQUhgPG3RuPOTpovmTf7HwJbfIG3RbKl3xdHfPG21b2sZc0Xn8bmZhMpa69DRNQcMEm6GQkjNWW+Te6T9Lpdyn//8MMPuHLlCh5++GH4+/sbnBs7diyWLFmCxx57DKmpqRg8eDDi4uIwYcIE1NbWYuPGjXjhhRfQunVrTJkyBQ899JCucMOpU6dQUFCA++67r8F9mj17Nnr27IkFCxZg/PjxyMjIwIcffoiPP/7Y4vNmzZqFAQMG4N1338WIESOwbds2bNq0yWAK2ty5c3H33XejVatWGDduHKRSKQ4cOIDs7Gy8+uqrADQV7rZu3YrbbrsNcrncYmLWUNr1SDdT+ruuLi0DkH2uFAfPFuOuzq49H1epUuPna1Xthpv4EnpbfAgA4I+TRaipVcPdQnUx/REOcyqqVXh5bTb+NaYTANzwqIw1CVm4n+KGq+oIIfD8twdxsbQabUK9kToy4YauY44tvsBb+4XYmr446ou3re5F5jEBIiKyDgs33KyEkcBT2cCUH4CxSzT//9Qhu+2PtGTJEgwZMsQoQQI0SdLevXtx8OBBDBo0CGvWrMH69evRtWtX3HHHHcjMzNS1Xbx4McaNG4fHH38cHTp0wLRp01BRUXFDferevTtWr16NVatWITExEXPnzsX8+fMNKtuZctttt+GTTz7Bu+++iy5duiA9PR1PP/20wTS65ORk/PDDD/j555/Rs2dP9O7dG++99x5iYmJ0bd555x1s3rwZ0dHR6NGjxw29BlOEENh5rfT3wPa2TZIAIKsRbCr7e+5llFxVIsTHdELRLtwHwd7uqFKq8efpK/VeLyUxEhN7tTI6Hunvgf/rHQOpBPh67xmMW/w7bnt96w2X7tYmZBYXXArgspmKdKao1AIZuZfxfdY5LPghB5tzLkIuk2DRhG7wcnfNf28yVySisd7H0fciIqLmyzX/y97YSGVAbH+H3GrDhg1mz/Xq1Qv6dTjGjBljci0PoFnP8+677+Ldd9+16r7bt2+3eH7s2LEYO3as2fMnT540eXzatGmYNm2aweO6U+uSk5ORnJxs9tojRozAiBEjAEBXuMEWci+V41zxVbi7SdE71nb/8tolOgAAkH2uBCq1cOkveZuubSCbkhhusp8SieYL6w8H8/F77mUktak/TscLygEA9/eKRlKbYIPRgNvigzFzxZ/400QC2dDS3SmJkYgN8UJeoWExljBfBWpVAhfLqjHxs91Y9UgfBHm7N3hRPADc0yVKt0kwERERNR1Mksip3n77bQwdOhTe3t7YtGkTvvzyy3qn6TmKdqpd7zbB8HS33RS++DAfeLnLUFGjQu6lcrQL97XZtW1JqVLjp780pb/vtJCU3BYfgh8O5iMj9zKeHmr5mmeKKpF5sggSCTBrcFtE+hsWERmaEAE/TzmKKmqMntvQvZSOXixDXmElpBLgowe6o6ZWrUuAzl25igmfZiD3UgVGfrALKiFQYKYqnbn9lgDg2/3nMCQh3OXLmBIREVHDcLodOVVmZiaGDh2KTp064ZNPPsGiRYvwj3/8w6l90k6rWrPvLACgf9sQm15fJpXoRh8OuPCUu90nLuNKpRLB3u4W1+70vba+4c8zV1BZU2vxmt9nndM9p26CBGjWIJlKkLQaspfS13+cAQAMuSUcwxMjDaZntQr2woppvRHgKUd+aZVBggRcH7XaePC82f2WtGy13xIRERG5Do4kkVOtXr3a2V0wYGpa1ac7chEd6GnT0YIuLf2RmVeEA2eLce+t0Ta7ri1pN5Ad1jECbjLz/57SKsgLLQI8ca74Kv44ecVskQshBNb+qUmSRpnZB8lWpbura1X4br8myZ3Qy3R8o4O8IJeZHo3SpjxPrsqC0kICpJ+0cTE8ERFR08GRJKJrtNOq6q47KSyvwfRl+60uGmAN7bqkg2dLbHbN+ugXHsjIvWxx9KNWb6rdXZ0sJ4cSiUQ3mvR7bqHZdtnnSpF7qQIKNylSEiNMtrFV6e7NORdxpVKJCD8PDGhrOmnLzCvCpXLzo1YALCZI+m52vyUiIiJyLRxJAgyKHVDTVN97rFILs9OqGroWxhraCneH80tRXauCws22+2nVZWqELNLC/jLaaW+BXnL0blN/mey+8cFYs+8sfj9+2Wyb7/7UjOwMTQiHr4fpnbTrK90tgWZfnPpKd6/K1Ey1u+/WlmZHwWyZ2NhiA1wiIiJyHc16JEku13xRq6ysrKclNXba91j7nteVmVdkNIKkryFrYazRMtATQd7uUKoEDueX2eSa5pgbIdOuuzE1Qvbjtap2yfVMtdPqG6dZt5V9vgQllUqj87UqNTYc0OwlNqa76al2wPXS3YBuS2YDAkDqiASLieqZokrsOl4IiQQWpzJam9gEebub7Iu2j5FWJG1ERETUuDTrkSSZTIaAgAAUFBQAALy8vAw2MrU1tVqNmpoaVFVVQSpt1vmp3dSNsRAClZWVKCgoQEBAgNmNZm21FsZaEokEnVv6Y/uRSzhwphhdr02/s7WGjpCp1AK7T1zG+msFFpI7mp4WV1e4nwfiQr2Re6kCGScuG02n23W8EIXlNQjydkd/M9PftFISI7F4UneTJbfD/RQYfEu4xeev3qsZReoXH4LoIC+z7awdtXrlrgTMWLEfEsCgnfY3RX1JGxERETU+zTpJAoCICM2XOW2iZE9CCFy9ehWenp52TcaaM3MxDggI0L3XpthqLUxDdGkZoEmSzhbb7Jp1NWSErORqjVFiMue7Q5g30vSUvLpuiw/RJEm5hUZJ0rprBRtGdI6E3IqRqZTESAxNiNDtXeSjcMNzaw7gYmk1vso4hYf7xZp8Xq1KjTV7NdP6xve0XBBDO2o1fZnlBCglMRKLpcZJW4SF6YpERETUuDX7JEkikSAyMhJhYWFQKo2nCdmSUqnEzp07MWDAALPTvujmmIqxXC43O4KkZau1MA3RJVpTBtyexRusHflKz87HVxmnjF77xVLrN3DtGxeMrzJO4fdcw3VJFdW1+OmviwCA0d1bWt13mVRiUDHuuZQOmPPdIfx7y1GM7tYCQd7uRs/ZeewSLpRWIdBLjqEJlkecAPOjVnUToLpJm6kNZ4mIiKjpaPZJkpZMJqv3i7Qt7lFbWwsPDw8mSXZyozHWH1Woy17TqjpfK95wvKAcX/9xGq2CvG3+xdvakS9TCRLQsKIVvdsEQyIBjhWUo6C0CmF+mnv/9NcFXFWqEBvijS4t/Rv+Iq6579ZofJVxCofzS/Hu5iN4dVQnozYrrxVsGNu9pdXFMKxNgOombURERNR0cWEM0TXaUYUAT8PkKsLfw6qRlIbae7II2u/hL3x7CBM/241+b2yzaalx7QiZJVLA4map1hatCPByR8coPwBAxonro0n6eyPdzDRT/aIOK/acxt8XSg3OF5RWYdvfmmmz9U21M3XtPnHBBhvOEhERUfPFJIlIT0piJB6/PQ4A0CMmACun9cauF+6weYKkrThXdxseUxXnGrK/UV36iUVdkms/U/q2tupa1kzd01a5++24Zr+kgtIq3Z9HdYuy6j6W9G4TjOGJEVALYMEPOQal3b/ZfxYqtUCPmEC0Dfe96XsRERFR88UkiaiOwmsbjHaLDrTLqEJ9FecAzfQ2lVogPTsf/d7Yhomf7caTq7JuaLQpJTESHSJ8jI5rR8iGWVnBzpqpe9c3ldWMJK0/cB5qAfSICURMsLfVfbbkpTtvgbubFL8dv4wthzUjR0IIfP2HZqpdQ0eRiIiIiOrimiSiOi6VVQMAwvwUdrm+tRXn3t9yBB9uyzVKprSjTdZOAayorsWJS5p9ot65rwvcpBKDdTcqtbBZ0YqerYPgJpXg7JWrOFNUeX2qXTfzeyM1VHSQF/7RLxYfb8/Fqz/8BYWbBJl5V3DqciW83WW4uzOrzREREdHN4UgSUR3aaWWhvvZJkqytOPeBiQQJMB5tqs/uE5dRo1KjZaAnxnRrYbTuxtIGrg0tWuGtcNMVZ3jl+2z8db4UMglwdyfbJi6P3x4PPw83nCq6ismf/4EPfzkOQBObnUcv2fReRERE1PwwSSKqQzeSZMM9kfTZ4rrWFlMAgO1HNEnDoPahZgsnaItWRNQp8tDQohXp2fk4crHc4L5uMin25F229LQG23XsEkqrao2OV9aojNZ0ERERETUUp9sR1VFwLUmy10iSNXsy+XvKUXy1/n276huVEkJg+1HNup2B7cIstr3ZvYC0xSjqvqbqWnWDpgfWR7umyxJrSpYTERERmcORJCI91bUqFFdqkpMwOyVJ1kxve/C21lZdq75RqbzCCpwpugq5zLo9fm60FLalYhRa1k4PrI+1a7qsGWUjIiIiMoVJEpEebWU7d5kU/p722/C3vultM+9oi0h/D6MkSksCINKKYgo7rq3P6dk6CD4K+w0cOzJxsXZNl7XtiIiIiOridDsiPZf0ptrdzMan1qhvelvqiARMX7YfEpje7NWaYgraJGlgu1Ab996QIxMXa9d02WtNGRERETV9HEki0lNQqvkSH2KnqXZ1WZreZm60yd1NatX6niqlChnX9isa1N7yeqSb5cjERbum62ZH2YiIiIjMYZJEpOdSubaynWOSpPqkJEZi1wt3YOW03ki9W7OOqaZWjU4tA+p97p68IlTXqhHh54F24cabydqSIxMXW5YsJyIiIjKFSRKRnoJS+1a2uxHa0aYH+8WiTxtN8YXv9p2t93k7rCj9bcs+OjJxsVXJciIiIiJTuCaJSI+rjSTVNa5HS2ScuIxv9p/FzDviLSY/10t/23c9kpY2cUnbkGNQxCHC3wOpIxJsnrjcbMlyIiIiInOYJBHpccWRJH3DO0Vg7vfZOHW5En+cvGJ2+tqZokqcuFQBmVSC29qGOKx/jk5ctKNsRERERLbE6XZEeq6PJLlmZTQvdzfc1VkzIvPNvjNm222/VtWuR6tA+HnYr5S5KTe61xIRERGRq2CSRKTn0rXqdq46kgQA43pEAwB+PJiPyppak22065EGtnfMVDsiIiKipoRJEtE1QgiXX5MEAD1bByIm2AsVNSpsOnTB6HxNrRq/5xYCcNx6JCIiIqKmhEkS0TXFlUooVZptW4N93J3cG/MkEgnGdW8JAFhjYsrd3pNFqKxRIcRHgYRIP0d3j4iIiKjRY5JEdI12FCnASw6Fm8zJvbFsTI+WkEiA3SeKcKao0uDcjmvrkQa2C4WU64GIiIiIGoxJEtE12sp2rjzVTqtFgCdui9NUrfumzp5J27keiYiIiOimMEkiuuZSuesXbdA3rodmyt23+89CrdZME8wvuYojF8sglQD94x1X+puIiIioKWGSRHTN9ZEk1yz/XVdyxwj4Ktxw9spV7MkrAgDsvDbVrkt0AAK9XXddFREREZErY5JEdM2lMtfeSLYuT3cZ7u6i2TNJW8BBO9VuULswp/WLiIiIqLFjkkR0TUFZ41mTpKXdM2njwXxsybmIX/4uAAD0a8updkREREQ3ikkS0TWNbSQJALq3CkC4rwJVtWr846u9qKpVAwBmLN+P9Ox8J/eOiIiIqHFikkR0TUFZ4yrcAAA//XUBF68ld/oullZh+jImSkREREQ3gkkS0TWXGtl0O5VaIG1Djslz4tr/p23IgUotTLYhIiIiItOYJJFZKrVARu5lfJ91Dhm5l5v0l+0qpQqlVbUAgNBGUt0uM68I+SVVZs8LAPklVci8VvmOiIiIiKzj5uwOkGtKz85H2oYcgy/hkf4eSB2RgJTEyAZdS6UWyMwrQkFZFcJ8PdArNggyqcTWXb4p2lEkdzcp/Dwax18L7fRAW7UjIiIiIo3G8W2QHCo9Ox/Tl+1H3XGjCyWadS6LJ3W3OlGyZbJlT/qV7SQS10rgzLF2P6fGsu8TERERkavgdDsyoF3nYmpiXUPXuWiTrbpTwrTJlisVFWiMle16xQYh0t8D5lI6CTQJaa/YIEd2i4iIiKjRY5JEBmy1zsWWyZYjXLo2Ja2xFG0AAJlUgtQRCQBglChpH6eOSHC5qY1EREREro5JEhmw1TqXxlZUoDGOJAFASmIkFk/qjgh/wyl1Ef4eDZoWSURERETXcU0SGbDVOpfGVlTg+pqkxrd+JyUxEkMTIly+OAYRERFRY8EkiQxo17lcKKkyOVVOAs0oRX3rXBpbUYHGOpKkJZNK0Ccu2NndICIiImoSON2ODOivczFFwLp1LkHecrMFBQDXKypQ0Mg2kiUiIiIi+3FqkrRw4UL07NkTvr6+CAsLw6hRo3DkyBGDNlVVVZgxYwaCg4Ph4+ODsWPH4uLFi07qcfOgXediLhFSqS0/v+SqEo/plRBvDEUFGvtIEhERERHZjlOTpB07dmDGjBnYvXs3Nm/eDKVSiWHDhqGiokLX5umnn8aGDRuwZs0a7NixA+fPn8eYMWOc2OvmIbljBGTX8pe5dydg5bTemDYgFgDw3DcHcPRimcnnqdQCT6z8E3mFFWgR4Im3xnV2+aICarVAYXnjXZNERERERLbl1DVJ6enpBo+/+OILhIWFYd++fRgwYABKSkqwZMkSrFixAnfccQcAYOnSpbjllluwe/du9O7d2xndbhYKy2tQoxKQSoBJvWPg7iZFz9aB+OtcKX7PvYxH/7cP38+8DX4ecoPnvfXTEew4egkecik+ndwDHaP8MaZ7S2w9fBGP/G8fAGD9zH4uNWJzpbIGtWoBiQQI9nF3dneIiIiIyMlcqnBDSUkJACAoSLNOZd++fVAqlRgyZIiuTYcOHdCqVStkZGSYTJKqq6tRXV2te1xaWgoAUCqVUCqV9ux+vbT3d3Y/rHHykiZuYb4KSIQKSqUKAPDuvZ0wevFu5BVW4OlVf+KD8V2w/0wxCsqqcbygHJ/syAMAvD46Ee1CvXSv9fZ2wWgb5o1jBRXYk3sJyR3D7dLvG4lx/hXNyGWglxxQq6BUq+zSt6aiMX2OGzPG2f4YY/tjjO2PMbY/xtgxHBVna68vEUK4xG6earUaI0eORHFxMXbt2gUAWLFiBR588EGDpAcAevXqhdtvvx1vvPGG0XXmzZuHtLQ0o+MrVqyAl5eXfTrfBO0vlODLYzK08RV4MtEwaThdDvw7W4ZaIYGHTKBKZbiuqFOgGv/oYLxwac0JKXZdlGJAhBpjY+tZ2ORAfxdLsPiwDJFeAi92YYJERERE1FRVVlbi/vvvR0lJCfz8/My2c5mRpBkzZiA7O1uXIN2oOXPm4JlnntE9Li0tRXR0NIYNG2YxEI6gVCqxefNmDB06FHK5vP4nONHZX/OAY8fQqU0U7ryzk9H5fM/DWJF5xihBAoDsK1LIYroZjxYduoBdqw+iAP64884+dun3jcS4+s/zwOFstIkMwZ139rBLv5qSxvQ5bswYZ/tjjO2PMbY/xtj+GGPHcFSctbPM6uMSSdLMmTPxww8/YOfOnWjZsqXueEREBGpqalBcXIyAgADd8YsXLyIiIsLktRQKBRQK4/UucrncZT7YrtQXc/JLNaN30UHeRn1VqQV+OXLJ4vNf23QEwzu3MKhe16dtKADgyMUyVCgFArzst/6nITG+XFkLAAj393D598WVNIbPcVPAONsfY2x/jLH9Mcb2xxg7hr3jbO21nVrdTgiBmTNnYu3atdi2bRtiY2MNzvfo0QNyuRxbt27VHTty5AhOnz6NPn3sMxJBGueuXAUAtAz0NDqXmVeE/JIqs88VAPJLqpCZV2RwPMzXA21CvSEEjM45E8t/ExEREZE+p44kzZgxAytWrMD3338PX19fXLhwAQDg7+8PT09P+Pv74+GHH8YzzzyDoKAg+Pn5YdasWejTpw8r29nZ2WtJUgsTSVJBmfkEqb52SbHBOHGpAnvyijCso+nRQEfT9pPlv4mIiIgIcHKStHjxYgDAoEGDDI4vXboUU6dOBQC89957kEqlGDt2LKqrq5GcnIyPP/7YwT1tXoQQuiSpZaBxsQtrkwlT7Xq3CcLKzNPYk3e5QX1SqQUy84pQUFaFMF8P9IoNstlGtBxJIiIiIiJ9Tk2SrCms5+HhgY8++ggfffSRA3pEAHClUomr10p+RwUYJzq9YoMQ6e+BCyVVMPUOSqDZMLZXbJDRuaTYYABAzvlSlFYpjfZZMiU9Ox9pG3IMpvhF+nsgdUSCTTak1SZJYUySiIiIiAhOXpNErunslUoAmqRB4SYzOi+TSpA6IgGAJiHSp32cOiLB5EhPhL8HYoK9oBbA3pP1r0tKz87H9GX7jdZAXSipwvRl+5GenV//C6oHR5KIiIiISB+TJDJiqWiDVkpiJBZP6o4If8ORpgh/Dyye1N3iCE/StRGmPScsJ0kqtUDahhyTo1XaY2kbcqBS3/hWX1drVCir1lS340gSEREREQEuUgKcXMv1og2WN99NSYzE0ISIBq8VSooNxuq9Z7G7ngp3Dami1ycu2OK1zNGOInnIpfBR8K8DERERETFJIhO00+0sjSRpyaSSBicoSW00I0nZ50pQXl1rNjlpaBU9lVpgT14R9hVKEJxXhD7xYfUmbPqV7SQS2xSCICIiIqLGjUkSGTlXfG0kKaD+JOlGtAz0QstAT5y9chX7Tl3BwHahJttZW0XPW+FWp7iDDF8d22tVcQeuRyIiIiKiurgmiYyctWJN0s3SVrnbc8J8KXBtFb36xneeWLkfj91gcYcCVrYjIiIiojqYJJEBIYRe4QbLa5JuhnbK3R4L65K0VfTMlRkHgHBfBSpr1Cafb01xB44kEREREVFdTJLIQOnVWl21N3tNtwOA3tdGkg6eLcbVGpXZdimJkegQ4WN0PMLfA59M6o537uti8T76xR1Mub4miUkSEREREWlwTRIZOHOtaEOIjzs83Y33SLKV6CBPRPp7IL+kCvtPX8Ft8SEm2+07dQV/XyiHTAK8P6Eb1EIYVNH7PuucVfczVwSCI0lEREREVBeTJDJg76INWhKJBEmxQViXdR57Tlw2myR99MtxAMC4HtEY0SXK6Ly1xR3Mtbu+Jsm66xARERFR08fpdmTgrAPWI2kltdFMuTO3X1L2uRJs+7sAUgkwfVCcyTb1FXeQAIj014w8mcKRJCIiIiKqi0kSGTjngMp2WknXEpesM8WoUhqvS9KOIo3sEoXWId4mr6Et7gDAbKKUOiLB5H5JKrVAYTmr2xERERGRISZJZEC7kWwLByRJsSHeCPVVoKZWjawzxQbnjl4sw6bsCwCAGbfHW7xOSmIkFk/qjgh/wylz3goZFk/qbnafpKKKGqgFIJEAQd7uN/5CiIiIiKhJYZJEBrRrkhwxkqRdlwQAe04YTrn7+Noo0vDECLQN9633WimJkdj1wh1Y9tCt6B+hKQneMsDT4kay2mIOwd4KuMn4V4GIiIiINPjNkAxo1yS1CLD/miTg+rqkPXnXN5U9WViB9QfOA6h/FEmfTKpJulJaqiGRAEculputagdwPRIRERERmcYkiXTKqpQouaoE4JjpdgDQ+9pI0v7TV1BTqxkBWrw9F2oB3NEhDIkt/Bt8TR850DHSDwDw2/FCs+2uV7ZjkkRERERE1zFJIh3tVLtALzl8FI6pDh8f5oNgb3dUKdU4eLYY54qv4tv9ZwE0bBSprn7xmhGqX4+aT5I4kkREREREpjBJIp2zRdem2jloFAnQrEvq2ToQAPDF7yeRtv4v1KoFbosPRo+YwBu+7m1xmiRp57FCCCFMtrnEkSQiIiIiMoGbyZKOrmiDg9YjAUB6dj5+z9WsR/rhYL7ueK/WwTd13W6tAuApl6GwvBp/XyjDLdem3+njSBIRERERmcKRJNJxZPlvQJMgTV+2H6VVtUbn3t9yFOnZ+SaeZR2FmxS922jWO+06ZnrKnbaoA5MkIiIiItLHJIl0zjpwI1mVWiBtQw5MT4TTSNuQA5XaUgvL+rUNBQDsPHbJ5Pnr0+08TJ4nIiIiouaJSRLpXN8jyf7T7TLzipBfYr48twCQX1KFzLwis23qM6BtiO5eVUqV0fkCTrcjIiIiIhOYJJHO9T2S7D+SZGn/ohtpZ0p8mA8i/DxQXavGHycNk62K6lpU1mgSJxZuICIiIiJ9TJIIAFBZU4uiihoAjlmTZO0Ut5uZCieRSND/2mjSr3XWJWlHkbzcZfB2ULlzIiIiImocmCQRAODctVEkXw83+HvK7X6/XrFBiPT3gMTMeQmASH8P9Lq22eyN6mcmSWL5byIiIiIyh0kSAdAv2uCY8t8yqQSpIxIAwChR0j5OHZEAmdRcGmWdfvGaJOlwfqnB1D1WtiMiIiIic5gkEQDgbLHjKttppSRGYvGk7ojwN5xSF+HvgcWTuiMlMfKm7xHso0BiC80eSb8dvz6axMp2RERERGQOF2MQAL09khxQtEFfSmIkhiZEIDOvCAVlVQjz1Uyxu9kRJH3924Yi+1wpfj1aiNHdWgJgZTsiIiIiMo9JEgG4vibJkSNJWjKpBH3igu12/f7xIVi8PRe/Hi+EEAISiUQ3ksQkiYiIiIjq4nQ7AuDYjWQdrUfrQHjIpbhUVo0jF8sAcCSJiIiIiMxjkkQAHLuRrKMp3GTo3UYzUvXrUc26JFa3IyIiIiJzmCQRqpQqXdLQFEeSAM26JADYeewSAHC6HRERERGZxSSJdKNI3u4yh+yR5AzaTWUz84pQUV2LyxWsbkdEREREpjFJIr2iDV6QSGxXVc6VtA3zQbifAtW1avz01wUIAUglQJC3u7O7RkREREQuhkkS6Yo2tGiiU+0AQCKR6Kbcfbv/LAAgxEdh01LjRERERNQ0MEkinCvW7JHUVNcjaWmn3P2eexkA1yMRERERkWlMkqhJl//Wd1u8JkkSQvNYLpNApRZO7BERERERuSImSXR9ul1A0yv/rW/vySK46U2vyzpTgn5vbEN6dr4Te0VEREREroZJEukVbmi6I0np2fmYvmw/auuMHF0oqcL0ZfuZKBERERGRDpOkZq6mVo2LZVUAmm7hBpVaIG1DDkxNrNMeS9uQw6l3RERERASASVKzl19yFUIAHnIpgptoOezMvCLkl1SZPS8A5JdUITOvyHGdIiIiIiKXxSSpmTvbDPZIKigznyDdSDsiIiIiatqYJDVzZ69oyn+3CGiaU+0AIMzXw6btiIiIiKhpY5LUzDWHog29YoMQ6e8Bc+NkEgCR/h7oFRvkyG4RERERkYtiktTM6cp/N+EkSSaVIHVEAgAYJUrax6kjEiCTNs3phkRERETUMG4NaaxWq7Fjxw78+uuvOHXqFCorKxEaGopu3bphyJAhiI6Otlc/yU7OFl9fk9SUpSRGYvGk7kjbkGNQxCHC3wOpIxKQkhjpxN4RERERkSuxKkm6evUq3nnnHSxevBhFRUXo2rUroqKi4OnpiePHj2PdunWYNm0ahg0bhrlz56J379727jfZSHOYbqeVkhiJoQkRyMwrQkFZFcJ8NVPsOIJERERERPqsSpLatWuHPn364LPPPsPQoUMhl8uN2pw6dQorVqzAhAkT8PLLL2PatGk27yzZVq1KjQulmlGVlk24cIM+mVSCPnHBzu4GEREREbkwq5Kkn3/+GbfccovFNjExMZgzZw6effZZnD592iadI/vKL6mCSi3g7iZFiI/C2d0hIiIiInIJVhVuqC9B0ieXyxEXF3fDHSLH0RVtCPCElFPOiIiIiIgANLBwg77a2lr85z//wfbt26FSqXDbbbdhxowZ8PDgXjONxbni5rMeiYiIiIjIWjecJD3xxBM4evQoxowZA6VSia+++gp79+7FypUrbdk/siPtRrJMkoiIiIiIrrM6SVq7di1Gjx6te/zzzz/jyJEjkMlkAIDk5GRWtWtkzulNtyMiIiIiIg2rN5P9/PPPMWrUKJw/fx4A0L17dzz22GNIT0/Hhg0b8Pzzz6Nnz5526yjZlkot8Nf5UgDAVaUKKrVwco+IiIiIiFyD1UnShg0bMHHiRAwaNAgffPABPv30U/j5+eHll1/GK6+8gujoaKxYscKefSUbSc/OR783tiEnX5MkffRLLvq9sQ3p2flO7hkRERERkfNZnSQBwPjx45GZmYlDhw4hOTkZkyZNwr59+5CVlYWPPvoIoaGh9uon2Uh6dj6mL9uP/JIqg+MXSqowfdl+JkpERERE1Ow1KEkCgICAAHz66ad46623MHnyZDz33HOoqqqq/4nkdCq1QNqGHJiaWKc9lrYhh1PviIiIiKhZszpJOn36NO677z506tQJDzzwANq2bYt9+/bBy8sLXbp0waZNmxp88507d2LEiBGIioqCRCLBunXrDM5PnToVEonE4CclJaXB9yGNzLwioxEkfQKaDWYz84oc1ykiIiIiIhdjdZI0efJkSKVSvPXWWwgLC8Ojjz4Kd3d3pKWlYd26dVi4cCHuu+++Bt28oqICXbp0wUcffWS2TUpKCvLz83U/LDF+4wrKrBvxs7YdEREREVFTZHUJ8L179+LAgQOIi4tDcnIyYmNjdeduueUW7Ny5E59++mmDbj58+HAMHz7cYhuFQoGIiAirr1ldXY3q6mrd49JSTXECpVIJpVLZoP7Zmvb+zupHsJd1b3ewl5vTY3WjnB3j5oAxdgzG2f4YY/tjjO2PMbY/xtgxHBVna68vEUJYtQBl4MCBaNmyJaZMmYItW7bg8OHD2LBhw0110qAjEgnWrl2LUaNG6Y5NnToV69atg7u7OwIDA3HHHXfg1VdfRXBwsNnrzJs3D2lpaUbHV6xYAS8vL5v1tzFSC2DefhlKagBAYqKFQIA7kNpdBamp00REREREjVhlZSXuv/9+lJSUwM/Pz2w7q5OkU6dOYfbs2Th8+DC6du2Kt956C1FRUTbrsKkkadWqVfDy8kJsbCxyc3Px0ksvwcfHBxkZGbpNbOsyNZIUHR2NwsJCi4FwBKVSic2bN2Po0KGQy+VO6cOrG//GlxmnjY5rc6IPJnRBcsdwx3bKhlwhxk0dY+wYjLP9Mcb2xxjbH2Nsf4yxYzgqzqWlpQgJCak3SbJ6ul1MTAy++eYbm3TOWhMmTND9uVOnTujcuTPi4uKwfft2DB482ORzFAoFFAqF0XG5XO4yH2xn9aVWpcbOY5cBAD4KN5RX1+rORfh7IHVEAlISIx3eL3twpfe7qWKMHYNxtj/G2P4YY/tjjO2PMXYMe8fZ2mtblSRVVFTA29vb6ps3tL212rRpg5CQEBw/ftxskkTmrf3zHPIKKxDk7Y7tzw7CX+dLUVBWhTBfD/SKDYKMc+yIiIiIiKyrbhcfH4/XX38d+fnmNxoVQmDz5s0YPnw4Fi1aZLMO6jt79iwuX76MyMimMdrhSEqVGou2HQMAPDawDfw85egTF4x7urZAn7hgJkhERERERNdYNZK0fft2vPTSS5g3bx66dOmCW2+9FVFRUfDw8MCVK1eQk5ODjIwMuLm5Yc6cOXj00Uetunl5eTmOHz+ue5yXl4esrCwEBQUhKCgIaWlpGDt2LCIiIpCbm4vnn38e8fHxSE5OvrFX24x9s+8szhRdRYiPAv/Xu7Wzu0NERERE5LKsSpLat2+Pb7/9FqdPn8aaNWvw66+/4vfff8fVq1cREhKCbt264bPPPsPw4cPNFlQwZe/evbj99tt1j5955hkAwJQpU7B48WIcPHgQX375JYqLixEVFYVhw4ZhwYIFJtcckXnVtSp8sFUzivT4oDh4ulv/HhERERERNTdWF24AgFatWmH27NmYPXu2TW4+aNAgWCqu99NPP9nkPs3d6j/O4HxJFcL9FLg/qZWzu0NERERE5NKsWpNEjVeVUoUPf9FMaZx5ezw85BxFIiIiIiKyhElSE7diz2lcLK1GlL8H7usZ7ezuEBERERG5vAZNt6PGQaUWyMwrwrkrlXh/i2Yt0qzBbaFw4ygSEREREVF9mCQ1MenZ+UjbkIP8kirdMZkE8PXgW01EREREZA1+c25C0rPzMX3ZftQthaESwKwVf8JNKkFKIveYIiIiIiKypMFrklq3bo358+fj9OnT9ugP3SCVWiBtQ45RgqQvbUMOVGpLLYiIiIiIqMFJ0lNPPYXvvvsObdq0wdChQ7Fq1SpUV1fbo29kgkotkJF7Gd9nnUNG7mVd0pOZV2Qwxa4uASC/pAqZeUUO6ikRERERUeN0Q0lSVlYWMjMzccstt2DWrFmIjIzEzJkzsX//fnv0ka5Jz85Hvze2YeJnu/HkqixM/Gw3+r2xDZsO5eO344VWXaOgzHwiRUREREREN1ECvHv37li0aBHOnz+P1NRU/Pe//0XPnj3RtWtXfP755xY3iaWG0643qjtalF9ShenL9+v2QqpPmK+HPbpHRERERNRk3HDhBqVSibVr12Lp0qXYvHkzevfujYcffhhnz57FSy+9hC1btmDFihW27GuzZc16IwDwdJfhao3K5DkJgAh/D/SKDbJ5/4iIiIiImpIGJ0n79+/H0qVLsXLlSkilUkyePBnvvfceOnTooGszevRo9OzZ06Ydbc7qW2+k9diAOLy/5SgAGCRUkmv/nzoiATKpxOh5RERERER0XYOn2/Xs2RPHjh3D4sWLce7cObz99tsGCRIAxMbGYsKECTbrZHNn7Tqi1iFeWDypOyL8DafURfh7YPGk7iz/TURERERkhQaPJJ04cQIxMTEW23h7e2Pp0qU33CkyZO06ojBfD/SJC8bQhAhk5hWhoKwKYb6aKXYcQSIiIiIisk6Dk6SCggJcuHABSUlJBsf37NkDmUyGW2+91WadI41esUGI9PcwO+Wu7nojmVSCPnHBDuwhEREREVHT0eDpdjNmzMCZM2eMjp87dw4zZsywSafIkEwqwfMpHUye43ojIiIiIiLbanCSlJOTg+7duxsd79atG3JycmzSKTJ26dq6pLqJENcbERERERHZVoOn2ykUCly8eBFt2rQxOJ6fnw83txuuKE4WVNeq8N9f8wAAr41KREywN9cbERERERHZSYOzmmHDhmHOnDn4/vvv4e/vDwAoLi7GSy+9hKFDh9q8gwR8u+8cCsqqEenvgTHdW8Ld7Yb3ACYiIiIiono0OEl6++23MWDAAMTExKBbt24AgKysLISHh+N///ufzTvY3NWq1PjPzlwAwD/6t2GCRERERERkZw1Oklq0aIGDBw9i+fLlOHDgADw9PfHggw9i4sSJkMvl9uhjs7Yx+wJOXa5EoJccE3tFO7s7RERERERN3g0tIvL29sYjjzxi675QHUIILN6uGUWa2jcWXu5c80VEREREZG83/K07JycHp0+fRk1NjcHxkSNH3nSnSGP7kUs4nF8Kb3cZpvS1vIEvERERERHZRoOTpBMnTmD06NE4dOgQJBIJhBAAAIlEU2FNpVLZtofN2MfbjwMA7k9qhQAvdyf3hoiIiIioeWhwFYAnn3wSsbGxKCgogJeXF/766y/s3LkTt956K7Zv326HLjZPf5wswh8nr8BdJsU/+rep/wlERERERGQTDR5JysjIwLZt2xASEgKpVAqpVIp+/fph4cKFeOKJJ/Dnn3/ao5/Nzse/aEaRxvZoiXA/Dyf3hoiIiIio+WjwSJJKpYKvry8AICQkBOfPnwcAxMTE4MiRI7btXTOjUgtk5F7Gx78cxy9HLkEC4LGBHEUiIiIiInKkBo8kJSYm4sCBA4iNjUVSUhLefPNNuLu749NPP0WbNvxCf6PSs/ORtiEH+SVVumMKuRSH80sRE+ztxJ4RERERETUvDR5J+uc//wm1Wg0AmD9/PvLy8tC/f39s3LgRixYtsnkHm4P07HxMX7bfIEECgCqlGtOX7Ud6dr6TekZERERE1Pw0eCQpOTlZ9+f4+Hj8/fffKCoqQmBgoK7CHVlPpRZI25ADYaFN2oYcDE2IgEzK+BIRERER2VuDRpKUSiXc3NyQnZ1tcDwoKIgJ0g3KzCsyGkHSJwDkl1QhM6/IcZ0iIiIiImrGGpQkyeVytGrVinsh2VBBmfkE6UbaERERERHRzWnwmqSXX34ZL730EoqKOLJhC2G+1pX3trYdERERERHdnAavSfrwww9x/PhxREVFISYmBt7ehpXX9u/fb7PONQe9YoMQ7qfAxdJqk+clACL8PdArNsixHSMiIiIiaqYanCSNGjXKDt1ovmRSCTq3DMDmnItG57SrvFJHJLBoAxERERGRgzQ4SUpNTbVHP5qtYxfL8MvfBQCAQC85rlQqdeci/D2QOiIBKYmRzuoeEREREVGz0+AkiWxHCIFXvs9GrVpgyC3h+M//9UBmXhEKyqoQ5quZYscRJCIiIiIix2pwkiSVSi2W+2blO+utP3Aeu08UQeEm1U2p6xMX7OxuERERERE1aw1OktauXWvwWKlU4s8//8SXX36JtLQ0m3WsqSurUuK1Hw8DAGbeHo/oIC8n94iIiIiIiIAbSJLuueceo2Pjxo1Dx44d8fXXX+Phhx+2Sceauve3HENBWTVaB3vhkYFtnN0dIiIiIiK6psH7JJnTu3dvbN261VaXa3JUaoE9eUXYVyjBmn1nsfS3PABA2j2JULjJnNw7IiIiIiLSsknhhqtXr2LRokVo0aKFLS7X5KRn5yNtQw7yS6oAyIBjOQCAbtEBGNgu1LmdIyIiIiIiAw1OkgIDAw0KNwghUFZWBi8vLyxbtsymnWsK0rPzMX3ZfggT5/48U4z07HyW+CYiIiIiciENTpLee+89gyRJKpUiNDQUSUlJCAwMtGnnGjuVWiBtQ47JBAnQbBabtiEHQxMiWOqbiIiIiMhFNDhJmjp1qh260TRl5hVdm2JnmgCQX1KFzLwilv4mIiIiInIRDS7csHTpUqxZs8bo+Jo1a/Dll1/apFNNRUGZ+QTpRtoREREREZH9NThJWrhwIUJCQoyOh4WF4V//+pdNOtVUhPl62LQdERERERHZX4OTpNOnTyM2NtboeExMDE6fPm2TTjUVvWKDEOnvAXOrjSQAIv090Cs2yJHdIiIiIiIiCxqcJIWFheHgwYNGxw8cOIDgYK6r0SeTSpA6IgEAjBIl7ePUEQks2kBERERE5EIanCRNnDgRTzzxBH755ReoVCqoVCps27YNTz75JCZMmGCPPjZqKYmRWDypOyL8DafURfh7YPGk7iz/TURERETkYhpc3W7BggU4efIkBg8eDDc3zdPVajUmT57MNUlmpCRGYmhCBLrN/xmlVbVYOCoB9/VqzREkIiIiIiIX1OAkyd3dHV9//TVeffVVZGVlwdPTE506dUJMTIw9+tdkyKQSyGWagbvOLf2ZIBERERERuagGJ0labdu2Rdu2bW3ZFyIiIiIiIqdr8JqksWPH4o033jA6/uabb+Lee++1SaeIiIiIiIicpcFJ0s6dO3HnnXcaHR8+fDh27txpk041RSq1gFKlBgAcPFsClVo4uUdERERERGRKg5Ok8vJyuLu7Gx2Xy+UoLS21SaeamvTsfPR7YxtKq2oBAHPW5aDfG9uQnp3v5J4REREREVFdDU6SOnXqhK+//tro+KpVq5CQkGCTTjUl6dn5mL5sP/JLqgyOXyipwvRl+5koERERERG5mAYnSa+88goWLFiAKVOm4Msvv8SXX36JyZMn47XXXsMrr7zSoGvt3LkTI0aMQFRUFCQSCdatW2dwXgiBuXPnIjIyEp6enhgyZAiOHTvW0C47jUotkLYhB6Ym1mmPpW3I4dQ7IiIiIiIX0uAkacSIEVi3bh2OHz+Oxx9/HLNnz8bZs2exZcsWjBo1qkHXqqioQJcuXfDRRx+ZPP/mm29i0aJF+OSTT7Bnzx54e3sjOTkZVVVVJtu7msy8IqMRJH0CQH5JFTLzihzXKSIiIiIisuiGSoDfdddduOuuu4yOZ2dnIzEx0errDB8+HMOHDzd5TgiB999/H//85z9xzz33AAC++uorhIeHY926dZgwYcKNdN2hCsqsS+asbUdERERERPZ3w/skaZWVlWHlypX473//i3379kGlUtmiX8jLy8OFCxcwZMgQ3TF/f38kJSUhIyPDbJJUXV2N6upq3WNtMQmlUgmlUmmTvlkr2Mu68AZ7uTm8b02VNo6Mp/0wxo7BONsfY2x/jLH9Mcb2xxg7hqPibO31bzhJ2rlzJ/773//iu+++Q1RUFMaMGWN22tyNuHDhAgAgPDzc4Hh4eLjunCkLFy5EWlqa0fGff/4ZXl5eNuufNdQCCHCXobgGACQmWggEuAOXcnZj42GHdq3J27x5s7O70OQxxo7BONsfY2x/jLH9Mcb2xxg7hr3jXFlZaVW7BiVJFy5cwBdffIElS5agtLQU9913H6qrq7Fu3TqXqWw3Z84cPPPMM7rHpaWliI6OxrBhw+Dn5+fw/shbX8SsVQcAwKCAg+Ta/746pguSO4abeCbdCKVSic2bN2Po0KGQy+XO7k6TxBg7BuNsf4yx/THG9scY2x9j7BiOirO1WxZZnSSNGDECO3fuxF133YX3338fKSkpkMlk+OSTT264k5ZEREQAAC5evIjIyEjd8YsXL6Jr165mn6dQKKBQKIyOy+Vyp3yw7+7aEm5uMqRtyDEo4hDh74HUEQlISYy08Gy6Uc56v5sTxtgxGGf7Y4ztjzG2P8bY/hhjx7B3nK29ttVJ0qZNm/DEE09g+vTpaNu27Q13zFqxsbGIiIjA1q1bdUlRaWkp9uzZg+nTp9v9/raUkhiJoQkRyDhegJ9/3YNh/ZPQJz4MMqmpKXhERERERORMVpcA37VrF8rKytCjRw8kJSXhww8/RGFh4U3dvLy8HFlZWcjKygKgKdaQlZWF06dPQyKR4KmnnsKrr76K9evX49ChQ5g8eTKioqIaXGrcFcikEiTFBqFHiEBSbBATJCIiIiIiF2V1ktS7d2989tlnyM/Px6OPPopVq1YhKioKarUamzdvRllZWYNvvnfvXnTr1g3dunUDADzzzDPo1q0b5s6dCwB4/vnnMWvWLDzyyCPo2bMnysvLkZ6eDg8Pjwbfi4iIiIiIyBoN3kzW29sbDz30EHbt2oVDhw5h9uzZeP311xEWFoaRI0c26FqDBg2CEMLo54svvgAASCQSzJ8/HxcuXEBVVRW2bNmCdu3aNbTLREREREREVmtwkqSvffv2ePPNN3H27FmsXLnSVn0iIiIiIiJymptKkrRkMhlGjRqF9evX2+JyRERERERETmOTJImIiIiIiKipYJJERERERESkh0kSERERERGRHiZJREREREREepgkERERERER6WGSREREREREpIdJEhERERERkR4mSURERERERHqYJBEREREREelhkkRERERERKSHSZKjqFWQnNqFFkUZkJzaBahVzu4RERERERGZ4ObsDjQLOeuB9BfgVnoetwLAqcWAXxSQ8gaQMNLZvSMiIiIiIj0cSbK3nPXA6slA6XnD46X5muM5653TLyIiIiIiMolJkj2pVUD6CwCEiZPXjqW/yKl3REREREQuhEmSPZ363XgEyYAASs9p2hERERERkUtgkmRP5Rdt246IiIiIiOyOSZI9+YTbth0REREREdkdkyR7iumrqWIHiZkGEsCvhaYdERERERG5BCZJ9iSVacp8AzBOlK49Tnld046IiIiIiFwCkyR7SxgJ3PcV4BtheNwvSnOc+yQREREREbkUbibrCAkjgTaDgNejAQC141fBrf0wjiAREREREbkgjiQ5il5CJFr1YYJEREREROSimCQRERERERHpYZJERERERESkh0kSERERERGRHiZJREREREREepgkERERERER6WGSREREREREpIdJEhERERERkR4mSURERERERHqYJBEREREREelhkkRERERERKSHSRIREREREZEeJklERERERER6mCQRERERERHpYZJERERERESkh0kSERERERGRHiZJREREREREepgkERERERER6WGSREREREREpIdJEhERERERkR4mSURERERERHqYJBEREREREelhkkRERERERKSHSRIREREREZEeJklERERERER6mCQRERERERHpYZJERERERESkh0kSERERERGRHiZJREREREREepgkERERERER6WGSREREREREpIdJEhERERERkR4mSURERERERHqYJBEREREREelx6SRp3rx5kEgkBj8dOnRwdreIiIiIiKgJc3N2B+rTsWNHbNmyRffYzc3lu0xERERERI2Yy2ccbm5uiIiIcHY3iIiIiIiomXD5JOnYsWOIioqCh4cH+vTpg4ULF6JVq1Zm21dXV6O6ulr3uLS0FACgVCqhVCrt3l+zlErIdX+sBZzZlyZM+x479b1u4hhjx2Cc7Y8xtj/G2P4YY/tjjB3DUXG29voSIYSwa09uwqZNm1BeXo727dsjPz8faWlpOHfuHLKzs+Hr62vyOfPmzUNaWprR8RUrVsDLy8veXTZLpqrG3QenAQB+6PwZVDKF0/pCRERERNQcVVZW4v7770dJSQn8/PzMtnPpJKmu4uJixMTE4N1338XDDz9sso2pkaTo6GgUFhZaDITd1VRA/lYMAKDyqVzIvf2d15cmTKlUYvPmzRg6dCjkcnn9T6AGY4wdg3G2P8bY/hhj+2OM7Y8xdgxHxbm0tBQhISH1JkkuP91OX0BAANq1a4fjx4+bbaNQKKBQGI/SyOVy536wxfV7y+Vu/EtmZ05/v5sBxtgxGGf7Y4ztjzG2P8bY/hhjx7B3nK29tkuXAK+rvLwcubm5iIyMdHZXiIiIiIioiXLpJOnZZ5/Fjh07cPLkSfz+++8YPXo0ZDIZJk6c6OyuERERERFRE+XS0+3Onj2LiRMn4vLlywgNDUW/fv2we/duhIaGOrtrRERERETURLl0krRq1Spnd4GIiIiIiJoZl55uR0RERERE5GhMkoiIiIiIiPQwSSIiIiIiItLDJImIiIiIiEgPkyQiIiIiIiI9TJKIiIiIiIj0MEkiIiIiIiLSwySJiIiIiIhID5MkIiIiIiIiPUySiIiIiIiI9DBJIiIiIiIi0sMkiYiIiIiISA+TJCIiIiIiIj1MkoiIiIiIiPQwSSIiIiIiItLDJImIiIiIiEgPkyQiIiIiIiI9TJKIiIiIiIj0MEkiIiIiIiLSwySJiIiIiIhID5MkIiIiIiIiPUySiIiIiIiI9DBJIiIiIiIi0sMkiYiIiIiISA+TJCIiIiIiIj1MkoiIiIiIiPQwSSIiIiIiItLDJImIiIiIiEgPkyQiIiIiIiI9TJKIiIiIiIj0MEkiIiIiIiLSwySJiIiIiIhID5MkIiIiIiIiPUySiIiIiIiI9DBJIiIiIiIi0sMkiYiIiIiISA+TJCIiIiIiIj1MkoiIiIiIiPQwSSIiIiIiItLDJImIiIiIiEgPkyQiIiIiIiI9TJKIiIiIiIj0MEkiIiIiIiLSwySJiIiIiIhID5MkIiIiIiIiPUySiIiIiIiI9DBJIiIiIiIi0sMkiYiIiIiISA+TJCIiIiIiIj1MkoiIiIiIiPQwSSIiIiIiItLDJImIiIiIiEgPkyQiIiIiIiI9TJKIiIiIiIj0MEkiIiIiIiLS4+bsDjQbapXuj5LTGUD7YYBUZtzm1O9A+UXAJxyI6ds82tjwPpJTu9CiKAOSU35AmwGu/bobYxvG2GFtHBJnF3zdTS7GtmrjSn1pqjG2VRvGuPG0YYxdp40LkgghhLM7UZ+PPvoIb731Fi5cuIAuXbrggw8+QK9evax6bmlpKfz9/VFSUgI/Pz8799SMnPXApueBsvzrx/yigJQ3gISR19ukvwCUnm9ebVypL2zD96o5tXGlvrAN36um0saV+sI2fK8aS5trlEolNm7ciDvvvBNyuRz2Ym1u4PJJ0tdff43Jkyfjk08+QVJSEt5//32sWbMGR44cQVhYWL3Pd3qSlLMeWD0ZQN0wSzT/d99Xmv9vjm36zgJ+/8A1+sI2fK+aUxu+n42nDd+rxtOG71XjacP3ynXa6CVKTJIaKCkpCT179sSHH34IAFCr1YiOjsasWbPw4osv1vt8pyZJahXwfqJh9mxAAvhGaP6oP8rULNoAkEgBoXaBvrCN5Tbge9Wk2oDvZ6NpA75XjaYN+F41mjbge+UqbfyigKcO6abeMUlqgJqaGnh5eeGbb77BqFGjdMenTJmC4uJifP/990bPqa6uRnV1te5xaWkpoqOjUVhY6PAkSXJqF9yWjXLoPYmIiIiIGoPaSesgYvoB0CRJmzdvxtChQ+2eJIWEhNSbJLl04YbCwkKoVCqEh4cbHA8PD8fff/9t8jkLFy5EWlqa0fGff/4ZXl5edumnOS2KMnCrQ+9IRERERNQ4ZP36E879VWpwbPPmzXa9Z2VlpVXtXDpJuhFz5szBM888o3usHUkaNmyYE0aS/IBTix16TyIiIiKixqBr/2R0ccJIkjVcOkkKCQmBTCbDxYsXDY5fvHgRERERJp+jUCigUCiMjsvlcrsG3KQ2AzTzLUvzYbxoDdDM2YzUrF9rdm1wbU6wcIG+sA3fq+bUBnw/G00b8L1qNG3A96rRtAHfK1dp4xcFNxMl1e39nd3aa0vt1gMbcHd3R48ePbB161bdMbVaja1bt6JPnz5O7JmVpDJNiUMAmk+KvmuPh7/RTNtIgD4zXaQvbMP3qjm14fvZeNrwvWo8bfheNZ42fK9cpk3K6y69X5JLJ0kA8Mwzz+Czzz7Dl19+icOHD2P69OmoqKjAgw8+6OyuWSdhpKbEoV+k4XG/qOulD5trm2ELXKcvbMP3qjm14fvZeNrwvWo8bfheNZ42fK9cp40Lc+nqdloffvihbjPZrl27YtGiRUhKSrLquU7fJ0lLrULtiZ3I+vUndO2fbHJ40eV2P3axHa+tacMY2/+9Yowd08YhcXbB193kYmyrNq7Ul6YaY1u1YYwbTxvG2HXagCXAHc5lkiQ47s1vzhhj+2OMHYNxtj/G2P4YY/tjjO2PMXYMV0uSpHbrARERERERUSPEJImIiIiIiEgPkyQiIiIiIiI9TJKIiIiIiIj0MEkiIiIiIiLSwySJiIiIiIhID5MkIiIiIiIiPUySiIiIiIiI9DBJIiIiIiIi0sMkiYiIiIiISI+bsztgb0IIAEBpaamTewIolUpUVlaitLQUcrnc2d1pkhhj+2OMHYNxtj/G2P4YY/tjjO2PMXYMR8VZmxNocwRzmnySVFZWBgCIjo52ck+IiIiIiMgVlJWVwd/f3+x5iagvjWrk1Go1zp8/D19fX0gkEqf2pbS0FNHR0Thz5gz8/Pyc2pemijG2P8bYMRhn+2OM7Y8xtj/G2P4YY8dwVJyFECgrK0NUVBSkUvMrj5r8SJJUKkXLli2d3Q0Dfn5+/EtmZ4yx/THGjsE42x9jbH+Msf0xxvbHGDuGI+JsaQRJi4UbiIiIiIiI9DBJIiIiIiIi0sMkyYEUCgVSU1OhUCic3ZUmizG2P8bYMRhn+2OM7Y8xtj/G2P4YY8dwtTg3+cINREREREREDcGRJCIiIiIiIj1MkoiIiIiIiPQwSSIiIiIiItLDJImIiIiIiEgPkyQH+uijj9C6dWt4eHggKSkJmZmZzu5SozBv3jxIJBKDnw4dOujOV1VVYcaMGQgODoaPjw/Gjh2LixcvGlzj9OnTuOuuu+Dl5YWwsDA899xzqK2tdfRLcRk7d+7EiBEjEBUVBYlEgnXr1hmcF0Jg7ty5iIyMhKenJ4YMGYJjx44ZtCkqKsIDDzwAPz8/BAQE4OGHH0Z5eblBm4MHD6J///7w8PBAdHQ03nzzTXu/NJdSX5ynTp1q9NlOSUkxaMM4m7dw4UL07NkTvr6+CAsLw6hRo3DkyBGDNrb6/bB9+3Z0794dCoUC8fHx+OKLL+z98lyGNXEeNGiQ0Wf5scceM2jDOJu3ePFidO7cWbeJZp8+fbBp0ybdeX6Ob159MeZn2PZef/11SCQSPPXUU7pjjeqzLMghVq1aJdzd3cXnn38u/vrrLzFt2jQREBAgLl686OyuubzU1FTRsWNHkZ+fr/u5dOmS7vxjjz0moqOjxdatW8XevXtF7969Rd++fXXna2trRWJiohgyZIj4888/xcaNG0VISIiYM2eOM16OS9i4caN4+eWXxXfffScAiLVr1xqcf/3114W/v79Yt26dOHDggBg5cqSIjY0VV69e1bVJSUkRXbp0Ebt37xa//vqriI+PFxMnTtSdLykpEeHh4eKBBx4Q2dnZYuXKlcLT01P85z//cdTLdLr64jxlyhSRkpJi8NkuKioyaMM4m5ecnCyWLl0qsrOzRVZWlrjzzjtFq1atRHl5ua6NLX4/nDhxQnh5eYlnnnlG5OTkiA8++EDIZDKRnp7u0NfrLNbEeeDAgWLatGkGn+WSkhLdecbZsvXr14sff/xRHD16VBw5ckS89NJLQi6Xi+zsbCEEP8e2UF+M+Rm2rczMTNG6dWvRuXNn8eSTT+qON6bPMpMkB+nVq5eYMWOG7rFKpRJRUVFi4cKFTuxV45Camiq6dOli8lxxcbGQy+VizZo1umOHDx8WAERGRoYQQvNFVSqVigsXLujaLF68WPj5+Ynq6mq79r0xqPvlXa1Wi4iICPHWW2/pjhUXFwuFQiFWrlwphBAiJydHABB//PGHrs2mTZuERCIR586dE0II8fHHH4vAwECDGL/wwguiffv2dn5FrslcknTPPfeYfQ7j3DAFBQUCgNixY4cQwna/H55//nnRsWNHg3uNHz9eJCcn2/sluaS6cRZC8wVT/4tQXYxzwwUGBor//ve//BzbkTbGQvAzbEtlZWWibdu2YvPmzQZxbWyfZU63c4Camhrs27cPQ4YM0R2TSqUYMmQIMjIynNizxuPYsWOIiopCmzZt8MADD+D06dMAgH379kGpVBrEtkOHDmjVqpUuthkZGejUqRPCw8N1bZKTk1FaWoq//vrLsS+kEcjLy8OFCxcMYurv74+kpCSDmAYEBODWW2/VtRkyZAikUin27NmjazNgwAC4u7vr2iQnJ+PIkSO4cuWKg16N69u+fTvCwsLQvn17TJ8+HZcvX9adY5wbpqSkBAAQFBQEwHa/HzIyMgyuoW3TXH9/142z1vLlyxESEoLExETMmTMHlZWVunOMs/VUKhVWrVqFiooK9OnTh59jO6gbYy1+hm1jxowZuOuuu4xi0dg+y242vRqZVFhYCJVKZfCGA0B4eDj+/vtvJ/Wq8UhKSsIXX3yB9u3bIz8/H2lpaejfvz+ys7Nx4cIFuLu7IyAgwOA54eHhuHDhAgDgwoULJmOvPUeGtDExFTP9mIaFhRmcd3NzQ1BQkEGb2NhYo2tozwUGBtql/41JSkoKxowZg9jYWOTm5uKll17C8OHDkZGRAZlMxjg3gFqtxlNPPYXbbrsNiYmJAGCz3w/m2pSWluLq1avw9PS0x0tySabiDAD3338/YmJiEBUVhYMHD+KFF17AkSNH8N133wFgnK1x6NAh9OnTB1VVVfDx8cHatWuRkJCArKwsfo5txFyMAX6GbWXVqlXYv38//vjjD6Nzje13MpMkcnnDhw/X/blz585ISkpCTEwMVq9e3Sx+4VDTNWHCBN2fO3XqhM6dOyMuLg7bt2/H4MGDndizxmfGjBnIzs7Grl27nN2VJs1cnB955BHdnzt16oTIyEgMHjwYubm5iIuLc3Q3G6X27dsjKysLJSUl+OabbzBlyhTs2LHD2d1qUszFOCEhgZ9hGzhz5gyefPJJbN68GR4eHs7uzk3jdDsHCAkJgUwmM6recfHiRURERDipV41XQEAA2rVrh+PHjyMiIgI1NTUoLi42aKMf24iICJOx154jQ9qYWPq8RkREoKCgwOB8bW0tioqKGPeb0KZNG4SEhOD48eMAGGdrzZw5Ez/88AN++eUXtGzZUnfcVr8fzLXx8/NrVv9QYy7OpiQlJQGAwWeZcbbM3d0d8fHx6NGjBxYuXIguXbrg3//+Nz/HNmQuxqbwM9xw+/btQ0FBAbp37w43Nze4ublhx44dWLRoEdzc3BAeHt6oPstMkhzA3d0dPXr0wNatW3XH1Go1tm7dajAXlqxTXl6O3NxcREZGokePHpDL5QaxPXLkCE6fPq2LbZ8+fXDo0CGDL5ubN2+Gn5+fbpidrouNjUVERIRBTEtLS7Fnzx6DmBYXF2Pfvn26Ntu2bYNardb9h6VPnz7YuXMnlEqlrs3mzZvRvn37ZjMFrKHOnj2Ly5cvIzIyEgDjXB8hBGbOnIm1a9di27ZtRtMObfX7oU+fPgbX0LZpLr+/64uzKVlZWQBg8FlmnBtGrVajurqan2M70sbYFH6GG27w4ME4dOgQsrKydD+33norHnjgAd2fG9Vn2aZlIMisVatWCYVCIb744guRk5MjHnnkEREQEGBQvYNMmz17tti+fbvIy8sTv/32mxgyZIgICQkRBQUFQghNOclWrVqJbdu2ib1794o+ffqIPn366J6vLSc5bNgwkZWVJdLT00VoaGizLgFeVlYm/vzzT/Hnn38KAOLdd98Vf/75pzh16pQQQlMCPCAgQHz//ffi4MGD4p577jFZArxbt25iz549YteuXaJt27YGpamLi4tFeHi4+L//+z+RnZ0tVq1aJby8vJpFaWotS3EuKysTzz77rMjIyBB5eXliy5Ytonv37qJt27aiqqpKdw3G2bzp06cLf39/sX37doOyvZWVlbo2tvj9oC03+9xzz4nDhw+Ljz76qFmV9a0vzsePHxfz588Xe/fuFXl5eeL7778Xbdq0EQMGDNBdg3G27MUXXxQ7duwQeXl54uDBg+LFF18UEolE/Pzzz0IIfo5twVKM+Rm2n7pVAxvTZ5lJkgN98MEHolWrVsLd3V306tVL7N6929ldahTGjx8vIiMjhbu7u2jRooUYP368OH78uO781atXxeOPPy4CAwOFl5eXGD16tMjPzze4xsmTJ8Xw4cOFp6enCAkJEbNnzxZKpdLRL8Vl/PLLLwKA0c+UKVOEEJoy4K+88ooIDw8XCoVCDB48WBw5csTgGpcvXxYTJ04UPj4+ws/PTzz44IOirKzMoM2BAwdEv379hEKhEC1atBCvv/66o16iS7AU58rKSjFs2DARGhoq5HK5iImJEdOmTTP6hxPG2TxTsQUgli5dqmtjq98Pv/zyi+jatatwd3cXbdq0MbhHU1dfnE+fPi0GDBgggoKChEKhEPHx8eK5554z2GNGCMbZkoceekjExMQId3d3ERoaKgYPHqxLkITg59gWLMWYn2H7qZskNabPskQIIWw7NkVERERERNR4cU0SERERERGRHiZJREREREREepgkERERERER6WGSREREREREpIdJEhERERERkR4mSURERERERHqYJBEREREREelhkkRERERERKSHSRIREZEeiUSCdevWObsbRETkREySiIjIZUydOhUSicToJyUlxdldIyKiZsTN2R0gIiLSl5KSgqVLlxocUygUTuoNERE1RxxJIiIil6JQKBAREWHwExgYCEAzFW7x4sUYPnw4PD090aZNG3zzzTcGzz906BDuuOMOeHp6Ijg4GI888gjKy8sN2nz++efo2LEjFAoFIiMjMXPmTIPzhYWFGD16NLy8vNC2bVusX79ed+7KlSt44IEHEBoaCk9PT7Rt29YoqSMiosaNSRIRETUqr7zyCsaOHYsDBw7ggQcewIQJE3D48GEAQEVFBZKTkxEYGIg//vgDa9aswZYtWwySoMWLF2PGjBl45JFHcOjQIaxfvx7x8fEG90hLS8N9992HgwcP4s4778QDDzyAoqIi3f1zcnKwadMmHD58GIsXL0ZISIjjAkBERHYnEUIIZ3eCiIgI0KxJWrZsGTw8PAyOv/TSS3jppZcgkUjw2GOPYfHixbpzvXv3Rvfu3fHxxx/js88+wwsvvIAzZ87A29sbALBx40aMGDEC58+fR3h4OFq0aIEHH3wQr776qsk+SCQS/POf/8SCBQsAaBIvHx8fbNq0CSkpKRg5ciRCQkLw+eef2ykKRETkbFyTRERELuX22283SIIAICgoSPfnPn36GJzr06cPsrKyAACHDx9Gly5ddAkSANx2221Qq9U4cuQIJBIJzp8/j8GDB1vsQ+fOnXV/9vb2hp+fHwoKCgAA06dPx9ixY7F//34MGzYMo0aNQt++fW/otRIRkWtikkRERC7F29vbaPqbrXh6elrVTi6XGzyWSCRQq9UAgOHDh+PUqVPYuHEjNm/ejMGDB2PGjBl4++23bd5fIiJyDq5JIiKiRmX37t1Gj2+55RYAwC233IIDBw6goqJCd/63336DVCpF+/bt4evri9atW2Pr1q031YfQ0FBMmTIFy5Ytw/vvv49PP/30pq5HRESuhSNJRETkUqqrq3HhwgWDY25ubrriCGvWrMGtt96Kfv36Yfny5cjMzMSSJUsAAA888ABSU1MxZcoUzJs3D5cuXcKsWbPwf//3fwgPDwcAzJs3D4899hjCwsIwfPhwlJWV4bfffsOsWbOs6t/cuXPRo0cPdOzYEdXV1fjhhx90SRoRETUNTJKIiMilpKenIzIy0uBY+/bt8ffffwPQVJ5btWoVHn/8cURGRmLlypVISEgAAHh5eeGnn37Ck08+iZ49e8LLywtjx47Fu+++q7vWlClTUFVVhffeew/PPvssQkJCMG7cOKv75+7ujjlz5uDkyZPw9PRE//79sWrVKhu8ciIichWsbkdERI2GRCLB2rVrMWrUKGd3hYiImjCuSSIiIiIiItLDJImIiIiIiEgP1yQREVGjwRniRETkCBxJIiIiIiIi0sMkiYiIiIiISA+TJCIiIiIiIj1MkoiIiIiIiPQwSSIiIiIiItLDJImIiIiIiEgPkyQiIiIiIiI9TJKIiIiIiIj0/D9J9+EIXcgYaAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Loop 2 Plot: Accuracy VS Epochs\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot Acc Retain and Acc Forget over epochs\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(df[\"Epochs\"], df[\"AccRetain\"], label='Acc Retain', marker='o')\n",
        "plt.plot(df[\"Epochs\"], df[\"AccForget\"], label='Acc Forget', marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Accuracy over Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "-Nlun4Km2qql",
        "outputId": "e0aca231-0921-4761-fbce-a3a43c66b673"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "1      0        0.0  11.160714   5.810602   6.675236          -0.084199   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "1        11.096108  \n",
            "Epoch 0 completed in 0.60 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "2     50        0.0  15.267856   4.849536    3.06631          -0.048116   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "2          1.63892  \n",
            "Epoch 50 completed in 0.66 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "3    100        0.0  16.484375   5.965597   2.890397          -0.073017   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "3         1.827085  \n",
            "Epoch 100 completed in 0.69 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "4    150        0.0  17.299107   5.413851   2.935211          -0.073995   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "4         1.838088  \n",
            "Epoch 150 completed in 0.67 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "5    200        0.0  20.189732   6.034798   2.875735          -0.071155   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "5         1.755188  \n",
            "Epoch 200 completed in 0.67 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "6    250        0.0  20.502232   5.027403   2.609459          -0.073615   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "6         1.710651  \n",
            "Epoch 250 completed in 0.69 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "7    300        0.0  21.015625    5.93826   3.315621          -0.072952   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "7          1.64891  \n",
            "Epoch 300 completed in 0.66 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "8    350        0.0  22.566964   5.738645   2.752894          -0.073518   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "8          1.61849  \n",
            "Epoch 350 completed in 0.72 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "9    400        0.0  24.631697   6.262642   3.007032          -0.072953   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "9         1.625016  \n",
            "Epoch 400 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "10    450        0.0  21.986607   6.283006   3.190322          -0.069235   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "10         1.609514  \n",
            "Epoch 450 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "11    500        0.0  28.593749    5.83252   2.443482           -0.07048   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "11         1.593363  \n",
            "Epoch 500 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "12    550        0.0  26.294643   6.532331   2.743359          -0.068684   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "12          1.58068  \n",
            "Epoch 550 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "13    600        0.0  26.618305   6.195158   2.794003          -0.067923   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "13         1.540625  \n",
            "Epoch 600 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "14    650        0.0  26.930803    6.24622   2.900066          -0.066193   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "14         1.515084  \n",
            "Epoch 650 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "15    700        0.0  31.160715    6.37046   2.668676          -0.066089   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "15         1.530018  \n",
            "Epoch 700 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "16    750        0.0  27.444196   6.603393   2.920724          -0.064386   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "16         1.509609  \n",
            "Epoch 750 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "17    800        0.0  31.562501   6.066182   2.636746          -0.064372   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "17         1.496912  \n",
            "Epoch 800 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "18    850        0.0  30.892858   6.158723   2.526125          -0.061922   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "18         1.511949  \n",
            "Epoch 850 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "19    900        0.0  30.814731   6.286603   2.670557          -0.061816   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "19         1.505267  \n",
            "Epoch 900 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "20    950        0.0  30.970982   6.206618    2.52396          -0.060007   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "20          1.47483  \n",
            "Epoch 950 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "21   1000        0.0  31.607142   6.286054   2.613488          -0.059166   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "21         1.474771  \n",
            "Epoch 1000 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "22   1050        0.0  30.937499    6.37705   2.699546          -0.058862   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "22         1.473367  \n",
            "Epoch 1050 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "23   1100        0.0  31.830359   6.219743   2.558342          -0.057815   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "23         1.472667  \n",
            "Epoch 1100 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "24   1150        0.0  32.377231   6.204006   2.485442          -0.057218   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "24         1.462233  \n",
            "Epoch 1150 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "25   1200        0.0  32.834822   6.303796   2.507657          -0.056216   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "25         1.443524  \n",
            "Epoch 1200 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "26   1250        0.0  33.069196   6.232448   2.552155          -0.057103   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "26         1.445807  \n",
            "Epoch 1250 completed in 0.75 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "27   1300        0.0  32.254463   6.378603   2.656782           -0.05663   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "27         1.449234  \n",
            "Epoch 1300 completed in 0.74 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "28   1350        0.0  32.220984   6.288646   2.632915          -0.056048   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "28         1.456635  \n",
            "Epoch 1350 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "29   1400        0.0  33.459821   6.345007   2.520245          -0.055879   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "29         1.439064  \n",
            "Epoch 1400 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "30   1450        0.0  32.790178   6.307036   2.627475          -0.056333   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "30         1.450557  \n",
            "Epoch 1450 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "31   1500        0.0  33.191964   6.255028    2.56295          -0.056642   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "31         1.436874  \n",
            "Epoch 1500 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "32   1550        0.0  33.203125   6.320127   2.575119          -0.055892   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "32         1.429836  \n",
            "Epoch 1550 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "33   1600        0.0  32.946429   6.328045   2.574134          -0.055483   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "33          1.44129  \n",
            "Epoch 1600 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "34   1650        0.0  32.991073   6.309247   2.609568          -0.055675   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "34         1.441677  \n",
            "Epoch 1650 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "35   1700        0.0  32.901785   6.342864   2.610939          -0.055807   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "35         1.449301  \n",
            "Epoch 1700 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "36   1750        0.0  32.912946   6.303087   2.603558           -0.05501   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "36         1.443499  \n",
            "Epoch 1750 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "37   1800        0.0   33.14732   6.314439   2.584718          -0.055976   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "37         1.430254  \n",
            "Epoch 1800 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "38   1850        0.0  32.935268   6.374766   2.615531          -0.054703   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "38         1.441961  \n",
            "Epoch 1850 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "39   1900        0.0  32.745537   6.335638   2.614877          -0.055149   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "39         1.439829  \n",
            "Epoch 1900 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "40   1950        0.0  32.879463   6.326529   2.615515          -0.054795   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "40         1.433628  \n",
            "Epoch 1950 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "41   2000        0.0  32.756695   6.316337   2.622032          -0.054799   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "41         1.433848  \n",
            "Epoch 2000 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "42   2050        0.0    32.8125   6.320398   2.620486          -0.055193   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "42         1.445676  \n",
            "Epoch 2050 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "43   2100        0.0  32.756695   6.335186   2.618562          -0.055236   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "43         1.419452  \n",
            "Epoch 2100 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "44   2150        0.0  32.656249   6.348586   2.633603          -0.054289   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "44         1.433446  \n",
            "Epoch 2150 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "45   2200        0.0   32.66741   6.353219   2.631103          -0.055685   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "45         1.429125  \n",
            "Epoch 2200 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "46   2250        0.0  32.834822   6.348626   2.623656          -0.055299   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "46         1.431484  \n",
            "Epoch 2250 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "47   2300        0.0  32.779017   6.348495   2.623807          -0.055375   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "47         1.437028  \n",
            "Epoch 2300 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "48   2350        0.0  32.700893   6.351537   2.630195          -0.054925   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "48          1.42444  \n",
            "Epoch 2350 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "49   2400        0.0  32.745537   6.353337   2.631228          -0.055055   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "49         1.429983  \n",
            "Epoch 2400 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "50   2450        0.0    32.8125   6.344598   2.628126          -0.054989   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "50         1.442101  \n",
            "Epoch 2450 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "51   2500        0.0  32.801339   6.344803   2.630202          -0.055276   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "51         1.440359  \n",
            "Epoch 2500 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "52   2550        0.0    32.8125   6.348246   2.631367          -0.054699   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "52          1.43471  \n",
            "Epoch 2550 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "53   2600        0.0  32.834822   6.348098   2.630141          -0.054678   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "53         1.432411  \n",
            "Epoch 2600 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "54   2650        0.0  32.801339   6.348427   2.630782          -0.055331   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "54         1.444287  \n",
            "Epoch 2650 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "55   2700        0.0  32.790178   6.349885   2.632758          -0.054939   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "55         1.435023  \n",
            "Epoch 2700 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "56   2750        0.0  32.767856   6.350772   2.632057          -0.055677   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "56         1.443926  \n",
            "Epoch 2750 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "57   2800        0.0  32.779017   6.350194   2.631565          -0.055076   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "57         1.443976  \n",
            "Epoch 2800 completed in 0.68 seconds\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-961623f6e07a>\u001b[0m in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;31m## Take n_generator_iter steps on generator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0midx_pseudo\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mn_repeat_batch\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mn_generator_iter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0mstudent_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstudent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_pseudo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mteacher_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_pseudo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mavgpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    279\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/pooling.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m   1265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1266\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madaptive_avg_pool2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36madaptive_avg_pool2d\u001b[0;34m(input, output_size)\u001b[0m\n\u001b[1;32m   1258\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madaptive_avg_pool2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[0m_output_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_list_with_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1260\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madaptive_avg_pool2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_output_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# Loop 3:\n",
        "# total_pseudo_batches = 4000\n",
        "# threshold = 0.02\n",
        "\n",
        "import time\n",
        "\n",
        "idx_pseudo = 0\n",
        "total_n_pseudo_batches = 4000\n",
        "n_pseudo_batches = 0\n",
        "running_gen_loss = []\n",
        "running_stu_loss = []\n",
        "\n",
        "threshold = 0.02\n",
        "KL_temperature = 2\n",
        "AT_beta = 250\n",
        "\n",
        "df = pd.DataFrame(columns=[\"Epochs\", \"AccForget\", \"AccRetain\", \"ErrForget\", \"ErrRetain\", \"MeanGeneratorLoss\", \"MeanStudentLoss\"])\n",
        "\n",
        "df = pd.concat([df, pd.DataFrame([{\"Epochs\": 0, \"AccForget\": AccForget, \"AccRetain\": AccRetain, \"ErrForget\": ErrForget,\n",
        "                                   \"ErrRetain\": ErrRetain, \"MeanGeneratorLoss\": None, \"MeanStudentLoss\": None}])], ignore_index=True)\n",
        "\n",
        "zero_count = 0\n",
        "\n",
        "while n_pseudo_batches < total_n_pseudo_batches:\n",
        "    epoch_start_time = time.time()  # Record the start time of the epoch\n",
        "\n",
        "    x_pseudo = generator.__next__()\n",
        "\n",
        "    preds = model(x_pseudo)\n",
        "    mask = (torch.softmax(preds.detach(), dim=1)[:, 0] <= threshold)\n",
        "    x_pseudo = x_pseudo[mask]\n",
        "\n",
        "    if x_pseudo.size(0) == 0:\n",
        "        zero_count += 1\n",
        "        if zero_count > 100:\n",
        "            print(\"Generator Stopped Producing datapoints corresponding to retain classes.\")\n",
        "        continue\n",
        "    else:\n",
        "        zero_count = 0\n",
        "\n",
        "    ## Take n_generator_iter steps on generator\n",
        "    if idx_pseudo % n_repeat_batch < n_generator_iter:\n",
        "        student_logits = student(x_pseudo)\n",
        "\n",
        "        teacher_logits = model(x_pseudo)\n",
        "\n",
        "        generator_total_loss = KT_loss_generator(student_logits, teacher_logits, KL_temperature=KL_temperature)\n",
        "\n",
        "        optimizer_generator.zero_grad()\n",
        "        generator_total_loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(generator.parameters(), 5)\n",
        "        optimizer_generator.step()\n",
        "        running_gen_loss.append(generator_total_loss.cpu().detach())\n",
        "\n",
        "    elif idx_pseudo % n_repeat_batch < (n_generator_iter + n_student_iter):\n",
        "        with torch.no_grad():\n",
        "            teacher_logits = model(x_pseudo)\n",
        "\n",
        "        student_logits = student(x_pseudo)\n",
        "\n",
        "        student_activations_list = list(student_activations.values())\n",
        "        teacher_activations_list = list(teacher_activations.values())\n",
        "\n",
        "        student_total_loss = KT_loss_student(student_logits, student_activations_list,\n",
        "                                             teacher_logits, teacher_activations_list,\n",
        "                                             KL_temperature=KL_temperature, AT_beta=AT_beta)\n",
        "\n",
        "        optimizer_student.zero_grad()\n",
        "        student_total_loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(student.parameters(), 5)\n",
        "        optimizer_student.step()\n",
        "        running_stu_loss.append(student_total_loss.cpu().detach())\n",
        "\n",
        "    if (idx_pseudo + 1) % n_repeat_batch == 0:\n",
        "        if (n_pseudo_batches % 50 == 0):\n",
        "            MeanGLoss = np.mean(running_gen_loss)\n",
        "            running_gen_loss = []\n",
        "            MeanSLoss = np.mean(running_stu_loss)\n",
        "            running_stu_loss = []\n",
        "\n",
        "            history_forget = [evaluate(student, forget_valid_dl, device=DEVICE)]\n",
        "            AccForget = history_forget[0][\"Acc\"]*100\n",
        "            ErrForget = history_forget[0][\"Loss\"]\n",
        "\n",
        "            history_retain = [evaluate(student, retain_valid_dl, device=DEVICE)]\n",
        "            AccRetain = history_retain[0][\"Acc\"]*100\n",
        "            ErrRetain = history_retain[0][\"Loss\"]\n",
        "\n",
        "            df = pd.concat([df, pd.DataFrame([{\"Epochs\": n_pseudo_batches, \"AccForget\": AccForget, \"AccRetain\": AccRetain, \"ErrForget\": ErrForget,\n",
        "                                               \"ErrRetain\": ErrRetain, \"MeanGeneratorLoss\": MeanGLoss, \"MeanStudentLoss\": MeanSLoss}])], ignore_index=True)\n",
        "            print(df.iloc[-1:])\n",
        "            scheduler_student.step(history_retain[0][\"Loss\"])\n",
        "            scheduler_generator.step(history_forget[0][\"Loss\"])\n",
        "\n",
        "            epoch_end_time = time.time()  # Record the end time of the epoch\n",
        "            epoch_duration = epoch_end_time - epoch_start_time  # Calculate the duration\n",
        "            print(f\"Epoch {n_pseudo_batches} completed in {epoch_duration:.2f} seconds\")\n",
        "\n",
        "\n",
        "        n_pseudo_batches += 1\n",
        "\n",
        "    idx_pseudo += 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "hSkpxBUz2pOR",
        "outputId": "327231e3-9db2-48b1-86e4-459b848d9d15"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Loop 3 Plot: Accuracy VS Epochs\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot Acc Retain and Acc Forget over epochs\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(df[\"Epochs\"], df[\"AccRetain\"], label='Acc Retain', marker='o')\n",
        "plt.plot(df[\"Epochs\"], df[\"AccForget\"], label='Acc Forget', marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Accuracy over Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFoJDk9-7Q_h",
        "outputId": "3b21a99c-941c-4690-9ad2-7593857c6a4c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "1      0        0.0  11.160714   3.787273   9.179292          -0.082138   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "1        11.117161  \n",
            "Epoch 0 completed in 0.65 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "2     50  13.950893  12.377232   2.901126   3.570164          -0.055822   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "2         1.789993  \n",
            "Epoch 50 completed in 0.65 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "3    100   5.691964  17.712054   2.672401   2.830077           -0.06952   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "3         1.652292  \n",
            "Epoch 100 completed in 0.69 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "4    150   7.589286  17.455357   3.175813   2.848802          -0.075911   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "4         1.795016  \n",
            "Epoch 150 completed in 0.74 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "5    200   1.116071  18.984374   4.390365   2.877123          -0.074057   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "5         1.687133  \n",
            "Epoch 200 completed in 0.66 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "6    250   3.348214  23.002233   3.678261   2.754472          -0.073783   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "6         1.651126  \n",
            "Epoch 250 completed in 0.65 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "7    300   4.910714  22.901785    3.03007    2.69699          -0.072171   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "7         1.548291  \n",
            "Epoch 300 completed in 0.65 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "8    350   1.450893  25.133929   3.549869   2.423307          -0.071851   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "8         1.539156  \n",
            "Epoch 350 completed in 0.67 seconds\n",
            "  Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "9    400   3.794643  23.504464   3.151375   2.869773          -0.072008   \n",
            "\n",
            "   MeanStudentLoss  \n",
            "9         1.550299  \n",
            "Epoch 400 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "10    450   2.455357  23.080356   3.058584   2.743745          -0.071748   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "10         1.499301  \n",
            "Epoch 450 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "11    500   2.790179  27.466518   3.885428   2.494288          -0.071659   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "11         1.525148  \n",
            "Epoch 500 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "12    550   4.575893  30.066964   3.979433   2.473322          -0.069502   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "12          1.55179  \n",
            "Epoch 550 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "13    600   6.361607  29.296875   3.385516   2.719359          -0.067672   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "13         1.509274  \n",
            "Epoch 600 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "14    650   5.915179  30.546874   3.647027   2.426288          -0.066782   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "14         1.478054  \n",
            "Epoch 650 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "15    700      9.375   32.66741   3.074648   2.342884            -0.0661   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "15         1.489951  \n",
            "Epoch 700 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "16    750   4.352679  30.390626   4.374932   2.634683          -0.064287   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "16         1.454712  \n",
            "Epoch 750 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "17    800   2.790179  30.546874   4.922889   2.869523          -0.063161   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "17         1.424056  \n",
            "Epoch 800 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "18    850   2.901786  33.058035   4.201622   2.325484          -0.063338   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "18         1.426834  \n",
            "Epoch 850 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "19    900     4.6875  30.234376   4.062218   2.577672          -0.061585   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "19         1.420102  \n",
            "Epoch 900 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "20    950   4.129464  32.131696   4.576529   2.467246          -0.061775   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "20         1.394248  \n",
            "Epoch 950 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "21   1000   6.473214  29.620537   3.741979   3.006836          -0.061359   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "21         1.381069  \n",
            "Epoch 1000 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "22   1050     4.6875  31.227678   4.842482   2.649873          -0.059231   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "22         1.377767  \n",
            "Epoch 1050 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "23   1100   6.696428  34.486607   4.130179   2.477484          -0.058717   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "23         1.373456  \n",
            "Epoch 1100 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "24   1150   7.700893  33.604911    3.95306   2.538645          -0.058624   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "24         1.369331  \n",
            "Epoch 1150 completed in 0.64 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "25   1200   7.142857  34.720981   3.866649   2.469323          -0.057169   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "25          1.36531  \n",
            "Epoch 1200 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "26   1250   7.700893  34.620535    4.17943   2.572528          -0.056405   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "26         1.357963  \n",
            "Epoch 1250 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "27   1300   4.017857  35.055804    4.37234   2.566713          -0.056177   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "27         1.350672  \n",
            "Epoch 1300 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "28   1350   5.803571  34.553573   4.549568   2.637916          -0.054906   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "28         1.347463  \n",
            "Epoch 1350 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "29   1400   4.575893  34.564734    4.91785   2.621436          -0.054913   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "29         1.346115  \n",
            "Epoch 1400 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "30   1450   7.254464  35.234374   4.114984   2.625639          -0.054727   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "30         1.332817  \n",
            "Epoch 1450 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "31   1500   7.254464   34.82143    4.09971   2.651707          -0.053831   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "31         1.335562  \n",
            "Epoch 1500 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "32   1550    7.03125  35.424107   4.143707   2.528423          -0.054019   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "32         1.345218  \n",
            "Epoch 1550 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "33   1600   5.691964  35.290179   4.466652   2.584663          -0.053587   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "33         1.323156  \n",
            "Epoch 1600 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "34   1650   6.808036  35.279018   4.470943   2.590111          -0.053776   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "34          1.33202  \n",
            "Epoch 1650 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "35   1700     7.8125  35.680804   4.072927   2.577615          -0.053664   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "35         1.339795  \n",
            "Epoch 1700 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "36   1750   6.473214  35.256696   4.377283   2.651782          -0.053162   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "36         1.340765  \n",
            "Epoch 1750 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "37   1800   7.366072  35.680804   4.288057   2.594496          -0.053426   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "37         1.326417  \n",
            "Epoch 1800 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "38   1850   6.361607  35.535714   4.353201   2.637368          -0.053305   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "38         1.332584  \n",
            "Epoch 1850 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "39   1900   7.366072  35.881695   4.243078   2.565955          -0.052994   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "39         1.343557  \n",
            "Epoch 1900 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "40   1950   7.477678   35.60268   4.202811   2.580459          -0.052953   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "40         1.322413  \n",
            "Epoch 1950 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "41   2000   7.254464  35.915178   4.250056   2.549233           -0.05293   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "41         1.330846  \n",
            "Epoch 2000 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "42   2050   7.477678  35.546875   4.221383   2.609177          -0.052928   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "42         1.332699  \n",
            "Epoch 2050 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "43   2100   7.589286  35.502231   4.223283   2.593469          -0.053152   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "43         1.339143  \n",
            "Epoch 2100 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "44   2150   7.589286  35.758927   4.216554   2.578391          -0.052386   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "44         1.328543  \n",
            "Epoch 2150 completed in 0.70 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "45   2200   7.477678  35.680804   4.228822    2.58071          -0.053598   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "45         1.328298  \n",
            "Epoch 2200 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "46   2250   7.477678  35.647321   4.238976    2.59642          -0.053057   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "46         1.322144  \n",
            "Epoch 2250 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "47   2300   7.589286  35.736609   4.202723   2.585398          -0.053126   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "47         1.330497  \n",
            "Epoch 2300 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "48   2350   7.477678  35.803571   4.231812   2.580095          -0.053323   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "48         1.328086  \n",
            "Epoch 2350 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "49   2400   7.477678  35.703126   4.246831   2.582204          -0.053118   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "49         1.328961  \n",
            "Epoch 2400 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "50   2450   7.477678  35.647321   4.248146   2.586086          -0.052747   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "50         1.332628  \n",
            "Epoch 2450 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "51   2500   7.477678  35.658482   4.267501   2.592232          -0.052699   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "51         1.329174  \n",
            "Epoch 2500 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "52   2550   7.477678  35.703126   4.260265   2.588786          -0.053123   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "52         1.325744  \n",
            "Epoch 2550 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "53   2600   7.477678  35.613838   4.266592   2.591465          -0.052769   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "53          1.32516  \n",
            "Epoch 2600 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "54   2650   7.477678  35.624999   4.271938   2.592482          -0.053388   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "54          1.32953  \n",
            "Epoch 2650 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "55   2700   7.477678  35.647321   4.266678   2.590436          -0.053138   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "55         1.325108  \n",
            "Epoch 2700 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "56   2750   7.477678  35.691965   4.258345   2.586223          -0.052902   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "56         1.331337  \n",
            "Epoch 2750 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "57   2800   7.477678  35.669643   4.262046   2.587441          -0.052782   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "57         1.327454  \n",
            "Epoch 2800 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "58   2850   7.477678  35.647321   4.258121   2.587381          -0.053066   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "58         1.339857  \n",
            "Epoch 2850 completed in 0.73 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "59   2900   7.477678  35.658482   4.257046    2.58915          -0.053087   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "59         1.326325  \n",
            "Epoch 2900 completed in 0.75 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "60   2950   7.477678  35.658482    4.25362   2.587851          -0.052914   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "60         1.326183  \n",
            "Epoch 2950 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "61   3000   7.477678  35.658482   4.251406   2.587032          -0.053222   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "61         1.333794  \n",
            "Epoch 3000 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "62   3050   7.477678  35.658482   4.253837   2.587022          -0.052421   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "62         1.318227  \n",
            "Epoch 3050 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "63   3100   7.477678  35.647321   4.253839   2.587202          -0.052791   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "63         1.333176  \n",
            "Epoch 3100 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "64   3150   7.477678   35.63616   4.252916   2.586621          -0.052993   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "64         1.332302  \n",
            "Epoch 3150 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "65   3200   7.477678  35.624999   4.257276   2.587419          -0.053001   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "65         1.323706  \n",
            "Epoch 3200 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "66   3250   7.477678  35.613838   4.255788   2.586142          -0.053143   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "66           1.3288  \n",
            "Epoch 3250 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "67   3300   7.477678  35.613838   4.254866   2.586089          -0.053108   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "67         1.329482  \n",
            "Epoch 3300 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "68   3350   7.477678   35.63616   4.254452   2.585243          -0.053346   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "68         1.328107  \n",
            "Epoch 3350 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "69   3400   7.477678  35.647321   4.257262   2.585919          -0.052905   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "69         1.328471  \n",
            "Epoch 3400 completed in 0.72 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "70   3450   7.477678  35.647321   4.256039    2.58556          -0.053203   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "70         1.332014  \n",
            "Epoch 3450 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "71   3500   7.477678  35.680804    4.25731   2.585184          -0.053716   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "71         1.329793  \n",
            "Epoch 3500 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "72   3550   7.477678  35.658482   4.256769   2.585376          -0.052923   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "72         1.316016  \n",
            "Epoch 3550 completed in 0.71 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "73   3600   7.477678  35.703126   4.258329   2.585811          -0.053529   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "73          1.33287  \n",
            "Epoch 3600 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "74   3650   7.477678  35.669643   4.257464   2.585755          -0.052776   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "74         1.327709  \n",
            "Epoch 3650 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "75   3700   7.477678  35.647321   4.257379   2.586221          -0.052547   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "75         1.321979  \n",
            "Epoch 3700 completed in 0.67 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "76   3750   7.477678  35.647321   4.261288   2.586911          -0.052923   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "76         1.325332  \n",
            "Epoch 3750 completed in 0.69 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "77   3800   7.477678  35.647321   4.261611   2.587027          -0.053295   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "77         1.336096  \n",
            "Epoch 3800 completed in 0.65 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "78   3850   7.477678  35.647321   4.262179   2.587268          -0.052962   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "78          1.32071  \n",
            "Epoch 3850 completed in 0.68 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "79   3900   7.477678  35.624999   4.260774   2.587577          -0.052905   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "79         1.324839  \n",
            "Epoch 3900 completed in 0.66 seconds\n",
            "   Epochs  AccForget  AccRetain  ErrForget  ErrRetain  MeanGeneratorLoss  \\\n",
            "80   3950   7.477678  35.647321   4.259459   2.587146           -0.05302   \n",
            "\n",
            "    MeanStudentLoss  \n",
            "80          1.32188  \n",
            "Epoch 3950 completed in 0.71 seconds\n"
          ]
        }
      ],
      "source": [
        "# Loop 4:\n",
        "# total_pseudo_batches = 4000\n",
        "# threshold = 4\n",
        "\n",
        "import time\n",
        "\n",
        "idx_pseudo = 0\n",
        "total_n_pseudo_batches = 4000\n",
        "n_pseudo_batches = 0\n",
        "running_gen_loss = []\n",
        "running_stu_loss = []\n",
        "\n",
        "threshold = 4\n",
        "KL_temperature = 2\n",
        "AT_beta = 250\n",
        "\n",
        "df = pd.DataFrame(columns=[\"Epochs\", \"AccForget\", \"AccRetain\", \"ErrForget\", \"ErrRetain\", \"MeanGeneratorLoss\", \"MeanStudentLoss\"])\n",
        "\n",
        "df = pd.concat([df, pd.DataFrame([{\"Epochs\": 0, \"AccForget\": AccForget, \"AccRetain\": AccRetain, \"ErrForget\": ErrForget,\n",
        "                                   \"ErrRetain\": ErrRetain, \"MeanGeneratorLoss\": None, \"MeanStudentLoss\": None}])], ignore_index=True)\n",
        "\n",
        "zero_count = 0\n",
        "\n",
        "while n_pseudo_batches < total_n_pseudo_batches:\n",
        "    epoch_start_time = time.time()  # Record the start time of the epoch\n",
        "\n",
        "    x_pseudo = generator.__next__()\n",
        "\n",
        "    preds = model(x_pseudo)\n",
        "    mask = (torch.softmax(preds.detach(), dim=1)[:, 0] <= threshold)\n",
        "    x_pseudo = x_pseudo[mask]\n",
        "\n",
        "    if x_pseudo.size(0) == 0:\n",
        "        zero_count += 1\n",
        "        if zero_count > 100:\n",
        "            print(\"Generator Stopped Producing datapoints corresponding to retain classes.\")\n",
        "        continue\n",
        "    else:\n",
        "        zero_count = 0\n",
        "\n",
        "    ## Take n_generator_iter steps on generator\n",
        "    if idx_pseudo % n_repeat_batch < n_generator_iter:\n",
        "        student_logits = student(x_pseudo)\n",
        "\n",
        "        teacher_logits = model(x_pseudo)\n",
        "\n",
        "        generator_total_loss = KT_loss_generator(student_logits, teacher_logits, KL_temperature=KL_temperature)\n",
        "\n",
        "        optimizer_generator.zero_grad()\n",
        "        generator_total_loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(generator.parameters(), 5)\n",
        "        optimizer_generator.step()\n",
        "        running_gen_loss.append(generator_total_loss.cpu().detach())\n",
        "\n",
        "    elif idx_pseudo % n_repeat_batch < (n_generator_iter + n_student_iter):\n",
        "        with torch.no_grad():\n",
        "            teacher_logits = model(x_pseudo)\n",
        "\n",
        "        student_logits = student(x_pseudo)\n",
        "\n",
        "        student_activations_list = list(student_activations.values())\n",
        "        teacher_activations_list = list(teacher_activations.values())\n",
        "\n",
        "        student_total_loss = KT_loss_student(student_logits, student_activations_list,\n",
        "                                             teacher_logits, teacher_activations_list,\n",
        "                                             KL_temperature=KL_temperature, AT_beta=AT_beta)\n",
        "\n",
        "        optimizer_student.zero_grad()\n",
        "        student_total_loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(student.parameters(), 5)\n",
        "        optimizer_student.step()\n",
        "        running_stu_loss.append(student_total_loss.cpu().detach())\n",
        "\n",
        "    if (idx_pseudo + 1) % n_repeat_batch == 0:\n",
        "        if (n_pseudo_batches % 50 == 0):\n",
        "            MeanGLoss = np.mean(running_gen_loss)\n",
        "            running_gen_loss = []\n",
        "            MeanSLoss = np.mean(running_stu_loss)\n",
        "            running_stu_loss = []\n",
        "\n",
        "            history_forget = [evaluate(student, forget_valid_dl, device=DEVICE)]\n",
        "            AccForget = history_forget[0][\"Acc\"]*100\n",
        "            ErrForget = history_forget[0][\"Loss\"]\n",
        "\n",
        "            history_retain = [evaluate(student, retain_valid_dl, device=DEVICE)]\n",
        "            AccRetain = history_retain[0][\"Acc\"]*100\n",
        "            ErrRetain = history_retain[0][\"Loss\"]\n",
        "\n",
        "            df = pd.concat([df, pd.DataFrame([{\"Epochs\": n_pseudo_batches, \"AccForget\": AccForget, \"AccRetain\": AccRetain, \"ErrForget\": ErrForget,\n",
        "                                               \"ErrRetain\": ErrRetain, \"MeanGeneratorLoss\": MeanGLoss, \"MeanStudentLoss\": MeanSLoss}])], ignore_index=True)\n",
        "            print(df.iloc[-1:])\n",
        "            scheduler_student.step(history_retain[0][\"Loss\"])\n",
        "            scheduler_generator.step(history_forget[0][\"Loss\"])\n",
        "\n",
        "            epoch_end_time = time.time()  # Record the end time of the epoch\n",
        "            epoch_duration = epoch_end_time - epoch_start_time  # Calculate the duration\n",
        "            print(f\"Epoch {n_pseudo_batches} completed in {epoch_duration:.2f} seconds\")\n",
        "\n",
        "\n",
        "        n_pseudo_batches += 1\n",
        "\n",
        "    idx_pseudo += 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "R8WN9y5xAG9L",
        "outputId": "677fcc26-0db6-4e10-fd28-0a120368b3e8"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Loop 4 Plot: Accuracy VS Epochs\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot Acc Retain and Acc Forget over epochs\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(df[\"Epochs\"], df[\"AccRetain\"], label='Acc Retain', marker='o')\n",
        "plt.plot(df[\"Epochs\"], df[\"AccForget\"], label='Acc Forget', marker='o')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Accuracy over Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}