hands_on_tutorial_InvictaSpringSchool.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GMvandeVen/continual-learning/blob/master/hands_on_tutorial_InvictaSpringSchool.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hands-on tutorial: Continual Learning\n",
        "\n",
        "**You can make your own copy of this notebook by selecting File->Save a copy in Drive from the menu bar above.**"
      ],
      "metadata": {
        "id": "pB15rzv-5A-i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This Colab Notebook was originally used for a hands-on tutorial at the [INVICTA Spring School](https://invicta.inesctec.pt/) in March 2024. The estimated time for this tutorial is 2 hours. At the school, this hands-on tutorial was preceded by a 3-hour interactive lecutre, for which the slides can be found [here](https://gmvandeven.github.io/files/slides/InvictaSpringSchool_Mar2024.pdf)."
      ],
      "metadata": {
        "id": "aBduCQ3QONvx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Things you'll learn in this session:\n",
        "- How to set up a simple continual learning experiment\n",
        "- How to implement EWC, replay and EWC+replay\n",
        "- How to evaluate and visualize the results of a continual learning experiment"
      ],
      "metadata": {
        "id": "DvB7ZIPdQr5c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This tutorial is based on code from this repository: https://github.com/GMvandeVen/continual-learning.\n",
        "\n",
        "Other popular libraries for continual learning are [Avalanche](https://github.com/ContinualAI/avalanche) and [Mammoth](https://github.com/aimagelab/mammoth)."
      ],
      "metadata": {
        "id": "OS7kSVkOth4g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "WojfTislQ6dg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load required libraries\n",
        "First, let's load some packages that we are going to need. We use [PyTorch](https://pytorch.org/) as our main deep learning library."
      ],
      "metadata": {
        "id": "anTIGQYWnm--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Standard libraries\n",
        "import numpy as np\n",
        "import copy\n",
        "import tqdm\n",
        "# Pytorch\n",
        "import torch\n",
        "from torch.nn import functional as F\n",
        "from torchvision import datasets, transforms\n",
        "# For visualization\n",
        "from torchvision.utils import make_grid\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "LNJi8AZPQ8HO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download data\n",
        "For this tutorial we will use the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), to construct different types of conitnual learning experiments."
      ],
      "metadata": {
        "id": "lCOLAWSkyA5A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MNIST_trainset = datasets.MNIST(root='data/', train=True, download=True,\n",
        "                                transform=transforms.ToTensor())\n",
        "MNIST_testset = datasets.MNIST(root='data/', train=False, download=True,\n",
        "                               transform=transforms.ToTensor())\n",
        "config = {'size': 28, 'channels': 1, 'classes': 10}"
      ],
      "metadata": {
        "id": "9mngyfO-RzyP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Visualization functions\n",
        "def multi_context_barplot(axis, accs, title=None):\n",
        "    '''Generate barplot using the values in [accs].'''\n",
        "    contexts = len(accs)\n",
        "    axis.bar(range(contexts), accs, color='k')\n",
        "    axis.set_ylabel('Testing Accuracy (%)')\n",
        "    axis.set_xticks(range(contexts), [f'Context {i+1}' for i in range(contexts)])\n",
        "    if title is not None:\n",
        "        axis.set_title(title)\n",
        "\n",
        "def plot_examples(axis, dataset, context_id=None):\n",
        "    '''Plot 25 examples from [dataset].'''\n",
        "    data_loader = torch.utils.data.DataLoader(dataset, batch_size=25, shuffle=True)\n",
        "    image_tensor, _ = next(iter(data_loader))\n",
        "    image_grid = make_grid(image_tensor, nrow=5, pad_value=1) # pad_value=0 would give black borders\n",
        "    axis.imshow(np.transpose(image_grid.numpy(), (1,2,0)))\n",
        "    if context_id is not None:\n",
        "        axis.set_title(\"Context {}\".format(context_id+1))\n",
        "    axis.axis('off')"
      ],
      "metadata": {
        "id": "yv-AN2xn1mZd",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 1: Catastrophic forgetting - Permuted MNIST\n",
        "Let's start by trying to set up a simple continual learning experiment, to check whether there is indeed catastrophic forgetting."
      ],
      "metadata": {
        "id": "sFEmAjmVQfRs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set up the benchmark (Permuted MNIST)\n",
        "For this we will use \"Permuted MNIST\". In this continual learning experiment, in each context (or task), the neural network must learn to classify the ten MNIST digits. However, in each context a different permutation is applied to the pixels of all images.\n",
        "\n",
        "Permuted MNIST was first used in this paper: https://arxiv.org/abs/1312.6211."
      ],
      "metadata": {
        "id": "wdbrA_V6jrI_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's start by specifying a function and a dataset class that we will use to create the various contexts (or tasks) of Permuted MNIST."
      ],
      "metadata": {
        "id": "tm0iox7zklFq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to apply a given permutation the pixels of an image.\n",
        "def permutate_image_pixels(image, permutation):\n",
        "    '''Permutate the pixels of [image] according to [permutation].'''\n",
        "\n",
        "    if permutation is None:\n",
        "        return image\n",
        "    else:\n",
        "        c, h, w = image.size()\n",
        "        image = image.view(c, -1)\n",
        "        image = image[:, permutation]  #--> same permutation for each channel\n",
        "        image = image.view(c, h, w)\n",
        "        return image"
      ],
      "metadata": {
        "id": "DHjgX-YuyvFj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Class to create a dataset with images that have all been transformed in the same way.\n",
        "class TransformedDataset(torch.utils.data.Dataset):\n",
        "    '''To modify an existing dataset with a transform.\n",
        "    Useful for creating different permutations of MNIST without loading the data multiple times.'''\n",
        "\n",
        "    def __init__(self, original_dataset, transform=None, target_transform=None):\n",
        "        super().__init__()\n",
        "        self.dataset = original_dataset\n",
        "        self.transform = transform\n",
        "        self.target_transform = target_transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dataset)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        (input, target) = self.dataset[index]\n",
        "        if self.transform:\n",
        "            input = self.transform(input)\n",
        "        if self.target_transform:\n",
        "            target = self.target_transform(target)\n",
        "        return (input, target)"
      ],
      "metadata": {
        "id": "PlVMxtWVyhaa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's use these tools to create a Permuted MNIST benchmark with 2 contexts."
      ],
      "metadata": {
        "id": "oGt1bpQkk8Kw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "contexts = 2"
      ],
      "metadata": {
        "id": "EklsnmDolDbR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify for each context the permutations to use (with no permutation for the first context)\n",
        "permutations = [None] + [np.random.permutation(config['size']**2) for _ in range(contexts-1)]"
      ],
      "metadata": {
        "id": "XGx9n5ezlvzf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify for each context the transformed train- and testset\n",
        "train_datasets = []\n",
        "test_datasets = []\n",
        "for context_id, perm in enumerate(permutations):\n",
        "    train_datasets.append(TransformedDataset(\n",
        "        MNIST_trainset, transform=transforms.Lambda(lambda x, p=perm: permutate_image_pixels(x, p)),\n",
        "    ))\n",
        "    test_datasets.append(TransformedDataset(\n",
        "        MNIST_testset, transform=transforms.Lambda(lambda x, p=perm: permutate_image_pixels(x, p)),\n",
        "    ))"
      ],
      "metadata": {
        "id": "AVAF1WGnyVlY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the contexts\n",
        "figure, axis = plt.subplots(1, contexts, figsize=(3*contexts, 4))\n",
        "\n",
        "for context_id in range(len(train_datasets)):\n",
        "    plot_examples(axis[context_id], train_datasets[context_id], context_id=context_id)"
      ],
      "metadata": {
        "id": "w0tTkEFZy-VU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set up the model\n",
        "Now it is time to define the neural network model that we will sequentially train on these two contexts."
      ],
      "metadata": {
        "id": "A9GSVyeAl1nh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We start by specifying some \"helper functions\" and \"helper code\" that make it easier to specify the model. If you are interested you can have a look at the code in the cell below, but that is not needed to follow the rest of this tutorial.\n",
        "\n",
        "**It is however needed to run the code in the below cell!**"
      ],
      "metadata": {
        "id": "xwfRu6HTmoSS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Helper functions\n",
        "\n",
        "class Identity(torch.nn.Module):\n",
        "    '''A nn-module to simply pass on the input data.'''\n",
        "    def forward(self, x):\n",
        "        return x\n",
        "\n",
        "    def __repr__(self):\n",
        "        tmpstr = self.__class__.__name__ + '()'\n",
        "        return tmpstr\n",
        "\n",
        "\n",
        "class Flatten(torch.nn.Module):\n",
        "    '''A nn-module to flatten a multi-dimensional tensor to 2-dim tensor.'''\n",
        "    def forward(self, x):\n",
        "        batch_size = x.size(0)   # first dimenstion should be batch-dimension.\n",
        "        return x.view(batch_size, -1)\n",
        "\n",
        "    def __repr__(self):\n",
        "        tmpstr = self.__class__.__name__ + '()'\n",
        "        return tmpstr\n",
        "\n",
        "\n",
        "class fc_layer(torch.nn.Module):\n",
        "    '''Fully connected layer, with possibility of returning \"pre-activations\".\n",
        "\n",
        "    Input:  [batch_size] x ... x [in_size] tensor\n",
        "    Output: [batch_size] x ... x [out_size] tensor'''\n",
        "\n",
        "    def __init__(self, in_size, out_size, nl=torch.nn.ReLU(), bias=True):\n",
        "        super().__init__()\n",
        "        self.bias = bias\n",
        "        self.linear = torch.nn.Linear(in_size, out_size, bias=bias)\n",
        "        if isinstance(nl, torch.nn.Module):\n",
        "            self.nl = nl\n",
        "        elif nl==\"relu\":\n",
        "            self.nl = torch.nn.ReLU()\n",
        "        elif nl==\"leakyrelu\":\n",
        "          self.nl = torch.nn.LeakyReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        pre_activ = self.linear(x)\n",
        "        output = self.nl(pre_activ) if hasattr(self, 'nl') else pre_activ\n",
        "        return output\n",
        "\n",
        "\n",
        "class MLP(torch.nn.Module):\n",
        "    '''Module for a multi-layer perceptron (MLP).\n",
        "\n",
        "    Input:  [batch_size] x ... x [size_per_layer[0]] tensor\n",
        "    Output: (tuple of) [batch_size] x ... x [size_per_layer[-1]] tensor'''\n",
        "\n",
        "    def __init__(self, input_size=1000, output_size=10, layers=2,\n",
        "                 hid_size=1000, hid_smooth=None, size_per_layer=None,\n",
        "                 nl=\"relu\", bias=True, output='normal'):\n",
        "        '''sizes: 0th=[input], 1st=[hid_size], ..., 1st-to-last=[hid_smooth], last=[output].\n",
        "        [input_size]       # of inputs\n",
        "        [output_size]      # of units in final layer\n",
        "        [layers]           # of layers\n",
        "        [hid_size]         # of units in each hidden layer\n",
        "        [hid_smooth]       if None, all hidden layers have [hid_size] units, else # of units linearly in-/decreases s.t.\n",
        "                             final hidden layer has [hid_smooth] units (if only 1 hidden layer, it has [hid_size] units)\n",
        "        [size_per_layer]   None or <list> with for each layer number of units (1st element = number of inputs)\n",
        "                                --> overwrites [input_size], [output_size], [layers], [hid_size] and [hid_smooth]\n",
        "        [nl]               <str>; type of non-linearity to be used (options: \"relu\", \"leakyrelu\", \"none\")\n",
        "        [output]           <str>; if - \"normal\", final layer is same as all others\n",
        "                                     - \"none\", final layer has no non-linearity\n",
        "                                     - \"sigmoid\", final layer has sigmoid non-linearity'''\n",
        "\n",
        "        super().__init__()\n",
        "        self.output = output\n",
        "\n",
        "        # get sizes of all layers\n",
        "        if size_per_layer is None:\n",
        "            hidden_sizes = []\n",
        "            if layers > 1:\n",
        "                if (hid_smooth is not None):\n",
        "                    hidden_sizes = [int(x) for x in np.linspace(hid_size, hid_smooth, num=layers-1)]\n",
        "                else:\n",
        "                    hidden_sizes = [int(x) for x in np.repeat(hid_size, layers - 1)]\n",
        "            size_per_layer = [input_size] + hidden_sizes + [output_size] if layers>0 else [input_size]\n",
        "        self.layers = len(size_per_layer)-1\n",
        "\n",
        "        # set label for this module\n",
        "        # -determine \"non-default options\"-label\n",
        "        nd_label = \"{bias}{nl}\".format(\n",
        "            bias=\"\" if bias else \"n\",\n",
        "            nl=\"l\" if nl==\"leakyrelu\" else (\"n\" if nl==\"none\" else \"\"),\n",
        "        )\n",
        "        nd_label = \"{}{}\".format(\"\" if nd_label==\"\" else \"-{}\".format(nd_label),\n",
        "                                 \"\" if output==\"normal\" else \"-{}\".format(output))\n",
        "        # -set label\n",
        "        size_statement = \"\"\n",
        "        for i in size_per_layer:\n",
        "            size_statement += \"{}{}\".format(\"-\" if size_statement==\"\" else \"x\", i)\n",
        "        self.label = \"F{}{}\".format(size_statement, nd_label) if self.layers>0 else \"\"\n",
        "\n",
        "        # set layers\n",
        "        for lay_id in range(1, self.layers+1):\n",
        "            # number of units of this layer's input and output\n",
        "            in_size = size_per_layer[lay_id-1]\n",
        "            out_size = size_per_layer[lay_id]\n",
        "            # define and set the fully connected layer\n",
        "            layer = fc_layer(\n",
        "                in_size, out_size, bias=bias,\n",
        "                nl=(\"none\" if output==\"none\" else nn.Sigmoid()) if (\n",
        "                    lay_id==self.layers and not output==\"normal\"\n",
        "                ) else nl,\n",
        "            )\n",
        "            setattr(self, 'fcLayer{}'.format(lay_id), layer)\n",
        "\n",
        "        # if no layers, add \"identity\"-module to indicate in this module's representation nothing happens\n",
        "        if self.layers<1:\n",
        "            self.noLayers = Identity()\n",
        "\n",
        "    def forward(self, x):\n",
        "        for lay_id in range(1, self.layers + 1):\n",
        "            x = getattr(self, \"fcLayer{}\".format(lay_id))(x)\n",
        "        return x\n",
        "\n",
        "    @property\n",
        "    def name(self):\n",
        "        return self.label"
      ],
      "metadata": {
        "cellView": "form",
        "id": "W5zR4KSamLJT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's specify a class for a basic neural network classifier model."
      ],
      "metadata": {
        "id": "UOymyzR9p3ob"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Classifier(torch.nn.Module):\n",
        "    '''Model for classifying images.'''\n",
        "\n",
        "    def __init__(self, image_size, image_channels, output_units,\n",
        "                 fc_layers=3, fc_units=1000, fc_nl=\"relu\", bias=True):\n",
        "\n",
        "        super().__init__()\n",
        "\n",
        "       # Flatten image to 2D-tensor\n",
        "        self.flatten = Flatten()\n",
        "\n",
        "        # Specify the fully connected hidden layers\n",
        "        input_size = image_channels * image_size * image_size\n",
        "        self.fcE = MLP(input_size=input_size, output_size=fc_units, layers=fc_layers-1,\n",
        "                       hid_size=fc_units, nl=fc_nl, bias=bias)\n",
        "        mlp_output_size = fc_units if fc_layers>1 else self.input_size\n",
        "\n",
        "        # Specify the final linear classifier layer\n",
        "        self.classifier = fc_layer(mlp_output_size, output_units, nl='none')\n",
        "\n",
        "    def forward(self, x):\n",
        "        flatten_x = self.flatten(x)\n",
        "        final_features = self.fcE(flatten_x)\n",
        "        out = self.classifier(final_features)\n",
        "        return out"
      ],
      "metadata": {
        "id": "s5hXbEWan7w0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create an instance of such a classifier model, and print some details of it to screen."
      ],
      "metadata": {
        "id": "SoADM8X_qCSk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the architectural layout of the network to use\n",
        "fc_lay = 4        #--> number of fully-connected layers\n",
        "fc_units = 40     #--> number of units in each hidden layer\n",
        "fc_nl = \"relu\"    #--> what non-linearity to use?"
      ],
      "metadata": {
        "id": "pQJISF0xqJ2B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the model\n",
        "model = Classifier(image_size=config['size'], image_channels=config['channels'],\n",
        "                   output_units=config['classes'],\n",
        "                   fc_layers=fc_lay, fc_units=fc_units, fc_nl=fc_nl)"
      ],
      "metadata": {
        "id": "JHKjU4MUqT4f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the network architecture\n",
        "model"
      ],
      "metadata": {
        "id": "LsSDP19EqZHs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print info regarding number of parameters\n",
        "total_params = 0\n",
        "for param in model.parameters():\n",
        "    n_params = index_dims = 0\n",
        "    for dim in param.size():\n",
        "        n_params = dim if index_dims==0 else n_params*dim\n",
        "        index_dims += 1\n",
        "    total_params += n_params\n",
        "print( \"--> this network has {} parameters (~{}K)\"\n",
        "      .format(total_params, round(total_params / 1000)))"
      ],
      "metadata": {
        "id": "sPe52AA8qY4U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train on first context\n",
        "It's time to start training the model on the first context (i.e., the regular MNIST dataset)."
      ],
      "metadata": {
        "id": "Pg_98FO7q7SI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To do this, let's define a function to that can train a given neural network (i.e., `model`) on a particular dataset (i.e., `dataset`). We can then later re-use this function, for example to train the network on the dataset of the second context.\n",
        "\n",
        "We can also specify the number of iterations we want to train for, the learning rate and the mini batch-size."
      ],
      "metadata": {
        "id": "jbtG3Elh5wbV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(model, dataset, iters, lr, batch_size):\n",
        "    # Define the optimizer\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999))\n",
        "\n",
        "    # Set model in training-mode\n",
        "    model.train()\n",
        "\n",
        "    # Initialize # iters left on current data-loader(s)\n",
        "    iters_left = 1\n",
        "\n",
        "    # Define tqdm progress bar(s)\n",
        "    progress_bar = tqdm.tqdm(range(1, iters+1))\n",
        "\n",
        "    # Loop over all iterations\n",
        "    for batch_index in range(1, iters+1):\n",
        "\n",
        "        # Update # iters left on current data-loader(s) and, if needed, create new one(s)\n",
        "        iters_left -= 1\n",
        "        if iters_left==0:\n",
        "            data_loader = iter(torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
        "                                                          shuffle=True, drop_last=True))\n",
        "            iters_left = len(data_loader)\n",
        "\n",
        "        # Sample training data of current context\n",
        "        x, y = next(data_loader)\n",
        "\n",
        "        # Reset optimizer\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Run model\n",
        "        y_hat = model(x)\n",
        "\n",
        "        # Calculate prediction loss\n",
        "        loss = torch.nn.functional.cross_entropy(input=y_hat, target=y, reduction='mean')\n",
        "\n",
        "        # Calculate training-accuracy (in %)\n",
        "        accuracy = (y == y_hat.max(1)[1]).sum().item()*100 / x.size(0)\n",
        "\n",
        "        # Backpropagate errors\n",
        "        loss.backward()\n",
        "\n",
        "        # Take the optimizer step\n",
        "        optimizer.step()\n",
        "\n",
        "        # Update progress bar\n",
        "        progress_bar.set_description(\n",
        "        '<CLASSIFIER> | training loss: {loss:.3} | training accuracy: {prec:.3}% |'\n",
        "            .format(loss=loss.item(), prec=accuracy)\n",
        "        )\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    # Close the progress bar\n",
        "    progress_bar.close()"
      ],
      "metadata": {
        "id": "yNJOs8Vj54Ub"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we need to choose the training hyperparameters (i.e., learning rate, batch size and number of iterations)."
      ],
      "metadata": {
        "id": "DlFBimOt7Tbx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iters = 200       # for how many iterations to train?\n",
        "lr = 0.01         # learning rate\n",
        "batch_size = 128  # size of mini-batches"
      ],
      "metadata": {
        "id": "ITZJbebd7cZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "And let's train our neural network on the first context."
      ],
      "metadata": {
        "id": "XyTV4Ee_7d9a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model on the first context\n",
        "train(model, dataset=train_datasets[0], iters=iters, lr=lr, batch_size=batch_size)"
      ],
      "metadata": {
        "id": "GJrEXhHwtO69"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluate\n",
        "Did the training work? Let's find out by evaluating the performance of the trained model on the MNIST test set."
      ],
      "metadata": {
        "id": "_XeVQWFWtSo9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, let's start by specifying a function to evaluate any given model (i.e., `model`) on a specific dataset (i.e., `dataset`)."
      ],
      "metadata": {
        "id": "gYkoS0NYyYC-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def test_acc(model, dataset, test_size=None, batch_size=128):\n",
        "    '''Evaluate accuracy (% samples classified correctly) of a classifier ([model]) on [dataset].'''\n",
        "\n",
        "    # Set model to eval()-mode\n",
        "    mode = model.training\n",
        "    model.eval()\n",
        "\n",
        "    # Loop over batches in [dataset]\n",
        "    data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
        "                                              shuffle=True, drop_last=False)\n",
        "    total_tested = total_correct = 0\n",
        "    for x, y in data_loader:\n",
        "        # -break on [test_size] (if \"None\", full dataset is used)\n",
        "        if test_size:\n",
        "            if total_tested >= test_size:\n",
        "                break\n",
        "        # -evaluate model\n",
        "        with torch.no_grad():\n",
        "            scores = model(x)\n",
        "        _, predicted = torch.max(scores, 1)\n",
        "        # -update statistics\n",
        "        total_correct += (predicted == y).sum().item()\n",
        "        total_tested += len(x)\n",
        "    accuracy = total_correct*100 / total_tested\n",
        "\n",
        "    # Set model back to its initial mode, print result on screen (if requested) and return it\n",
        "    model.train(mode=mode)\n",
        "\n",
        "    return accuracy"
      ],
      "metadata": {
        "id": "oeR-wzk7trnq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's use this function to evaluate the performance of the model on both the test data from context 1 (i.e., regular MNIST, on which the model was just trained) and on the test data from context 2 (i.e., a permuted version of MNIST, on which the model has not yet been trained)."
      ],
      "metadata": {
        "id": "oy0jxo7GzTZS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate accuracy per context and print to screen\n",
        "print(\"\\n Accuracy (in %) of the model on test-set of:\")\n",
        "context1_accs = []\n",
        "for i in range(contexts):\n",
        "    acc = test_acc(model, test_datasets[i], test_size=None)\n",
        "    print(\" - Context {}: {:.1f}\".format(i+1, acc))\n",
        "    context1_accs.append(acc)"
      ],
      "metadata": {
        "id": "RLbnEFYizT-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model does well on test data from context 1, but is around chance level for test data from context 2.\n",
        "\n",
        "This is not very surprising, as we have not yet trained the model on context 2!"
      ],
      "metadata": {
        "id": "tDX1C8e80Ous"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Store model copy (for later)\n",
        "Before continuing to train the model on the second context, let's store a copy of the model after training on the first context. We will later use this model copy to try out different ways of training the same model on the second context (i.e., we will try out different continual learning methods)."
      ],
      "metadata": {
        "id": "nuSXFD-O00ZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_after_context1 = copy.deepcopy(model)"
      ],
      "metadata": {
        "id": "hfDXSlr903z-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train on second context\n",
        "Now let's continue to train our model on the second context, and see what happens."
      ],
      "metadata": {
        "id": "KG5vmXzrtVY-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Continue to train the model on the second context\n",
        "train(model, dataset=train_datasets[1], iters=iters, lr=lr, batch_size=batch_size)"
      ],
      "metadata": {
        "id": "Qm8nl4vZ8Qg8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluate"
      ],
      "metadata": {
        "id": "gzz4tXXFtXbT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate accuracy per context and print to screen\n",
        "print(\"\\n Accuracy (in %) of the model on test-set of:\")\n",
        "context2_accs = []\n",
        "for i in range(contexts):\n",
        "    acc = test_acc(model, test_datasets[i], test_size=None)\n",
        "    print(\" - Context {}: {:.1f}\".format(i+1, acc))\n",
        "    context2_accs.append(acc)"
      ],
      "metadata": {
        "id": "FHVaWONJtsps"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train on both contexts at the same time"
      ],
      "metadata": {
        "id": "Rq6PsBLitcRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a new model with same architecture\n",
        "model_joint = Classifier(image_size=config['size'], image_channels=config['channels'],\n",
        "                         output_units=config['classes'],\n",
        "                         fc_layers=fc_lay, fc_units=fc_units, fc_nl=fc_nl)"
      ],
      "metadata": {
        "id": "ix_3i8cittOj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a joint dataset with data from both contexts\n",
        "joint_trainset = torch.utils.data.ConcatDataset(train_datasets)"
      ],
      "metadata": {
        "id": "y4trFHRl9jEk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use the same training-hyperparameters, except that we double the batch size."
      ],
      "metadata": {
        "id": "Ldr-Z3Xa9Vgq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size_joint = 256"
      ],
      "metadata": {
        "id": "ON_nH7Ap94R6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the joint model\n",
        "train(model_joint, dataset=joint_trainset, iters=iters, lr=lr, batch_size=batch_size_joint)"
      ],
      "metadata": {
        "id": "HP73reGQ-KlI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "print(\"\\n Accuracy (in %) of the model on test-set of:\")\n",
        "joint_accs = []\n",
        "for i in range(contexts):\n",
        "    acc = test_acc(model_joint, test_datasets[i], test_size=None)\n",
        "    print(\" - Context {}: {:.1f}\".format(i+1, acc))\n",
        "    joint_accs.append(acc)"
      ],
      "metadata": {
        "id": "ngV6LI73-zKV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualize results"
      ],
      "metadata": {
        "id": "jot0FyzK--uE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize\n",
        "figure, axis = plt.subplots(1, 4, figsize=(15, 5))\n",
        "\n",
        "title='After training on context 1, \\nbut not yet training on context 2'\n",
        "multi_context_barplot(axis[0], context1_accs, title)\n",
        "\n",
        "title='After first training on context 1, \\nand then training on context 2'\n",
        "multi_context_barplot(axis[1], context2_accs, title)\n",
        "\n",
        "axis[2].axis('off')\n",
        "\n",
        "title='After jointly training on both contexts'\n",
        "multi_context_barplot(axis[3], joint_accs, title)"
      ],
      "metadata": {
        "id": "OJEcM-8i_CAV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have observed **catastrophic forgetting**! When our neural network model first learns context 1 and is then trained on context 2, the model's performance on data from context 1 substantially drops (left panels). When the same neural network model is instead trained on both context 1 and 2 at the same time, the model is able to learn both contexts well (right panel), demonstrating that the forgetting cannot be explained by limited model capacity."
      ],
      "metadata": {
        "id": "pWWeiBRcyaCH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 2: Overcoming catastrophic forgetting - EWC & replay"
      ],
      "metadata": {
        "id": "HrAUoOovzzMf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we found catastrophic forgetting, let's try out some methods to mitigate the forgetting.\n",
        "\n",
        "We will start by exploring two methods: EWC and replay. For both methods, the training on the first context is the same as before. We can therefore use as starting point the copy of the model that we stored after finishing training on the first context."
      ],
      "metadata": {
        "id": "q6ox4ZT10EGB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_ewc = copy.deepcopy(model_after_context1)\n",
        "model_replay = copy.deepcopy(model_after_context1)"
      ],
      "metadata": {
        "id": "z9FGHT8Z161d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Elastic Weight Consolidation (EWC)\n",
        "EWC is a popular parameter regularization strategy for continual learning. It was introduced in the paper \"[Overcoming catastrophic forgetting in neural networks\n",
        "](https://www.pnas.org/doi/abs/10.1073/pnas.1611835114)\" (Kirkpatrick et al., 2017; *PNAS*).\n",
        "\n",
        "EWC computes a diagonal approximation to the [Fisher Information matrix]((https://en.wikipedia.org/wiki/Fisher_information) to estimate for each parameter of the network how important it is for the performance on the previous context. During training on the next context, these parameter importance estimates are then used to penalize changes to the parameters, with changes to the most important parameters penalized most.\n",
        "\n"
      ],
      "metadata": {
        "id": "s7dQ5itP0RJc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "When training on context $k>1$, the EWC regularization term is given by:\n",
        "$$\n",
        "\\mathcal{L}^{(k)}_{\\text{regularization}_{\\text{EWC}}}\\left(\\boldsymbol{\\theta}\\right) = \\frac{1}{2} \\sum_{i=1}^{N_{\\text{params}}} \\tilde{F}_{ii}^{(k)} \\left(\\theta_i - \\hat{\\theta}_{i}^{(k)} \\right)^2\n",
        "$$\n",
        "whereby $\\hat{\\theta}_{i}^{(k)}$ is the $i^{\\text{th}}$ element of $\\hat{\\boldsymbol{\\theta}}^{\\left(k\\right)}$, which is the vector with parameter values at the end of training of task $k$, and $\\tilde{F}_{ii}^{(k)}$ is an approximation of $F_{ii}^{(k)}$, the $i^{\\text{th}}$ diagonal element of $\\boldsymbol{F}^{(k)}$, which is the Fisher Information matrix of task $k$ evaluated at $\\hat{\\boldsymbol{\\theta}}^{(k)}$."
      ],
      "metadata": {
        "id": "OcXpF4N6kyc_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "(Technically, the above regularization term is for `[Online EWC](https://arxiv.org/abs/1805.06370)'. The original version of EWC did something weird, as explained in [this blog post](https://www.inference.vc/comment-on-overcoming-catastrophic-forgetting-in-nns-are-multiple-penalties-needed-2/).)"
      ],
      "metadata": {
        "id": "uRJG6pGDoj_O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Fisher Information matrix is defined as the covariance of the `score', which is the partial derivative with respect to $\\boldsymbol{\\theta}$ of the natural logarithm of the likelihood function. The $i^{\\text{th}}$ diagonal element of the Fisher Information on context $k$ is therefore given by:\n",
        "\n",
        "$$\n",
        "F_{ii}^{(k)} = \\mathbb{E}_{\\boldsymbol{x}\\sim Q_{\\boldsymbol{x}}^{(k)}} \\left[ \\ \\mathbb{E}_{p_{\\hat{\\boldsymbol{\\theta}}^{(k)}}} \\left[ \\left(  \\left. \\frac{\\delta \\log{p_{\\boldsymbol{\\theta}}\\left(Y=y|\\boldsymbol{x}\\right)}}{\\delta \\theta_i} \\right\\rvert_{\\boldsymbol{\\theta}=\\hat{\\boldsymbol{\\theta}}^{(k)}} \\right)^2 \\right] \\right]\n",
        "$$\n",
        "\n",
        "whereby $Q_{\\boldsymbol{x}}^{(k)}$ is the input distribution of context $k$, $p_{\\boldsymbol{\\theta}}$ is the conditional distribution of $y$ given $\\boldsymbol{x}$ defined by the neural network with parameters $\\boldsymbol{\\theta}$, and $\\hat{\\boldsymbol{\\theta}}^{(k)}$ is the vector with parameter values after finisihing training on context $k$."
      ],
      "metadata": {
        "id": "C_8xnOyI-D5_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The outer expectation in the above equation can be approximated using a random sample from the training set of context $k$. Because there is only a finit number of possible classes, the inner expectation in the above equation can be calculated for each sample exactly:\n",
        "$$\n",
        "\\tilde{F}_{ii}^{(k)} = \\frac{1}{|S^{(k)}|} \\sum_{\\boldsymbol{x}\\in S^{(k)}} \\left( \\sum_{c=1}^{N_{\\text{classes}}} \\tilde{y}_c \\left( \\left. \\frac{\\delta\\log p_{\\boldsymbol{\\theta}}\\left(Y=c|\\boldsymbol{x}\\right)}{\\delta\\theta_i} \\right\\rvert_{\\boldsymbol{\\theta}=\\hat{\\boldsymbol{\\theta}}^{(k)}} \\right)^2 \\right)\n",
        "$$\n",
        "whereby $S^{(k)}$ is the random sample of training data of context $k$ and $\\tilde{y}_c = p_{\\hat{\\boldsymbol{\\theta}}^{(k)}}\\left(Y=c|\\boldsymbol{x}\\right)$ (i.e., the probability that input $\\boldsymbol{x}$ belongs to class $c$ as predicted by the model after finishing training on context $k$)."
      ],
      "metadata": {
        "id": "8MMh_ZZslXpd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In the literature or on GitHub, often different, rather crude approximations of the Fisher Information are used ([example](https://github.com/ContinualAI/avalanche/blob/dbdc3804b11710b85b0e564b13034f487c7cf806/avalanche/training/plugins/ewc.py#L132-L186)). Be careful, as the quality of this approximation might influence the results!**"
      ],
      "metadata": {
        "id": "Uitfc1Hom_cC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def estimate_fisher(model, dataset, n_samples, ewc_gamma=1.):\n",
        "    '''Estimate diagonal of Fisher Information matrix for [model] on [dataset] using [n_samples].'''\n",
        "\n",
        "    # Prepare <dict> to store estimated Fisher Information matrix\n",
        "    est_fisher_info = {}\n",
        "    for n, p in model.named_parameters():\n",
        "        n = n.replace('.', '__')\n",
        "        est_fisher_info[n] = p.detach().clone().zero_()\n",
        "\n",
        "    # Set model to evaluation mode\n",
        "    mode = model.training\n",
        "    model.eval()\n",
        "\n",
        "    # Create data-loader to give batches of size 1\n",
        "    data_loader = torch.utils.data.DataLoader(dataset, batch_size=1)\n",
        "\n",
        "    # Estimate the FI-matrix for [n_samples] batches of size 1\n",
        "    for index,(x,y) in enumerate(data_loader):\n",
        "        # break from for-loop if max number of samples has been reached\n",
        "        if n_samples is not None:\n",
        "            if index > n_samples:\n",
        "                break\n",
        "        # run forward pass of model\n",
        "        output = model(x)\n",
        "        # calculate the FI-matrix\n",
        "        with torch.no_grad():\n",
        "            label_weights = F.softmax(output, dim=1)  #--> get weights, with no gradient tracked\n",
        "        # - loop over all classes\n",
        "        for label_index in range(output.shape[1]):\n",
        "            label = torch.LongTensor([label_index])\n",
        "            negloglikelihood = F.cross_entropy(output, label)\n",
        "            # Calculate gradient of negative loglikelihood for this class\n",
        "            model.zero_grad()\n",
        "            negloglikelihood.backward(retain_graph=True if (label_index+1)<output.shape[1] else False)\n",
        "            # Square gradients and keep running sum (using the weights)\n",
        "            for n, p in model.named_parameters():\n",
        "                n = n.replace('.', '__')\n",
        "                if p.grad is not None:\n",
        "                    est_fisher_info[n] += label_weights[0][label_index] * (p.grad.detach() ** 2)\n",
        "\n",
        "    # Normalize by sample size used for estimation\n",
        "    est_fisher_info = {n: p/index for n, p in est_fisher_info.items()}\n",
        "\n",
        "    # Store new values in the network\n",
        "    for n, p in model.named_parameters():\n",
        "        n = n.replace('.', '__')\n",
        "        # -mode (=MAP parameter estimate)\n",
        "        model.register_buffer('{}_EWC_param_values'.format(n,), p.detach().clone())\n",
        "        # -precision (approximated by diagonal Fisher Information matrix)\n",
        "        if hasattr(model, '{}_EWC_estimated_fisher'.format(n)):\n",
        "            existing_values = getattr(model, '{}_EWC_estimated_fisher'.format(n))\n",
        "            est_fisher_info[n] += ewc_gamma * existing_values\n",
        "        model.register_buffer('{}_EWC_estimated_fisher'.format(n), est_fisher_info[n])\n",
        "\n",
        "    # Set model back to its initial mode\n",
        "    model.train(mode=mode)"
      ],
      "metadata": {
        "id": "TdBqM3I3-PTc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We need to define a new training function for EWC by adding the above regularization term to the loss."
      ],
      "metadata": {
        "id": "zr9yPQcQ9sNl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# (only the steps that differ from the original `train`-function are commented)\n",
        "def train_ewc(model, dataset, iters, lr, batch_size, current_context, ewc_lambda=100.):\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999))\n",
        "    model.train()\n",
        "    iters_left = 1\n",
        "    progress_bar = tqdm.tqdm(range(1, iters+1))\n",
        "\n",
        "    for batch_index in range(1, iters+1):\n",
        "        iters_left -= 1\n",
        "        if iters_left==0:\n",
        "            data_loader = iter(torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
        "                                                           shuffle=True, drop_last=True))\n",
        "            iters_left = len(data_loader)\n",
        "        x, y = next(data_loader)\n",
        "        optimizer.zero_grad()\n",
        "        y_hat = model(x)\n",
        "        loss = torch.nn.functional.cross_entropy(input=y_hat, target=y, reduction='mean')\n",
        "\n",
        "        # Compute the EWC-regularization term, and add it to the loss (except if first context)\n",
        "        if current_context>1:\n",
        "            ewc_losses = []\n",
        "            for n, p in model.named_parameters():\n",
        "                # Retrieve stored mode (MAP estimate) and precision (Fisher Information matrix)\n",
        "                n = n.replace('.', '__')\n",
        "                mean = getattr(model, '{}_EWC_param_values'.format(n))\n",
        "                fisher = getattr(model, '{}_EWC_estimated_fisher'.format(n))\n",
        "                # Calculate weight regularization loss\n",
        "                ewc_losses.append((fisher * (p-mean)**2).sum())\n",
        "            ewc_loss = (1./2)*sum(ewc_losses)\n",
        "            total_loss = loss + ewc_lambda*ewc_loss\n",
        "        else:\n",
        "            total_loss = loss\n",
        "\n",
        "        accuracy = (y == y_hat.max(1)[1]).sum().item()*100 / x.size(0)\n",
        "        total_loss.backward()\n",
        "        optimizer.step()\n",
        "        progress_bar.set_description(\n",
        "        '<CLASSIFIER> | training loss: {loss:.3} | training accuracy: {prec:.3}% |'\n",
        "            .format(loss=total_loss.item(), prec=accuracy)\n",
        "        )\n",
        "        progress_bar.update(1)\n",
        "    progress_bar.close()"
      ],
      "metadata": {
        "id": "JjDl0lsg0Vs_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's train the model on the second context using EWC."
      ],
      "metadata": {
        "id": "8ikYkBt-JMm6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Estimate the FI-matrix (and store it as attribute in the network)\n",
        "estimate_fisher(model_ewc, train_datasets[0], n_samples=200)"
      ],
      "metadata": {
        "id": "mvW1FKRNJU2F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train on the second context using EWC parameter regularization\n",
        "ewc_lambda = 100   #--> this is a \"continual learning hyperparameter\", setting these is a delicate\n",
        "                   #    business. Here we ignore that and just use one that gives good performance.\n",
        "train_ewc(model_ewc, train_datasets[1], iters=iters, lr=lr, batch_size=batch_size,\n",
        "          current_context=2, ewc_lambda=ewc_lambda)"
      ],
      "metadata": {
        "id": "9UjjXJtLJUg7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "... and evaluate its performance."
      ],
      "metadata": {
        "id": "60zj34suJVYJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "print(\"\\n Accuracy (in %) of the model on test-set of:\")\n",
        "ewc_accs = []\n",
        "for i in range(contexts):\n",
        "    acc = test_acc(model_ewc, test_datasets[i], test_size=None)\n",
        "    print(\" - Context {}: {:.1f}\".format(i+1, acc))\n",
        "    ewc_accs.append(acc)"
      ],
      "metadata": {
        "id": "jiNB3edeJZ-L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "That worked well! The performance on the first context barely dropped while the network learned the second context."
      ],
      "metadata": {
        "id": "vSfLqS87Vm5e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Experience Replay\n",
        "For comparison, now let's train another model copy on the second context using 'experience replay'.\n",
        "\n",
        "The typical approach is to store a relatively small amount of samples from previous contexts, and revisit those when training on a new context. We thus first need to populate a memory buffer with some samples from the first context. We select these samples using class-balanced random sampling from the training set (other approaches are possible here, how to optimally select the samples to store in the memory buffer is an active field of research)."
      ],
      "metadata": {
        "id": "bLC3-drp0TIk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Helper dataset classes for constructing memory buffer\n",
        "class SubDataset(torch.utils.data.Dataset):\n",
        "    '''To sub-sample a dataset, taking only those samples with label in [sub_labels].\n",
        "\n",
        "    After this selection of samples has been made, it is possible to transform the target-labels,\n",
        "    which can be useful when doing continual learning with fixed number of output units.'''\n",
        "\n",
        "    def __init__(self, original_dataset, sub_labels, target_transform=None):\n",
        "        super().__init__()\n",
        "        self.dataset = original_dataset\n",
        "        self.sub_indeces = []\n",
        "        for index in range(len(self.dataset)):\n",
        "            if hasattr(original_dataset, \"targets\"):\n",
        "                if self.dataset.target_transform is None:\n",
        "                    label = self.dataset.targets[index]\n",
        "                else:\n",
        "                    label = self.dataset.target_transform(self.dataset.targets[index])\n",
        "            else:\n",
        "                label = self.dataset[index][1]\n",
        "            if label in sub_labels:\n",
        "                self.sub_indeces.append(index)\n",
        "        self.target_transform = target_transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.sub_indeces)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        sample = self.dataset[self.sub_indeces[index]]\n",
        "        if self.target_transform:\n",
        "            target = self.target_transform(sample[1])\n",
        "            sample = (sample[0], target)\n",
        "        return sample\n",
        "\n",
        "\n",
        "class MemorySetDataset(torch.utils.data.Dataset):\n",
        "    '''Create dataset from list of <np.arrays> with shape (N, C, H, W) (i.e., with N images each).\n",
        "\n",
        "    The images at the i-th entry of [memory_sets] belong to class [i],\n",
        "    unless a [target_transform] is specified\n",
        "    '''\n",
        "\n",
        "    def __init__(self, memory_sets, target_transform=None):\n",
        "        super().__init__()\n",
        "        self.memory_sets = memory_sets\n",
        "        self.target_transform = target_transform\n",
        "\n",
        "    def __len__(self):\n",
        "        total = 0\n",
        "        for class_id in range(len(self.memory_sets)):\n",
        "            total += len(self.memory_sets[class_id])\n",
        "        return total\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        total = 0\n",
        "        for class_id in range(len(self.memory_sets)):\n",
        "            examples_in_this_class = len(self.memory_sets[class_id])\n",
        "            if index < (total + examples_in_this_class):\n",
        "                class_id_to_return = class_id if self.target_transform is None else self.target_transform(class_id)\n",
        "                example_id = index - total\n",
        "                break\n",
        "            else:\n",
        "                total += examples_in_this_class\n",
        "        image = torch.from_numpy(self.memory_sets[class_id][example_id])\n",
        "        return (image, class_id_to_return)"
      ],
      "metadata": {
        "id": "06uwJLSlYfYs",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill the memory buffer using class-balanced random sampling\n",
        "def fill_memory_buffer(memory_sets, dataset, buffer_size_per_class, class_indeces):\n",
        "    '''This function is rather slow and can be optimized.'''\n",
        "    for class_id in class_indeces:\n",
        "        # Create dataset with only instances of one class\n",
        "        class_dataset = SubDataset(original_dataset=dataset, sub_labels=[class_id])\n",
        "\n",
        "        # Randomly select which indeces to store in the buffer\n",
        "        n_total = len(class_dataset)\n",
        "        indeces_selected = np.random.choice(n_total, size=min(buffer_size_per_class, n_total),\n",
        "                                            replace=False)\n",
        "\n",
        "        # Select those indeces\n",
        "        memory_set = []\n",
        "        for k in indeces_selected:\n",
        "            memory_set.append(class_dataset[k][0].numpy())\n",
        "\n",
        "        # Add this [memory_set] as a [n]x[ich]x[isz]x[isz] to the list of [memory_sets]\n",
        "        memory_sets.append(np.array(memory_set))\n",
        "\n",
        "    return memory_sets"
      ],
      "metadata": {
        "id": "Ll7YdfMo0VSl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "buffer_size_per_class = 20\n",
        "memory_sets = []\n",
        "# The next command is unneccesary slow, apologies! Bonus question: optimize this implementation :)\n",
        "memory_sets = fill_memory_buffer(memory_sets, train_datasets[0],\n",
        "                                 buffer_size_per_class=buffer_size_per_class,\n",
        "                                 class_indeces=list(range(10)))\n",
        "buffer_dataset = MemorySetDataset(memory_sets)"
      ],
      "metadata": {
        "id": "0Bbg05iTOye4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we also need to define a new training-function that revisits data from the memory buffer along with training on the data from the new context."
      ],
      "metadata": {
        "id": "XA5cDdKSO_Pq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# (only the steps that differ from the original `train`-function are commented)\n",
        "def train_replay(model, dataset, iters, lr, batch_size, current_context, buffer_dataset=None):\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999))\n",
        "    model.train()\n",
        "    iters_left = 1\n",
        "    iters_left_replay = 1\n",
        "    progress_bar = tqdm.tqdm(range(1, iters+1))\n",
        "\n",
        "    for batch_index in range(1, iters+1):\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Data from current context\n",
        "        iters_left -= 1\n",
        "        if iters_left==0:\n",
        "            data_loader = iter(torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
        "                                                           shuffle=True, drop_last=True))\n",
        "            iters_left = len(data_loader)\n",
        "        x, y = next(data_loader)\n",
        "        y_hat = model(x)\n",
        "        loss = torch.nn.functional.cross_entropy(input=y_hat, target=y, reduction='mean')\n",
        "        accuracy = (y == y_hat.max(1)[1]).sum().item()*100 / x.size(0)\n",
        "\n",
        "        # Replay data from memory buffer\n",
        "        if buffer_dataset is not None:\n",
        "          iters_left_replay -= 1\n",
        "          if iters_left_replay==0:\n",
        "              batch_size_to_use = min(batch_size, len(buffer_dataset))\n",
        "              data_loader_replay = iter(torch.utils.data.DataLoader(buffer_dataset,\n",
        "                                                                    batch_size_to_use, shuffle=True,\n",
        "                                                                    drop_last=True))\n",
        "              iters_left_replay = len(data_loader_replay)\n",
        "          x_, y_ = next(data_loader_replay)\n",
        "          y_hat_ = model(x_)\n",
        "          loss_replay = torch.nn.functional.cross_entropy(input=y_hat_, target=y_, reduction='mean')\n",
        "\n",
        "        # Combine both losses to approximate the joint loss over both contexts\n",
        "        # (i.e., the loss on the replayed data has weight proportional to number of contexts so far)\n",
        "        if buffer_dataset is not None:\n",
        "            rnt = 1./current_context\n",
        "            total_loss = rnt*loss + (1-rnt)*loss_replay\n",
        "        else:\n",
        "            total_loss = loss\n",
        "\n",
        "        total_loss.backward()\n",
        "        optimizer.step()\n",
        "        progress_bar.set_description(\n",
        "        '<CLASSIFIER> | training loss: {loss:.3} | training accuracy: {prec:.3}% |'\n",
        "            .format(loss=total_loss.item(), prec=accuracy)\n",
        "        )\n",
        "        progress_bar.update(1)\n",
        "    progress_bar.close()"
      ],
      "metadata": {
        "id": "FVfyNqsMPIf4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train on the second context using experience replay\n",
        "train_replay(model_replay, train_datasets[1], iters=iters, lr=lr, batch_size=batch_size,\n",
        "             current_context=2, buffer_dataset=buffer_dataset)"
      ],
      "metadata": {
        "id": "XU8LLSWFUokA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "... and evaluate its performance"
      ],
      "metadata": {
        "id": "H35hMk3zVFs_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "print(\"\\n Accuracy (in %) of the model on test-set of:\")\n",
        "replay_accs = []\n",
        "for i in range(contexts):\n",
        "    acc = test_acc(model_replay, test_datasets[i], test_size=None)\n",
        "    print(\" - Context {}: {:.1f}\".format(i+1, acc))\n",
        "    replay_accs.append(acc)"
      ],
      "metadata": {
        "id": "hKuT_aFDVEn0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "That also worked!"
      ],
      "metadata": {
        "id": "t_ZmSg9hWJiV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compare\n",
        "Let's compare the performance of naive fine-tuning, EWC and experience replay."
      ],
      "metadata": {
        "id": "Rp4rvzgZVLBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure, axis = plt.subplots(1, 3, figsize=(12, 4))\n",
        "\n",
        "title='Fine-tuning'\n",
        "multi_context_barplot(axis[0], context2_accs, title)\n",
        "\n",
        "title='EWC \\n(lambda: {})'.format(ewc_lambda)\n",
        "multi_context_barplot(axis[1], ewc_accs, title)\n",
        "\n",
        "title='Replay \\n(buffer: {} samples per class)'.format(buffer_size_per_class)\n",
        "multi_context_barplot(axis[2], replay_accs, title)"
      ],
      "metadata": {
        "id": "YWWDQqiFVSc0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **ASSIGNMENT**: Combine EWC and replay\n",
        "Train another model copy on the second context using *both* EWC and experience replay."
      ],
      "metadata": {
        "id": "kuzoBMh8fh2i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_ewc_replay = copy.deepcopy(model_after_context1)"
      ],
      "metadata": {
        "id": "E-vo75W6f1tU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Start by defining the training function that can be used to train the model on the new context using both EWC and experience replay."
      ],
      "metadata": {
        "id": "CxUvSooRgxiZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_ewc_replay(model, dataset, buffer_dataset, iters, lr, batch_size, ewc_lambda):\n",
        "    pass\n",
        "    # TO BE COMPLETED (tip: use the above training functions as example / starting point)"
      ],
      "metadata": {
        "id": "5r96gtzkgWzZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Possible Answer\n",
        "def train_ewc_replay(model, dataset, iters, lr, batch_size, current_context,\n",
        "                     ewc_lambda=100., buffer_dataset=None):\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999))\n",
        "    model.train()\n",
        "    iters_left = 1\n",
        "    iters_left_replay = 1\n",
        "    progress_bar = tqdm.tqdm(range(1, iters+1))\n",
        "\n",
        "    for batch_index in range(1, iters+1):\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Data from current context\n",
        "        iters_left -= 1\n",
        "        if iters_left==0:\n",
        "            data_loader = iter(torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
        "                                                           shuffle=True, drop_last=True))\n",
        "            iters_left = len(data_loader)\n",
        "        x, y = next(data_loader)\n",
        "        y_hat = model(x)\n",
        "        loss = torch.nn.functional.cross_entropy(input=y_hat, target=y, reduction='mean')\n",
        "        accuracy = (y == y_hat.max(1)[1]).sum().item()*100 / x.size(0)\n",
        "\n",
        "        # Replay data from memory buffer\n",
        "        if buffer_dataset is not None:\n",
        "            iters_left_replay -= 1\n",
        "            if iters_left_replay==0:\n",
        "                batch_size_to_use = min(batch_size, len(buffer_dataset))\n",
        "                data_loader_replay = iter(torch.utils.data.DataLoader(buffer_dataset,\n",
        "                                                                      batch_size_to_use,\n",
        "                                                                      shuffle=True,\n",
        "                                                                      drop_last=True))\n",
        "                iters_left_replay = len(data_loader_replay)\n",
        "            x_, y_ = next(data_loader_replay)\n",
        "            y_hat_ = model(x_)\n",
        "            loss_replay = torch.nn.functional.cross_entropy(input=y_hat_, target=y_,\n",
        "                                                            reduction='mean')\n",
        "\n",
        "        # Compute the EWC-regularization term, and add it to the loss\n",
        "        if current_context>1:\n",
        "            ewc_losses = []\n",
        "            for n, p in model.named_parameters():\n",
        "                # Retrieve stored mode (MAP estimate) and precision (Fisher Information matrix)\n",
        "                n = n.replace('.', '__')\n",
        "                mean = getattr(model, '{}_EWC_param_values'.format(n))\n",
        "                fisher = getattr(model, '{}_EWC_estimated_fisher'.format(n))\n",
        "                # Calculate weight regularization loss\n",
        "                ewc_losses.append((fisher * (p-mean)**2).sum())\n",
        "            ewc_loss = (1./2)*sum(ewc_losses)\n",
        "        else:\n",
        "            ewc_loss = 0.\n",
        "\n",
        "        # Combine all three losses\n",
        "        if buffer_dataset is not None:\n",
        "            rnt = 1./current_context\n",
        "            total_loss = rnt*loss + (1-rnt)*loss_replay + ewc_lambda*ewc_loss\n",
        "        else:\n",
        "            total_loss = loss + ewc_lambda*ewc_loss\n",
        "\n",
        "        total_loss.backward()\n",
        "        optimizer.step()\n",
        "        progress_bar.set_description(\n",
        "        '<CLASSIFIER> | training loss: {loss:.3} | training accuracy: {prec:.3}% |'\n",
        "            .format(loss=total_loss.item(), prec=accuracy)\n",
        "        )\n",
        "        progress_bar.update(1)\n",
        "    progress_bar.close()"
      ],
      "metadata": {
        "id": "qON7O-O1ozuM",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now test your function by training the model, ..."
      ],
      "metadata": {
        "id": "1T2XW32xhAl3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the hyperparameter for EWC\n",
        "ewc_lambda_with_replay = 100 # YOU CAN EXPLORE OTHER VALUES\n",
        "# (if you want to do a new try, first 'reset' [model_ewc_replay] by running the command\n",
        "#  `model_ewc_replay = copy.deepcopy(model_after_context1)` at the top of the assignment)\n",
        "\n",
        "# Compute the Fisher Information matrix (and store it as attribute in the network)\n",
        "estimate_fisher(model_ewc_replay, train_datasets[0], n_samples=200)\n",
        "\n",
        "# Train on the second context using EWC and experience replay\n",
        "train_ewc_replay(model_ewc_replay, train_datasets[1], iters=iters, lr=lr, batch_size=batch_size,\n",
        "                 current_context=2, ewc_lambda=ewc_lambda_with_replay,\n",
        "                 buffer_dataset=buffer_dataset)"
      ],
      "metadata": {
        "id": "bVKKz3xdhBa9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "... evaluating it, ..."
      ],
      "metadata": {
        "id": "xCAXSuUGhu4B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Accuracy (in %) of the model on test-set of:\")\n",
        "ewc_replay_accs = []\n",
        "for i in range(contexts):\n",
        "    acc = test_acc(model_ewc_replay, test_datasets[i], test_size=None)\n",
        "    print(\" - Context {}: {:.1f}\".format(i+1, acc))\n",
        "    ewc_replay_accs.append(acc)"
      ],
      "metadata": {
        "id": "Damfry4kh7d7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "... and comparing its performance with the performance of the individual methods."
      ],
      "metadata": {
        "id": "E5zUtbJ3h1EG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure, axis = plt.subplots(1, 4, figsize=(16, 4))\n",
        "\n",
        "title='Fine-tuning'\n",
        "multi_context_barplot(axis[0], context2_accs, title)\n",
        "\n",
        "title='EWC \\n(lambda: {})'.format(ewc_lambda)\n",
        "multi_context_barplot(axis[1], ewc_accs, title)\n",
        "\n",
        "title='Replay \\n(buffer: {} samples per class)'.format(buffer_size_per_class)\n",
        "multi_context_barplot(axis[2], replay_accs, title)\n",
        "\n",
        "title='EWC + replay \\n(lambda: {} - buffer: {} per class)'.format(ewc_lambda_with_replay,\n",
        "                                                                          buffer_size_per_class)\n",
        "multi_context_barplot(axis[3], ewc_replay_accs, title)"
      ],
      "metadata": {
        "id": "QWR5Nw4bh7Im"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 3: Class-incremental learning - Split MNIST\n",
        "We saw that on Permuted MNIST with two contexts, both EWC and experience replay (with a relatively small buffer of 20 samples per class) are able to succesfully prevent a large part of the catastrophic forgetting.\n",
        "\n",
        "Now let's look at a different type of continual learning problem. As discussed in the lecture, when it comes to supervised continual learning, three fundamental types - or 'scenarios' - can be distinguished: **task-incremental learning**, **domain-incremental learning** and **class-incremental learning**."
      ],
      "metadata": {
        "id": "tW8VSZ_90BDN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "uxpSI2BttGa1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Screenshot 2024-03-18 at 19.00.26.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "ac1wTL2duGD2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ASSIGNMENT: What scenario was used for Permuted MNIST?\n",
        "\n",
        "What type of 'scenario' was the permuted MNIST problem that we explored above? Was it task-incremental, domain-incremental or class-incremental? Try to motivate your answer."
      ],
      "metadata": {
        "id": "R_oxTtVTrM5g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Possible Answer\n",
        "\n",
        "'''\n",
        "The Permuted MNIST problem consisted of two contexts: normal MNIST (context 1) and MNIST\n",
        "with permuted input images (context 2).\n",
        "\n",
        "After learning both contexts, when the model was evaluated, the model was not told to\n",
        "which context an image belongs (i.e., the model was not told whether the image to be\n",
        "classified was permuted or not), but the model also did not need to identify to\n",
        "which context an image belongs (i.e., the model did not need to predict whether\n",
        "the image to be classified had permuted pixels or not; it only needed to predict\n",
        "the original digit displayed in the image).\n",
        "This thus means that the above Permuted MNIST problem was an example of a domain-incremental\n",
        "learning problem.\n",
        "\n",
        "Another way to motivate that this problem is an example of domain-incremental\n",
        "learning, is to say that in both context 1 (normal MNIST) and context 2 (MNIST with\n",
        "permuted input images), the 'type of problem' is the same (i.e., to identify the\n",
        "digit displayed in the original image), but the 'domain' or 'context' is changing (i.e.,\n",
        "the order/permutation in which the image pixels are presented).\n",
        "''';"
      ],
      "metadata": {
        "id": "CIvJrwERry3C",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's explore a **class-incremental learning** problem. For this we will no longer use Permuted MNIST (because it is a bit unintuitive to perform Permuted MNIST according to the class-incremental learning scenario), but we will use Split MNIST, which was introduced in the lecture."
      ],
      "metadata": {
        "id": "Hbp9I_kpstgv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set up the benchmark (Split MNIST)\n",
        "We will split the MNIST dataset up in five contexts with two different classes per context."
      ],
      "metadata": {
        "id": "Ho1dVPxFuKPj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "contexts = 5\n",
        "classes_per_context = 2\n",
        "# Generate labels-per-context\n",
        "labels_per_context = [\n",
        "    list(np.array(range(classes_per_context))+classes_per_context*context_id) for context_id in range(contexts)\n",
        "]\n",
        "# Split the train and test datasets up into sub-datasets, one for each context\n",
        "train_datasets = []\n",
        "test_datasets = []\n",
        "for labels in labels_per_context:\n",
        "    train_datasets.append(SubDataset(MNIST_trainset, labels))\n",
        "    test_datasets.append(SubDataset(MNIST_testset, labels))"
      ],
      "metadata": {
        "id": "aBAcIMoG-voX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the contexts\n",
        "figure, axis = plt.subplots(1, contexts, figsize=(3*contexts, 4))\n",
        "\n",
        "for context_id in range(len(train_datasets)):\n",
        "    plot_examples(axis[context_id], train_datasets[context_id], context_id=context_id)"
      ],
      "metadata": {
        "id": "kVr-Qc3LaJAw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set up the model\n",
        "We use the same network architecture as before."
      ],
      "metadata": {
        "id": "VLBAv9CyFRzc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the architectural layout of the network to use\n",
        "fc_lay = 4        #--> number of fully-connected layers\n",
        "fc_units = 40     #--> number of units in each hidden layer\n",
        "fc_nl = \"relu\"    #--> what non-linearity to use?"
      ],
      "metadata": {
        "id": "JBmaNwqAlXQ6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the model\n",
        "model = Classifier(image_size=config['size'], image_channels=config['channels'],\n",
        "                   output_units=config['classes'],\n",
        "                   fc_layers=fc_lay, fc_units=fc_units, fc_nl=fc_nl)"
      ],
      "metadata": {
        "id": "vmCYbrRDliwp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare for evaluation throughout training\n",
        "Below we will use the same continual learning strategies as before (fine-tuning, EWC and replay) to train models on the five contexts of Split MNIST. When we do this, we want to keep track of the performance of the model while it is sequentially trained on these different contexts. For that we will define some functions here.\n",
        "\n",
        "As is common in the continual learning literature, we will evaluate the performance of the model only after finishing training on each new task. (But see [this paper](https://openreview.net/forum?id=Zy350cRstc6) for an interesting phenomenon that can be observed if we would evaluate the model on previous tasks after each training iteration on the new task.)"
      ],
      "metadata": {
        "id": "1zoLaBgvutQ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to initiate a dict-object in which performance throughout training is logged.\n",
        "def initiate_result_dict(n_contexts):\n",
        "    '''Initiate <dict> with accuracy-measures to keep track of.'''\n",
        "    result_dict = {}\n",
        "    result_dict[\"acc per context\"] = {}\n",
        "    for i in range(n_contexts):\n",
        "        result_dict[\"acc per context\"][\"context {}\".format(i+1)] = []\n",
        "    result_dict[\"average_contexts_so_far\"] = []  # average accuracy over all contexts so far\n",
        "    result_dict[\"average_all_contexts\"] = []     # average accuracy over all contexts\n",
        "    result_dict[\"context\"] = []                  # number of contexts so far\n",
        "    return result_dict"
      ],
      "metadata": {
        "id": "S5KOtI2Axyn8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_all(model, datasets, current_context, test_size=None, result_dict=None, verbose=False):\n",
        "    '''Evaluate accuracy of a classifier (=[model]) on all contexts in [datasets].'''\n",
        "\n",
        "    n_contexts = len(datasets)\n",
        "\n",
        "    # Evaluate accuracy of model on all contexts\n",
        "    precs = []\n",
        "    for i in range(n_contexts):\n",
        "        precs.append(test_acc(model, datasets[i], test_size=test_size))\n",
        "\n",
        "    # Compute average accuracy both for all contexts seen so far, and for all contexts\n",
        "    ave_so_far = sum([precs[context_id] for context_id in range(current_context)]) / current_context\n",
        "    ave_all = sum([precs[context_id] for context_id in range(n_contexts)]) / n_contexts\n",
        "\n",
        "    # Print results on screen\n",
        "    if verbose:\n",
        "        print(' => ave accuracy (contexts so far): {:.3f}'.format(ave_so_far))\n",
        "        print(' => ave accuracy (all contexts):    {:.3f}'.format(ave_all))\n",
        "\n",
        "    # Add results to [result_dict]\n",
        "    if result_dict is not None:\n",
        "        for i in range(n_contexts):\n",
        "            result_dict['acc per context']['context {}'.format(i+1)].append(precs[i])\n",
        "        result_dict['average_all_contexts'].append(ave_all)\n",
        "        result_dict['average_contexts_so_far'].append(ave_so_far)\n",
        "        result_dict['context'].append(current_context)"
      ],
      "metadata": {
        "id": "N_j_o9RCuyvB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compare fine-tuning, EWC and experience replay on Split MNIST"
      ],
      "metadata": {
        "id": "MooCLNx7luDs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Fine-tuning"
      ],
      "metadata": {
        "id": "qRNzQe6vt0mf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy from the base-model\n",
        "model_finetune = copy.deepcopy(model)"
      ],
      "metadata": {
        "id": "s78p_YQQtX-M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initiate a `results_dict` to keep track of performance throughout the continual training.\n",
        "result_dict_finetune = initiate_result_dict(contexts)"
      ],
      "metadata": {
        "id": "L0FKJoYRy7lp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For fine-tuning, we can simply re-use the `train`-function we had defined above to train the model on a given dataset in \"the standard way\" (i.e., without using any specific continual learning strategy)."
      ],
      "metadata": {
        "id": "WrMqMENnuHRg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sequentially train the model on all contexts using finetuning\n",
        "for context_id in range(contexts):\n",
        "    # train the model on this context\n",
        "    train(model_finetune, dataset=train_datasets[context_id], iters=iters, lr=lr,\n",
        "          batch_size=batch_size)\n",
        "    # evaluate the performance of the model after training on this context\n",
        "    test_all(model_finetune, test_datasets, context_id+1, test_size=None,\n",
        "             result_dict=result_dict_finetune, verbose=True)"
      ],
      "metadata": {
        "id": "g0y65f07tVie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Visualization function\n",
        "def plot_lines(list_with_lines, x_axes=None, line_names=None, colors=None, title=None,\n",
        "               title_top=None, xlabel=None, ylabel=None, ylim=None, figsize=None, list_with_errors=None, errors=\"shaded\",\n",
        "               x_log=False, with_dots=False, linestyle='solid', h_line=None, h_label=None, h_error=None,\n",
        "               h_lines=None, h_colors=None, h_labels=None, h_errors=None):\n",
        "    '''Generates a figure containing multiple lines in one plot.\n",
        "\n",
        "    :param list_with_lines: <list> of all lines to plot (with each line being a <list> as well)\n",
        "    :param x_axes:          <list> containing the values for the x-axis\n",
        "    :param line_names:      <list> containing the names of each line\n",
        "    :param colors:          <list> containing the colors of each line\n",
        "    :param title:           <str> title of plot\n",
        "    :param title_top:       <str> text to appear on top of the title\n",
        "    :return: f:             <figure>\n",
        "    '''\n",
        "\n",
        "    # if needed, generate default x-axis\n",
        "    if x_axes == None:\n",
        "        n_obs = len(list_with_lines[0])\n",
        "        x_axes = list(range(n_obs))\n",
        "\n",
        "    # if needed, generate default line-names\n",
        "    if line_names == None:\n",
        "        n_lines = len(list_with_lines)\n",
        "        line_names = [\"line \" + str(line_id) for line_id in range(n_lines)]\n",
        "\n",
        "    # make plot\n",
        "    size = (12,7) if figsize is None else figsize\n",
        "    f, axarr = plt.subplots(1, 1, figsize=size)\n",
        "\n",
        "    # add error-lines / shaded areas\n",
        "    if list_with_errors is not None:\n",
        "        for line_id, name in enumerate(line_names):\n",
        "            if errors==\"shaded\":\n",
        "                axarr.fill_between(x_axes, list(np.array(list_with_lines[line_id]) + np.array(list_with_errors[line_id])),\n",
        "                                   list(np.array(list_with_lines[line_id]) - np.array(list_with_errors[line_id])),\n",
        "                                   color=None if (colors is None) else colors[line_id], alpha=0.25)\n",
        "            else:\n",
        "                axarr.plot(x_axes, list(np.array(list_with_lines[line_id]) + np.array(list_with_errors[line_id])), label=None,\n",
        "                           color=None if (colors is None) else colors[line_id], linewidth=1, linestyle='dashed')\n",
        "                axarr.plot(x_axes, list(np.array(list_with_lines[line_id]) - np.array(list_with_errors[line_id])), label=None,\n",
        "                           color=None if (colors is None) else colors[line_id], linewidth=1, linestyle='dashed')\n",
        "\n",
        "    # mean lines\n",
        "    for line_id, name in enumerate(line_names):\n",
        "        axarr.plot(x_axes, list_with_lines[line_id], label=name,\n",
        "                   color=None if (colors is None) else colors[line_id],\n",
        "                   linewidth=4, marker='o' if with_dots else None, linestyle=linestyle if type(linestyle)==str else linestyle[line_id])\n",
        "\n",
        "    # add horizontal line\n",
        "    if h_line is not None:\n",
        "        axarr.axhline(y=h_line, label=h_label, color=\"grey\")\n",
        "        if h_error is not None:\n",
        "            if errors == \"shaded\":\n",
        "                axarr.fill_between([x_axes[0], x_axes[-1]],\n",
        "                                   [h_line + h_error, h_line + h_error], [h_line - h_error, h_line - h_error],\n",
        "                                   color=\"grey\", alpha=0.25)\n",
        "            else:\n",
        "                axarr.axhline(y=h_line + h_error, label=None, color=\"grey\", linewidth=1, linestyle='dashed')\n",
        "                axarr.axhline(y=h_line - h_error, label=None, color=\"grey\", linewidth=1, linestyle='dashed')\n",
        "\n",
        "    # add horizontal lines\n",
        "    if h_lines is not None:\n",
        "        h_colors = colors if h_colors is None else h_colors\n",
        "        for line_id, new_h_line in enumerate(h_lines):\n",
        "            axarr.axhline(y=new_h_line, label=None if h_labels is None else h_labels[line_id],\n",
        "                          color=None if (h_colors is None) else h_colors[line_id])\n",
        "            if h_errors is not None:\n",
        "                if errors == \"shaded\":\n",
        "                    axarr.fill_between([x_axes[0], x_axes[-1]],\n",
        "                                       [new_h_line + h_errors[line_id], new_h_line+h_errors[line_id]],\n",
        "                                       [new_h_line - h_errors[line_id], new_h_line - h_errors[line_id]],\n",
        "                                       color=None if (h_colors is None) else h_colors[line_id], alpha=0.25)\n",
        "                else:\n",
        "                    axarr.axhline(y=new_h_line+h_errors[line_id], label=None,\n",
        "                                  color=None if (h_colors is None) else h_colors[line_id], linewidth=1,\n",
        "                                  linestyle='dashed')\n",
        "                    axarr.axhline(y=new_h_line-h_errors[line_id], label=None,\n",
        "                                  color=None if (h_colors is None) else h_colors[line_id], linewidth=1,\n",
        "                                  linestyle='dashed')\n",
        "\n",
        "    # finish layout\n",
        "    # -set y-axis\n",
        "    if ylim is not None:\n",
        "        axarr.set_ylim(ylim)\n",
        "    # -add axis-labels\n",
        "    if xlabel is not None:\n",
        "        axarr.set_xlabel(xlabel)\n",
        "    if ylabel is not None:\n",
        "        axarr.set_ylabel(ylabel)\n",
        "    # -add title(s)\n",
        "    if title is not None:\n",
        "        axarr.set_title(title)\n",
        "    if title_top is not None:\n",
        "        f.suptitle(title_top)\n",
        "    # -add legend\n",
        "    if line_names is not None:\n",
        "        axarr.legend()\n",
        "    # -set x-axis to log-scale\n",
        "    if x_log:\n",
        "        axarr.set_xscale('log')\n",
        "\n",
        "    # return the figure\n",
        "    return f"
      ],
      "metadata": {
        "cellView": "form",
        "id": "tK2ztl4O2SeF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize performance on each context throughout the continual training\n",
        "plot_list = []\n",
        "for i in range(contexts):\n",
        "    plot_list.append(result_dict_finetune[\"acc per context\"][\"context {}\".format(i + 1)])\n",
        "figure = plot_lines(\n",
        "    plot_list, x_axes=result_dict_finetune[\"context\"],\n",
        "    line_names=['context {}'.format(i + 1) for i in range(contexts)],\n",
        "    title=\"Fine-tuning\", ylabel=\"Test Accuracy (%)\",\n",
        "    xlabel=\"Number of contexts trained so far\", figsize=(10,5),\n",
        ")"
      ],
      "metadata": {
        "id": "izJVfClC0Nwe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "That doesn't look great! The performance on data from each context drops to zero as soon as the next context has been learned.\n",
        "\n",
        "Can this be fixed with EWC or replay?\n",
        "\n"
      ],
      "metadata": {
        "id": "XoOvQDxU0H7V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### EWC"
      ],
      "metadata": {
        "id": "qZpWQSzct6-j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy from the base-model\n",
        "model_ewc = copy.deepcopy(model)"
      ],
      "metadata": {
        "id": "A0zJrd324EYi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initiate a `results_dict` to keep track of performance throughout the continual training.\n",
        "result_dict_ewc = initiate_result_dict(contexts)"
      ],
      "metadata": {
        "id": "6b_toOZG4HK2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a hyperparameter for EWC\n",
        "ewc_lambda = 100"
      ],
      "metadata": {
        "id": "KUvoqpvC462f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sequentially train the model on all contexts using EWC\n",
        "for context_id in range(contexts):\n",
        "    # Train the model on this context\n",
        "    train_ewc(model_ewc, train_datasets[context_id], iters=iters, lr=lr, batch_size=batch_size,\n",
        "              current_context=context_id+1, ewc_lambda=ewc_lambda)\n",
        "    # Estimate/update the FI-matrix (which is stored as attribute in the network)\n",
        "    estimate_fisher(model_ewc, train_datasets[context_id], n_samples=200)\n",
        "    # Evaluate the performance of the model after training on this context\n",
        "    test_all(model_ewc, test_datasets, context_id+1, test_size=None,\n",
        "             result_dict=result_dict_ewc, verbose=True)"
      ],
      "metadata": {
        "id": "UaTlFqCU4L23"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize performance on each context throughout the continual training\n",
        "plot_list = []\n",
        "for i in range(contexts):\n",
        "    plot_list.append(result_dict_ewc[\"acc per context\"][\"context {}\".format(i + 1)])\n",
        "figure = plot_lines(\n",
        "    plot_list, x_axes=result_dict_ewc[\"context\"],\n",
        "    line_names=['context {}'.format(i + 1) for i in range(contexts)],\n",
        "    title=\"EWC\", ylabel=\"Test Accuracy (%)\",\n",
        "    xlabel=\"Number of contexts trained so far\", figsize=(10,5),\n",
        ")"
      ],
      "metadata": {
        "id": "cnwEr6D86XXz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On this problem, EWC does not seem to help!"
      ],
      "metadata": {
        "id": "A3veN7vFDgtg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Experience Replay"
      ],
      "metadata": {
        "id": "xKga8yfYt8qI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy from the base-model\n",
        "model_replay = copy.deepcopy(model)"
      ],
      "metadata": {
        "id": "PNUKP7xL7Mit"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initiate a `results_dict` to keep track of performance throughout the continual training.\n",
        "result_dict_replay = initiate_result_dict(contexts)"
      ],
      "metadata": {
        "id": "w47-mCHp7PU1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select how many samples per class can be stored in the memory buffer\n",
        "buffer_size_per_class = 20"
      ],
      "metadata": {
        "id": "ysrAtTYN9Xq8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sequentially train the model on all contexts using Experience Replay\n",
        "memory_sets = []\n",
        "buffer_dataset = None\n",
        "for context_id in range(contexts):\n",
        "    # Train the model on this context\n",
        "    train_replay(model_replay, train_datasets[context_id], iters=iters, lr=lr,\n",
        "                 batch_size=batch_size, current_context=context_id+1, buffer_dataset=buffer_dataset)\n",
        "    # Update memory buffer\n",
        "    classes_in_this_context = list(range(classes_per_context*context_id,\n",
        "                                         classes_per_context*(context_id+1)))\n",
        "    memory_sets = fill_memory_buffer(memory_sets, train_datasets[context_id],\n",
        "                                     buffer_size_per_class=buffer_size_per_class,\n",
        "                                     class_indeces=classes_in_this_context)\n",
        "    buffer_dataset = MemorySetDataset(memory_sets)\n",
        "    # Evaluate the performance of the model after training on this context\n",
        "    test_all(model_replay, test_datasets, context_id+1, test_size=None,\n",
        "             result_dict=result_dict_replay, verbose=True)"
      ],
      "metadata": {
        "id": "PqEQ0psH7S5_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize performance on each context throughout the continual training\n",
        "plot_list = []\n",
        "for i in range(contexts):\n",
        "    plot_list.append(result_dict_replay[\"acc per context\"][\"context {}\".format(i + 1)])\n",
        "figure = plot_lines(\n",
        "    plot_list, x_axes=result_dict_replay[\"context\"],\n",
        "    line_names=['context {}'.format(i + 1) for i in range(contexts)],\n",
        "    title=\"Experience Replay ({} samples per class)\".format(buffer_size_per_class),\n",
        "    ylabel=\"Test Accuracy (%)\", xlabel=\"Number of contexts trained so far\", figsize=(10,5),\n",
        ")"
      ],
      "metadata": {
        "id": "Cd6cVn4596Z2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Experience replay does help on this type of continual learning problem!"
      ],
      "metadata": {
        "id": "0h0_dqswDxIF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Visual comparison fine-tuning, EWC and replay"
      ],
      "metadata": {
        "id": "PeRrxlPMD9Ih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "key = \"average_contexts_so_far\"\n",
        "plot_list = [result_dict_finetune[key], result_dict_ewc[key], result_dict_replay[key]]\n",
        "line_names = ['Fine-tuning', 'EWC', 'Experience Replay']\n",
        "figure = plot_lines(\n",
        "    plot_list, x_axes=result_dict_replay[\"context\"], line_names=line_names,\n",
        "    title=\"Comparison (performance on all contexts so far)\",\n",
        "    ylabel=\"Test Accuracy (%)\", xlabel=\"Number of contexts trained so far\", figsize=(10,5),\n",
        ")"
      ],
      "metadata": {
        "id": "BlMgaQw3EDNG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = \"average_all_contexts\"\n",
        "plot_list = [result_dict_finetune[key], result_dict_ewc[key], result_dict_replay[key]]\n",
        "line_names = ['Fine-tuning', 'EWC', 'Experience Replay']\n",
        "figure = plot_lines(\n",
        "    plot_list, x_axes=result_dict_replay[\"context\"], line_names=line_names,\n",
        "    title=\"Comparison (performance on all contexts)\",\n",
        "    ylabel=\"Test Accuracy (%)\", xlabel=\"Number of contexts trained so far\", figsize=(10,5),\n",
        ")"
      ],
      "metadata": {
        "id": "jDvPRbb6NunA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "(Note that the lines of `fine-tuning` and `EWC` might well overlap almost completely.)"
      ],
      "metadata": {
        "id": "BnzPs8sLNiiW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **ASSIGNMENT**: Combine EWC and replay on class-incremental Split MNIST\n",
        "Train another model copy on the class-incremental version of Split MNIST, now again using *both* EWC and experience replay."
      ],
      "metadata": {
        "id": "nnHbEqUclFzL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_ewc_replay = copy.deepcopy(model)\n",
        "result_dict_ewc_replay = initiate_result_dict(contexts)"
      ],
      "metadata": {
        "id": "9Z4mW-SGJ2Mz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TO BE COMPLETED"
      ],
      "metadata": {
        "id": "o8uoeyclJsol"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Possible Answer\n",
        "\n",
        "# Select hyperparameter for EWC\n",
        "ewc_lambda_with_replay = 100\n",
        "\n",
        "# Sequentially train the model on all contexts using both EWC and Experience Replay\n",
        "memory_sets = []\n",
        "buffer_dataset = None\n",
        "for context_id in range(contexts):\n",
        "\n",
        "    # Train the model on this context\n",
        "    train_ewc_replay(model_ewc_replay, train_datasets[context_id], iters=iters, lr=lr,\n",
        "                     batch_size=batch_size, current_context=context_id+1,\n",
        "                     ewc_lambda=ewc_lambda_with_replay, buffer_dataset=buffer_dataset)\n",
        "    # NOTE: depending on how you had written your `train_ewc_replay` function, you might need to\n",
        "    #       adjust it to make it suitable for the more general case in which the function is used\n",
        "    #       for arbitrary contexts\n",
        "\n",
        "    # Estimate/update the FI-matrix (which is stored as attribute in the network)\n",
        "    estimate_fisher(model_ewc_replay, train_datasets[context_id], n_samples=200)\n",
        "\n",
        "    # Update memory buffer\n",
        "    classes_in_this_context = list(range(classes_per_context*context_id,\n",
        "                                         classes_per_context*(context_id+1)))\n",
        "    memory_sets = fill_memory_buffer(memory_sets, train_datasets[context_id],\n",
        "                                     buffer_size_per_class=buffer_size_per_class,\n",
        "                                     class_indeces=classes_in_this_context)\n",
        "    buffer_dataset = MemorySetDataset(memory_sets)\n",
        "\n",
        "    # Evaluate the performance of the model after training on this context\n",
        "    test_all(model_ewc_replay, test_datasets, context_id+1, test_size=None,\n",
        "             result_dict=result_dict_ewc_replay, verbose=True)"
      ],
      "metadata": {
        "id": "NCIl4npjKA4b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize performance on each context throughout the continual training\n",
        "plot_list = []\n",
        "for i in range(contexts):\n",
        "    plot_list.append(result_dict_ewc_replay[\"acc per context\"][\"context {}\".format(i + 1)])\n",
        "figure = plot_lines(\n",
        "    plot_list, x_axes=result_dict_ewc_replay[\"context\"],\n",
        "    line_names=['context {}'.format(i + 1) for i in range(contexts)],\n",
        "    title=\"EWC + Experience Replay ({} samples per class)\".format(buffer_size_per_class),\n",
        "    ylabel=\"Test Accuracy (%)\", xlabel=\"Number of contexts trained so far\", figsize=(10,5),\n",
        ")"
      ],
      "metadata": {
        "id": "H8Bv45h-LAOo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Is there a benefit of \"EWC + Experience Replay\" over only \"Experience Replay\"? Let's compare them more directly."
      ],
      "metadata": {
        "id": "lYRQToDAOHCC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "key = \"average_contexts_so_far\"\n",
        "plot_list = [result_dict_finetune[key], result_dict_ewc[key], result_dict_replay[key],\n",
        "             result_dict_ewc_replay[key]]\n",
        "line_names = ['Fine-tuning', 'EWC', 'Experience Replay', 'EWC + Experience Replay']\n",
        "figure = plot_lines(\n",
        "    plot_list, x_axes=result_dict_replay[\"context\"], line_names=line_names,\n",
        "    title=\"Comparison (performance on all contexts so far)\",\n",
        "    ylabel=\"Test Accuracy (%)\", xlabel=\"Number of contexts trained so far\", figsize=(10,5),\n",
        ")"
      ],
      "metadata": {
        "id": "Y7tYuOJqLm1F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bonus Part: Task- and domain-incremental learning version of Split MNIST\n",
        "As a *bonus exercise*, let's try to think about how to adapt the code above such that we can run the same experiment, except that we perform Split MNIST according to the task- and domain-incremental learning scenarios rather than the class-incremental learning scenario."
      ],
      "metadata": {
        "id": "tysftIoWOY-u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recall from the lecture that Split MNIST (just like any other sequence of classification tasks), can be performed according to each of the three scenarios:\n",
        "\n",
        "![Screenshot 2024-03-14 at 15.02.19.png](data:image/png;base64,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)\n",
        "\n"
      ],
      "metadata": {
        "id": "qnzRpciWOnSY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Domain-incremental Split MNIST\n",
        "To create the domain-incrmental learning version of Split MNIST, the number of possible output classes needs to be changed from ten (one output class for each digit) to two (one output class for `odd` and one output class for `even`).\n",
        "\n",
        "To implement this, we need to change both the way the benchmark is defined (e.g., in the second context, the digits '2' and '3' should no longer be labelled with `y=2` and `y=3`, but instead with `y=0` and `y=1`), and we need to change the way the classifier is defined (as it should now have two output units rather than ten)."
      ],
      "metadata": {
        "id": "AS-70pNrv4E_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task-incremental Split MNIST\n",
        "To create the task-incremental version of Split MNIST, the context label needs to be provided as input to the model. Usually, this context label will then be used to enable a \"multi-headed output layer\", meaning that there is a separate output layer per task.\n",
        "\n",
        "One option to implement this is to keep the number of output classes at ten and to still have the same, single output layer as in the class-incremental learning case, but to always mask out the outputs that are not in the current context.\n",
        "\n",
        "Another option to implement this is to set the number of output classes to two (as in the domain-incremental learning case) and to define a separate output layer per task.\n",
        "\n"
      ],
      "metadata": {
        "id": "7Ipjh-G0wjwh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "oYI2Bbr4zCDE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "$^{\\text{a}}$ With task-incremental learning, at the computational level, there is no difference between whether the algorithm must return the within-context label or the global label, because the within-context label can be combined with the context label (which is provided as input) to get the global label."
      ],
      "metadata": {
        "id": "vFDIFf_KzLu-"
      }
    }
  ]
}