functional_regularisation_for_continual_learning/frcl.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksftVXR8lUeP"
      },
      "source": [
        "Copyright 2020 DeepMind Technologies Limited.\n",
        "\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEf5_rHhlXmd"
      },
      "source": [
        "# [Functional Regluarisation for Continual Learning with Gaussian Processes](https://arxiv.org/abs/1901.11356) (Published at ICLR 2020)\n",
        "\n",
        "![Screen Shot 2020-09-29 at 7.59.29 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)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqifXRtRPcGw"
      },
      "source": [
        "**Functional Regluarisation for Continual Learning (FRCL)** is a method designed to tackle the Continual Learning (CL) problem, combining recent ideas and advances in Bayesian methods, Functional Regularisation and Experience Replay. FRCL is one of the few CL algorithms that provide a principled criterion to choose representative training points for replay. In addition, the Bayesian formulation provides uncertainty estimates that can be used in the task-agonstic setting.\n",
        "\n",
        "\n",
        "This code implements training and replicates published results presented for the Permuted MNIST and Sequential Omniglot datasets. If you find this code useful, please cite the paper:\n",
        "\n",
        "```\n",
        "@article{titsias2019functional,\n",
        "  title={Functional regularisation for continual learning using gaussian processes},\n",
        "  author={Titsias, Michalis K and Schwarz, Jonathan and Matthews, AG de G and Pascanu, Razvan and Teh, Yee Whye},\n",
        "  journal={ICLR 2020},\n",
        "  year={2020}\n",
        "}\n",
        "```\n",
        "If you have any questions, please do not hestiate to reach out to the\n",
        "code authors:\n",
        "\n",
        "*   Jonathan Schwarz (schwarzjn@google.com)\n",
        "*   Michalis Titsias (mtitsias@google.com)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDn_lVxg3Z2G"
      },
      "source": [
        "# Installation and Setup\n",
        "\n",
        "We begin by installing and importing all necessary dependencies.\n",
        "\n",
        "In order to allow efficient training, please ensure to use a colab kernel with a GPU by doing the following:\n",
        "\n",
        " \u003e `Edit` -\u003e `Notebook settings` -\u003e select GPU under `Hardware accelerator` -\u003e `Save`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "GQ18Kd5F3uKe"
      },
      "outputs": [],
      "source": [
        "#@title Installing and Importing Dependencies\n",
        "\n",
        "print('Installing necessary libraries...')\n",
        "\n",
        "def install_libraries():\n",
        "  !pip install gpflow\n",
        "  !pip install dm-sonnet\n",
        "  !pip install tfa-nightly\n",
        "\n",
        "import IPython\n",
        "  \n",
        "with IPython.utils.io.capture_output() as captured:\n",
        "  install_libraries()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "__3eqm3q3sr-"
      },
      "outputs": [],
      "source": [
        "#@title Imports\n",
        "\n",
        "import collections\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_addons as tfa\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "# Python 3 type annotations\n",
        "from typing import Optional, Sequence, Text, Tuple"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhCA3yCJVWxA"
      },
      "source": [
        "Let's define a few useful contants and set some global properties related to plotting and logging."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "WY9arMfM1as2"
      },
      "outputs": [],
      "source": [
        "#@title Setup\n",
        "\n",
        "# TF logging level (disables some gpflow internal warnings we can safely ignore)\n",
        "tf.get_logger().setLevel('ERROR')\n",
        "\n",
        "# Plotting options\n",
        "sns.set_style(\"white\")\n",
        "matplotlib.use('Agg') \n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LAxYQ5gVtIX"
      },
      "source": [
        "Run the code below to check that you chose the correct colab runtime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "H-ogeIN5QV8X"
      },
      "outputs": [],
      "source": [
        "#@title Test for GPU\n",
        "\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  print('NOT using a GPU. Please follow the instructions above.')\n",
        "else:\n",
        "  print(\"Using a GPU. You're good to go.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdtWhHZvjz7S"
      },
      "source": [
        "# Setting up Data\n",
        "\n",
        "As a first step, we will setup the CL problems to be used in the code below. Specifically, the code below implements the following experiments:\n",
        "\n",
        "* Permuted MNIST (Proposed as a CL benchmark in [3])\n",
        "* Sequential Omniglot (Proposed as a CL benchmark in [2])\n",
        "\n",
        "We start by defining a few useful objects that hold data at various stages throughout the training pipeline:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "QYOn2oR-k0Of"
      },
      "outputs": [],
      "source": [
        "#@title Data utility objects\n",
        "\n",
        "# Holds underlying data source for all tasks. `task_metadata` contains all\n",
        "# metadata necessary to split this data into a sequence of tasks.\n",
        "RawData = collections.namedtuple(\n",
        "    'RawData', ['inputs', 'outputs', 'task_metadata'])\n",
        "\n",
        "# `RawData` split into train/valid \u0026 test sets for a specific task.\n",
        "DatasetSplit = collections.namedtuple(\n",
        "    'DatasetSplit', ['train', 'valid', 'test',\n",
        "                     'num_train', 'num_valid', 'num_test'])\n",
        "\n",
        "# TF iterators used to fetch data in the training pipeline.\n",
        "# `train`: Training dataset. Possibly augmented.\n",
        "# `discr_search`: Dataset to choose replay/inducing points from. Not augmented.\n",
        "# `discr_search_eval`: Dataset to evaluate inducing point approximation on.\n",
        "#  Either training or validation set. Not augmented.\n",
        "# `test`: Test dataset. Not augmented.\n",
        "Iterators = collections.namedtuple(\n",
        "    'Iterators', ['train', 'discr_search', 'discr_search_eval', 'test'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AaTKGUDXWr3T"
      },
      "source": [
        "And next define a few functions to operate on those objects, performing converstions along with the necessary processing of those objects."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "OQS5wddJWr-T"
      },
      "outputs": [],
      "source": [
        "#@title Data utility functions\n",
        "\n",
        "def make_tf_dataset(inputs: tf.Tensor,\n",
        "                    outputs: tf.Tensor,\n",
        "                    num_datapoints: int) -\u003e tf.data.Dataset:\n",
        "  \"\"\"Create a dataset for supervised learning.\n",
        "\n",
        "  Args:\n",
        "    inputs: A tensor of shape `[num_datapoitns, ...]`.\n",
        "    outputs: Associated outputs. A tensor of shape `[num_datapoitns, ...]`.\n",
        "    num_datapoints: Number of expected datapoints.\n",
        "  Returns:\n",
        "    dataset: A tf.data.Dataset instance. Implements shuffling and repetition.\n",
        "  \"\"\"\n",
        "  dataset = tf.data.Dataset.from_tensor_slices((inputs, outputs))\n",
        "  return dataset.shuffle(num_datapoints).repeat()\n",
        "\n",
        "def create_iterators(data: RawData) -\u003e Iterators:\n",
        "  \"\"\"Create dataset tensorflow dataset iterators.\n",
        "\n",
        "  Args:\n",
        "    data: A RawData object.\n",
        "\n",
        "  Returns:\n",
        "    tensorflow Dataset iterators.\n",
        "  \"\"\"\n",
        "  # Setup dataset iterators\n",
        "  train_iterator = iter(data.train.batch(train_batch_size))\n",
        "  test_iterator = iter(data.test.batch(data.num_test))\n",
        "\n",
        "  # Dataset to choose inducing points from\n",
        "  discr_search_iterator = iter(data.train.batch(data.num_train))\n",
        "  if data.valid is not None:\n",
        "    # Dataset to evaluate the quality of the inducing approximation on\n",
        "    discr_search_eval_iterator = iter(data.valid.batch(data.num_valid))\n",
        "  else:\n",
        "    # Use only a quarter fo the train dataset to avoid long training times\n",
        "    discr_search_eval_iterator = iter(data.train.batch(data.num_train // 4))\n",
        "\n",
        "  return Iterators(\n",
        "      train_iterator,\n",
        "      discr_search_iterator,\n",
        "      discr_search_eval_iterator,\n",
        "      test_iterator)\n",
        "\n",
        "def concat(x: tf.Tensor, y: tf.Tensor) -\u003e tf.Tensor:\n",
        "  \"\"\"Concatenates two tensors along first dimension.\n",
        "\n",
        "  Args:\n",
        "    x: A tensor of shape `[N, ...]`.\n",
        "    y: A tensor of shape `[M, ...]`.\n",
        "  Returns:\n",
        "    A tensor of shape `[N+M, ...]`.\n",
        "  \"\"\"\n",
        "\n",
        "  return tf.concat([x, y], axis=0)\n",
        "\n",
        "def select_datapoints(\n",
        "    data: tf.Tensor,\n",
        "    select_idx: np.array) -\u003e tf.Tensor:\n",
        "  \"\"\"Select subset of datapoints provided by `select_idx` from `data`.\n",
        "\n",
        "  Args:\n",
        "    data: A tensor of shape `[num_datapoints, ...]`.\n",
        "    select_idx: Indices of data to be returned. Shape (N \u003c= num_datapoints, ...)\n",
        "  Returns:\n",
        "    A tensor of shape `[N, ...]`.\n",
        "  \"\"\"\n",
        "\n",
        "  return tf.gather(data, select_idx, axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hgLRArrYpdU"
      },
      "source": [
        "Let's define all functions necessary to generate the Permuted MNIST benchmark. This includes a function to fetch the data and apply some elimentary preprocessing (returning a `RawData` object) as well as one to split the data into tasks based on different random permuations (returning a `DatasetSplit` object)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "2upl5WdRpP5c"
      },
      "outputs": [],
      "source": [
        "#@title Permuted MNIST\n",
        "\n",
        "def generate_permutation(num_idx: int) -\u003e Tuple[np.array, np.array]:\n",
        "  \"\"\"Create a permuation of `num_idx` indices.\n",
        "\n",
        "  Args:\n",
        "    num_idx: Number of indices to be permuted\n",
        "\n",
        "  Returns:\n",
        "    perm: Permuted indices.\n",
        "    inv_perm: Inverse of `perm`. Needed to invert permuation.\n",
        "  \"\"\"\n",
        "  perm = np.random.permutation(range(num_idx))\n",
        "  inv_perm = np.argsort(perm)\n",
        "  return perm, inv_perm\n",
        "\n",
        "def prepare_mnist(num_mnist_tasks: int) -\u003e RawData:\n",
        "  \"\"\"Download MNIST data and create permutations.\n",
        "\n",
        "  Args:\n",
        "    num_mnist_tasks: How many tasks to use.\n",
        "\n",
        "  Returns:\n",
        "    Data re-organised with permutation masks to distinguish between tasks.\n",
        "  \"\"\"\n",
        "\n",
        "  mnist = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "  x_train, y_train = mnist[0]\n",
        "  x_test, y_test = mnist[1]\n",
        "\n",
        "  all_images = concat(x_train, x_test)\n",
        "  all_labels = concat(y_train, y_test)\n",
        "\n",
        "  # Safe scalling and casting\n",
        "  all_images = tf.image.convert_image_dtype(all_images, tf.float64)\n",
        "  # Reshape\n",
        "  all_images = snt.Flatten()(all_images)\n",
        "  num_indicies = all_images.shape[1]\n",
        "\n",
        "  # Define permutations\n",
        "  task_perms, task_inv_perms = zip(*[generate_permutation(num_indicies)\n",
        "                                     for _ in range(num_mnist_tasks)])\n",
        "\n",
        "  return RawData(all_images.numpy(), all_labels.numpy(), (task_perms, task_inv_perms))\n",
        "\n",
        "\n",
        "def load_mnist_permutation(\n",
        "    full_mnist: RawData,\n",
        "    task_id: int,\n",
        "    construct_valid: bool,\n",
        "    use_data_augmentationt: bool) -\u003e DatasetSplit:\n",
        "  \"\"\"Extract data for a single task by premuting and create a train/valid/test split.\n",
        "\n",
        "  Args:\n",
        "    full_mnist: Contains original MNIST data. Unpermuted.\n",
        "    task_id: Id of task requested.\n",
        "    construct_valid: Whether to construct a validation dataset.\n",
        "    use_data_augmentationt: Unused.\n",
        "\n",
        "  Returns:\n",
        "    Data re-organised with alphabet ids to distinguish between tasks.\n",
        "  \"\"\"\n",
        "  del use_data_augmentationt  # Unused\n",
        "  # Apply permutation for task given by `task_id`\n",
        "  permuted_inputs = full_mnist.inputs[:, full_mnist.task_metadata[0][task_id]]\n",
        "\n",
        "  num_train = 60000\n",
        "  num_test = 10000\n",
        "\n",
        "  # Choose a random permutation\n",
        "  x_test = permuted_inputs[-num_test:]\n",
        "  y_test = full_mnist.outputs[-num_test:]\n",
        "  x_train = permuted_inputs[:-num_test]\n",
        "  y_train = full_mnist.outputs[:-num_test]\n",
        "\n",
        "  # Construct an optional validation set\n",
        "  if construct_valid:\n",
        "    perm = np.random.permutation(num_train)\n",
        "\n",
        "    num_valid = 10000\n",
        "    num_train -= num_valid\n",
        "\n",
        "    x_valid = x_train[perm[:num_valid]]\n",
        "    y_valid = y_train[perm[:num_valid]]\n",
        "    x_train = x_train[perm[num_valid:]]\n",
        "    y_train = y_train[perm[num_valid:]]\n",
        "    valid_dataset = make_tf_dataset(x_valid, y_valid, num_valid)\n",
        "  else:\n",
        "    num_valid = 0\n",
        "    valid_dataset = None\n",
        "\n",
        "  train_dataset = make_tf_dataset(x_train, y_train, num_train)\n",
        "  test_dataset = make_tf_dataset(x_test, y_test, num_test)\n",
        "\n",
        "  return DatasetSplit(train_dataset, valid_dataset, test_dataset,\n",
        "                      num_train, num_valid, num_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__Bhwg5qWDH4"
      },
      "source": [
        "The Omiglot setup is fairly similar with the exception of the additional data augmentation (growing the size of the datset for each task 20-fold) proposed in [2]. This augmentation consists of random rotations and shifts of images which we implement below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "KGlT7BF9XgXa"
      },
      "outputs": [],
      "source": [
        "#@title Data augmentation functions\n",
        "\n",
        "def random_img_shift_and_rotation(\n",
        "    imgs: tf.Tensor,\n",
        "    degree_range: Sequence[float] = None,\n",
        "    shift_range: Sequence[float] = None) -\u003e tf.Tensor:\n",
        "  \"\"\"Augments image tensor by random shifts and rotations.\n",
        "\n",
        "  Args:\n",
        "    imgs: A tensor of shape (num_images, num_rows, num_columns, num_channels).\n",
        "    degree_range: Min./Max. degrees by which each image is rotated.\n",
        "    shift_range: Min./Max. diff (in both x/y) by which each image is shifted.\n",
        "\n",
        "  Returns:\n",
        "    Augmented Image(s) with the same type and shape as images.\n",
        "  \"\"\"\n",
        "\n",
        "  if degree_range is None:\n",
        "    degree_range = [-30.0, 30.0]\n",
        "\n",
        "  if shift_range is None:\n",
        "    shift_range = [-5.0, 5.0]\n",
        "\n",
        "  num_datapoints = imgs.shape[0]\n",
        "\n",
        "  # Random rotation\n",
        "  angles = np.random.uniform(\n",
        "      degree_range[0], degree_range[1], size=num_datapoints)\n",
        "  imgs = tfa.image.rotate(imgs, np.radians(angles))\n",
        "\n",
        "  # Random shift (https://stackoverflow.com/questions/42252040/how-to-translateor-shift-images-in-tensorflow/45662771)\n",
        "  diff = np.random.uniform(shift_range[0], shift_range[1],\n",
        "                           size=num_datapoints*2).reshape(num_datapoints, 2)\n",
        "  transforms = np.tile([[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]],\n",
        "                       [num_datapoints, 1]).astype(np.float32)\n",
        "  transforms[:, 2] = -diff[:, 0]\n",
        "  transforms[:, 5] = -diff[:, 1]\n",
        "\n",
        "  return tfa.image.transform(imgs, transforms)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQG-BsEHaowB"
      },
      "source": [
        "Setting up the rest of the functions for Seq. Omniglot:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "SEh1pp6LhnBX"
      },
      "outputs": [],
      "source": [
        "#@title Sequential Omniglot\n",
        "\n",
        "def prepare_omniglot(num_alphabets: int) -\u003e RawData:\n",
        "  \"\"\"Download Omniglot data and merge original train/test sets.\n",
        "\n",
        "  Args:\n",
        "    num_alphabets: How many alphabets to use. Must be \u003c= 50.\n",
        "\n",
        "  Returns:\n",
        "    Data re-organised with alphabet ids to distinguish between tasks.\n",
        "  \"\"\"\n",
        "  # The original train/test split is designed for few-shot learning. As this\n",
        "  # split no longer applies to us, we will merge this data and perform a manual\n",
        "  # train/test split later on.\n",
        "  omniglot_train_ds = tfds.load(\n",
        "      'omniglot', batch_size=-1, split='train',\n",
        "      as_supervised=False, shuffle_files=False)\n",
        "  omniglot_test_ds = tfds.load(\n",
        "      'omniglot', batch_size=-1, split='test',\n",
        "      as_supervised=False, shuffle_files=False)\n",
        "\n",
        "  # The provided train/test split does not apply, so we'll merge and re-organise\n",
        "  all_images = concat(\n",
        "      omniglot_train_ds['image'], omniglot_test_ds['image'])\n",
        "  all_labels = concat(\n",
        "      omniglot_train_ds['alphabet_char_id'],\n",
        "      omniglot_test_ds['alphabet_char_id'])\n",
        "\n",
        "  # Allows us to distinguish between tasks (i.e. alphabets)\n",
        "  all_alphabet_ids = concat(\n",
        "      omniglot_train_ds['alphabet'],\n",
        "      omniglot_test_ds['alphabet']).numpy()\n",
        "\n",
        "  # To determine the size of a prediction vector.\n",
        "  num_chars_per_alphabet = [tf.reduce_max(select_datapoints(all_labels, np.argwhere(all_alphabet_ids == t))).numpy()\n",
        "                            for t in range(num_alphabets)]\n",
        "\n",
        "  # Discard all data not needed\n",
        "  idx = np.argwhere(all_alphabet_ids \u003c num_alphabets).flatten()\n",
        "  all_alphabet_ids = all_alphabet_ids[idx]\n",
        "  all_images = select_datapoints(all_images, idx)\n",
        "  all_labels = select_datapoints(all_labels, idx)\n",
        "\n",
        "  return RawData(all_images, all_labels, (all_alphabet_ids, num_chars_per_alphabet))\n",
        "\n",
        "\n",
        "def load_omniglot_alphabet(\n",
        "    full_omniglot: RawData,\n",
        "    alphabet_id: int,\n",
        "    construct_valid: bool,\n",
        "    use_data_augmentationt: bool) -\u003e DatasetSplit:\n",
        "  \"\"\"Extract a single alphabet, preprocess and create a train/valid/test split.\n",
        "\n",
        "  Args:\n",
        "    full_omniglot: Contains Omniglot data of all alphabets.\n",
        "    alphabet_id: Id of alphabet requested. Must be \u003c= 50.\n",
        "    construct_valid: Whether to construct a validation dataset.\n",
        "    use_data_augmentationt: Whether to augment the dataset.\n",
        "\n",
        "  Returns:\n",
        "    Data re-organised with alphabet ids to distinguish between tasks.\n",
        "  \"\"\"\n",
        "  # Selecting an alphabet\n",
        "  idx = np.argwhere(full_omniglot.task_metadata[0] == alphabet_id).flatten()\n",
        "  alphabet_images = select_datapoints(full_omniglot.inputs, idx)\n",
        "  alphabet_labels = select_datapoints(full_omniglot.outputs, idx)\n",
        "\n",
        "  # Pre-processing\n",
        "  alphabet_images = tf.image.convert_image_dtype(alphabet_images, tf.float32)\n",
        "  alphabet_images = tf.image.rgb_to_grayscale(alphabet_images)\n",
        "  # Resize and invert colour (from black on white background to white on black).\n",
        "  alphabet_images = 1.0 - tf.image.resize(alphabet_images, [28, 28])\n",
        "  alphabet_images = tf.cast(alphabet_images, tf.float64)\n",
        "\n",
        "  num_datapoints = alphabet_labels.shape[0]\n",
        "\n",
        "  # Let's shuffle the datapoints randomnly.\n",
        "  perm = np.random.permutation(num_datapoints)\n",
        "  alphabet_images = select_datapoints(alphabet_images, perm)\n",
        "  alphabet_labels = select_datapoints(alphabet_labels, perm)\n",
        "\n",
        "  # Number of datapoints in alphabet\n",
        "  num_datapoints = alphabet_images.shape[0]\n",
        "  num_test = int(np.ceil(0.2 * num_datapoints))\n",
        "\n",
        "  # Choose a test set (20% of all data).\n",
        "  x_test = alphabet_images[:num_test]\n",
        "  y_test = alphabet_labels[:num_test]\n",
        "\n",
        "  # Construct an optional validation set (20% of all data).\n",
        "  if construct_valid:\n",
        "    num_valid = num_test\n",
        "\n",
        "    x_valid = alphabet_images[num_test:num_test+num_valid]\n",
        "    y_valid = alphabet_labels[num_test:num_test+num_valid]\n",
        "    valid_dataset = make_tf_dataset(x_valid, y_valid, num_valid)\n",
        "  else:\n",
        "    num_valid = 0\n",
        "    valid_dataset = None\n",
        "\n",
        "  # Use the remaining data for the training set.\n",
        "  num_train = num_datapoints - num_valid - num_test\n",
        "  x_train = alphabet_images[-num_train:]\n",
        "  y_train = alphabet_labels[-num_train:]\n",
        "\n",
        "  # Dataset Augmentation proposed in [2].\n",
        "  if use_data_augmentationt:\n",
        "    num_train *= 20\n",
        "    x_train = tf.concat([x_train] + [random_img_shift_and_rotation(x_train)\n",
        "                                     for _ in range(19)], axis=0)\n",
        "    y_train = tf.tile(y_train, [20])\n",
        "\n",
        "  train_dataset = make_tf_dataset(x_train, y_train, num_train)\n",
        "  test_dataset = make_tf_dataset(x_test, y_test, num_test)\n",
        "\n",
        "  return DatasetSplit(train_dataset, valid_dataset, test_dataset,\n",
        "                      num_train, num_valid, num_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViGySN-Yq5Td"
      },
      "source": [
        "# Implementing FRCL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lp-jvHiTElFs"
      },
      "source": [
        "There are two key architectural components: \n",
        "- (i) A neural network defining the feature mapping $\\phi(x)$.\n",
        "- (ii) A Sparse Variational Gaussian Process operating on the feature mapping. \n",
        "\n",
        "We will use standard networks for (i) and focus our attention primarily on the implementation of (ii). The code below uses Sonnet to build Neural Network Architectures and GPFlow to implement sparse GPs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "J9e-hWJ_r5IH"
      },
      "outputs": [],
      "source": [
        "#@title Import Sonnet \u0026 GPFlow\n",
        "\n",
        "import gpflow\n",
        "import sonnet as snt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-OFqE1gbxws"
      },
      "source": [
        "For Permuted MNIST, we use a standard Sonnet Multi-Layer Perceptron (MLP) which we augment by concatenating a bias term for the GP:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "03HfC6x0SaIO"
      },
      "outputs": [],
      "source": [
        "#@title Define an MLP Network to provide the feature mapping for MNIST\n",
        "\n",
        "class MLPNetworkWithBias(snt.nets.MLP):\n",
        "  \"\"\"Fully connected MLP base network using Sonnet.\"\"\"\n",
        "\n",
        "  def __call__(self, inputs: tf.Tensor, *args, **kwargs) -\u003e tf.Tensor:\n",
        "    \"\"\"Applies MLP to `inputs` and adds a column of ones to the feature matrix.\n",
        "\n",
        "    Args:\n",
        "      inputs: A Tensor of shape `[batch_size, num_input_dimensions]`.\n",
        "      *args: Arguments to snt.nets.MLP. See Sonnet documentation.\n",
        "      **kwargs: Named arguments to snt.nets.MLP. See Sonnet documentation.\n",
        "\n",
        "    Returns:\n",
        "      outputs: Model output and bias term of shape `[batch_size, output_size+1]`.\n",
        "    \"\"\"\n",
        "    outputs = super(MLPNetworkWithBias, self).__call__(inputs, *args, **kwargs)\n",
        "\n",
        "    # Add a column of ones to the feature vector to account for the bias\n",
        "    outputs = tf.concat(\n",
        "        [outputs, tf.ones((outputs.shape[0], 1), dtype=outputs.dtype)], axis=1)\n",
        "\n",
        "    return outputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5j5lOSib7cE"
      },
      "source": [
        "For Sequential Omniglot, we use a good old-fashioned ConvNet with MaxPooling which we define below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "9_QDJa5kj5_o"
      },
      "outputs": [],
      "source": [
        "#@title Define a Conv. Network to provide the feature mapping for Omniglot\n",
        "\n",
        "class ConvNetworkWithBias(snt.Module):\n",
        "  \"\"\"Builds a module out of a sequence of callables.\"\"\"\n",
        "\n",
        "  def __init__(self,\n",
        "               output_sizes: Sequence[int],\n",
        "               data_format: Text = 'NHWC',\n",
        "               conv_padding: Text = 'SAME',\n",
        "               conv_kernel_shape: Sequence[int] = None,\n",
        "               conv_kernel_stride: Sequence[int] = None,\n",
        "               maxpool_padding: Text = 'VALID',\n",
        "               maxpool_kernel_shape: Sequence[int] = None,\n",
        "               maxpool_kernel_stride: Sequence[int] = None,\n",
        "               name: Optional[Text] = None):\n",
        "    \"\"\"Constructor for ConvNetworkWithBias.\n",
        "\n",
        "    Args:\n",
        "      output_sizes: Defines the number of output channels for each ConvLayer.\n",
        "      data_format: Specifies semantics for each input batch dimension.\n",
        "      conv_padding: Either `SAME` or `VALID`.\n",
        "      conv_kernel_shape: Size of the convolutional kernel.\n",
        "      conv_kernel_stride: Convolution stride.\n",
        "      maxpool_padding: Either `SAME` or `VALID`.\n",
        "      maxpool_kernel_shape: Size of the maxpool kernel.\n",
        "      maxpool_kernel_stride: MaxPool stride.\n",
        "      name: Optional model name.\n",
        "    \"\"\"\n",
        "    super(ConvNetworkWithBias, self).__init__(name=name)\n",
        "\n",
        "    if conv_kernel_shape is None:\n",
        "      conv_kernel_shape = [3, 3]\n",
        "    if conv_kernel_stride is None:\n",
        "      conv_kernel_stride = [1, 1]\n",
        "    if maxpool_padding is None:\n",
        "      maxpool_padding = 'VALID'\n",
        "    if maxpool_kernel_shape is None:\n",
        "      maxpool_kernel_shape = [1, 2, 2, 1]\n",
        "    if maxpool_kernel_stride is None:\n",
        "      maxpool_kernel_stride = [1, 2, 2, 1]\n",
        "\n",
        "    self._output_sizes = output_sizes\n",
        "    self._num_layers = len(self._output_sizes)\n",
        "    self._conv_kernel_shapes = [conv_kernel_shape] * self._num_layers\n",
        "    self._conv_strides = [conv_kernel_stride] * self._num_layers\n",
        "    self._maxpool_kernel_shapes = [maxpool_kernel_shape] * self._num_layers\n",
        "    self._maxpool_kernel_strides = [maxpool_kernel_stride] * self._num_layers\n",
        "\n",
        "    # Instantiate modules\n",
        "    self._conv_modules = list(\n",
        "        snt.Conv2D(  # pylint: disable=g-complex-comprehension\n",
        "            output_channels=self._output_sizes[i],\n",
        "            kernel_shape=self._conv_kernel_shapes[i],\n",
        "            stride=self._conv_strides[i],\n",
        "            padding=conv_padding,\n",
        "            data_format=data_format,\n",
        "            name='conv_2d_{}'.format(i))\n",
        "        for i in range(self._num_layers))\n",
        "    \n",
        "    self._maxpool_modules = list(\n",
        "        lambda x: tf.nn.max_pool(x, \n",
        "                                 ksize=self._maxpool_kernel_shapes[i],\n",
        "                                 strides=self._maxpool_kernel_strides[i],\n",
        "                                 padding=maxpool_padding,\n",
        "                                 data_format=data_format,\n",
        "                                 name='maxpool_2d_{}'.format(i)) \n",
        "        for i in range(self._num_layers))\n",
        "    self._flatten = snt.Flatten(name='Flatten')\n",
        "\n",
        "  def __call__(self, inputs: tf.Tensor) -\u003e tf.Tensor:\n",
        "    \"\"\"Applies ConvNet to `inputs` and adds a column of ones to the feature matrix.\n",
        "\n",
        "    Args:\n",
        "      inputs: A Tensor of shape `[batch_size, height, width, num_channels]`.\n",
        "\n",
        "    Returns:\n",
        "      outputs: Model output and bias term `[batch_size, output_size+1]`.\n",
        "    \"\"\"\n",
        "\n",
        "    # Ensure correct data type\n",
        "    original_dtype = inputs.dtype\n",
        "    outputs = tf.cast(inputs, tf.float32)\n",
        "\n",
        "    for conv_layer, maxpool_layer in zip(\n",
        "        self._conv_modules, self._maxpool_modules):\n",
        "      outputs = conv_layer(outputs)\n",
        "      outputs = maxpool_layer(outputs)\n",
        "      outputs = tf.nn.relu(outputs)\n",
        "\n",
        "    outputs = self._flatten(outputs)\n",
        "    # Add a column of ones to the feature vector to account for the bias\n",
        "    outputs = tf.concat(\n",
        "        [outputs, tf.ones((outputs.shape[0], 1), dtype=outputs.dtype)], axis=1)\n",
        "\n",
        "    return tf.cast(outputs, original_dtype)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5UQZKq-HGV2"
      },
      "source": [
        "Let's turn our attention to (ii) Implementing the GP to work on the feature embedding. This is the heart of FRCL:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "wYuUL3P-3el8"
      },
      "outputs": [],
      "source": [
        "#@title Implementing FRCL\n",
        "\n",
        "class BaseInducingApproximation(object):\n",
        "  \"\"\"Holds the parameters of a posterior approximation.\"\"\"\n",
        "  def __init__(self,\n",
        "               q_z: tf.Tensor,\n",
        "               q_mean: tf.Variable,\n",
        "               q_sqrt: tf.Variable,\n",
        "               q_z_init: tf.Tensor):\n",
        "    \"\"\"Constructor for BaseInducingApproximation.\n",
        "\n",
        "    Args:\n",
        "      q_z: Inducing points. A Tensor of shape `[num_inducing_points, num_input_features]`.\n",
        "      q_mean: Mean of the variational distribution of shape `[num_inducing_points, num_classes]`.\n",
        "      q_sqrt: Cholesky (sqrt) matrices of the variational distribution `[]`.\n",
        "      q_z_init: Initial inducing points. A Tensor of shape `[num_inducing_points, num_input_features]`.\n",
        "    \"\"\"\n",
        "    self.q_z = q_z\n",
        "    self.q_mean = q_mean\n",
        "    self.q_sqrt = q_sqrt\n",
        "    self.q_z_init = q_z_init\n",
        "\n",
        "\n",
        "class InducingApproximation(BaseInducingApproximation):\n",
        "  \"\"\"Creates a variational approximation in function/GP space.\"\"\"\n",
        "\n",
        "  def __init__(self,\n",
        "               num_inducing_points: int,\n",
        "               num_classes: int,\n",
        "               inducing_init_value: tf.Tensor):\n",
        "    \"\"\"Constructor for InducingApproximation.\n",
        "\n",
        "    Args:\n",
        "      num_inducing_points: Number of inducing points to use.\n",
        "      num_classes: Number of classes in classification problem.\n",
        "      inducing_init_value: Initial value. A Tensor of shape `[num_inducing_points, num_input_features]`.\n",
        "    \"\"\"\n",
        "    q_mean = tf.Variable(np.zeros(\n",
        "        (num_inducing_points, num_classes)).astype(np.float64))\n",
        "    q_sqrt = tf.Variable(np.array(\n",
        "        [np.eye(num_inducing_points).astype(np.float64)\n",
        "         for _ in range(num_classes)]).T)\n",
        "\n",
        "    q_z = tf.constant(inducing_init_value)\n",
        "    q_z_init = tf.constant(inducing_init_value)\n",
        "    super(InducingApproximation, self).__init__(q_z, q_mean, q_sqrt, q_z_init)\n",
        "\n",
        "\n",
        "class WeightSpaceApproximation(object):\n",
        "  \"\"\"Creates a variational approximation in weight space.\"\"\"\n",
        "\n",
        "  def __init__(self,\n",
        "               num_features: int,\n",
        "               num_classes: int,\n",
        "               rng: np.random.RandomState):\n",
        "    \"\"\"Constructor for WeightSpaceApproximation.\n",
        "\n",
        "    Args:\n",
        "      num_features: Number of inducing points to use.\n",
        "      num_classes: Number of classes in classification problem.\n",
        "      rng: Random number generator.\n",
        "    \"\"\"\n",
        "    scalar = 1.0/np.sqrt(num_features)\n",
        "    self.q_w_mean = tf.Variable(scalar*rng.randn(\n",
        "        num_features, num_classes).astype(np.float64))\n",
        "    self.q_w_sqrt = tf.Variable(np.array(\n",
        "        [np.eye(num_features).astype(np.float64)\n",
        "         for _ in range(num_classes)]).T)\n",
        "\n",
        "class ContinualGPmodel(object):\n",
        "  \"\"\"Continual learning GP-based model tha uses variational inducing points.\"\"\"\n",
        "\n",
        "  def __init__(self,\n",
        "               num_input_dimensions: int,\n",
        "               num_features: int,\n",
        "               num_classes: int,\n",
        "               base_network: snt.Module,\n",
        "               likelihood: gpflow.likelihoods,\n",
        "               noise_variance: float = 1e-3):\n",
        "    \"\"\"Constructor for continual GP model.\n",
        "\n",
        "    Args:\n",
        "      num_input_dimensions: Dimensionality of the input space.\n",
        "      num_features: Size of the feature including the bias term.\n",
        "      num_classes: Number of classes in classification problem.\n",
        "      base_network: Provides the feature mappping.\n",
        "      likelihood: Likelihood for the datatype at hand.\n",
        "      noise_variance: Added to diagonal of covariance matrix.\n",
        "    \"\"\"\n",
        "\n",
        "    self.num_input_dimensions = num_input_dimensions\n",
        "    self.num_features = num_features\n",
        "    self.num_classes = num_classes\n",
        "    self.past_inducing_approxs = []\n",
        "    self.current_inducing_approx = None\n",
        "    self.current_weight_space_approx = None\n",
        "    self.base_network = base_network\n",
        "    self.likelihood = likelihood\n",
        "    # Constant noise/jitter to be added only to the inducing point covariances\n",
        "    self.noise_variance = tf.constant(noise_variance, dtype=tf.float64)\n",
        "\n",
        "  def get_weight_space_approx(self, rng):\n",
        "    \"\"\"Initialise a new weight space approximation.\n",
        "\n",
        "    Args:\n",
        "      rng: Random number generator.\n",
        "    \"\"\"\n",
        "    self.current_weight_space_approx = WeightSpaceApproximation(\n",
        "        self.num_features, self.num_classes, rng)\n",
        "\n",
        "  def covariance_self(self, matrix_a: tf.Tensor) -\u003e tf.Tensor:\n",
        "    \"\"\"Compute linear kernel plus noise for features with themself.\n",
        "\n",
        "    Args:\n",
        "      matrix_a: Matrix of shape `[batch, num_features]`.\n",
        "\n",
        "    Returns:\n",
        "      Covariance matrix of shape `[batch, batch]`.\n",
        "\n",
        "    \"\"\"\n",
        "    return tf.matmul(\n",
        "        matrix_a, matrix_a, transpose_b=True) + self.noise_variance * tf.eye(\n",
        "            tf.cast(matrix_a.shape[0], tf.int32), dtype=tf.float64)\n",
        "\n",
        "  def covariance_cross(self,\n",
        "                       matrix_a: tf.Tensor,\n",
        "                       matrix_b: tf.Tensor) -\u003e tf.Tensor:\n",
        "    \"\"\"Compute linear kernel for features with other features.\n",
        "\n",
        "    No noise in these case because features are assumed distinct.\n",
        "\n",
        "    Args:\n",
        "      matrix_a: Tensor of shape `[batch_a, num_features]`.\n",
        "      matrix_b: Tensor of shape  `[batch_b, num_features]`.\n",
        "\n",
        "    Returns:\n",
        "      Covariance matrix of shape `[batch_a, batch_b]`.\n",
        "\n",
        "    \"\"\"\n",
        "    return tf.matmul(matrix_a, matrix_b, transpose_b=True)\n",
        "\n",
        "  def covariance_diag(self, matrix: tf.Tensor) -\u003e tf.Tensor:\n",
        "    \"\"\"Compute diagonal of linear kernel matrix for features with themself.\n",
        "\n",
        "    Args:\n",
        "      matrix: Tensor of shape `[batch, num_features]`.\n",
        "\n",
        "    Returns:\n",
        "      Diagonal covariance vector of shape `[batch]`.\n",
        "    \"\"\"\n",
        "\n",
        "    # We do not add noise_variance/jitter to this diagonal, but only to the\n",
        "    # inducing matrix, so inducing variables are noisy function values and\n",
        "    # through them we approximate the exact noise-free GP model.\n",
        "    return tf.reduce_sum(matrix * matrix, axis=1)\n",
        "\n",
        "  def objective_weight_space(self,\n",
        "                             inputs: tf.Tensor,\n",
        "                             outputs: tf.Tensor,\n",
        "                             num_task_points: int):\n",
        "    \"\"\"Compute the variational objective for the model.\n",
        "\n",
        "    Args:\n",
        "      inputs: A Tensor of shape `[batch_size, num_input_dimensions]`.\n",
        "      outputs: Class labels. Shape (batch, num_classes).\n",
        "      num_task_points: Total number of data points in task.\n",
        "\n",
        "    Returns:\n",
        "      variational objective for the bound.\n",
        "    \"\"\"\n",
        "    batch_size = tf.cast(inputs.shape[0], tf.int32)\n",
        "\n",
        "    # Add KL divergences for the all previous tasks.\n",
        "    # Rightmost term of Eq. (4) in the paper.\n",
        "    kl_historical = 0\n",
        "    for inducing_approx in self.past_inducing_approxs:\n",
        "      # z_features shape (num_inducing_points, num_features)\n",
        "      z_features = self.base_network(inducing_approx.q_z)\n",
        "      # p_cov shape (num_inducing_points, num_inducing_points)\n",
        "      p_cov = self.covariance_self(z_features)\n",
        "      kl_historical += gpflow.kullback_leiblers.gauss_kl(\n",
        "          inducing_approx.q_mean,\n",
        "          tf.transpose(inducing_approx.q_sqrt, (2, 0, 1)),\n",
        "          K=p_cov)\n",
        "\n",
        "    # For the current task we do inference in the weight space\n",
        "    data_features = self.base_network(inputs)\n",
        "    data_mean = tf.matmul(data_features,\n",
        "                          self.current_weight_space_approx.q_w_mean)\n",
        "\n",
        "    tr_q_w_sqrt = tf.compat.v1.matrix_band_part(tf.transpose(\n",
        "        self.current_weight_space_approx.q_w_sqrt, (2, 0, 1)), -1, 0)\n",
        "    expand_data_features = tf.tile(\n",
        "        tf.expand_dims(data_features, 0), [self.num_classes, 1, 1])\n",
        "    feat_w_sqrt = tf.matmul(expand_data_features, tr_q_w_sqrt)\n",
        "    data_var = tf.transpose(tf.reduce_sum(tf.square(feat_w_sqrt), axis=2))\n",
        "\n",
        "    # Middle term of Eq. (4) in the paper.\n",
        "    kl_current = gpflow.kullback_leiblers.gauss_kl(\n",
        "        self.current_weight_space_approx.q_w_mean,\n",
        "        tf.transpose(self.current_weight_space_approx.q_w_sqrt, (2, 0, 1)),\n",
        "        K=None)\n",
        "\n",
        "    # Left term of Eq. (4) in the paper.\n",
        "    exp_likes = self.likelihood.variational_expectations(\n",
        "        data_mean, data_var, outputs)\n",
        "    # Scale this correclty by the total number of datapoints in the task.\n",
        "    ratio = tf.cast(\n",
        "        num_task_points, tf.float64) / tf.cast(batch_size, tf.float64)\n",
        "    reconstruction_cost = tf.reduce_sum(exp_likes) * ratio\n",
        "\n",
        "    # Full objective. Eq. (4) in the paper.\n",
        "    objective = kl_historical + kl_current - reconstruction_cost\n",
        "    return objective, reconstruction_cost, kl_current\n",
        "\n",
        "  def trace_term(self,\n",
        "                 inputs: tf.Tensor,\n",
        "                 q_z: tf.Tensor) -\u003e tf.Tensor:\n",
        "    \"\"\"Compute the trace term. Useful for search over inducing points.\n",
        "\n",
        "    Args:\n",
        "      inputs: A Tensor of shape `[batch_size, num_input_dimensions]`.\n",
        "      q_z: Inducing points of shape `[num_inducing_points, num_input_dimensions]`.\n",
        "\n",
        "    Returns:\n",
        "      variational objective for the bound.\n",
        "    \"\"\"\n",
        "\n",
        "    # Shape [num_inducing_points, num_features]\n",
        "    inducing_features = self.base_network(q_z)\n",
        "    # Shape [batch_size, num_features]\n",
        "    data_features = self.base_network(inputs)\n",
        "\n",
        "    # Compute covariance\n",
        "    kmn = self.covariance_cross(inducing_features, data_features)\n",
        "    kmm = self.covariance_self(inducing_features)\n",
        "    knn = self.covariance_diag(data_features)\n",
        "\n",
        "    # Implements Equation (6) in the paper and speeds up computations by\n",
        "    # applying the matrix inversion formula if needed.\n",
        "    if q_z.shape[0] \u003c= self.num_features:\n",
        "      lm = tf.compat.v1.cholesky(kmm)\n",
        "      v = tf.compat.v1.matrix_triangular_solve(lm, kmn, lower=True)\n",
        "      fvar = knn - tf.reduce_sum(tf.square(v), 0)\n",
        "    else:\n",
        "      phimphim = tf.matmul(inducing_features, inducing_features,\n",
        "                           transpose_a=True)\n",
        "      phimphim_noise = phimphim + self.noise_variance * tf.eye(\n",
        "          tf.cast(self.num_features, tf.int32), dtype=tf.float64)\n",
        "\n",
        "      sqrt_phimphin_noise = tf.compat.v1.cholesky(phimphim_noise)\n",
        "\n",
        "      inv_sqrt_phimphin_noise_phim = tf.compat.v1.matrix_triangular_solve(\n",
        "          sqrt_phimphin_noise, tf.transpose(inducing_features),\n",
        "          lower=True)\n",
        "      v = tf.matmul(inv_sqrt_phimphin_noise_phim, kmn)\n",
        "\n",
        "      fvar = knn - (1.0/self.noise_variance)*(\n",
        "          tf.reduce_sum(tf.square(kmn), 0) - tf.reduce_sum(tf.square(v), 0))\n",
        "\n",
        "    return tf.reduce_sum(fvar)\n",
        "\n",
        "  def function_space_prediction(self,\n",
        "                                inputs: tf.Tensor,\n",
        "                                q_z: tf.Tensor,\n",
        "                                q_mean: tf.Variable,\n",
        "                                q_sqrt: tf.Variable,\n",
        "                                outputs: tf.Tensor = None):\n",
        "    \"\"\"Compute predictions from the model in function/GP space.\n",
        "\n",
        "    Args:\n",
        "      inputs: Tensor of shape `[batch, num_input_dimensions]`.\n",
        "      q_z: Inducing points of shape `[num_inducing_points, input_dimensions]`.\n",
        "      q_mean: Mean of the variational distribution of shape `[num_inducing_points, num_classes]`.\n",
        "      q_sqrt: Cholesky (sqrt) matrices of the variational distribution `[]`.\n",
        "      outputs: Class labels. Optional tensor of Shape `[batch, num_clsses]`.\n",
        "\n",
        "    Returns:\n",
        "      When outputs=None, it returns predictive mean and variance from model\n",
        "      otherwise it returns the log predictive density for the outputs.\n",
        "    \"\"\"\n",
        "\n",
        "    inducing_features = self.base_network(q_z)\n",
        "    data_features = self.base_network(inputs)\n",
        "\n",
        "    # Compute covariance\n",
        "    kmn = self.covariance_cross(inducing_features, data_features)\n",
        "    kmm = self.covariance_self(inducing_features)\n",
        "    knn = self.covariance_diag(data_features)\n",
        "\n",
        "    func_mean, func_var = gpflow.conditionals.base_conditional(\n",
        "        kmn, kmm, knn, f=q_mean, q_sqrt=tf.transpose(q_sqrt, (2, 0, 1)),\n",
        "        full_cov=False, white=False)\n",
        "\n",
        "    if outputs is None:\n",
        "      return self.likelihood.predict_mean_and_var(func_mean, func_var)\n",
        "    else:\n",
        "      return self.likelihood.predict_density(func_mean, func_var, outputs)\n",
        "\n",
        "  def complete_task_weight_space(self,\n",
        "                                 z: tf.Tensor,\n",
        "                                 z_init: tf.Tensor):\n",
        "    \"\"\"Completes training of current task in weight space.\n",
        "\n",
        "    Args:\n",
        "      z: Final inducing points for current task of shape `[num_inducing_points, num_features]`.\n",
        "      z_init: Initial inducing points for current task of shape  `[num_inducing_points, num_features]`.\n",
        "    \"\"\"\n",
        "\n",
        "    current_inducing_features = self.base_network(z)\n",
        "    current_q_mean = tf.matmul(\n",
        "        current_inducing_features, self.current_weight_space_approx.q_w_mean)\n",
        "    noise_matrix = self.noise_variance * tf.eye(\n",
        "        tf.cast(tf.shape(z)[0], tf.int32), dtype=tf.float64)\n",
        "    noise_matrix = tf.tile(\n",
        "        tf.expand_dims(noise_matrix, 0), [self.num_classes, 1, 1])\n",
        "    tr_q_w_sqrt = tf.linalg.band_part(tf.transpose(\n",
        "        self.current_weight_space_approx.q_w_sqrt, (2, 0, 1)), -1, 0)\n",
        "    expand_current_inducing_features = tf.tile(\n",
        "        tf.expand_dims(current_inducing_features, 0), [self.num_classes, 1, 1])\n",
        "    feat_w_sqrt = tf.matmul(expand_current_inducing_features, tr_q_w_sqrt)\n",
        "    q_cov = tf.matmul(feat_w_sqrt, feat_w_sqrt, transpose_b=True)\n",
        "    q_cov = q_cov + noise_matrix\n",
        "    current_q_sqrt = tf.transpose(tf.compat.v1.cholesky(q_cov), (1, 2, 0))\n",
        "\n",
        "    self.past_inducing_approxs.append(\n",
        "        BaseInducingApproximation(z, tf.identity(current_q_mean),\n",
        "                                  tf.identity(current_q_sqrt), z_init))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGRQ3-vmpVOu"
      },
      "source": [
        "The only remaning bit is to define the evaluation functions that allow us to measure model performance:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "iDfyq2E8pUha"
      },
      "outputs": [],
      "source": [
        "#@title Evaluation utility functions\n",
        "\n",
        "def get_accuracy_rate(\n",
        "    predictions: np.array,\n",
        "    labels: np.array) -\u003e float:\n",
        "  \"\"\"Compute mean classification accuracy.\n",
        "\n",
        "  Args:\n",
        "    predictions: A numpy array of shape `[batch_size, input_size]`.\n",
        "    labels: Ground truth labels of shape `[batch_size, input_size]`.\n",
        "\n",
        "  Returns:\n",
        "    outputs: Model output and bias term `[batch_size, output_size+1]`.\n",
        "  \"\"\"\n",
        "  return np.mean(np.equal(np.argmax(predictions, axis=1), labels))\n",
        "\n",
        "def evaluate_on_all_tasks(\n",
        "    eval_iterators: Sequence[Iterators],\n",
        "    model: ContinualGPmodel) -\u003e Sequence[float]:\n",
        "  \"\"\"Evaluates models on all tasks. Assumes iterators provide entire dataset.\n",
        "\n",
        "  Args:\n",
        "    eval_iterators: Provides entire data to evaluate model on. One per task.\n",
        "    model: A ContinualGPmodel to be evaluated.\n",
        "\n",
        "  Returns:\n",
        "    List of accuracies achieved on each task.\n",
        "  \"\"\"\n",
        "\n",
        "  # Evaluate test accuracies for all tasks\n",
        "  eval_accuracies = []\n",
        "  for task, eval_iterator in enumerate(eval_iterators):\n",
        "    # Fetch the whole eval dataset\n",
        "    eval_input, eval_output = next(eval_iterator)\n",
        "\n",
        "    # Model prediction\n",
        "    pred_prob = model.function_space_prediction(\n",
        "        eval_input,\n",
        "        model.past_inducing_approxs[task].q_z,\n",
        "        model.past_inducing_approxs[task].q_mean,\n",
        "        model.past_inducing_approxs[task].q_sqrt)[0]\n",
        "\n",
        "    test_accuracy_rate = get_accuracy_rate(pred_prob, eval_output)\n",
        "    eval_accuracies.append(test_accuracy_rate)\n",
        "\n",
        "  return eval_accuracies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nnl_sw4cqrfN"
      },
      "source": [
        "# Training\n",
        "\n",
        "Now that we've set up the model, let's define a basic Continual Learning Setup. The next code block allows you to choose between the two data sources and vary some of the fundamental CL experimental parameters. The values suggested below are those used in the paper to report result."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "DfBnBfSYDhqK"
      },
      "outputs": [],
      "source": [
        "#@title Defining the training protocol\n",
        "\n",
        "exp_name = 'Permuted MNIST'  #@param [\"Permuted MNIST\", \"Sequential Omniglot\"] {allow-input: false}\n",
        "\n",
        "# Random number generator\n",
        "rng_seed = 42  #@param\n",
        "rng_state = np.random.RandomState(rng_seed)\n",
        "\n",
        "#@markdown P-MNIST: 10 Omniglot: 50\n",
        "num_tasks = 11  #@param {type:\"slider\", min:1, max:1000, step:1}\n",
        "\n",
        "# General training settings (Default: Permuted MNIST)\n",
        "learning_rate = 1e-3  #@param\n",
        "#@markdown P-MNIST: 128 Omniglot: 32\n",
        "train_batch_size = 128  #@param {type:\"slider\", min:1, max:1000, step:1}\n",
        "#@markdown P-MNIST: 2000 Omniglot: 2500\n",
        "num_train_iters = 2000  #@param {type:\"slider\", min:1, max:5000, step:1}\n",
        "use_validation_dataset = False  #@param {type:\"boolean\"}\n",
        "\n",
        "\n",
        "# FRCL settings\n",
        "which_discrete_loss = 'trace_term'  #@param [\"trace_term\", \"random\"] {allow-input: false}\n",
        "num_optim_steps_discrete_search = 1000  #@param {type:\"slider\", min:1, max:5000, step:1}\n",
        "\n",
        "#@markdown Only used for Omniglot\n",
        "use_data_augmentation = False  #@param {type:\"boolean\"}\n",
        "\n",
        "\n",
        "if exp_name == 'Permuted MNIST':\n",
        "  tasks = list(range(num_tasks))\n",
        "  download_data = prepare_mnist\n",
        "  load_task_data = load_mnist_permutation\n",
        "\n",
        "  # MLP Size. Based on the experimental protocol in [1].\n",
        "  hidden_units = [100, 100]\n",
        "\n",
        "  num_output_dim = 10\n",
        "\n",
        "  # Instantiate network that defines the feature mapping\n",
        "  BaseNetwork = MLPNetworkWithBias\n",
        "\n",
        "  if use_data_augmentation:\n",
        "    print('Data augmentation is non-standard with Permuted MNIST. Ignoring option.')\n",
        "    use_data_augmentation = False\n",
        "elif exp_name == 'Sequential Omniglot':\n",
        "  tasks = list(range(num_tasks))\n",
        "  assert num_tasks \u003c= 50, 'Only 50 tasks are available'\n",
        "\n",
        "  download_data = prepare_omniglot\n",
        "  load_task_data = load_omniglot_alphabet\n",
        "\n",
        "  # ConvNet output channels. Based on the experimental protocol in [2].\n",
        "  hidden_units = [64, 64, 64, 64]\n",
        "\n",
        "  # This is the maximum number of characters in any Omniglot alphabet\n",
        "  num_output_dim = 54\n",
        "\n",
        "  # Instantiate network that defines the feature mapping\n",
        "  BaseNetwork = ConvNetworkWithBias\n",
        "else:\n",
        "  print('unknown dataset')\n",
        "\n",
        "print('\\nDownloading dataset...')\n",
        "with IPython.utils.io.capture_output() as captured:\n",
        "  raw_data = download_data(num_tasks)\n",
        "\n",
        "#@markdown P-MNIST: 200 Omniglot: 2 (to be multiplied by the #characters/alphabet)\n",
        "num_inducing_points_per_task = 200  #@param {type:\"slider\", min:1, max:1000, step:1}\n",
        "\n",
        "# The specification of inducing points is absolute for P-MNIST and relative to the number of characters/alphabet for Omniglot\n",
        "if exp_name == 'Permuted MNIST':\n",
        "  num_inducing_points_per_task = [num_inducing_points_per_task] * num_tasks\n",
        "else:\n",
        "  num_inducing_points_per_task = [num_inducing_points_per_task * t for t in\n",
        "                                  raw_data.task_metadata[1]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Sz-EzPNDxum"
      },
      "source": [
        "Let's setup data iterators and instatiate an FRCL model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "V-6cBCCfDu8u"
      },
      "outputs": [],
      "source": [
        "#@title Instantiate model\n",
        "\n",
        "num_pixels = 784\n",
        "num_gp_features = hidden_units[-1] + 1  # Add one to account for the bias\n",
        "\n",
        "# Instantiate Continal Learning Gaussian Process Model\n",
        "model_likelihood = gpflow.likelihoods.MultiClass(num_output_dim)\n",
        "network = BaseNetwork(output_sizes=hidden_units)\n",
        "FRCL = ContinualGPmodel(num_pixels, num_gp_features,\n",
        "                        num_output_dim, network, model_likelihood)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOIN529ADhLP"
      },
      "source": [
        "Let's start training. The code below runs the main training loop:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "eQiqr9a10skX"
      },
      "outputs": [],
      "source": [
        "#@title Main training loop\n",
        "\n",
        "discr_search_losses = []\n",
        "test_accuracies = []\n",
        "test_iterators = []\n",
        "\n",
        "for t in range(num_tasks):\n",
        "  print('Learning task {}'.format(t+1))\n",
        "  discr_search_loss = []\n",
        "\n",
        "  task_data = load_task_data(raw_data, t,\n",
        "                             use_validation_dataset,\n",
        "                             use_data_augmentation)\n",
        "  iterators = create_iterators(task_data)\n",
        "\n",
        "  # Get a weight space approximation for the current task\n",
        "  FRCL.get_weight_space_approx(rng_state)\n",
        "  # Set up a task-specific optimiser\n",
        "  optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n",
        "\n",
        "  # --- Task Training ---\n",
        "  print('\\tStarting optimisation')\n",
        "  for training_index in range(num_train_iters):\n",
        "    train_inputs, train_outputs = next(iterators.train)\n",
        "\n",
        "    def loss_fn():\n",
        "      train_loss, _, _ = FRCL.objective_weight_space(\n",
        "          train_inputs, train_outputs, task_data.num_train)\n",
        "\n",
        "      return train_loss\n",
        "\n",
        "    optimizer.minimize(loss_fn)\n",
        "\n",
        "  print('\\tFinished training')\n",
        "\n",
        "  # --- Discrete inducing point Optimisation ---\n",
        "  num_inducing_points_for_task = num_inducing_points_per_task[t]\n",
        "\n",
        "  # Select a random set of inducing inputs from the discrete search set\n",
        "  x_discr = next(iterators.discr_search)[0]\n",
        "  perm_train = np.random.permutation(task_data.num_train)\n",
        "\n",
        "  z_idx = perm_train[-num_inducing_points_for_task:]\n",
        "  z_ = select_datapoints(x_discr, z_idx)\n",
        "\n",
        "  if 'random' == which_discrete_loss:\n",
        "    print('\\tUsing random inducing points')\n",
        "  elif 'trace_term' == which_discrete_loss:\n",
        "    # Indicates a candidate training point to replace the inducing point with\n",
        "    train_set_id = 0\n",
        "\n",
        "    # Data to evaluate inducing set on\n",
        "    discr_search_input, _ = next(iterators.discr_search_eval)\n",
        "    current_loss = -FRCL.trace_term(discr_search_input, z_)\n",
        "    discr_search_loss.append((0, current_loss, z_))\n",
        "\n",
        "    accepted_moves = 0\n",
        "    print('\\tOptimising inducing points using discrete search.')\n",
        "    for disr_search_iter in range(num_optim_steps_discrete_search):\n",
        "      inducing_set_id = disr_search_iter % num_inducing_points_for_task\n",
        "      z_idx_proposed = z_idx.copy()\n",
        "\n",
        "      # Replace inducing point and re-evaluate\n",
        "      z_idx_proposed[inducing_set_id] = perm_train[train_set_id]\n",
        "      proposed_loss = -FRCL.trace_term(\n",
        "          discr_search_input, select_datapoints(x_discr, z_idx_proposed))\n",
        "\n",
        "      if proposed_loss \u003e current_loss:\n",
        "        z_idx = z_idx_proposed.copy()\n",
        "        current_loss = proposed_loss\n",
        "        accepted_moves += 1\n",
        "\n",
        "        discr_search_loss.append(\n",
        "            (disr_search_iter, current_loss, z_))\n",
        "\n",
        "      if train_set_id == task_data.num_train-1:\n",
        "        perm_train = np.random.permutation(task_data.num_train)\n",
        "        train_set_id = 0\n",
        "      else:\n",
        "        train_set_id += 1\n",
        "\n",
        "    print('\\tAccepted moves: {}'.format(accepted_moves))\n",
        "  discr_search_losses.append(discr_search_loss)\n",
        "\n",
        "  # --- Complete the task ---\n",
        "  FRCL.complete_task_weight_space(z_, discr_search_loss[0][2])\n",
        "\n",
        "  # --- Evaluation on all tasks thus far ---\n",
        "  test_iterators.append(iterators.test)\n",
        "  test_accuracies.append(\n",
        "      evaluate_on_all_tasks(test_iterators, FRCL))\n",
        "\n",
        "print('Done.')\n",
        "\n",
        "mean_test_accuracy = list(map(np.mean, test_accuracies))\n",
        "\n",
        "print('\\n---\\nFinal accuracy: {}\\n---'.format(mean_test_accuracy[-1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UmeQb-3bOW7"
      },
      "source": [
        "Let's visualise overall (first column) along with task-specific performance that allows us to visualise forward/backward transfer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "BmZwTixhRv67"
      },
      "outputs": [],
      "source": [
        "#@title Visualising overall and per-task accuracies\n",
        "\n",
        "from matplotlib.ticker import MaxNLocator\n",
        "\n",
        "\n",
        "def _format_axis(axis: matplotlib.pyplot.axis,\n",
        "                 y_limit: Sequence[float],\n",
        "                 title_str: Text = None):\n",
        "  \"\"\"Format axis.\n",
        "\n",
        "  Args:\n",
        "    axis: Axis to format.\n",
        "    y_limit: Limit to apply on the y-axis.\n",
        "    title_str: String to use for axis title.\n",
        "  \"\"\"\n",
        "  axis.set_xlim((0.5, num_tasks + 0.5))\n",
        "  axis.set_ylim(y_limit)\n",
        "  # Force only integer tirkcs\n",
        "  axis.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "\n",
        "  if title_str is not None:\n",
        "    axis.set_title(title_str)\n",
        "\n",
        "\n",
        "colours = sns.color_palette()\n",
        "\n",
        "fig = plt.figure(figsize=(10, 10))\n",
        "layout = (int(np.ceil(num_tasks / 2) + 1), 2)\n",
        "ylim = (np.min([np.min(t) for t in test_accuracies]) * 0.975, 1.0)\n",
        "\n",
        "# Plot the overall mean accuracy\n",
        "ax = plt.subplot2grid(layout, (0, 0), colspan=2)\n",
        "ax.plot(range(1, num_tasks + 1), mean_test_accuracy, marker='o', c='k')\n",
        "ax.set_xlabel('Evaluated after learning task')\n",
        "ax.set_ylabel('Test accuracy (%)')\n",
        "_format_axis(\n",
        "    ax, ylim,\n",
        "    'Overall mean test accuracy: FRCL ({})'.format(which_discrete_loss))\n",
        "\n",
        "# Plot results for each task\n",
        "for t in range(num_tasks):\n",
        "  ax = plt.subplot2grid(layout, (t // 2 + 1, t % 2))\n",
        "  ax.plot(\n",
        "      range(t + 1, num_tasks + 1),\n",
        "      test_accuracies[-t - 1],\n",
        "      marker='o',\n",
        "      c=colours[t])\n",
        "\n",
        "  _format_axis(ax, ylim, 'Test accuracy for task: {}'.format(t + 1))\n",
        "\n",
        "sns.despine()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bR0yr75Bbfgh"
      },
      "source": [
        "The code below allows us to look at some of the inducing points we optimised for. Each row shows inducing points for a different task:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "61mZYuu9MTH6"
      },
      "outputs": [],
      "source": [
        "#@title Visualising inducing points\n",
        "\n",
        "num_inducing_points_per_task_to_show = 20  #@param\n",
        "max_number_of_tasks = 5  #@param\n",
        "\n",
        "max_number_of_tasks = min(max_number_of_tasks, num_tasks)\n",
        "num_inducing_points_per_task_to_show = min(\n",
        "    num_inducing_points_per_task_to_show, max(num_inducing_points_per_task))\n",
        "\n",
        "fig, axarr = plt.subplots(\n",
        "    max_number_of_tasks,\n",
        "    num_inducing_points_per_task_to_show,\n",
        "    figsize=(10, 10))\n",
        "\n",
        "for i in range(max_number_of_tasks):\n",
        "  task_inducing_points = FRCL.past_inducing_approxs[i].q_z.numpy()\n",
        "\n",
        "  if 'Permuted MNIST' == exp_name:\n",
        "    task_inv_perm = raw_data.task_metadata[1][i]\n",
        "    # Apply inverse permutation to allow visualisation\n",
        "    task_inducing_points = task_inducing_points[:, task_inv_perm]\n",
        "  task_inducing_points = task_inducing_points.reshape([-1, 28, 28])\n",
        "  for j in range(num_inducing_points_per_task_to_show):\n",
        "    axarr[i, j].imshow(task_inducing_points[j], cmap='gray')\n",
        "    axarr[i, j].axis('off')\n",
        "\n",
        "plt.subplots_adjust(wspace=0, hspace=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VhaEvkSdhYWQ"
      },
      "source": [
        "# References\n",
        "\n",
        "[1] Zenke, Friedemann, Ben Poole, and Surya Ganguli. \"Continual learning through synaptic intelligence.\" Proceedings of machine learning research 70 (2017): 3987.\n",
        "\n",
        "[2] Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., \u0026 Hadsell, R. (2018). Progress \u0026 compress: A scalable framework for continual learning. arXiv preprint arXiv:1805.06370.\n",
        "\n",
        "[3] Goodfellow, Ian J., et al. \"An empirical investigation of catastrophic forgetting in gradient-based neural networks.\" arXiv preprint arXiv:1312.6211 (2013)."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "last_runtime": {},
      "name": "Functional Regularisation for Continual Learning",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}