{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBFXQGKYUc4X"
      },
      "source": [
        "##### Copyright 2022 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "1z4xy2gTUc4a"
      },
      "outputs": [],
      "source": [
        "#@title 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": "L2MHy42s5wl6"
      },
      "source": [
        "# Left and Right Bend Video Classification with a 3D convolutional neural network\n",
        "\n",
        "Original code from: https://www.tensorflow.org/tutorials/video/video_classification\n",
        "\n",
        "Adapted for bend classification - original code was for action recognition"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "VARIENT = \"Optic_flow_narrow\"\n",
        "# VARIENT = \"Optic_flow_wide\"\n",
        "# VARIENT = \"RGB_narrow\"\n",
        "# VARIENT = \"RGB_wide\"\n",
        "\n",
        "\n",
        "if \"RGB\" in VARIENT:\n",
        "  MODE = \"RGB\"\n",
        "else:\n",
        "  MODE = \"OPTIC_FLOW\"\n",
        "\n",
        "# if \"wide\" in VARIENT:\n",
        "#   batch_size = 32\n",
        "# else:\n",
        "#   batch_size = 64\n",
        "\n",
        "batch_size = 32\n",
        "\n",
        "\n",
        "CHANNELS = 3 # OR SET TO 2\n",
        "\n",
        "NUMBER_OF_CLASSES = 4 #3,4,7"
      ],
      "metadata": {
        "id": "MEOMjBfD7jtx"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Ih_df2q0kw4"
      },
      "source": [
        "## Setup\n",
        "\n",
        "Begin by installing and importing some necessary libraries, including:\n",
        "[tqdm](https://github.com/tqdm/tqdm) to use a progress bar, [OpenCV](https://opencv.org/) to process video files, [einops](https://github.com/arogozhnikov/einops/tree/master/docs) for performing more complex tensor operations, and [`tensorflow_docs`](https://github.com/tensorflow/docs/tree/master/tools/tensorflow_docs) for embedding data in a Jupyter notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "KEbL4Mwi01PV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "815a2126-8790-40e3-a609-20c3268b4f17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m644.9/644.9 MB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m43.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m106.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m110.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow-text 2.18.1 requires tensorflow<2.19,>=2.18.0, but you have tensorflow 2.19.0 which is incompatible.\n",
            "tf-keras 2.18.0 requires tensorflow<2.19,>=2.18, but you have tensorflow 2.19.0 which is incompatible.\n",
            "tensorflow-decision-forests 1.11.0 requires tensorflow==2.18.0, but you have tensorflow 2.19.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install tqdm opencv-python einops --quiet\n",
        "!pip install -U tensorflow keras --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "gg0otuqb0hIf"
      },
      "outputs": [],
      "source": [
        "import tqdm\n",
        "import random\n",
        "import pathlib\n",
        "import itertools\n",
        "import collections\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "import cv2\n",
        "import einops\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import layers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RANDOM_SEED = 42 # for dataset slip and SMOTE reproducability\n",
        "random.seed(RANDOM_SEED)\n",
        "np.random.seed(RANDOM_SEED)"
      ],
      "metadata": {
        "id": "PGJuCi1GSTob"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "rRrSzl1xuJQ2"
      },
      "outputs": [],
      "source": [
        "DATASET = \"RGB_OF_LARGE\"\n",
        "\n",
        "DATASET_FILE = f\"{DATASET}.zip\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "5fPbu7XGjG63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99,
          "referenced_widgets": [
            "81cdc74adc1d4f6b9e6b28bc10fe7150",
            "f52c622632a34113b0fe5966bdf61d7e",
            "447f713a0fee47ad8ea3504f3430e4fe",
            "0dc1131f08154879910d5b5798f43718",
            "2e034686c33d4b00b81d50a7372f7c69",
            "1037c16b880d4545aded5d6d300d5b0e",
            "062c40afed0d4cf596308b12770cc156",
            "8d30990533124d27a4c7b405641dd5c8",
            "333a41172a014dc88b6ad5ce6b1ca6a2",
            "e728fb4514e94207a9c6e8f4637936cb",
            "7faa39e9e4924a38bca8834b35f79fad",
            "b6092ae13bf546dfae7451d51e2a69dd",
            "8755ec7264b841d3a5fe31568a7d3c9f",
            "b9a68e39f4f64825803e45dd6dc2c61b",
            "e1ce49025fa0453db0329c8f17515011",
            "3ccabe253c4e4aab8ddfcf3185c42137",
            "5e44838ef34f4d1b8f1bde78f70e5fe5",
            "4e8e7ca05dfd431785c0768e16391c16",
            "4d850f5d0a59401c83d8289400ec0e24",
            "75c7f5f8095649e5a4d8a8e3a94e1223",
            "5c2beb28131e4c7e9dd22e2d5c131db3",
            "691af6caf19b458fa50eaaf7ec1e3675"
          ]
        },
        "outputId": "f4d11c2a-640f-41e7-8a18-90814de8fbbf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 1 files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81cdc74adc1d4f6b9e6b28bc10fe7150"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "RGB_OF_LARGE.zip:   0%|          | 0.00/1.80G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6092ae13bf546dfae7451d51e2a69dd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "from huggingface_hub import snapshot_download\n",
        "\n",
        "# set HF_TOKEN in your enviroment\n",
        "snapshot_download(repo_id=\"aap9002/RGB_Optic_Flow_Bend_Classification\",  repo_type=\"dataset\", allow_patterns=f\"*{DATASET_FILE}\", local_dir='./')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ctk9A57-6ABq"
      },
      "source": [
        "## Load and preprocess video data\n",
        "\n",
        "The hidden cell below defines helper functions to download a slice of data from our custom dataset, and load it into a `tf.data.Dataset`. You can learn more about the specific preprocessing steps in the [Loading video data tutorial](../load_data/video.ipynb), which walks you through this code in more detail.\n",
        "\n",
        "The `FrameGenerator` class at the end of the hidden block is the most important utility here. It creates an iterable object that can feed data into the TensorFlow data pipeline. Specifically, this class contains a Python generator that loads the video frames along with its encoded label. The generator (`__call__`) function yields the frame array produced by `frames_from_video_file` and a one-hot encoded vector of the label associated with the set of frames."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "nB2aOTU35r9_"
      },
      "outputs": [],
      "source": [
        "def list_files_per_class(local_zip_file):\n",
        "    \"\"\"\n",
        "    List the files in each class of the dataset from a local zip file.\n",
        "\n",
        "    Args:\n",
        "      local_zip_file: Path to the local zip file.\n",
        "\n",
        "    Returns:\n",
        "      A dictionary mapping each class to a list of file names belonging to that class.\n",
        "    \"\"\"\n",
        "    files_by_class = {}\n",
        "    with zipfile.ZipFile(local_zip_file, 'r') as zip_ref:\n",
        "        for zip_info in zip_ref.infolist():\n",
        "            # Skip directories.\n",
        "            if not zip_info.is_dir():\n",
        "                filename = zip_info.filename\n",
        "                if not filename.endswith('.avi'):\n",
        "                  continue\n",
        "                class_name = get_class(filename)  # Assumes get_class is defined elsewhere.\n",
        "                if class_name is None: # skip if no class name retuened\n",
        "                  continue\n",
        "\n",
        "                if class_name not in files_by_class:\n",
        "                    files_by_class[class_name] = []\n",
        "                files_by_class[class_name].append(filename)\n",
        "\n",
        "    print(f\"Found {len(files_by_class)} classes.\")\n",
        "    for cls, files in files_by_class.items():\n",
        "        print(f\"{cls}: {len(files)} files\")\n",
        "    return files_by_class\n",
        "\n",
        "def get_class(fname, MAX_DISTANCE_ALLOWED = 50, MIN_SPEED_ALLOWED = 15):\n",
        "  \"\"\"\n",
        "    Retrieve the name of the class given a filename.\n",
        "\n",
        "    Args:\n",
        "      fname: Name of the file in the UCF101 dataset.\n",
        "\n",
        "    Return:\n",
        "      Class that the file belongs to.\n",
        "  \"\"\"\n",
        "  if VARIENT not in fname:\n",
        "    return None\n",
        "\n",
        "  angle = fname.split('_')[-4]\n",
        "\n",
        "  distance = fname.split('_')[-3]\n",
        "\n",
        "  speed = fname.split('_')[-5]\n",
        "\n",
        "  if int(distance) > MAX_DISTANCE_ALLOWED:\n",
        "    return None\n",
        "\n",
        "  if float(speed) < MIN_SPEED_ALLOWED:\n",
        "    return None\n",
        "\n",
        "  # print(f\"Angle: {angle}, Distance: {distance}, Speed: {speed}\"\n",
        "\n",
        "  streight = 1\n",
        "  light = 5\n",
        "  moderate = 10\n",
        "\n",
        "  angle = float(angle)\n",
        "\n",
        "  if abs(angle) <= streight:\n",
        "    class_name = 'streight'\n",
        "  elif int(angle) < 0:\n",
        "    if NUMBER_OF_CLASSES == 3:\n",
        "      return 'left'\n",
        "    if abs(int(angle)) < light:\n",
        "      class_name = 'light_left'\n",
        "      if NUMBER_OF_CLASSES == 4:\n",
        "        return None\n",
        "    elif abs(int(angle)) < moderate:\n",
        "      class_name = 'moderate_left'\n",
        "    else:\n",
        "      class_name = 'sharp_left'\n",
        "  else:\n",
        "    if NUMBER_OF_CLASSES == 3:\n",
        "      return 'right'\n",
        "    if int(angle) < light:\n",
        "      class_name = 'light_right'\n",
        "      if NUMBER_OF_CLASSES == 4:\n",
        "        return None\n",
        "    elif int(angle) < moderate:\n",
        "      class_name = 'moderate_right'\n",
        "    else:\n",
        "      class_name = 'sharp_right'\n",
        "\n",
        "  return class_name\n",
        "\n",
        "def unzip_from_local_zip(local_zip_file, to_dir, file_names=None):\n",
        "    \"\"\"\n",
        "    Extract selected files (if file_names is provided) or all files from a local zip file into the given directory,\n",
        "    flattening the directory structure.\n",
        "\n",
        "    Args:\n",
        "      local_zip_file: Path to the local zip file.\n",
        "      to_dir: Directory where the files will be extracted.\n",
        "      file_names: Optional list of file names to extract. If None, all files are extracted.\n",
        "    \"\"\"\n",
        "    with zipfile.ZipFile(local_zip_file, 'r') as zip_ref:\n",
        "        if file_names is None:\n",
        "            file_infos = [info for info in zip_ref.infolist() if not info.is_dir()]\n",
        "        else:\n",
        "            file_infos = []\n",
        "            for fn in file_names:\n",
        "                try:\n",
        "                    info = zip_ref.getinfo(fn)\n",
        "                    file_infos.append(info)\n",
        "                except KeyError:\n",
        "                    print(f\"Warning: {fn} not found in the zip file.\")\n",
        "        for info in tqdm.tqdm(file_infos, desc=\"Extracting files\"):\n",
        "            fn = info.filename\n",
        "            # Determine the class name for the file (assumes get_class is defined)\n",
        "            class_name = get_class(fn)\n",
        "            target_dir = to_dir / class_name\n",
        "            target_dir.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "            # Extract the file to the target directory.\n",
        "            zip_ref.extract(info, path=str(target_dir))\n",
        "\n",
        "            # The extracted file may include subdirectories; flatten it.\n",
        "            extracted_file = target_dir / fn\n",
        "            file_basename = pathlib.Path(fn).name\n",
        "            output_file = target_dir / file_basename\n",
        "\n",
        "            if extracted_file != output_file:\n",
        "                extracted_file.rename(output_file)\n",
        "\n",
        "def split_class_lists(files_for_class, count):\n",
        "  \"\"\"\n",
        "    Returns the list of files belonging to a subset of data as well as the remainder of\n",
        "    files that need to be downloaded.\n",
        "\n",
        "    Args:\n",
        "      files_for_class: Files belonging to a particular class of data.\n",
        "      count: Number of files to download.\n",
        "\n",
        "    Return:\n",
        "      split_files: Files belonging to the subset of data.\n",
        "      remainder: Dictionary of the remainder of files that need to be downloaded.\n",
        "  \"\"\"\n",
        "  split_files = []\n",
        "  remainder = {}\n",
        "  for cls in files_for_class:\n",
        "    split_files.extend(files_for_class[cls][:count])\n",
        "    remainder[cls] = files_for_class[cls][count:]\n",
        "  return split_files, remainder\n",
        "\n",
        "def parse_subset(zip_path, splits, download_dir):\n",
        "  \"\"\"\n",
        "    Download a subset of the UFC101 dataset and split them into various parts, such as\n",
        "    training, validation, and test.\n",
        "\n",
        "    Args:\n",
        "      zip_url: Zip URL containing data.\n",
        "      num_classes: Number of labels.\n",
        "      splits: Dictionary specifying the training, validation, test, etc. (key) division of data\n",
        "              (value is number of files per split).\n",
        "      download_dir: Directory to download data to.\n",
        "\n",
        "    Return:\n",
        "      dir: Posix path of the resulting directories containing the splits of data.\n",
        "  \"\"\"\n",
        "\n",
        "  files_for_class = list_files_per_class(zip_path)\n",
        "\n",
        "  classes = list(files_for_class.keys())\n",
        "\n",
        "  for cls in classes:\n",
        "    new_files_for_class = files_for_class[cls]\n",
        "    rng = random.Random(RANDOM_SEED + hash(cls))  # seed per class for determinism\n",
        "    rng.shuffle(new_files_for_class)\n",
        "    files_for_class[cls] = new_files_for_class\n",
        "\n",
        "  # Only use the number of classes you want in the dictionary\n",
        "  files_for_class = {x: files_for_class[x] for x in list(files_for_class)}\n",
        "\n",
        "  dirs = {}\n",
        "\n",
        "  for split_name in splits:\n",
        "        split_dir = download_dir / split_name\n",
        "        split_files = []\n",
        "        for cls in classes:\n",
        "            total = len(files_for_class[cls])\n",
        "            # Compute counts based on percentages.\n",
        "            train_count = int(round(total * splits.get(\"train\", 0)))\n",
        "            val_count = int(round(total * splits.get(\"val\", 0)))\n",
        "            # For test, we take the remaining files.\n",
        "            test_count = total - train_count - val_count\n",
        "\n",
        "            if split_name == \"train\":\n",
        "                selected = files_for_class[cls][:train_count]\n",
        "            elif split_name == \"val\":\n",
        "                selected = files_for_class[cls][train_count:train_count + val_count]\n",
        "            elif split_name == \"test\":\n",
        "                selected = files_for_class[cls][train_count + val_count:]\n",
        "            else:\n",
        "                raise ValueError(f\"Unknown split name: {split_name}\")\n",
        "            split_files.extend(selected)\n",
        "        print(f\"{split_name}: extracting {len(split_files)} files\")\n",
        "        unzip_from_local_zip(zip_path, split_dir, file_names=split_files)\n",
        "        dirs[split_name] = split_dir\n",
        "\n",
        "  return dirs\n",
        "\n",
        "def format_frames(frame, output_size):\n",
        "  \"\"\"\n",
        "    Pad and resize an image from a video.\n",
        "\n",
        "    Args:\n",
        "      frame: Image that needs to resized and padded.\n",
        "      output_size: Pixel size of the output frame image.\n",
        "\n",
        "    Return:\n",
        "      Formatted frame with padding of specified output size.\n",
        "  \"\"\"\n",
        "  frame = tf.image.convert_image_dtype(frame, tf.float32)\n",
        "  frame = tf.image.resize_with_pad(frame, *output_size)\n",
        "  if CHANNELS == 2:\n",
        "    # remove last channel since all zeros\n",
        "    frame = frame[..., :CHANNELS]\n",
        "  return frame\n",
        "\n",
        "def frames_from_video_file(video_path, n_frames, output_size = (224,224), frame_step = 2):\n",
        "  \"\"\"\n",
        "    Creates frames from each video file present for each category.\n",
        "\n",
        "    Args:\n",
        "      video_path: File path to the video.\n",
        "      n_frames: Number of frames to be created per video file.\n",
        "      output_size: Pixel size of the output frame image.\n",
        "\n",
        "    Return:\n",
        "      An NumPy array of frames in the shape of (n_frames, height, width, channels).\n",
        "  \"\"\"\n",
        "  # Read each video frame by frame\n",
        "  result = []\n",
        "  src = cv2.VideoCapture(str(video_path))\n",
        "\n",
        "  video_length = src.get(cv2.CAP_PROP_FRAME_COUNT)\n",
        "\n",
        "  need_length = 1 + (n_frames - 1) * frame_step\n",
        "\n",
        "  if need_length > video_length:\n",
        "    start = 0\n",
        "  else:\n",
        "    max_start = video_length - need_length\n",
        "    start = random.randint(0, max_start + 1)\n",
        "\n",
        "  src.set(cv2.CAP_PROP_POS_FRAMES, start)\n",
        "  # ret is a boolean indicating whether read was successful, frame is the image itself\n",
        "  ret, frame = src.read()\n",
        "\n",
        "  result.append(format_frames(frame, output_size))\n",
        "\n",
        "  for _ in range(n_frames - 1):\n",
        "    for _ in range(frame_step):\n",
        "      ret, frame = src.read()\n",
        "    if ret:\n",
        "      frame = format_frames(frame, output_size)\n",
        "      result.append(frame)\n",
        "    else:\n",
        "      result.append(np.zeros_like(result[0]))\n",
        "  src.release()\n",
        "\n",
        "  if not MODE == \"OPTIC_FLOW\":\n",
        "    result = np.array(result)[..., [2, 1, 0]]\n",
        "\n",
        "  return result\n",
        "\n",
        "class FrameGenerator:\n",
        "  def __init__(self, path, n_frames, training = False):\n",
        "    \"\"\" Returns a set of frames with their associated label.\n",
        "\n",
        "      Args:\n",
        "        path: Video file paths.\n",
        "        n_frames: Number of frames.\n",
        "        training: Boolean to determine if training dataset is being created.\n",
        "    \"\"\"\n",
        "    self.path = path\n",
        "    self.n_frames = n_frames\n",
        "    self.training = training\n",
        "    self.class_names = sorted(set(p.name for p in self.path.iterdir() if p.is_dir()))\n",
        "    self.class_ids_for_name = dict((name, idx) for idx, name in enumerate(self.class_names))\n",
        "\n",
        "  def get_files_and_class_names(self):\n",
        "    video_paths = list(self.path.glob('*/*.avi'))\n",
        "    classes = [p.parent.name for p in video_paths]\n",
        "    return video_paths, classes\n",
        "\n",
        "  def __call__(self):\n",
        "    video_paths, classes = self.get_files_and_class_names()\n",
        "\n",
        "    pairs = list(zip(video_paths, classes))\n",
        "\n",
        "    if self.training:\n",
        "      rng = random.Random(RANDOM_SEED)\n",
        "      rng.shuffle(pairs)\n",
        "\n",
        "    for path, name in pairs:\n",
        "      video_frames = frames_from_video_file(path, self.n_frames)\n",
        "      label = self.class_ids_for_name[name] # Encode labels\n",
        "      yield video_frames, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "OYY7PkdJFM4Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f4cddf9-de91-433a-9467-e42c0371fd17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4 classes.\n",
            "sharp_right: 163 files\n",
            "sharp_left: 168 files\n",
            "moderate_left: 376 files\n",
            "moderate_right: 344 files\n",
            "train: extracting 736 files\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting files: 100%|██████████| 736/736 [00:00<00:00, 1431.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val: extracting 211 files\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting files: 100%|██████████| 211/211 [00:00<00:00, 1393.94it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test: extracting 104 files\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting files: 100%|██████████| 104/104 [00:00<00:00, 1405.77it/s]\n"
          ]
        }
      ],
      "source": [
        "download_dir = pathlib.Path('./left_right_subset/')\n",
        "\n",
        "subset_paths = parse_subset(DATASET_FILE,\n",
        "                        splits = {\"train\": 0.7, \"val\": 0.2, \"test\": 0.1},\n",
        "                        download_dir = download_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n90nVnByhgCY"
      },
      "source": [
        "Split Distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "bh5Hr-SnAvmH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "568d2271-e452-4777-8b0b-31eca8a4eba4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def get_dataset_size(set):\n",
        "  return len(list(subset_paths[set].glob('*/*.avi')))\n",
        "\n",
        "sets = ['train', 'val', 'test']\n",
        "\n",
        "plt.bar(x = range(len(sets)), height = [get_dataset_size(s) for s in sets])\n",
        "plt.xlabel('Dataset')\n",
        "plt.ylabel('Size')\n",
        "plt.xticks(range(len(sets)), sets)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLiw0pjWhco6"
      },
      "source": [
        "Class Distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "V12E2aNzhce0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "43c84303-9287-4bf5-8890-667e808c4d42"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def count_class_size(class_name):\n",
        "  return len(list(subset_paths['train'].glob(f'{class_name}/*')))\n",
        "\n",
        "fg = FrameGenerator(subset_paths['train'], 1, training=True)\n",
        "labels = list(fg.class_ids_for_name.keys())\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.title('Training Set Class Distribution')\n",
        "plt.bar(x = range(len(labels)), height = [count_class_size(c) for c in labels])\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Size')\n",
        "plt.xticks(range(len(labels)), labels)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0O3ttIzpFZJ"
      },
      "source": [
        "Create the training, validation, and test sets (`train_ds`, `val_ds`, and `test_ds`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "cellView": "form",
        "id": "lq86IyGDJjTX"
      },
      "outputs": [],
      "source": [
        "n_frames = 10\n",
        "\n",
        "output_signature = (tf.TensorSpec(shape = (None, None, None, CHANNELS), dtype = tf.float32),\n",
        "                    tf.TensorSpec(shape = (), dtype = tf.int16))\n",
        "\n",
        "train_ds = tf.data.Dataset.from_generator(FrameGenerator(subset_paths['train'], n_frames, training=True),\n",
        "                                          output_signature = output_signature)\n",
        "\n",
        "# # Batch the data\n",
        "train_ds = train_ds.batch(batch_size)\n",
        "\n",
        "val_ds = tf.data.Dataset.from_generator(FrameGenerator(subset_paths['val'], n_frames),\n",
        "                                        output_signature = output_signature)\n",
        "val_ds = val_ds.batch(batch_size)\n",
        "\n",
        "test_ds = tf.data.Dataset.from_generator(FrameGenerator(subset_paths['test'], n_frames),\n",
        "                                         output_signature = output_signature)\n",
        "\n",
        "test_ds = test_ds.batch(batch_size)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.pipeline import Pipeline\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from collections import Counter\n",
        "\n",
        "# Assuming train_ds is your original training dataset\n",
        "# Extract all data from the dataset into NumPy arrays\n",
        "X_list, y_list = [], []\n",
        "for frames, label in train_ds.unbatch().as_numpy_iterator():\n",
        "    X_list.append(frames)\n",
        "    y_list.append(label)\n",
        "\n",
        "X_train = np.array(X_list)\n",
        "y_train = np.array(y_list)\n",
        "\n",
        "# Example: Suppose each sample has shape (n_frames, height, width, channels)\n",
        "n_samples, n_frames, height, width, channels = X_train.shape\n",
        "\n",
        "# Flatten each sample to a 1D vector:\n",
        "X_train_flat = X_train.reshape(n_samples, -1)\n",
        "\n",
        "# 2) figure out your “pivot” size = the second-largest class count\n",
        "counts = Counter(y_train)\n",
        "# sorted counts in descending order\n",
        "sorted_counts = sorted(counts.values(), reverse=True)\n",
        "pivot = sorted_counts[1]\n",
        "\n",
        "# 3) build sampling strategies\n",
        "#   a) under-sample any class bigger than pivot ↓\n",
        "under_strategy = {cls: pivot for cls, cnt in counts.items() if cnt > pivot}\n",
        "\n",
        "#   b) over-sample any class smaller than pivot ↑\n",
        "over_strategy  = {cls: pivot for cls, cnt in counts.items() if cnt < pivot}\n",
        "\n",
        "# 4) chain them in a Pipeline\n",
        "pipe = Pipeline([\n",
        "    ('under', RandomUnderSampler(sampling_strategy=under_strategy,\n",
        "                                 random_state=RANDOM_SEED)),\n",
        "    ('over',  RandomOverSampler (sampling_strategy=over_strategy,\n",
        "                                 random_state=RANDOM_SEED)),\n",
        "])\n",
        "\n",
        "X_res_flat, y_res = pipe.fit_resample(X_train_flat, y_train)\n",
        "\n",
        "# Convert the flattened synthetic samples back to the original shape:\n",
        "X_res = X_res_flat.reshape(-1, n_frames, height, width, channels)\n",
        "\n",
        "# Create a new balanced tf.data.Dataset:\n",
        "balanced_train_ds = (\n",
        "    tf.data.Dataset.from_tensor_slices((X_res, y_res))\n",
        "      .shuffle(buffer_size=1024)\n",
        "      .batch(batch_size)\n",
        "      .repeat()\n",
        "      .prefetch(tf.data.AUTOTUNE)\n",
        ")\n",
        "\n",
        "train_ds = balanced_train_ds"
      ],
      "metadata": {
        "id": "HYGq3WqFazzO"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compare class counts before and after\n",
        "print(\"Before undersampling / oversampling:\", Counter(y_train))\n",
        "print(\"After undersampling / oversampling:\", Counter(y_res))\n",
        "before_counts = [count_class_size(c) for c in labels]\n",
        "after = Counter(y_res)\n",
        "after_counts = [after[c] for c in range(len(labels))]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.title('Training Set Class Distribution - Undersampling / Oversampling to Second Largest Class')\n",
        "x = np.arange(len(labels))\n",
        "width = 0.35\n",
        "# draw bars offset to the left and right\n",
        "plt.bar(x - width/2, before_counts, width, label='Before undersampling / oversampling')\n",
        "plt.bar(x + width/2, after_counts,  width, label='After undersampling / oversampling')\n",
        "\n",
        "# add some labels\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(x, labels)             # put the class names on the x-axis\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "v4JXOk99_G_D",
        "outputId": "2fc0a9ee-c28a-4e12-e311-393ec0140845",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before undersampling / oversampling: Counter({np.int16(0): 263, np.int16(1): 241, np.int16(2): 118, np.int16(3): 114})\n",
            "After undersampling / oversampling: Counter({np.int16(0): 241, np.int16(1): 241, np.int16(2): 241, np.int16(3): 241})\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "lnujtvwefpNd",
        "outputId": "899853c2-f6a5-4104-e450-28c38f09940e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for tensorflow-docs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "(32, 10, 224, 224, 3)\n",
            "(32,) tf.Tensor([3 2 1 1 1 0 2 1 1 1 3 2 3 1 0 1 1 1 1 1 1 1 2 2 1 3 0 1 2 2 1 2], shape=(32,), dtype=int16)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<img 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\"/>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Display example gif - code from https://www.tensorflow.org/tutorials/load_data/video\n",
        "!pip install -q git+https://github.com/tensorflow/docs\n",
        "\n",
        "import imageio\n",
        "from tensorflow_docs.vis import embed\n",
        "\n",
        "def to_gif(images):\n",
        "  converted_images = np.clip(images * 255, 0, 255).astype(np.uint8)\n",
        "  if CHANNELS == 2:\n",
        "    # convert back to RGB by adding zero channel\n",
        "    converted_images = np.concatenate([converted_images, np.zeros_like(converted_images[..., :1])], axis=-1)\n",
        "  imageio.mimsave('./animation.gif', converted_images, fps=1)\n",
        "  return embed.embed_file('./animation.gif')\n",
        "\n",
        "frames, label = next(iter(train_ds))\n",
        "print(frames.shape)\n",
        "print(label.shape, label)\n",
        "to_gif(frames[7])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzogoGA4pQW0"
      },
      "source": [
        "## Create the model\n",
        "\n",
        "The following 3D convolutional neural network model is based off the paper [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/abs/1711.11248v3) by D. Tran et al. (2017). The paper compares several versions of 3D ResNets. Instead of operating on a single image with dimensions `(height, width)`, like standard ResNets, these operate on video volume `(time, height, width)`. The most obvious approach to this problem would be replace each 2D convolution (`layers.Conv2D`) with a 3D convolution (`layers.Conv3D`).\n",
        "\n",
        "This tutorial uses a (2 + 1)D convolution with [residual connections](https://arxiv.org/abs/1512.03385). The (2 + 1)D convolution allows for the decomposition of the spatial and temporal dimensions, therefore creating two separate steps. An advantage of this approach is that factorizing the convolutions into spatial and temporal dimensions saves parameters.\n",
        "\n",
        "For each output location a 3D convolution combines all the vectors from a 3D patch of the volume to create one vector in the output volume.\n",
        "\n",
        "![3D convolutions](https://www.tensorflow.org/images/tutorials/video/3DCNN.png)\n",
        "\n",
        "This operation is takes `time * height * width * channels` inputs and produces `channels` outputs (assuming the number of input and output channels are the same. So a 3D convolution layer with a kernel size of `(3 x 3 x 3)` would need a weight-matrix with `27 * channels ** 2` entries. The reference paper found that a more effective & efficient approach was to factorize the convolution. Instead of a single 3D convolution to process the time and space dimensions, they proposed a \\\"(2+1)D\\\" convolution which processes the space and time dimensions separately. The figure below shows the factored spatial and temporal convolutions of a (2 + 1)D convolution.\n",
        "\n",
        "![(2+1)D convolutions](https://www.tensorflow.org/images/tutorials/video/2plus1CNN.png)\n",
        "\n",
        "The main advantage of this approach is that it reduces the number of parameters. In the (2 + 1)D convolution the spatial convolution takes in data of the shape `(1, width, height)`, while the temporal convolution takes in data of the shape `(time, 1, 1)`. For example, a (2 + 1)D convolution with kernel size `(3 x 3 x 3)` would need weight matrices of size `(9 * channels**2) + (3 * channels**2)`, less than half as many as the full 3D convolution. This tutorial implements (2 + 1)D ResNet18, where each convolution in the resnet is replaced by a (2+1)D convolution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "GZcB_7dg-EZJ"
      },
      "outputs": [],
      "source": [
        "# Define the dimensions of one frame in the set of frames created\n",
        "HEIGHT = 224\n",
        "WIDTH = 224"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "yD_sDIBlNu7K"
      },
      "outputs": [],
      "source": [
        "class Conv2Plus1D(keras.layers.Layer):\n",
        "  def __init__(self, filters, kernel_size, padding):\n",
        "    \"\"\"\n",
        "      A sequence of convolutional layers that first apply the convolution operation over the\n",
        "      spatial dimensions, and then the temporal dimension.\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    self.seq = keras.Sequential([\n",
        "        # Spatial decomposition\n",
        "        layers.Conv3D(filters=filters,\n",
        "                      kernel_size=(1, kernel_size[1], kernel_size[2]),\n",
        "                      padding=padding),\n",
        "        # Temporal decomposition\n",
        "        layers.Conv3D(filters=filters,\n",
        "                      kernel_size=(kernel_size[0], 1, 1),\n",
        "                      padding=padding)\n",
        "        ])\n",
        "\n",
        "  def call(self, x):\n",
        "    return self.seq(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-fCAddqEORZ"
      },
      "source": [
        "A ResNet model is made from a sequence of residual blocks.\n",
        "A residual block has two branches. The main branch performs the calculation, but is difficult for gradients to flow through.\n",
        "The residual branch bypasses the main calculation and mostly just adds the input to the output of the main branch.\n",
        "Gradients flow easily through this branch.\n",
        "Therefore, an easy path from the loss function to any of the residual block's main branch will be present.\n",
        "This avoids the vanishing gradient problem.\n",
        "\n",
        "Create the main branch of the residual block with the following class. In contrast to the standard ResNet structure this uses the custom `Conv2Plus1D` layer instead of `layers.Conv2D`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "tjxAKHwn6mTJ"
      },
      "outputs": [],
      "source": [
        "class ResidualMain(keras.layers.Layer):\n",
        "  \"\"\"\n",
        "    Residual block of the model with convolution, layer normalization, and the\n",
        "    activation function, ReLU.\n",
        "  \"\"\"\n",
        "  def __init__(self, filters, kernel_size):\n",
        "    super().__init__()\n",
        "    self.seq = keras.Sequential([\n",
        "        Conv2Plus1D(filters=filters,\n",
        "                    kernel_size=kernel_size,\n",
        "                    padding='same'),\n",
        "        layers.LayerNormalization(),\n",
        "        layers.ReLU(),\n",
        "        Conv2Plus1D(filters=filters,\n",
        "                    kernel_size=kernel_size,\n",
        "                    padding='same'),\n",
        "        layers.LayerNormalization()\n",
        "    ])\n",
        "\n",
        "  def call(self, x):\n",
        "    return self.seq(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CevmZ9qsdpWC"
      },
      "source": [
        "To add the residual branch to the main branch it needs to have the same size. The `Project` layer below deals with cases where the number of channels is changed on the branch. In particular, a sequence of densely-connected layer followed by normalization is added.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "znrk5BrL6kuq"
      },
      "outputs": [],
      "source": [
        "class Project(keras.layers.Layer):\n",
        "  \"\"\"\n",
        "    Project certain dimensions of the tensor as the data is passed through different\n",
        "    sized filters and downsampled.\n",
        "  \"\"\"\n",
        "  def __init__(self, units):\n",
        "    super().__init__()\n",
        "    self.seq = keras.Sequential([\n",
        "        layers.Dense(units),\n",
        "        layers.LayerNormalization()\n",
        "    ])\n",
        "\n",
        "  def call(self, x):\n",
        "    return self.seq(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8zycXGvfnak"
      },
      "source": [
        "Use `add_residual_block` to introduce a skip connection between the layers of the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "urjVgqvw-TlB"
      },
      "outputs": [],
      "source": [
        "def add_residual_block(input, filters, kernel_size):\n",
        "  \"\"\"\n",
        "    Add residual blocks to the model. If the last dimensions of the input data\n",
        "    and filter size does not match, project it such that last dimension matches.\n",
        "  \"\"\"\n",
        "  out = ResidualMain(filters,\n",
        "                     kernel_size)(input)\n",
        "\n",
        "  res = input\n",
        "  # Using the Keras functional APIs, project the last dimension of the tensor to\n",
        "  # match the new filter size\n",
        "  if out.shape[-1] != input.shape[-1]:\n",
        "    res = Project(out.shape[-1])(res)\n",
        "\n",
        "  return layers.add([res, out])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bozog_0hFKrD"
      },
      "source": [
        "Resizing the video is necessary to perform downsampling of the data. In particular, downsampling the video frames allow for the model to examine specific parts of frames to detect patterns that may be specific to a certain action. Through downsampling, non-essential information can be discarded. Moreoever, resizing the video will allow for dimensionality reduction and therefore faster processing through the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "lQOWuc2I-QqK"
      },
      "outputs": [],
      "source": [
        "class ResizeVideo(keras.layers.Layer):\n",
        "  def __init__(self, height, width):\n",
        "    super().__init__()\n",
        "    self.height = height\n",
        "    self.width = width\n",
        "    self.resizing_layer = layers.Resizing(self.height, self.width)\n",
        "\n",
        "  def call(self, video):\n",
        "    \"\"\"\n",
        "      Use the einops library to resize the tensor.\n",
        "\n",
        "      Args:\n",
        "        video: Tensor representation of the video, in the form of a set of frames.\n",
        "\n",
        "      Return:\n",
        "        A downsampled size of the video according to the new height and width it should be resized to.\n",
        "    \"\"\"\n",
        "    # b stands for batch size, t stands for time, h stands for height,\n",
        "    # w stands for width, and c stands for the number of channels.\n",
        "    old_shape = einops.parse_shape(video, 'b t h w c')\n",
        "    images = einops.rearrange(video, 'b t h w c -> (b t) h w c')\n",
        "    images = self.resizing_layer(images)\n",
        "    videos = einops.rearrange(\n",
        "        images, '(b t) h w c -> b t h w c',\n",
        "        t = old_shape['t'])\n",
        "    return videos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9IqzCq--Uu9"
      },
      "source": [
        "Use the [Keras functional API](https://www.tensorflow.org/guide/keras/functional) to build the residual network."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "_bROfh_K-Wxs"
      },
      "outputs": [],
      "source": [
        "# Number of classes\n",
        "num_classes = len(labels)\n",
        "\n",
        "input_shape = (None, 10, HEIGHT, WIDTH, CHANNELS)\n",
        "input = layers.Input(shape=(input_shape[1:]))\n",
        "x = input\n",
        "\n",
        "x = Conv2Plus1D(filters=16, kernel_size=(3, 7, 7), padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.ReLU()(x)\n",
        "x = ResizeVideo(HEIGHT // 2, WIDTH // 2)(x)\n",
        "\n",
        "# Block 1\n",
        "x = add_residual_block(x, 16, (3, 3, 3))\n",
        "x = ResizeVideo(HEIGHT // 4, WIDTH // 4)(x)\n",
        "\n",
        "# Block 2\n",
        "x = add_residual_block(x, 32, (3, 3, 3))\n",
        "x = ResizeVideo(HEIGHT // 8, WIDTH // 8)(x)\n",
        "\n",
        "# Block 3\n",
        "x = add_residual_block(x, 64, (3, 3, 3))\n",
        "x = ResizeVideo(HEIGHT // 16, WIDTH // 16)(x)\n",
        "\n",
        "# Block 4\n",
        "x = add_residual_block(x, 128, (3, 3, 3))\n",
        "\n",
        "x = layers.GlobalAveragePooling3D()(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dense(num_classes)(x)\n",
        "\n",
        "model = keras.Model(input, x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "TiO0WylG-ZHM"
      },
      "outputs": [],
      "source": [
        "frames, label = next(iter(train_ds))\n",
        "model.build(frames)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "GAsKrM8r-bKM",
        "outputId": "4f879ac3-8a78-472f-a23f-ce2871bfab35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Visualize the model\n",
        "keras.utils.plot_model(model, expand_nested=True, dpi=60, show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1yvJJPnY-dMP"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "For this tutorial, choose the `tf.keras.optimizers.Adam` optimizer and the `tf.keras.losses.SparseCategoricalCrossentropy` loss function. Use the `metrics` argument to the view the accuracy of the model performance at every step."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Choose LR"
      ],
      "metadata": {
        "id": "iweYu2h-cRh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_dataset_size = get_dataset_size('train')\n",
        "\n",
        "print(training_dataset_size, \"videos in train split\")"
      ],
      "metadata": {
        "id": "QMlyf5cBKDRe",
        "outputId": "29a44232-9b48-4caf-9db7-304f05b62f65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "736 videos in train split\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import Callback\n",
        "import tensorflow.keras.backend as K\n",
        "\n",
        "class LRFinder(Callback):\n",
        "    def __init__(self, min_lr=1e-6, max_lr=1e-2, total_steps=100):\n",
        "        super().__init__()\n",
        "        self.min_lr = min_lr\n",
        "        self.max_lr = max_lr\n",
        "        self.total_steps = total_steps\n",
        "        self.history = {\"lr\": [], \"loss\": []}\n",
        "        self.step = 0\n",
        "\n",
        "    def on_batch_end(self, batch, logs=None):\n",
        "        self.step += 1\n",
        "        # exponential increase\n",
        "        lr = self.min_lr * (self.max_lr / self.min_lr) ** (self.step / self.total_steps)\n",
        "        self.model.optimizer.learning_rate.assign(lr)\n",
        "        # K.set_value(self.model.optimizer.lr, lr)\n",
        "\n",
        "        # record\n",
        "        self.history[\"lr\"].append(lr)\n",
        "        self.history[\"loss\"].append(logs.get(\"loss\"))\n",
        "\n",
        "        # stop if we hit the max\n",
        "        if self.step >= self.total_steps:\n",
        "            self.model.stop_training = True\n",
        "\n",
        "batches_per_epoch = training_dataset_size // batch_size\n",
        "total_steps = batches_per_epoch * 1\n",
        "\n",
        "lr_finder = LRFinder(\n",
        "    min_lr=1e-6,    # start very small\n",
        "    max_lr=1e-5,    # up to something large\n",
        "    total_steps=total_steps\n",
        ")"
      ],
      "metadata": {
        "id": "y2tI1pzNQZ2m"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              optimizer = keras.optimizers.Adam(learning_rate = 3e-6, weight_decay=1e-5),\n",
        "              metrics = [\n",
        "                  'accuracy'\n",
        "                  ])\n",
        "\n",
        "history = model.fit(x = train_ds,\n",
        "                    epochs = 300,\n",
        "                    validation_data = val_ds,\n",
        "                    steps_per_epoch = training_dataset_size // batch_size,\n",
        "                    callbacks=[lr_finder]\n",
        "                    )"
      ],
      "metadata": {
        "id": "pEGq5OP_byYh",
        "outputId": "34aecc10-2882-4567-a266-42173d7374c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 1s/step - accuracy: 0.2834 - loss: 1.4250 - val_accuracy: 0.1517 - val_loss: 1.5450\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
            "  self._interrupted_warning()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(lr_finder.history[\"lr\"], lr_finder.history[\"loss\"])\n",
        "plt.xscale(\"log\")\n",
        "plt.xlabel(\"Learning Rate\")\n",
        "plt.ylabel(\"Training Loss\")\n",
        "plt.title(\"LR Range Test\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Yj6uKErWb6SG",
        "outputId": "0eaf8026-a5e4-4798-995d-c3e027b7d7c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train"
      ],
      "metadata": {
        "id": "d-MjlErfcWFK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint_filepath = '/models/bend_classification.keras'\n",
        "os.makedirs(os.path.dirname(checkpoint_filepath), exist_ok=True)"
      ],
      "metadata": {
        "id": "Ar6_N-T8J4Ev"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              optimizer = keras.optimizers.Adam(learning_rate = 1e-4),\n",
        "              metrics = [\n",
        "                  'accuracy'\n",
        "                  ])"
      ],
      "metadata": {
        "id": "xr2GufxUhLKi"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "# Define the checkpoint callback\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    filepath=checkpoint_filepath,\n",
        "    monitor='val_loss',                 # Metric to monitor\n",
        "    verbose=1,                          # Verbosity mode\n",
        "    save_best_only=True,                # Save only the best model\n",
        "    save_weights_only=False,            # Save the entire model (architecture + weights)\n",
        "    mode='auto',                        # Mode for monitoring the metric\n",
        "    save_freq='epoch'                   # Save frequency\n",
        ")\n",
        "\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    patience=14,\n",
        "    restore_best_weights=True,\n",
        "    verbose=1\n",
        ")"
      ],
      "metadata": {
        "id": "erVWiU7RJo3c"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "VMrMUl2hOqMs",
        "outputId": "1d2ea32d-a53e-4391-cf40-9bef285a7632",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 1s/step - accuracy: 0.2949 - loss: 1.5206 - val_accuracy: 0.1564 - val_loss: 1.8336 - learning_rate: 1.0000e-04\n",
            "Epoch 2/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 2s/step - accuracy: 0.3029 - loss: 1.4348 - val_accuracy: 0.1564 - val_loss: 1.7598 - learning_rate: 1.0000e-04\n",
            "Epoch 3/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.3193 - loss: 1.4053 - val_accuracy: 0.3128 - val_loss: 1.6106 - learning_rate: 1.0000e-04\n",
            "Epoch 4/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 829ms/step - accuracy: 0.2917 - loss: 1.3873 - val_accuracy: 0.1754 - val_loss: 1.6847 - learning_rate: 1.0000e-04\n",
            "Epoch 5/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 820ms/step - accuracy: 0.2753 - loss: 1.4280 - val_accuracy: 0.1564 - val_loss: 1.6794 - learning_rate: 1.0000e-04\n",
            "Epoch 6/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 817ms/step - accuracy: 0.2800 - loss: 1.4003 - val_accuracy: 0.2464 - val_loss: 1.7273 - learning_rate: 1.0000e-04\n",
            "Epoch 7/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 819ms/step - accuracy: 0.3214 - loss: 1.3927 - val_accuracy: 0.2891 - val_loss: 1.5096 - learning_rate: 1.0000e-04\n",
            "Epoch 8/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 826ms/step - accuracy: 0.3286 - loss: 1.3639 - val_accuracy: 0.2654 - val_loss: 1.7860 - learning_rate: 1.0000e-04\n",
            "Epoch 9/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.2923 - loss: 1.3712 - val_accuracy: 0.1943 - val_loss: 1.5886 - learning_rate: 1.0000e-04\n",
            "Epoch 10/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.2887 - loss: 1.3782 - val_accuracy: 0.2180 - val_loss: 1.6112 - learning_rate: 1.0000e-04\n",
            "Epoch 11/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 825ms/step - accuracy: 0.2851 - loss: 1.3978 - val_accuracy: 0.2133 - val_loss: 1.4066 - learning_rate: 1.0000e-04\n",
            "Epoch 12/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.3086 - loss: 1.3750 - val_accuracy: 0.2370 - val_loss: 1.8136 - learning_rate: 1.0000e-04\n",
            "Epoch 13/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 823ms/step - accuracy: 0.3763 - loss: 1.3535 - val_accuracy: 0.2180 - val_loss: 1.4521 - learning_rate: 1.0000e-04\n",
            "Epoch 14/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 817ms/step - accuracy: 0.3329 - loss: 1.3492 - val_accuracy: 0.3318 - val_loss: 1.3305 - learning_rate: 1.0000e-04\n",
            "Epoch 15/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 817ms/step - accuracy: 0.3583 - loss: 1.3423 - val_accuracy: 0.1896 - val_loss: 1.5890 - learning_rate: 1.0000e-04\n",
            "Epoch 16/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 824ms/step - accuracy: 0.3305 - loss: 1.3414 - val_accuracy: 0.1896 - val_loss: 1.7112 - learning_rate: 1.0000e-04\n",
            "Epoch 17/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 822ms/step - accuracy: 0.3393 - loss: 1.3927 - val_accuracy: 0.3412 - val_loss: 1.3341 - learning_rate: 1.0000e-04\n",
            "Epoch 18/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 830ms/step - accuracy: 0.3515 - loss: 1.3436 - val_accuracy: 0.2559 - val_loss: 1.5157 - learning_rate: 1.0000e-04\n",
            "Epoch 19/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 826ms/step - accuracy: 0.3594 - loss: 1.3323 - val_accuracy: 0.1611 - val_loss: 1.7456 - learning_rate: 1.0000e-04\n",
            "Epoch 20/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 824ms/step - accuracy: 0.3297 - loss: 1.3420 - val_accuracy: 0.3365 - val_loss: 1.3082 - learning_rate: 1.0000e-04\n",
            "Epoch 21/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.3658 - loss: 1.3450 - val_accuracy: 0.2986 - val_loss: 1.3905 - learning_rate: 1.0000e-04\n",
            "Epoch 22/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 808ms/step - accuracy: 0.3677 - loss: 1.3161 - val_accuracy: 0.3744 - val_loss: 1.4936 - learning_rate: 1.0000e-04\n",
            "Epoch 23/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 823ms/step - accuracy: 0.3779 - loss: 1.3158 - val_accuracy: 0.3602 - val_loss: 1.3964 - learning_rate: 1.0000e-04\n",
            "Epoch 24/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 823ms/step - accuracy: 0.4299 - loss: 1.2853 - val_accuracy: 0.3033 - val_loss: 1.4052 - learning_rate: 1.0000e-04\n",
            "Epoch 25/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 823ms/step - accuracy: 0.3562 - loss: 1.3309 - val_accuracy: 0.3175 - val_loss: 1.3678 - learning_rate: 1.0000e-04\n",
            "Epoch 26/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 819ms/step - accuracy: 0.3804 - loss: 1.3077 - val_accuracy: 0.3555 - val_loss: 1.4551 - learning_rate: 1.0000e-04\n",
            "Epoch 27/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.3280 - loss: 1.3240\n",
            "Epoch 27: ReduceLROnPlateau reducing learning rate to 3.9999998989515007e-05.\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 813ms/step - accuracy: 0.3288 - loss: 1.3236 - val_accuracy: 0.3270 - val_loss: 1.3409 - learning_rate: 1.0000e-04\n",
            "Epoch 28/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 828ms/step - accuracy: 0.3788 - loss: 1.2891 - val_accuracy: 0.3791 - val_loss: 1.3064 - learning_rate: 4.0000e-05\n",
            "Epoch 29/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 823ms/step - accuracy: 0.4233 - loss: 1.2756 - val_accuracy: 0.4123 - val_loss: 1.2804 - learning_rate: 4.0000e-05\n",
            "Epoch 30/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 822ms/step - accuracy: 0.4427 - loss: 1.2645 - val_accuracy: 0.4028 - val_loss: 1.3581 - learning_rate: 4.0000e-05\n",
            "Epoch 31/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 817ms/step - accuracy: 0.4133 - loss: 1.2679 - val_accuracy: 0.3886 - val_loss: 1.3297 - learning_rate: 4.0000e-05\n",
            "Epoch 32/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 828ms/step - accuracy: 0.4570 - loss: 1.2644 - val_accuracy: 0.3697 - val_loss: 1.3354 - learning_rate: 4.0000e-05\n",
            "Epoch 33/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 821ms/step - accuracy: 0.4232 - loss: 1.2670 - val_accuracy: 0.3697 - val_loss: 1.3295 - learning_rate: 4.0000e-05\n",
            "Epoch 34/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 815ms/step - accuracy: 0.4205 - loss: 1.2661 - val_accuracy: 0.4645 - val_loss: 1.2779 - learning_rate: 4.0000e-05\n",
            "Epoch 35/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 833ms/step - accuracy: 0.4349 - loss: 1.2523 - val_accuracy: 0.4313 - val_loss: 1.2858 - learning_rate: 4.0000e-05\n",
            "Epoch 36/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 828ms/step - accuracy: 0.4433 - loss: 1.2526 - val_accuracy: 0.3744 - val_loss: 1.4640 - learning_rate: 4.0000e-05\n",
            "Epoch 37/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 817ms/step - accuracy: 0.4525 - loss: 1.2399 - val_accuracy: 0.2986 - val_loss: 1.6321 - learning_rate: 4.0000e-05\n",
            "Epoch 38/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 814ms/step - accuracy: 0.4181 - loss: 1.2204 - val_accuracy: 0.3981 - val_loss: 1.3342 - learning_rate: 4.0000e-05\n",
            "Epoch 39/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 821ms/step - accuracy: 0.4681 - loss: 1.2231 - val_accuracy: 0.3934 - val_loss: 1.3212 - learning_rate: 4.0000e-05\n",
            "Epoch 40/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 826ms/step - accuracy: 0.4471 - loss: 1.2013 - val_accuracy: 0.3460 - val_loss: 1.9079 - learning_rate: 4.0000e-05\n",
            "Epoch 41/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.4348 - loss: 1.2658\n",
            "Epoch 41: ReduceLROnPlateau reducing learning rate to 1.5999999595806004e-05.\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 822ms/step - accuracy: 0.4363 - loss: 1.2644 - val_accuracy: 0.3649 - val_loss: 1.4165 - learning_rate: 4.0000e-05\n",
            "Epoch 42/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.4277 - loss: 1.2425 - val_accuracy: 0.4265 - val_loss: 1.3299 - learning_rate: 1.6000e-05\n",
            "Epoch 43/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 828ms/step - accuracy: 0.4759 - loss: 1.2017 - val_accuracy: 0.4265 - val_loss: 1.2974 - learning_rate: 1.6000e-05\n",
            "Epoch 44/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 819ms/step - accuracy: 0.4466 - loss: 1.2077 - val_accuracy: 0.3886 - val_loss: 1.4453 - learning_rate: 1.6000e-05\n",
            "Epoch 45/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.4677 - loss: 1.2059 - val_accuracy: 0.4265 - val_loss: 1.2783 - learning_rate: 1.6000e-05\n",
            "Epoch 46/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 817ms/step - accuracy: 0.4688 - loss: 1.1959 - val_accuracy: 0.4028 - val_loss: 1.3460 - learning_rate: 1.6000e-05\n",
            "Epoch 47/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 827ms/step - accuracy: 0.4909 - loss: 1.1747 - val_accuracy: 0.4502 - val_loss: 1.2285 - learning_rate: 1.6000e-05\n",
            "Epoch 48/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 820ms/step - accuracy: 0.4261 - loss: 1.1955 - val_accuracy: 0.4171 - val_loss: 1.2215 - learning_rate: 1.6000e-05\n",
            "Epoch 49/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 822ms/step - accuracy: 0.4913 - loss: 1.1910 - val_accuracy: 0.4171 - val_loss: 1.2690 - learning_rate: 1.6000e-05\n",
            "Epoch 50/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 816ms/step - accuracy: 0.4832 - loss: 1.1533 - val_accuracy: 0.4502 - val_loss: 1.2553 - learning_rate: 1.6000e-05\n",
            "Epoch 51/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 822ms/step - accuracy: 0.4286 - loss: 1.2351 - val_accuracy: 0.4597 - val_loss: 1.2097 - learning_rate: 1.6000e-05\n",
            "Epoch 52/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 817ms/step - accuracy: 0.5000 - loss: 1.1893 - val_accuracy: 0.4550 - val_loss: 1.2459 - learning_rate: 1.6000e-05\n",
            "Epoch 53/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 816ms/step - accuracy: 0.5136 - loss: 1.1397 - val_accuracy: 0.4360 - val_loss: 1.3054 - learning_rate: 1.6000e-05\n",
            "Epoch 54/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 826ms/step - accuracy: 0.5249 - loss: 1.1290 - val_accuracy: 0.4313 - val_loss: 1.2206 - learning_rate: 1.6000e-05\n",
            "Epoch 55/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 825ms/step - accuracy: 0.4911 - loss: 1.1610 - val_accuracy: 0.3981 - val_loss: 1.2761 - learning_rate: 1.6000e-05\n",
            "Epoch 56/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 819ms/step - accuracy: 0.4844 - loss: 1.1451 - val_accuracy: 0.4550 - val_loss: 1.2925 - learning_rate: 1.6000e-05\n",
            "Epoch 57/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 819ms/step - accuracy: 0.4560 - loss: 1.1779 - val_accuracy: 0.4739 - val_loss: 1.2868 - learning_rate: 1.6000e-05\n",
            "Epoch 58/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.4858 - loss: 1.1349\n",
            "Epoch 58: ReduceLROnPlateau reducing learning rate to 6.399999983841554e-06.\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 813ms/step - accuracy: 0.4862 - loss: 1.1350 - val_accuracy: 0.4739 - val_loss: 1.2764 - learning_rate: 1.6000e-05\n",
            "Epoch 59/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.4686 - loss: 1.1855 - val_accuracy: 0.4360 - val_loss: 1.2896 - learning_rate: 6.4000e-06\n",
            "Epoch 60/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 819ms/step - accuracy: 0.5292 - loss: 1.1289 - val_accuracy: 0.4218 - val_loss: 1.2690 - learning_rate: 6.4000e-06\n",
            "Epoch 61/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 814ms/step - accuracy: 0.5168 - loss: 1.1396 - val_accuracy: 0.4882 - val_loss: 1.2619 - learning_rate: 6.4000e-06\n",
            "Epoch 62/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 818ms/step - accuracy: 0.5198 - loss: 1.1581 - val_accuracy: 0.4265 - val_loss: 1.2607 - learning_rate: 6.4000e-06\n",
            "Epoch 63/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 822ms/step - accuracy: 0.5052 - loss: 1.1462 - val_accuracy: 0.4645 - val_loss: 1.2391 - learning_rate: 6.4000e-06\n",
            "Epoch 64/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 820ms/step - accuracy: 0.5269 - loss: 1.0959 - val_accuracy: 0.4550 - val_loss: 1.2333 - learning_rate: 6.4000e-06\n",
            "Epoch 65/300\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.5167 - loss: 1.1433\n",
            "Epoch 65: ReduceLROnPlateau reducing learning rate to 2.5600000299164097e-06.\n",
            "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 815ms/step - accuracy: 0.5173 - loss: 1.1430 - val_accuracy: 0.4645 - val_loss: 1.2382 - learning_rate: 6.4000e-06\n",
            "Epoch 65: early stopping\n",
            "Restoring model weights from the end of the best epoch: 51.\n"
          ]
        }
      ],
      "source": [
        "reduce_lr = keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor='val_loss', factor=0.4, patience=7, min_lr=1e-6, verbose=1\n",
        ")\n",
        "\n",
        "history = model.fit(x = train_ds,\n",
        "                    epochs = 300,\n",
        "                    validation_data = val_ds,\n",
        "                    steps_per_epoch = training_dataset_size // batch_size,\n",
        "                    callbacks=[reduce_lr, early_stopping] #checkpoint_callback\n",
        "                    # callbacks=[lr_finder]\n",
        "                    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKUfMNVns2hu"
      },
      "source": [
        "### Visualize the results\n",
        "\n",
        "Create plots of the loss and accuracy on the training and validation sets:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "Cd5tpNrtOrs7",
        "outputId": "57b77a9b-d56f-43ad-9a6c-d4fae7cc4f69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1850x1050 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def plot_history(history):\n",
        "  \"\"\"\n",
        "    Plotting training and validation learning curves.\n",
        "\n",
        "    Args:\n",
        "      history: model history with all the metric measures\n",
        "  \"\"\"\n",
        "  fig, (ax1, ax2) = plt.subplots(2)\n",
        "\n",
        "  fig.set_size_inches(18.5, 10.5)\n",
        "\n",
        "  # Plot loss\n",
        "  ax1.set_title('Loss')\n",
        "  ax1.plot(history.history['loss'], label = 'train')\n",
        "  ax1.plot(history.history['val_loss'], label = 'test')\n",
        "  ax1.set_ylabel('Loss')\n",
        "\n",
        "  # Determine upper bound of y-axis\n",
        "  max_loss = max(history.history['loss'] + history.history['val_loss'])\n",
        "\n",
        "  ax1.set_ylim([0, np.ceil(max_loss)])\n",
        "  ax1.set_xlabel('Epoch')\n",
        "  ax1.legend(['Train', 'Validation'])\n",
        "\n",
        "  # Plot accuracy\n",
        "  ax2.set_title('Accuracy')\n",
        "  ax2.plot(history.history['accuracy'],  label = 'train')\n",
        "  ax2.plot(history.history['val_accuracy'], label = 'test')\n",
        "  ax2.set_ylabel('Accuracy')\n",
        "  ax2.set_ylim([0, 1])\n",
        "  ax2.set_xlabel('Epoch')\n",
        "  ax2.legend(['Train', 'Validation'])\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "plot_history(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJrGF0Sss8E0"
      },
      "source": [
        "## Evaluate the model\n",
        "\n",
        "Use Keras `Model.evaluate` to get the loss and accuracy on the test dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "Hev0hMCxOtfy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f51235b1-d758-4297-ffdd-be861665617c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 3s/step - accuracy: 0.4424 - loss: 1.2705\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.5096153616905212, 'loss': 1.207253098487854}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "model.evaluate(test_ds, return_dict=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-F73GxD1-yc8"
      },
      "source": [
        "To visualize model performance further, use a [confusion matrix](https://www.tensorflow.org/api_docs/python/tf/math/confusion_matrix). The confusion matrix allows you to assess the performance of the classification model beyond accuracy. In order to build the confusion matrix for this multi-class classification problem, get the actual values in the test set and the predicted values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "Yw-6rG5V-0L-"
      },
      "outputs": [],
      "source": [
        "def get_actual_predicted_labels(dataset):\n",
        "    print(\"Getting ground truths\")\n",
        "    actual = tf.concat([labels for _, labels in dataset], axis=0)\n",
        "    print(\"Getting predictions\")\n",
        "    preds_proba = model.predict(dataset)\n",
        "    predicted = tf.argmax(preds_proba, axis=1)\n",
        "    return actual, predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "aln6qWW_-2dk"
      },
      "outputs": [],
      "source": [
        "def plot_confusion_matrix(actual, predicted, labels, ds_type):\n",
        "  cm = tf.math.confusion_matrix(actual, predicted)\n",
        "  ax = sns.heatmap(cm, annot=True, fmt='g')\n",
        "  sns.set(rc={'figure.figsize':(12, 12)})\n",
        "  sns.set(font_scale=1.4)\n",
        "  ax.set_title('Confusion matrix of bend classification for ' + ds_type)\n",
        "  ax.set_xlabel('Predicted Action')\n",
        "  ax.set_ylabel('Actual Action')\n",
        "  plt.xticks(rotation=90)\n",
        "  plt.yticks(rotation=0)\n",
        "  ax.xaxis.set_ticklabels(labels)\n",
        "  ax.yaxis.set_ticklabels(labels)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_learning_rate(history):\n",
        "  fig, (ax1) = plt.subplots(1)\n",
        "\n",
        "  fig.set_size_inches(10, 10)\n",
        "\n",
        "  # Plot loss\n",
        "  ax1.set_title('Learning Rate')\n",
        "  ax1.plot(history.history['learning_rate'], label = 'Learning Rate')\n",
        "  ax1.set_ylabel('Learning Rate')\n",
        "  ax1.set_xlabel('Epoch')\n",
        "  ax1.legend(['Learning Rate'])\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "plot_learning_rate(history)"
      ],
      "metadata": {
        "id": "-3KgiwWYp3nt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "outputId": "1199abff-3ffe-48fa-84ab-e784707b9cba"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "tfQ3VAGd-4Az"
      },
      "outputs": [],
      "source": [
        "fg = FrameGenerator(subset_paths['train'], n_frames, training=True)\n",
        "labels = list(fg.class_ids_for_name.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "1ucGpbiA-5qi",
        "outputId": "cf36ffbf-8936-4df8-fb5b-399edad39d58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Getting ground truths\n",
            "Getting predictions\n",
            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "actual, predicted = get_actual_predicted_labels(val_ds)\n",
        "plot_confusion_matrix(actual, predicted, labels, 'Validation Set')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "Mfr7AT5T-7ZD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5e74667a-e6c4-49b6-9789-a6ede016fe73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Getting ground truths\n",
            "Getting predictions\n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2s/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
            "  self._interrupted_warning()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": 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M8+fPJztudHS08fPPPxtXr15NtjwuLs5Yu3atUbt2baNGjRrG5cuX0/34PahFixbmx3L48OHGnTt3zOvu37+frmP169fP8PT0NF588UXzdZ87d86oU6eO4enpafj5+aX9wKXi7NmzRs2aNQ1PT09jzJgxRkREhHndn3/+adSvX9/w9PQ0/vvf/6bY9/333zc8PT2N6dOnZ+ichpH8vVOrVi1j8+bN5nWRkZHG6NGjza+Pfz4OBw4cMKpXr27UqFHDmD9/vhEdHW1ed+7cOaNLly6Gp6enMWrUqGT7Pfie8/b2Nn744QcjLi7OvH7OnDnm10ZQUFCKx6lGjRqGp6en8fHHH5vPmZCQYPz8889GtWrVjOrVqxuenp7G3r170/043L9/32jXrp35Hvb3338nW3/jxg1j7ty5xs2bN1O9jn86cuSIsW3btmSPiWEYxs2bN42xY8canp6exuuvv55snTX3jP79+xuenp7Gu+++m+L9e/PmTePHH380bty4kWx5averJEnvjbQeu7TWnzlzxqhdu7bh6elpDB48OMV798yZM8Y333yTbFlgYKCxYcMG4969e8mWh4eHG1OnTjU8PT2Ndu3apbhnJ93TWrRokWqMSbLTvfRh7+Gk+2mjRo2M/fv3m5eHh4cbw4cPT/M9On36dPO1Nm/e3Dh69Kh53YP3uoxIOmZqj/+bb75peHp6Gj169DBOnTplXh4fH2/88MMPRtWqVY2aNWsa586dM68LCAgwPD09jXr16qV4XSUkJBj79u0z1q5dm2x5ev5dtuTixYtGtWrVzNexZMmSVP89SU10dLTRoUMHw9PT0+jfv78RHBycbN3kyZMNT09P44knnkj2b55hWH7vAch6DrZO3gBAkqSy9H9+k5pekZGR+uGHHyQlDrF4cDiEi4uLRo4cqQYNGig+Pj7NXimxsbEaPXp0suE57u7u+vDDD+Xs7KyQkJAsKVNOTVhYmG7fvq18+fKpV69ecnJKXixYqVKlDFXozJ49W5LUvXt39ejRQw4Oif8EODo66rXXXtPzzz8vKXHa5dTExsaqX79+5u0kycnJSSNHjlShQoUUGRkpf3//DF1jksaNG2vAgAHma3RyctL777+vXLly6cqVK7p8+bI+//xzFShQwLxPt27d5O3tLcMwtH379mTHc3FxUffu3VW8ePFkyx0dHfXss8/qlVdeUWxsrH799Ver4k1SoUIFTZo0KdmsPOnt9TBhwgQVL15chw4d0owZMxQTE6Phw4crMjJSzz//vDp16pShWL7//ntFR0fL09NTH3/8cbJhFo0bNzZXYPzyyy+6du1aho6dHrGxserfv7+5MktK7L/w8ccfq0KFCoqMjNT8+fOT7fPFF18oLi5O7777rl555ZVkVTYVKlTQjBkz5O7urlWrVpnvD//UoUMHvfrqq8mGI/Xr10+enp4yDEPbtm1Ltv3333+v2NhYVa9eXWPHjjWf02QyqXv37urSpYt5lq+MWLZsmQIDA1WwYEHNnz8/RUVD4cKF1adPn2TfFltSq1YtNWvWLEXlUaFChfTxxx+rePHi+vPPP3X9+nXzOmvuGUlVMm+88UaK6p5ChQrppZdeSnN4UGaaNm2a7t+/Lx8fH02dOjXFe7dy5crq27dvsmUVK1ZUmzZtlDt37mTL8+TJo7ffflv16tVTYGCgjh49mqmx2vO9NDXBwcFatWqVJGncuHHJhizmyZNHkyZNUunSpZP9G/pP8fHx+vLLL1WzZk3zsszua7N7927t2LFDpUqV0pw5c5JVUzg4OOjVV1/VSy+9pOjo6GSNh5New40aNUoxFMxkMqlhw4aZPgtemTJlNGrUKDk4OCgkJEQffvihmjdvriZNmqhPnz765ptv0qzMWb16tU6ePClvb29NmzZNHh4e5nUuLi5699131aJFC926dUvLli3L1LgBPBqSJwDsxr179yQp2Ye+jDhw4IDu3bunXLlyJRuO8aDXX39dUuIfaTExMSnW582bVx06dEixvGjRouY/cC5evGhVfBlVuHBhubq6Kjw8PEVyIKMCAwPNcb/22mupbvPGG29Ikk6fPq3Lly+nuk3Pnj1TLMuVK5eqV68uyfrH5j//+U+KZQUKFDA/5u3bt0/xAUmS+QNqWuc9evSovvzySw0YMEC9evVSjx491KNHD/MsGidOnLAq3iQdO3ZM8QE1vQoWLKgvvvhCjo6Omjt3rvr376+AgACVLVtWH330UYaPt2PHDkmJvSZSGzrUsWNHFS5cWLGxsdq9e7dVMVvi5OSkl156KcVyBwcH9erVS5KSJTJCQ0N16NAhOTk5pTn0rGTJkvL29lZ8fHyaM2uldk5Jqlu3rqSUr42kxymtYRGvvPJKqssfZtOmTZISX8uZlWyIjo7Wr7/+qnHjxumNN95Qz549za/hiIgIGYaR7DVszT0j6T22du1aJSQkZErcGRUdHW1+bfTr18+cjEiP+Ph4bdmyRR9//LH69u2rl156yfwYBQUFSUocmpRZ7P1empqdO3cqISFBpUqVUps2bVKsd3JyMr/u/5lsTFKpUqVUpx3OTOvWrZOUeL9Pa5r4Z555RlJiX58kScMejxw5Yu5F8jj06tVLy5Yt03PPPWf+9+nGjRvasWOHpkyZorZt22rUqFGKjIxMtl/SdXbt2jXNabCTnqe9e/dm4RUAyCh6ngCwG0nfev7zD430On/+vKTEDwOpfdCW/jcuODo6WiEhIapQoUKy9eXKlUuzZ0WRIkV04cIFRUREWBVfRjk4OOj111/X7Nmz1bdvX3l6esrX11d16tRRw4YNk03f/DBJj01ST4nUVK5cWY6OjoqPj9e5c+eS9eGQEj/sP1j58aCkD4vWPjZp9VMoXLiwzp07Z3G9lPI1ExcXp9GjR2v16tUWz3v79u2MB/uAKlWqPNL+Pj4+6t+/v2bOnKldu3bJ2dlZU6ZMSbO/R1rCw8PNFQhpjX13dnZWxYoVdfPmTfM3tZmpRIkSafaLSHqcLl26pJiYGLm4uOjkyZOSEl/nffr0SfO4Sd/eXrlyJdX1afUaSO01+eDjlNZzV6FCBTk5OWW4+iSpV0pS0uZRBQYGqm/fvgoODra43YOvYWvuGW+88YZ2796tuXPnatWqVWratKnq1q2r+vXrq1KlSplyLQ9z4cIFczI7I4/ftWvX1K9fv4cmRx71ff4ge7+XWoq5UqVKaSamku4bwcHB5vfogypXrpxp8aQl6Z6wefNmHTx4MNVtkmYVunr1qnlZnTp15OPjo3379qlNmzaqX7++GjZsqDp16qh+/fpp/j2QGby9vfXll18qPj5eZ86c0fHjx+Xv72/u4ebn56ewsDB98803Ka5z8eLF5j4t/xQeHi4p7fseANsgeQLAbiSVaVv7zVHSH5tFihRJc5sHu+Kn9seppaqXpD86DcOwKj5rDBkyRKVKldKPP/6okydP6vTp01qwYIFMJpN8fX31/vvvpzrzwD8lXaulb8SdnJxUsGBB3bhx47E/NmlNr5mUyHrY+n+ed968eVq9erVy5cql4cOH68knn1TJkiXl5uYmk8mk5cuXa8yYMVYNz0hP3BnRuHFj8+wYNWrUSFYWn14PPl+WXv9JH56zIgFo6bwPvu4iIiLk4uKiO3fuSJJiYmLMTXoteXBK8Ael9bpM7TX54HWn9V5wdHRUgQIFdOPGjYfG9KCkyrn0Nhy1JCEhQYMHD1ZwcLCqV6+uwYMHq0aNGipYsKD5Q+1LL72kAwcOpHgNZ/Se0bhxYy1YsEBz5szRvn375OfnJz8/P0mJH5iHDBmSarVCZkp67BwdHTP0QXfUqFEKCAhQmTJlNGzYMNWtW1dFihQxP0ZJs1Y96vv8QfZ+L01NUgyWEu4Prkt6jz7I2orQjLh7966kxGTaw5rRPng/MJlMmjNnjjkBuG/fPvOMSq6urmrfvr3efffddA+Zs4ajo6OqVq2qqlWrqkuXLgoPD9eoUaO0efNmbdu2TYcPH1adOnUk/e8609OcOq37HgDbIHkCwG7Ur19fixYt0tmzZ3Xz5s0Ml74/WDablgd7PWTlt1EPSusD/oPSqrYxmUzq1q2bunXrprCwMB06dEj79u3TunXrtHv3br3yyitas2ZNiv4A/5R0rTdv3kxzm7i4ON26dSvZ9tlV0oe/999/P9VhHZn5TfSjCA8P14gRIyQlfmg6fPiwFi5cmOHZph58vm7cuJFsDP2DkqousuL5tfS+e/B1l3TupA9jpUqV0h9//JHp8aTmweu+efNmqo9TfHy8Va+PPHny6Pbt2+ZvjB/F0aNHFRgYKFdXV33//fepfuhLK0Zr7hk+Pj7y8fHR/fv3dfjwYR08eFCbNm3SqVOnNGTIEM2dO1fNmjV75OtKS1KlVXx8vCIiItL1+rx+/bp27dolSfr6669TrSTKivd5dryXJsXwYH+cf3pwna1iTronfPbZZ+rSpUuG9s2dO7eGDRumYcOGKSgoSIcOHdLu3bu1efNmrVixQufOndNPP/2UYlr7rJI3b15NmDBBW7duVUJCQrLkibu7u+7evasFCxaYZwACkD3Q8wSA3XjqqadUoEABJSQkmKeAzIiKFStKkkJCQtL8Zj3pm55cuXKl+QEzsyVVJ1j6cJk0Nt+SQoUKqXXr1ho9erQ2bNig0qVL6/bt21q7du1D9016bKKiotIcS3/27FnFx8dL0mMr188qSUMdHmyM+KAjR448znDS9MEHHygkJES1atXSV199JUmaPHmyuaw7vfLmzWv+5jitbzPj4uLMw3WSXg+ZKTQ01FxB8E9nzpyRlNhkMekbbS8vL0mJ5fePK5n14ON09uzZVLc5f/68VZUKScMe/vrrL+sD/H9Jr99KlSqlmji5c+dOuqaJzeg9w83NTb6+vho0aJBWr15trjhZsmSJ9ReTDhUqVDBPD5vexy/pMSpQoECqiZO4uDjzFPL/9LDpxC3JjvfSpJgDAwPT7GuTdN948D36uCW9hx61KXu5cuX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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "actual, predicted = get_actual_predicted_labels(test_ds)\n",
        "plot_confusion_matrix(actual, predicted, labels, 'Test Set')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FefzeIZz-9aI"
      },
      "source": [
        "The precision and recall values for each class can also be calculated using a confusion matrix."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "dq95-56Z-_E2"
      },
      "outputs": [],
      "source": [
        "def calculate_classification_metrics(y_actual, y_pred, labels):\n",
        "  \"\"\"\n",
        "    Calculate the precision and recall of a classification model using the ground truth and\n",
        "    predicted values.\n",
        "\n",
        "    Args:\n",
        "      y_actual: Ground truth labels.\n",
        "      y_pred: Predicted labels.\n",
        "      labels: List of classification labels.\n",
        "\n",
        "    Return:\n",
        "      Precision and recall measures.\n",
        "  \"\"\"\n",
        "  cm = tf.math.confusion_matrix(y_actual, y_pred)\n",
        "  tp = np.diag(cm) # Diagonal represents true positives\n",
        "  precision = dict()\n",
        "  recall = dict()\n",
        "  for i in range(len(labels)):\n",
        "    col = cm[:, i]\n",
        "    fp = np.sum(col) - tp[i] # Sum of column minus true positive is false negative\n",
        "\n",
        "    row = cm[i, :]\n",
        "    fn = np.sum(row) - tp[i] # Sum of row minus true positive, is false negative\n",
        "\n",
        "    precision[labels[i]] = tp[i] / (tp[i] + fp) # Precision\n",
        "\n",
        "    recall[labels[i]] = tp[i] / (tp[i] + fn) # Recall\n",
        "\n",
        "  return precision, recall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "4jSEonYQ_BZt"
      },
      "outputs": [],
      "source": [
        "precision, recall = calculate_classification_metrics(actual, predicted, labels) # Test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "hXvTW1Df_DV8",
        "outputId": "9e1ac892-0ced-42c5-d2e1-eb5ab0ce5f1f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'moderate_left': np.float64(0.5),\n",
              " 'moderate_right': np.float64(0.5862068965517241),\n",
              " 'sharp_left': np.float64(0.3333333333333333),\n",
              " 'sharp_right': np.float64(0.4)}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "precision"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "be1yrQl5_EYF",
        "outputId": "9ae1848c-ab0d-4062-af7a-08d6e463a047",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'moderate_left': np.float64(0.6578947368421053),\n",
              " 'moderate_right': np.float64(0.5),\n",
              " 'sharp_left': np.float64(0.3125),\n",
              " 'sharp_right': np.float64(0.25)}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "recall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "ZAOWXZ0ER8wt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfba6a4f-d80f-4f6b-fb2b-5c3b7f978636"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'moderate_left': np.float64(0.5681818181818182),\n",
              " 'moderate_right': np.float64(0.5396825396825397),\n",
              " 'sharp_left': np.float64(0.3225806451612903),\n",
              " 'sharp_right': np.float64(0.3076923076923077)}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
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