notebooks/02_keyword_spotting_intro.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dsT1FPnBguaJ"
      },
      "source": [
        "# Keyword Spotting: general training notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0lYQXwCmh402"
      },
      "source": [
        "## Get scripts from GitHub and install wget\n",
        "Only required when running on Colab. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_V8fO0IpyMLd",
        "outputId": "54375c02-0a7d-4aa0-ab34-90b820a16853"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9674 sha256=783c880e859c5b163babbda9a961b81190a09a405565ca321869a3a58149c759\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/a8/c3/3cf2c14a1837a4e04bd98631724e81f33f462d86a1d895fae0\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n"
          ]
        }
      ],
      "source": [
        "! pip install wget"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7ZaKB5zhwiz",
        "outputId": "70f0bc51-1c1c-4956-8790-6d4a859c2ed4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-01-25 16:41:25--  https://raw.githubusercontent.com/NicolaZomer/Keyword_Spotting/main/utils/models_utils.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
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            "Saving to: ‘models_utils.py’\n",
            "\n",
            "\rmodels_utils.py       0%[                    ]       0  --.-KB/s               \rmodels_utils.py     100%[===================>]  10.76K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-01-25 16:41:25 (83.9 MB/s) - ‘models_utils.py’ saved [11020/11020]\n",
            "\n",
            "--2023-01-25 16:41:26--  https://raw.githubusercontent.com/NicolaZomer/Keyword_Spotting/main/utils/plot_utils.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n",
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            "Saving to: ‘plot_utils.py’\n",
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            "\n",
            "2023-01-25 16:41:26 (38.5 MB/s) - ‘plot_utils.py’ saved [2253/2253]\n",
            "\n",
            "--2023-01-25 16:41:26--  https://raw.githubusercontent.com/NicolaZomer/Keyword_Spotting/main/utils/preprocessing_utils.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
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            "Saving to: ‘preprocessing_utils.py’\n",
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            "\n",
            "2023-01-25 16:41:26 (48.4 MB/s) - ‘preprocessing_utils.py’ saved [9421/9421]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "! wget -nc https://raw.githubusercontent.com/NicolaZomer/Keyword_Spotting/main/utils/models_utils.py\n",
        "! wget -nc https://raw.githubusercontent.com/NicolaZomer/Keyword_Spotting/main/utils/plot_utils.py\n",
        "! wget -nc https://raw.githubusercontent.com/NicolaZomer/Keyword_Spotting/main/utils/preprocessing_utils.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFq83kJvyatv"
      },
      "source": [
        "## Import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Y_4uCFuXgucW"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pathlib\n",
        "import requests\n",
        "import wget\n",
        "import inspect\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "\n",
        "from IPython.display import display, Audio, Image\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, accuracy_score, log_loss, cohen_kappa_score\n",
        "\n",
        "from scipy.io import wavfile\n",
        "\n",
        "from models_utils import available_models, select_model\n",
        "import plot_utils \n",
        "\n",
        "plt.rcParams['font.size'] = '12'\n",
        "# %matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v58f7JtLicT4",
        "outputId": "fa753c11-e9ba-499d-cfee-2f53449c976e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-4-8b196a1e910a>:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU Available: True\n",
            "Version: 2.9.2\n"
          ]
        }
      ],
      "source": [
        "print(\"GPU Available:\", tf.test.is_gpu_available())\n",
        "print(\"Version:\", tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMYTM7mrgufm"
      },
      "source": [
        "To extract MFCC features from audio signals we use the Python library [python_speech_features](https://github.com/jameslyons/python_speech_features)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83mmHptRgufv",
        "outputId": "3e042f3c-bd58-4999-b6a5-dd0b9fc54af9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting python_speech_features\n",
            "  Downloading python_speech_features-0.6.tar.gz (5.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: python_speech_features\n",
            "  Building wheel for python_speech_features (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python_speech_features: filename=python_speech_features-0.6-py3-none-any.whl size=5889 sha256=d03ab041a39132b9330ae2b734f662c5b2e3f2233d5978885df68f356457bc45\n",
            "  Stored in directory: /root/.cache/pip/wheels/5b/60/87/28af2605138deac93d162904df42b6fdda1dab9b8757c62aa3\n",
            "Successfully built python_speech_features\n",
            "Installing collected packages: python_speech_features\n",
            "Successfully installed python_speech_features-0.6\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting visualkeras\n",
            "  Downloading visualkeras-0.0.2-py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from visualkeras) (7.1.2)\n",
            "Collecting aggdraw>=1.3.11\n",
            "  Downloading aggdraw-1.3.15-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (992 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m992.2/992.2 KB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.8/dist-packages (from visualkeras) (1.21.6)\n",
            "Installing collected packages: aggdraw, visualkeras\n",
            "Successfully installed aggdraw-1.3.15 visualkeras-0.0.2\n"
          ]
        }
      ],
      "source": [
        "# uncomment to install python_speech_features\n",
        "! pip install python_speech_features\n",
        "! pip install visualkeras\n",
        "\n",
        "from python_speech_features import logfbank, mfcc, delta\n",
        "import visualkeras\n",
        "\n",
        "import preprocessing_utils\n",
        "from preprocessing_utils import create_dataset, remove_file_starting_with"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7N6hBU0Tgugj"
      },
      "source": [
        "To perform Discrete Wavelet Transform on audio signals we use the Python library [PyWavelets](https://github.com/PyWavelets/pywt)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhiIcU5Xgugy",
        "outputId": "3f86e44a-683f-4d54-bd7b-c6d3e5c556b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.8/dist-packages (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from PyWavelets) (1.21.6)\n"
          ]
        }
      ],
      "source": [
        "# uncomment to install PyWavelets\n",
        "! pip install PyWavelets\n",
        "\n",
        "from pywt import dwt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5J6-132RguqS"
      },
      "source": [
        "## 1 - Data loading \n",
        "In this project we use the [Speech Commands dataset](https://www.tensorflow.org/datasets/catalog/speech_commands), which contains short (one-second long) audio clips of English commands, stored as audio files in the WAV format. More in detail, the version 0.02 of the dataset contains 105.829 utterances of 35 short words, by thousands of different people. It was released on April 11th 2018 under [Creative Commons BY 4.0 license](https://creativecommons.org/licenses/by/4.0/) and collected using crowdsourcing, through [AIY](https://aiyprojects.withgoogle.com/) by Google. Some of these words are \"yes\", \"no\", \"up\", \"down\", \"left\", \"right\", \"on\", \"off\", \"stop\" and \"go\".\n",
        "\n",
        "### 1.1 - Download data and create data folder\n",
        "We import both a smaller version of the Speech Commands dataset and the full version. The mini dataset can be used for testing and demo reasons, however the entire work will be focused only on the complete dataset (or a subset of it). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMEgF_1Oguqb",
        "outputId": "210f9f41-38d0-4974-e1ec-30c281931369"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz\n",
            "2428923189/2428923189 [==============================] - 10s 0us/step\n",
            "\n",
            "\n",
            "Working with the full dataset!\n"
          ]
        }
      ],
      "source": [
        "choose_dataset = 2 # 1 for mini, 2 for full\n",
        "download_also_mini = False\n",
        "\n",
        "DATASET_PATH_mini = 'data/mini_speech_commands'\n",
        "DATASET_PATH_full = 'data/full_speech_commands'\n",
        "\n",
        "data_dir_mini = pathlib.Path(DATASET_PATH_mini)\n",
        "if not data_dir_mini.exists() and download_also_mini:\n",
        "  tf.keras.utils.get_file(\n",
        "      'mini_speech_commands.zip',\n",
        "      origin=\"http://storage.googleapis.com/download.tensorflow.org/data/mini_speech_commands.zip\",\n",
        "      extract=True,\n",
        "      cache_dir='.', cache_subdir='data')\n",
        "  print('\\n')\n",
        "  \n",
        "data_dir_full = pathlib.Path(DATASET_PATH_full)\n",
        "if not data_dir_full.exists():\n",
        "  tf.keras.utils.get_file(\n",
        "      'full_speech_commands.zip',\n",
        "      origin=\"http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz\",\n",
        "      extract=True,\n",
        "      cache_dir='.', cache_subdir='data/full_speech_commands')\n",
        "  print('\\n')\n",
        "  \n",
        "if choose_dataset==1:\n",
        "  data_dir = data_dir_mini\n",
        "  data_path = DATASET_PATH_mini\n",
        "  print('Working with the mini dataset!')\n",
        "elif choose_dataset==2:\n",
        "  data_dir = data_dir_full\n",
        "  data_path = DATASET_PATH_full\n",
        "  print('Working with the full dataset!')\n",
        "else:\n",
        "  print(\"Error, 'choose_dataset' must be 1 or 2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TajS9BZPgurM"
      },
      "source": [
        "### 1.2 - Dataset organization\n",
        "The audio files are organized into folders based on the word they contain. For more details see \\[[Warden18](https://arxiv.org/abs/1804.03209)\\].\n",
        "\n",
        "**Mini dataset** <br>\n",
        "The dataset's audio clips are stored in 8 folders corresponding to each speech command: `down`, `go`, `left`, `no`, `right`, `stop`, `up`, `yes`, and `stop`. \n",
        "\n",
        "**Full dataset** <br>\n",
        "The dataset's audio clips are stored in 35 folders corresponding to each speech command: <br>\n",
        "`backward`, `bed`, `bird`, `cat`, `dog`, `down`, `eight`, `five`, `follow`, `forward`, `four`, `go`, `happy`, `house`, `learn`, `left`, `marvin`, `nine`, `no`, `off`, `on`, `one`, `right`, `seven`, `sheila`, `six`, `stop`, `three`, `tree`, `two`, `up`, `visual`, `wow`, `yes`, `zero`\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gE9p410cgur8",
        "outputId": "e8d92110-5bb5-403b-baac-cbca5a0b149a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Commands: ['backward', 'bed', 'bird', 'cat', 'dog', 'down', 'eight', 'five', 'follow', 'forward', 'four', 'go', 'happy', 'house', 'learn', 'left', 'marvin', 'nine', 'no', 'off', 'on', 'one', 'right', 'seven', 'sheila', 'six', 'stop', 'three', 'tree', 'two', 'up', 'visual', 'wow', 'yes', 'zero']\n",
            "\n",
            "Number of commands: 35\n"
          ]
        }
      ],
      "source": [
        "commands = np.array(tf.io.gfile.listdir(str(data_dir)))\n",
        "\n",
        "if choose_dataset==1:\n",
        "  mask = commands != 'README.md'\n",
        "\n",
        "elif choose_dataset==2:\n",
        "  mask = (\n",
        "      np.array(commands != 'README.md')                 &\n",
        "      np.array(commands != 'LICENSE')                   &\n",
        "      np.array(commands != '.DS_Store')                 &\n",
        "      np.array(commands != 'training_list.txt')         &  \n",
        "      np.array(commands != 'validation_list.txt')       &  \n",
        "      np.array(commands != 'testing_list.txt')          &  \n",
        "      np.array(commands != 'full_speech_commands.zip')  &\n",
        "      np.array(commands != '_background_noise_')        \n",
        "  )\n",
        "  \n",
        "commands = sorted(commands[mask])\n",
        "print('Commands:', commands)\n",
        "\n",
        "print('\\nNumber of commands: %i' %len(commands))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_HQ4GIRguvw"
      },
      "source": [
        "### 1.3 - Processing of the original audio files \n",
        "The original audio files were captured in a variety of formats and then converted to .WAV file at a 16000 sample rate. The audio was then trimmed to a one second length to align most utterances, using the [extract_loudest_section](https://github.com/petewarden/extract_loudest_section) tool. The audio files were then screened for silence or incorrect words, and arranged into folders by label.\n",
        "\n",
        "### 1.4 - Training, testing and validation sets\n",
        "The text files `validation_list.txt` and `testing_list.txt` available in the [Speech Commands dataset](https://www.tensorflow.org/datasets/catalog/speech_commands) contain the paths to all the files in each set, with each path on a new line. Any files that aren't in either of these lists can be considered to be part of the training set. The validation and test set sizes thus obtained are approximatly 10% of the size of the complete set. For more information on how the partition is made, see the README.md file of the dataset. \n",
        "\n",
        "The following code performs these operations:\n",
        "- Load the reference validation and testing files as separated Pandas dataframe objects. \n",
        "- Store the path of all audio files in a dictionary. \n",
        "- Create the reference file for training data, by storing in a Pandas dataframe object the paths of all files that aren't in the validation or testing list. If it has been already created, we simply import it as we did for the reference validation and testing files. \n",
        "\n",
        "To each file we associate the corresponding label (i.e. the name of the folder it is in), so that we get a proper labeling for each file to be used with our loss function. Of course, we still need to load the actual data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fygRaSrguv0",
        "outputId": "29f24dda-4b53-4b55-926c-0e8af1895f89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 105829 audio files in the dataset\n",
            "File downloaded from GitHub\n"
          ]
        }
      ],
      "source": [
        "training_list_path = data_path + \"/training_list.txt\"\n",
        "\n",
        "reference_df_val = pd.read_csv(data_path+\"/validation_list.txt\", sep=\"/\", header=None, names=['label', 'file'])\n",
        "reference_df_test = pd.read_csv(data_path+\"/testing_list.txt\", sep=\"/\", header=None, names=['label', 'file'])\n",
        "\n",
        "# all audio files\n",
        "all_files = {}\n",
        "for command in commands:\n",
        "    all_files[command] = os.listdir(data_path+'/'+command)\n",
        "    \n",
        "num_files = 0\n",
        "for x_ in all_files.values():\n",
        "    num_files += len(x_)\n",
        "        \n",
        "print('There are %i audio files in the dataset' %num_files)\n",
        "\n",
        "# reference file for training set\n",
        "# if already exists import it\n",
        "if pathlib.Path(training_list_path).exists(): \n",
        "    reference_df_train =  pd.read_csv(data_path+\"/training_list.txt\", sep=\"/\", header=None, names=['label', 'file'])\n",
        "    \n",
        "# else search if it can be dowloaded\n",
        "else: \n",
        "  training_list_url = 'https://raw.githubusercontent.com/NicolaZomer/Keyword_Spotting/main/training_list.txt'\n",
        "  response = requests.get(training_list_url)\n",
        "\n",
        "  if response.status_code == 200:\n",
        "      wget.download(training_list_url, out=str(data_path))\n",
        "      print('File downloaded from GitHub')\n",
        "\n",
        "      reference_df_train =  pd.read_csv(data_path+\"/training_list.txt\", sep=\"/\", header=None, names=['label', 'file'])\n",
        "\n",
        "  else: # create it and store the dataframe as a .txt file; creating the pd object can take some time\n",
        "      print('File not found, error '+str(response.status_code))\n",
        "\n",
        "      reference_df_train = pd.DataFrame(columns=['label', 'file'])\n",
        "      for i, command in enumerate(commands):\n",
        "          print(\"Processing command '%s', %i commands missing...\" %(command, len(commands)-i-1))\n",
        "          for file in all_files[command]:\n",
        "              if file in reference_df_val['file'].values:\n",
        "                  continue\n",
        "              if file in reference_df_test['file'].values:\n",
        "                  continue\n",
        "\n",
        "              reference_df_train.loc[len(reference_df_train)] = [command, file]\n",
        "\n",
        "      print('Done!')\n",
        "      print('\\n')\n",
        "\n",
        "      # store reference_df_train in .txt file\n",
        "      reference_df_train.to_csv(data_path+'/training_list.txt', header=None, index=None, sep='/', mode='a')\n",
        "\n",
        "# print the result\n",
        "# print('----------------------------')\n",
        "# print('TRAINING FILES:')\n",
        "# print(reference_df_train)\n",
        "# print('----------------------------')\n",
        "# print('VALIDATION FILES:')\n",
        "# print(reference_df_val)\n",
        "# print('----------------------------')\n",
        "# print('TESTING FILES:')\n",
        "# print(reference_df_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2naK4mEeguv5"
      },
      "source": [
        "Encode target labels with value between 0 and n_commands-1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "tyTfKJQbguv6"
      },
      "outputs": [],
      "source": [
        "label_to_class = {commands[i]:i for i in range(len(commands))}\n",
        "# label_to_class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "9bEaJKJkguv7"
      },
      "outputs": [],
      "source": [
        "class_to_label = {i:commands[i] for i in range(len(commands))}\n",
        "# class_to_label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vgwIDfSguv8",
        "outputId": "9e899968-ef00-4226-e4cb-f0db0168bc3d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TRAINING FILES:\n",
            "          label                   file  class\n",
            "0      backward  0165e0e8_nohash_0.wav      0\n",
            "1      backward  017c4098_nohash_0.wav      0\n",
            "2      backward  017c4098_nohash_1.wav      0\n",
            "3      backward  017c4098_nohash_2.wav      0\n",
            "4      backward  017c4098_nohash_3.wav      0\n",
            "...         ...                    ...    ...\n",
            "84838      zero  ffd2ba2f_nohash_1.wav     34\n",
            "84839      zero  ffd2ba2f_nohash_2.wav     34\n",
            "84840      zero  ffd2ba2f_nohash_3.wav     34\n",
            "84841      zero  ffd2ba2f_nohash_4.wav     34\n",
            "84842      zero  fffcabd1_nohash_0.wav     34\n",
            "\n",
            "[84843 rows x 3 columns]\n",
            "----------------------------\n",
            "VALIDATION FILES:\n",
            "      label                   file  class\n",
            "0     right  a69b9b3e_nohash_0.wav     22\n",
            "1     right  439c84f4_nohash_1.wav     22\n",
            "2     right  409c962a_nohash_1.wav     22\n",
            "3     right  dbaf8fc6_nohash_2.wav     22\n",
            "4     right  a6d586b7_nohash_1.wav     22\n",
            "...     ...                    ...    ...\n",
            "9976   four  d107dc42_nohash_0.wav     10\n",
            "9977   four  ad63d93c_nohash_0.wav     10\n",
            "9978   four  d3831f6a_nohash_1.wav     10\n",
            "9979   four  3c4aa5ef_nohash_3.wav     10\n",
            "9980   four  c6389ab0_nohash_0.wav     10\n",
            "\n",
            "[9981 rows x 3 columns]\n",
            "----------------------------\n",
            "TESTING FILES:\n",
            "       label                   file  class\n",
            "0      right  bb05582b_nohash_3.wav     22\n",
            "1      right  97f4c236_nohash_2.wav     22\n",
            "2      right  f2e59fea_nohash_3.wav     22\n",
            "3      right  fdb5155e_nohash_2.wav     22\n",
            "4      right  dc75148d_nohash_0.wav     22\n",
            "...      ...                    ...    ...\n",
            "11000   four  189cbabe_nohash_2.wav     10\n",
            "11001   four  8fe67225_nohash_3.wav     10\n",
            "11002   four  c9e251d2_nohash_1.wav     10\n",
            "11003   four  0c40e715_nohash_0.wav     10\n",
            "11004   four  8769c34c_nohash_3.wav     10\n",
            "\n",
            "[11005 rows x 3 columns]\n"
          ]
        }
      ],
      "source": [
        "reference_df_train['class'] = reference_df_train['label'].map(label_to_class)\n",
        "reference_df_val['class']   = reference_df_val['label'].map(label_to_class)\n",
        "reference_df_test['class']  = reference_df_test['label'].map(label_to_class)\n",
        "\n",
        "# print the result\n",
        "print('TRAINING FILES:')\n",
        "print(reference_df_train)\n",
        "print('----------------------------')\n",
        "print('VALIDATION FILES:')\n",
        "print(reference_df_val)\n",
        "print('----------------------------')\n",
        "print('TESTING FILES:')\n",
        "print(reference_df_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWHq02qWguwV"
      },
      "source": [
        "#### 1.3.1 - Smoke test\n",
        "We enable the possibility to perform some tests without loading the entire dataset by setting `smoke_test=True`. With this option the train, validation and test set are reduced to a fraction `smoke_test_frac` of elements. This can be useful to test if everything is working as intended before the full training, or if the current hardware is not powerful enough. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "kWb3R_eQguwW"
      },
      "outputs": [],
      "source": [
        "smoke_test = False\n",
        "smoke_test_frac = 0.2\n",
        "smoke_test_threshold = 1000 # minimum length of reduced datasets\n",
        "\n",
        "if smoke_test:\n",
        "    len_min = smoke_test_frac * min(len(reference_df_train), len(reference_df_val), len(reference_df_test))\n",
        "    if len_min < smoke_test_threshold:\n",
        "        raise Exception(f'One of the reduced datasets would have a number of samples < {smoke_test_threshold}! Select a larger value for \"smoke_test_frac\".')\n",
        "    else: \n",
        "        reference_df_train = reference_df_train.sample(frac=smoke_test_frac, replace=False)\n",
        "        reference_df_val   = reference_df_val.sample(frac=smoke_test_frac, replace=False)\n",
        "        reference_df_test  = reference_df_test.sample(frac=smoke_test_frac, replace=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdOpKmpwguwi",
        "outputId": "ef52aaab-dd51-4f21-f567-33c6e38e57e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TRAINING SET\n",
            "\tnumber of audio files: 84843\n",
            "\tpercentage of audio files: 80.17%\n",
            "VALIDATION SET\n",
            "\tnumber of audio files: 9981\n",
            "\tpercentage of audio files: 9.43%\n",
            "TEST SET\n",
            "\tnumber of audio files: 11005\n",
            "\tpercentage of audio files: 10.40%\n"
          ]
        }
      ],
      "source": [
        "print('TRAINING SET')\n",
        "print('\\tnumber of audio files: %i' %len(reference_df_train))\n",
        "print('\\tpercentage of audio files: %.2f%%' %(len(reference_df_train)/num_files*100))\n",
        "\n",
        "print('VALIDATION SET')\n",
        "print('\\tnumber of audio files: %i' %len(reference_df_val))\n",
        "print('\\tpercentage of audio files: %.2f%%' %(len(reference_df_val)/num_files*100))\n",
        "\n",
        "print('TEST SET')\n",
        "print('\\tnumber of audio files: %i' %len(reference_df_test))\n",
        "print('\\tpercentage of audio files: %.2f%%' %(len(reference_df_test)/num_files*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qpc9ckEFguwm"
      },
      "source": [
        "#### 1.3.2 - Dataset distribution\n",
        "Let's now plot the percentage of elements of each class in each set. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YhKiDxNuguwo",
        "outputId": "ad9468a7-ffee-432c-8cf9-ab6e51dc3cc6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x1512 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(3, 1, figsize=(14, 21))\n",
        "\n",
        "plot_utils.hist_class_distribution(reference_df_train, ax[0], commands)\n",
        "plot_utils.hist_class_distribution(reference_df_val, ax[1], commands)\n",
        "plot_utils.hist_class_distribution(reference_df_test, ax[2], commands)\n",
        "\n",
        "for i in range(3):\n",
        "    ax[i].set_ylabel('Normalized counts')\n",
        "    ax[i].grid(alpha=0.5)\n",
        "    ax[i].tick_params(axis='x', labelrotation=90)\n",
        "    \n",
        "ax[0].set_title('Training set distribution', fontsize=18)\n",
        "ax[1].set_title('Validation set distribution', fontsize=18)\n",
        "ax[2].set_title('Testing set distribution', fontsize=18)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OntieQwayQN"
      },
      "source": [
        "### 1.4 Noise loading \n",
        "We load here the available noisy audio signals and we defined the object `noise_dict`, which is required by the function `load_and_preprocessing_data` (see next section)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_Mm2LkLaxUT",
        "outputId": "2b89b0d4-4b57-40a9-9e05-e9086f8e0a30"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/content/preprocessing_utils.py:70: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
            "  _, data = wavfile.read(file_path)\n"
          ]
        }
      ],
      "source": [
        "noise_path = '_background_noise_'\n",
        "\n",
        "# load all noises \n",
        "noise_1 = preprocessing_utils.load_data('doing_the_dishes.wav', noise_path)\n",
        "noise_2 = preprocessing_utils.load_data('dude_miaowing.wav', noise_path)\n",
        "noise_3 = preprocessing_utils.load_data('exercise_bike.wav', noise_path)\n",
        "noise_4 = preprocessing_utils.load_data('pink_noise.wav', noise_path)\n",
        "noise_5 = preprocessing_utils.load_data('running_tap.wav', noise_path)\n",
        "noise_6 = preprocessing_utils.load_data('white_noise.wav', noise_path)\n",
        "\n",
        "noise_dict = {\n",
        "    '1': noise_1, \n",
        "    '2': noise_2, \n",
        "    '3': noise_3, \n",
        "    '4': noise_4, \n",
        "    '5': noise_5, \n",
        "    '6': noise_6\n",
        "}"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "af53Vbe4gu07"
      },
      "source": [
        "## 2 - Data Loading using the Dataset API\n",
        "As the dataset is quite large (>5GiB), there is the risk that it will not entirely fit in the memory, plus the preprocessing pipeline would be computationally intensive. For these reasons, we want to implement a proper and optimized data-loading pipeline, as reading the data can be the main bottleneck of the entire training process. This can be done by exploit the [Tensorflow Dataset API](https://www.tensorflow.org/guide/datasets).\n",
        "\n",
        "The main goal of this phase is to create a `tf.data.Dataset` object to efficiently load and preprocess your data. \n",
        "\n",
        "### 2.1 Loading and preprocessing\n",
        "First, to load and preprocess the data, we use the function `load_and_preprocess_data`, defined in the file `preprocessing_utils.py`. This function puts together all loading and preprocessing methods that can be explored in the notebook `B1_data_analysis_and_preprocessing_inspection.ipynb`. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPDcrdmCY17R",
        "outputId": "d0be93cf-1cd2-470e-88cc-109330213ef8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "def load_and_preprocess_data(file_name, file_label, data_path_=data_path):\n",
            "\n",
            "    # load data\n",
            "    data = load_data(file_name, file_label, data_path_=data_path_)\n",
            "    \n",
            "    # padding/trimming\n",
            "    data = padding_trimming(data)\n",
            "        \n",
            "    # TensorFlow takes as input 32-bit floating point data\n",
            "    return data.astype(np.float32)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(inspect.getsource(preprocessing_utils.load_and_preprocess_data))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "2fIv5VEdgu1a"
      },
      "source": [
        "### 2.2 - Dataset definition\n",
        "We now use the function `create_dataset`, defined in the file `preprocessing_utils.py`. This function initializes a `tf.data.Dataset` object and properly maps all the required processing. More details on this procedure can be found [here](https://www.tensorflow.org/guide/performance/datasets), along with additional tips and tricks for performance optimization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "IpL9t-wfcgSu"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "def create_dataset(df, is_train=True, data_path_=data_path, cache_file=None, shuffle=True, apply_random_shift=False, apply_background_noise=False, noise_dict=None, noise_reduction=0.5, features=1, batch_size=32):\n",
            "    '''\n",
            "    features:\n",
            "    - 1 for MFCC features [delta_order=2] (default)\n",
            "    - 2 for log Mel-filterbank energy features\n",
            "    - 3 for spectrogram\n",
            "    - 4 for Discrete Wavelet Transform + MFCC features\n",
            "    - 5 for MFCC features [delta_order=0]\n",
            "    - 6 for log Mel-filterbank energy features [winlen=32, winstep=15.5, nfilt=64]\n",
            "    - 7 for log Mel-filterbank energy features [winlen=25, winstep=8,    nfilt=80]\n",
            "    '''\n",
            "\n",
            "    # Convert DataFrame to lists\n",
            "    file_names  = df['file'].tolist()\n",
            "    file_labels = df['class'].tolist()\n",
            "\n",
            "    # Create a Dataset object\n",
            "    dataset = tf.data.Dataset.from_tensor_slices((file_names, file_labels))\n",
            "\n",
            "    # Map the \"load_and_preprocess_data\" function\n",
            "    dataset = dataset.map(lambda file_name, file_label: (tf.numpy_function(load_and_preprocess_data, [file_name, file_label, data_path_], tf.float32), file_label), num_parallel_calls=os.cpu_count())\n",
            "\n",
            "    if is_train:\n",
            "\n",
            "        # Cache\n",
            "        if cache_file:\n",
            "            dataset = dataset.cache(filename=cache_file)\n",
            "\n",
            "        # Shuffle\n",
            "        if shuffle:\n",
            "            dataset = dataset.shuffle(buffer_size=len(df))\n",
            "\n",
            "        # Repeat the dataset indefinitely\n",
            "        dataset = dataset.repeat()\n",
            "\n",
            "    # Map the \"random_time_shift\" function\n",
            "    if apply_random_shift:\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(random_time_shift, [data], tf.float32), label))\n",
            "\n",
            "    # Map the \"background_noise\" function\n",
            "    if apply_background_noise and np.random.uniform() < 0.8:\n",
            "        noise_list = [tf.convert_to_tensor(noise, dtype=tf.float32) for noise in noise_dict.values()]\n",
            "        dataset    = dataset.map(lambda data, label: (tf.numpy_function(background_noise, [data, -1, -1, noise_reduction, *noise_list], tf.float32), label))\n",
            "\n",
            "    # Extract features\n",
            "    if   features == 1:\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(get_mfcc,        [data], tf.float32), label))\n",
            "    elif features == 2:\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(get_logfbank,    [data], tf.float32), label))\n",
            "    elif features == 3:\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(get_spectrogram, [data], tf.float32), label))\n",
            "    elif features == 4:\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(get_dwt,         [data], tf.float32), label))\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(get_mfcc,        [data], tf.float32), label))\n",
            "    elif features == 5:\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(get_mfcc,        [data, 0], tf.float32), label))\n",
            "    elif features == 6:\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(get_logfbank,    [data, 16000, 32, 15.5, 64], tf.float32), label))\n",
            "    elif features == 7:\n",
            "        dataset = dataset.map(lambda data, label: (tf.numpy_function(get_logfbank,    [data, 16000, 25, 8, 80], tf.float32), label))\n",
            "\n",
            "    if not is_train:\n",
            "\n",
            "        # Cache\n",
            "        if cache_file:\n",
            "            dataset = dataset.cache(filename=cache_file)\n",
            "\n",
            "        # Shuffle\n",
            "        if shuffle:\n",
            "            dataset = dataset.shuffle(buffer_size=len(df))\n",
            "\n",
            "        # Repeat the dataset indefinitely\n",
            "        dataset = dataset.repeat()\n",
            "\n",
            "    # Batch\n",
            "    dataset = dataset.batch(batch_size=batch_size)\n",
            "\n",
            "    # Prefetch\n",
            "    dataset = dataset.prefetch(buffer_size=1)\n",
            "\n",
            "    # Steps\n",
            "    steps = int(np.ceil(len(df) / batch_size))\n",
            "\n",
            "    return dataset, steps\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(inspect.getsource(preprocessing_utils.create_dataset))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "g7wcCw0Kgu1b"
      },
      "source": [
        "### 2.3 - Dataset initialization\n",
        "Finally, we define the training, validation and testing dataset, applying the above function to each of the corresponding reference dataframes. We also evaluate the number of steps (*train_steps*, *val_steps* and *test_steps*) required to load and process the entire dataset (num. of samples / batch_size).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "i2c5zxUVgu1b"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "    features_to_extract:\n",
        "    - 1 for MFCC features (default)\n",
        "    - 2 for log Mel-filterbank energy features\n",
        "    - 3 for spectrogram\n",
        "    - 4 for Discrete Wavelet Transform + MFCC features\n",
        "    else the original signal without feature extraction is returned\n",
        "'''\n",
        "\n",
        "apply_bn = True\n",
        "features_to_extract = 2\n",
        "batch_size = 32\n",
        "\n",
        "train_dataset, train_steps = create_dataset(\n",
        "    reference_df_train,\n",
        "    is_train=True,\n",
        "    cache_file='train_cache',\n",
        "    shuffle=True,\n",
        "    apply_background_noise=apply_bn,\n",
        "    noise_dict=noise_dict,\n",
        "    features=features_to_extract,\n",
        "    batch_size=batch_size\n",
        ")\n",
        "\n",
        "val_dataset, val_steps = create_dataset(\n",
        "    reference_df_val,\n",
        "    is_train=False,\n",
        "    cache_file='val_cache',\n",
        "    shuffle=True,\n",
        "    apply_background_noise=False,\n",
        "    features=features_to_extract,\n",
        "    batch_size=batch_size\n",
        ")\n",
        "\n",
        "# important! shuffle=False\n",
        "test_dataset, test_steps = create_dataset(\n",
        "    reference_df_test,\n",
        "    is_train=False,\n",
        "    cache_file='test_cache',\n",
        "    shuffle=False,\n",
        "    apply_background_noise=False,\n",
        "    features=features_to_extract,\n",
        "    batch_size=batch_size\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dp116A5gu1c"
      },
      "source": [
        "## 3 - Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ehk_BTxgu1d"
      },
      "source": [
        "### 3.1 - Import model\n",
        "Here we select one of the available network architectures in the file `models_utils.py`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "3DXXbVvygu1e"
      },
      "outputs": [],
      "source": [
        "############### Sainath15 notation ###############\n",
        "t = 99 # input feature dimension in time\n",
        "f = 39 # input feature dimension in frequency\n",
        "\n",
        "# 1st CONV layer\n",
        "m = 20 # filter size in time\n",
        "r = 8  # filter size in frequency\n",
        "s = 1  # stride in time\n",
        "v = 1  # stride in frequency\n",
        "p = 1  # max-pooling in time\n",
        "q = 3  # max-pooling in frequency\n",
        "##################################################"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNe4K5-Ggu1e",
        "outputId": "6c4dface-66f8-42e9-b615-ae9df219848c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Available models:\n",
            "cnn_trad_fpool3\n",
            "cnn_one_fpool3\n",
            "cnn_one_fstride4\n",
            "cnn_one_fstride8\n",
            "custom_cnn_simple\n",
            "custom_cnn\n",
            "residual_block\n",
            "resnet\n",
            "RNNSpeechModel\n",
            "AttRNNSpeechModel\n"
          ]
        }
      ],
      "source": [
        "available_models()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvPRWpCRgu1f",
        "outputId": "febd9fcd-6172-4f69-a7ec-4ee556d04d12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Selected model: cnn_one_fpool3\n"
          ]
        }
      ],
      "source": [
        "features_to_input = { # input shapes\n",
        "    1 : (99, 39),     # MFCC features\n",
        "    2 : (99, 40),     # log Mel-filterbank energy features\n",
        "    3 : (98, 257),    # spectrogram\n",
        "    4 : (49, 39)      # Discrete Wavelet Transform + MFCC features\n",
        "}\n",
        "\n",
        "input_shape = features_to_input[features_to_extract]\n",
        "model_name = 'cnn_one_fpool3'\n",
        "model = select_model(model_name, input_shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zzeQKUFgu1n",
        "outputId": "fa0f8663-3a90-460e-c4f9-5418cfc13750"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"cnn_one_fpool3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " reshape (Reshape)           (None, 99, 40, 1)         0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 99, 40, 54)        13878     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 99, 13, 54)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 69498)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                2223968   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               4224      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 35)                4515      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,263,097\n",
            "Trainable params: 2,263,097\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"sparse_categorical_accuracy\"])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "1GP0qN222u-W",
        "outputId": "583f39d5-9f4e-4e6f-c173-edc63554bfb4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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fnwtlCRJX3MEUXyohSVIs7HDwGrvxUJCx256E394F3/kc9O4Z/PqTnslcPb76G9C4CS6/lUkTJxm7kiTFREk6nU5/0IdGj/oNf772D+zRq5I9enXJemhTczvPzVnDqUftxT59u3b4uVfnbuDV+Y3sXl7JbhVVWc/d2N7G/MZ1XDvgSGO3UGIX4JYHYf5S+N3Xg726m0rBjffB0/MysbuylsQVf+PA/fan5pXZwc2RJIViwYIFDBoUwJ93zi34uR8YvK2trZx75vHUrlrE0Ydmf4UV4ImXVrDs7UY+f+zeHX4mmUzz7GN1lDWXc8Su/QKZO3HlazS3t/H4wacHsl4hKdjYfXwe/HEqVHUJ+ChDGja1QiqdWTuVguZWunSpgDR07drxX8QkSdHX0t5O09q3OWDffaioqOjwc/UNDdStb6CysjKQua1trTRtWM8B++/v3BzPbWlqZt7smTsUvR94pKGiooKDDx4BezdzxXc/GsQeuWIsvDTvbf73/CHv+7k/NC6gdG5XLjswmFfHLmtuYPrbCwNZq5AUbOy+uAh+fw9lp36Lkt32zN2craQ2/9qQl2mSpFxI/vsxUi89RHWvXZk6dWqHn3v0iSf54Q9/SLLfIJq7VGc/eO0KqF9Ndffuzs3D3G49eu7wFd5AXi1cKPboUk1jso1kOkWiZKcfQVxQCjp2f/U3yr9wMaX7Dc3dHElSrLT965+kZj4Gx5xN5WvPMHTo9v8MuXfGDC6++GKS+w6F7gE86vLtJVC7AvbYl0qanZuHuRU07/CPFUf1bdalNEFFaYLFLY1hbyUvCj52zzJ2JUk7ru1f/yT1xD1w+vdh34M6/Ny9M2Zw5pln0b7PkOAibNWb0O8gqN7FuSHP3Z6iCl6A7oly5jfVhb2NnDN2JUnF5N2xO7jDzxVb/BXb3I4UX/CWVVDTHO/gNXYlScXE2HXuBymqM7yQucIb5+At2Nid/hJcM4nEYaeQ3rSRZM2LuZslSYqN9gUzYc7TcMKXobo7rF2e+UbdatrbWpk3bx4A06bP4Gc//znp3ftBsg3qV2c3uH4d1K2CvvtDogxamjJfb91Ee3u7c4Oau6EOalfC3oN3OnahKIO3IrZHGgoydlva4Lp7KXm0hpJuPUnXvEja2JUk7ah1K6CqO7z4wLu/3t7GxsY6zjnnHFpaWli0ZBnpsnJKNqyDDeu2WSQNdO7Rl+nmRkiUZ2LsXd9IsTHV7tyg5m7aCLvvm1XsQhEGb2Vpgk2pJLVtm9i1PJhnwUVBQcburDfg6gkwYE/oVkW3L/2MRJ99czNLkhRL66/+Lqkjz4SBI979jbXL6fHQzcydOxeA3fruRcNegyit7h7I3NaXHyPdqx903/Xd32hpokfT284Ncm559i89K7ozvCUlJRxU1TNWxxoKLnYbm+GPE+HKO+BbJ8OVXw34pRKSJEnvKLrgBRhS3Yua5vqwtxGIgovdp+fC+SMhnYZbLoVPHRz8DEmSpK0U3ZEGgMFVvXi+4e2wt5G1gorddRtg9BRYtBIu/xKMyOENcJIkSVsp4iu8hX2koWBiN52Gf74AXx8Fe+8BN//I2JUkSXlVlFd4D6zsweKWBlpTSSpKE2Fvp9MKJnaXr4WREzNndkd+Ewb2C25tSZKkHVSUwdulNME+Fd1YuGk9Q6t3/eAfiJCCiN32JEx4Cv7+GHz5WDjrKCgrvL9YSJKkeCjK4IXNxxqa6gsqeAsidl9fDn+YAN2rYNxF0G/3YNaVJEnaSUUbvIOrCuscb+Rjt6UNbn0IZrwA3z4FPnuojxqTJEmRULTBO6S6F0+sWhH2NnZI5GP3lUWZF0gM7Je5KW237N6GIkmSFKSiDd7BVb2oaaojnU5TEuErkZGO3YZmGDcDnq+BH54OR34o+/1JkiQFrCgfSwawW3klFaUJVrY1hb2VDkU6dp+cAxeMhEQJ3HKJsStJkiKraK/wAgzZfJV3r4quYW/lPSIbu+s2wLWT4c1V8MsvwyEDst+bJElBq32bxtq1DBs2DID169dTulce5rZuonH9eufmYW57e3KHP17UwTu4uifzm+s4rufeYW/lXSIZu+l05oa08ffBqYfBL74EXcqz35skSUFbUkPiodu45pprOeaIwwA4/Ohjyfm/6TatJ7F2MdeMHs0xnzrKuTmeO/6OO3b4R4o6eIdU9eL+uqVhb+NdIhm7y9Zmbkrb1AqjvgUH5OOvbpIk7YQlNZRNv4Epk+7hlJNO/M+XE4kcPw++aT1lq99kyuTJnHLyZ52b57kfpKiDd3BVL65dMTvsbfxH5GK3PQl3Pwl3Pg5fOR7OPBISRXvsW5IUdVtid+KEd8Vuzm2JsEmTOhVhzs3f3KIO3v6V3Vnd1kxjso1uiXD/eT5ysbtgWeYFEj26wo3/A3sWzgs6JElFyNh17vso6uAtKynlwMoeLGiu5yPd9ghtH5GK3U2t8NcH4f6X4Lufg09/1BdISJKibeUblM19Kv+xu6mRso21+Y8/53ZaUQcvwODqXsxvqgsteCMVuy8vhKvvgcH7wF8vgV27Z7+fHZVMkly3EtL5GylJKnzpthZ46QFGj7me/frtxbx587b7udbWVtKbNpIKam4yCfVvM/q669iv/77OzdXcVIqS9auZcu+9WUV20QfvkBBfMRyZ2G1ogrEz4MXX4OIz4PCh2e+lM+a+BUDTfX+Bsq0Ou7cnobmVrqVllJcEf3Z4Y7KNdGkJPXr2CHxtSVJ+lLS2sGef3owZPZoxo0d3+LmKigpKVy+mvDyY9KlLp+jbtw9jrr+OMddf59wcza0vgXE33JD1FeWiD97B1T2ZWvtm3udGInbT6cwLJP40FY76ENxyKXStzH4vnfHGKvjFraQvOwcOG/zO119eSOLy2xnb/3CO7dI78LHPNaziojefYeLUKRz32U8Hvr4kKT8WLFjAoEEB/Dnq3FjPNXirevF6cz3JdIpEDq4ibk8kYnfN+swLJJashivOg4P3z34fnbWyFn48Hr5/Wt5j94dL/sXEqZONXUkqcGFEmHMLb27RP2OqW6Kc3corWdzSmJd5ocduKgXTnoP/HgUD9oS//Cic2K1tgEtuhPOOgxM+/M7X8xS7d0+ayHGf/Uzg60uSpOgp+iu8kLnKO7+pjgGVu+R0Tuixu3QNjLwHWtvh2u9kgjcMDc1w6Xg46aPw+SPe+bqxK0mScsDg5Z0b106hf85mhBq77Un4xxNw1xNw/gmZyAzrBRKbWuGnN8MhAzJ72cLYlSRJOWLwknk02V1rX8/Z+qHGbs3SzAskduse/gsk2pNwxe3QtxdcdNo7z/c1diVJUg4ZvGSu8M5vys2jyUKL3eZW+OsD8ODL8L3PwYkfCfcFEqkU/O7uzP++7Bwo3XyF2diVJEk5ZvACe1VUsymVpLZtE7uWB/dYrtBi96UF8MeJMLQ/3HIJ9OyW/exspNNw3TRYVQsjv/nOs3aNXUmSlAcGL1BSUsJBVT2paa7j8PJgbuQKJXY3NMGYaZk3pv3oDDhsSPZzg3DbwzBzEYz+LlRWZL5m7EqSpDwp+seSbTGkuhc1zfWBrJX32E2n4bFZcMFIqOqSuaobldid/Czc/2+4+hvQvSrzNWNXkiTlkVd4Nxtc1YvnG97Oep28x+7qerhmMixfC1d+FT60X/Yzg/LwK/C3R+C6C2G3zY98M3YlSVKeeYV3s8wV3uxuXMtr7KZSMOVZ+MY1cFA/uOniaMXuczVw3b2ZK7tbngxh7EqSpBB4hXezAyt7sLilgdZUkorSRKd/Pq+xu3g1XD0BkqnMudj9+2Y/L0hz34Lf3gm//do7L7cwdiVJUkgM3s26lCbYp6IbCzetZ2h1555Vm7fYbWuHOx+HCU/BBSfC6YeH9wKJjryxEn5xK/z83HeuOBu7kiQpRAbvVoZU96Kmqb5TwZu32J2/JPMCiT16wE0/hD69sp8VtBXr4Mc3wUX/BZ8YnPmasStJkkJm8G5lcFXnzvHmJXabW+Ev92duAPv+aXD8iHBfINGRdRvgkvHwleMzewRjV5IkRYLBu5Uh1b14YtWKHfpsXmL3xddg5EQ4eH+45VLo2TX7ObnQ0Jy5svvZQzPHLMDYlSRJkWHwbmVwVS9qmupIp9OUvM9V1JzH7vqNcP20zMsaLjnzneMBUbSpFX56c2bfXzk+8zVjV5IkRUjE7ngK127llVSUJljZ1tThZ3IauyMGwCMz4YI/Zl7ScMul0Y7d9iT86vbMY8cuPDVz1MLYlSRJEeMV3m0M2XyVd6+K9x4fyGns9tsNfvpXWFUHv7kAhu6b/fq5lErBVXdBaSn85OzMr8auJEmKIK/wbmNwdU/mb+fGtZzF7pXnw1urMi+QGLIPjP+f6MduOg1/npp5y9sV50FZwtiVJEmR5RXebQyp6sX9dUvf9bWcxe73/wtuvj9zFODP34P+fbJfOx9ufQhmvwWjvwNdyo1dSZIUaV7h3ca2jybLSezeMB2O/BCMnQYnfAT+9N3Cid2JT8ODL2deGdytytiVJEmR5xXebfSv7M7qtmYak208n6wPPnbHToMeXTNPYrjpYujdM/t18+WhlzNvevvz92DX7sauJEkqCAbvNspKSjmwsgd/b1zCqNVzgovdWx+EcdOgqgt8+2Q49pBovkCiI8/NzzwqbdS3M09lMHYlSVKBMHi3o61HNSNbFpHev0/miuadj2e34IaNMHMhHDoILv9S5gpvIZn9Jvz2H3DV12FAX2NXkiQVFIN3G62pJMke1bBLVxIfC+YZuMkX5sMhB8DIbwayXl4tXAG/vC0T6sP6G7uSJKngGLzbqChN8KHyHiz62F4kLjw9sHWTc98IbK28Wb4WfvIX+J/T4WMHGbuSJKkg+ZQGbd/a9XDpeDj/hMx5Y2NXkiQVKINX79XQBJfeBCd/HE77pLErSZIKmsGrd2tuhZ/cDIcOhPOOM3YlSVLBM3j1jrb2zA1q++wO3/scvLLI2JUkSQXP4FVGKgW/vQvKE/DjL8DMN4xdSZIUCz6lQZBOw+gpULsB/vANmP2msStJkmLD4BX89UGYtxiu/Q7MW2zsSpKkWDF4i909T8MjM+G6C2HBcmNXkiTFjsFbzB78N9z1OPz5QnhzlbErSZJiyeAtVs++CmOmZ44xrFhn7EqSpNjyKQ3FaNYb8Pu74aqvQW2DsStJkmLN4C02ry/PPGv38i9Bc6uxK0mSYs/gLSbL1sBP/gIXnwGlpcauJEkqCgZvsVizHi69Cb52EuxSbexKkqSiYfAWgw1NcOl4OPUT0G93Y1eSJBUVgzfumlszxxg+cRAM3sfYlSRJRcfgjbO2drj8VujfGz4xmMQv/2bsSpKkomPwxlUyBb+5E7qUw/EfNnYlSVLRMnjjKJ2GaydDXSOcdhiJK+4wdiVJUtEyeOPoLw/Aa0vhnKNJXHmnsStJkoqawRs3dz8Jj8+C844n8Zu7jF1JklT0DN44eeAlmPAkfO0kEr+/x9iVJEnC4I2PZ+bB2Bnw9U+TGDXF2JUkSdrM4I2DmYvgDxPgghNJXDfd2JUkSdqKwVvoXl8Ov7odzj2GxPgHjF1JkqRtGLyFbOmazFvUPn84idsfM3YlSZK2w+AtVKvr4dLxcMKHSUx4xtiVJEnqgMFbiNZvhB+Ph0MHkZjxkrErSZL0PgzeQtPUApfdDAP2JPH4HGNXkiTpAxi8haS1HS6/FbpXUfr8AmNXkiRpB5SFvQHtoGQKfnMntLZRuuhtxu13hLErSZK0AwzeQpBOwzWTYPlaSlfUGbuSJEmdYPAWgvH3w8xFlNZuNHYlSZI6yeCNuruegIf+TWljK+P2N3YlSZI6y+CNsn++CH9/jNLWJOP2P9LYlSRJ2gkGb1Q9NRfGTKO0Pc24AcauJEnSzjJ4o+iVRXDVXZSm0owbcJSxK0mSlAWfwxs1ry2Dn99CadLYlSRJCoLBGyVLVsMlN1LanmLcAcauJElSEAzeqFhdDxeNoaS1nXEHfsrYlSRJCojBGwX1G+HC6yhpauWGA482diVJkgJk8IataRN8/3pK6jZyw0BjV5IkKWgGb5ha2+EHYylZWcs4Y1eSJCknDN6wJFNwyQ3wxirGDjqG4yr7BD7C2JUkSTJ4w5FOw8/+CnMXc/2gYzje2JUkScoZgzcMV94Bz7/GnwYdzUlVfQNf3tiVJEl6h8GbbyPvgcdmMWrQUXy2eq/Alzd2JUmS3s3gzaex02Ha8/x+4JGc2nXvwJc3diVJkt6rLOwNFI233oYn5/D/DvwkZ3TbN/DljV1JkqTtM3i3ozHZSurFGrh+SiDrJZ+aDUtX84sBH+eLu+wfyJpbM3YlSZI6ZvBuR1NtPcNayxn05IpA1pvXVs5h+x/C+T0PDGS9rRm7kiRJ78/g3Y4NGxq4vMdH+UjFHoGs11qWpKJrIpC1tmbsSpIkfTBvWttGezrFwk3rGVTVM7A1K0qNXUmSpLAYvNtYvKmB3uVVdEuUh72VDhm7kiRJO87g3cb85joGV/UKexsdMnYlSZI6x+DdRk1zHUOqoxm8xq4kSVLnGbzbqGmqZ3CA53eDYuxKkiTtHIN3G/MjeIXX2JUkSdp5Bu9W1rY105pK0re8Ouyt/IexK0mSlB2Ddys1zfUMru5FSUlJ2FsBjF1JkqQgGLxbqWmqY0hEntBg7EqSJAXD4N1KTUQeSWbsSpIkBcfg3cr8pvBvWDN2JUmSgmXwbtaSSrK0tZEDKncJbQ/GriRJUvAM3s1eb66nf5fuVJQmQplv7EqSJOWGwbtZTXN9aOd3jV1JkqTcMXg3C+uVwsauJElSbhm8m81vyv8TGoxdSZKk3DN4gXQ6vfmlEz3zNtPYlSRJyg+DF1jeupGq0gS7llXmZZ6xK0mSlD8GL5kb1vJ1ftfYlSRJyi+Dl8wrhfNxftfYlSRJyj+DF5jfXMeQHAevsStJkhQOg5fMI8kG5/BIg7ErSZIUnqIP3sZkG+vaNtG/S7ecrG/sSpIkhavog7emuY6BVT1JlAT/f4WxK0mSFL6iD97MCyd6Br6usStJkhQNRR+8uXgkmbErSZIUHQZvwI8kM3YlSZKipaiDtz2dYuGm9QwK6EiDsStJkhQ9RR28izc10Lu8im6J8qzXMnYlSZKiqaiDd35zMMcZjF1JkqToKurgrWmuy/qGNWNXkiQp2oo7eJvqs3okmbErSZIUfUUdvPOzuMJr7EqSJBWGog3etW3NtKaS9C2v7vTPGruSJEmFo2iDt6a5nsHVvSgpKenUzxm7kiRJhaV4g7epjiGdfEKDsStJklR4ijd4O/lIMmNXkiSpMBVt8M5v2vEb1oxdSZKkwlWUwduSSrK0tZEDKnf5wM8au5IkSYWtKIP39eZ6+nfpTkVp4n0/Z+xKkiQVvqIM3prm+g88v2vsSpIkxUOkg3d17aacrPtBrxQ2diVJkuIjssH79Mw1PDt7XU7Wnt/U8RMajF1JkqR4iWTwPj1zDd/8v39z9DHHBr52Op3e/NKJnu/5nrErSZIUP5EL3i2xe9eEiey5Z7/A19+USlJdWsauZZXv+rqxK0mSFE+RCt6tY/e443MTnQ3J1vdc3TV2JUmS4isywZuP2AVoSLa96/yusStJkhRvkQjefMUuQEN7K0M2B6+xK0mSFH+hB28+Yxc2X+Gt7mXsSpIkFYlQgzffsduSStKaSrKqdaOxK0mSVCRCC9619ZvyGrsAa1qaqCxN8KOlzxm7kiRJRaJsRz7U2NjIv19awRVjgxn6xEsrWLC4gVNPP4snn3qOJ596brufe+GFF9hYu5zfLdz+9zvr+foVlJaWGruSJElF5P8DnpB7aqaji1kAAAAASUVORK5CYII=",
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=700x292 at 0x7FF0D1807E20>"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "visualkeras.layered_view(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "Q0uoLXKPgu2G"
      },
      "outputs": [],
      "source": [
        "# Show the structure of the model through building blocks\n",
        "# tf.keras.utils.plot_model(model, to_file=f'{model_name}.png')\n",
        "# Image(f'{model_name}.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-khCC16dgu2K"
      },
      "source": [
        "## 4 - Model training and testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6AOKfTzRgu2Y"
      },
      "source": [
        "### 4.1 - Model training\n",
        "At this point we can train the model for some epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "9YgyrsX9ofph"
      },
      "outputs": [],
      "source": [
        "# uncomment to remove cache files\n",
        "# remove_file_starting_with('train_cache')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEr0gilQgu2d",
        "outputId": "d9c752a5-8e10-47da-ac3a-74427db1ccc8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "2652/2652 [==============================] - 377s 29ms/step - loss: 2.6906 - sparse_categorical_accuracy: 0.2066 - val_loss: 2.2459 - val_sparse_categorical_accuracy: 0.3069 - lr: 0.0010\n",
            "Epoch 2/40\n",
            "2652/2652 [==============================] - 41s 15ms/step - loss: 2.2279 - sparse_categorical_accuracy: 0.3192 - val_loss: 2.0804 - val_sparse_categorical_accuracy: 0.3712 - lr: 0.0010\n",
            "Epoch 3/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 2.0853 - sparse_categorical_accuracy: 0.3687 - val_loss: 1.9741 - val_sparse_categorical_accuracy: 0.3987 - lr: 0.0010\n",
            "Epoch 4/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 2.0104 - sparse_categorical_accuracy: 0.3872 - val_loss: 1.9264 - val_sparse_categorical_accuracy: 0.4149 - lr: 0.0010\n",
            "Epoch 5/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.9485 - sparse_categorical_accuracy: 0.4058 - val_loss: 1.9170 - val_sparse_categorical_accuracy: 0.4065 - lr: 0.0010\n",
            "Epoch 6/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.9045 - sparse_categorical_accuracy: 0.4199 - val_loss: 1.8560 - val_sparse_categorical_accuracy: 0.4348 - lr: 0.0010\n",
            "Epoch 7/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.8666 - sparse_categorical_accuracy: 0.4314 - val_loss: 1.8585 - val_sparse_categorical_accuracy: 0.4214 - lr: 0.0010\n",
            "Epoch 8/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.8440 - sparse_categorical_accuracy: 0.4371 - val_loss: 1.7801 - val_sparse_categorical_accuracy: 0.4522 - lr: 0.0010\n",
            "Epoch 9/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.8191 - sparse_categorical_accuracy: 0.4430 - val_loss: 1.7521 - val_sparse_categorical_accuracy: 0.4685 - lr: 0.0010\n",
            "Epoch 10/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.7959 - sparse_categorical_accuracy: 0.4489 - val_loss: 1.7583 - val_sparse_categorical_accuracy: 0.4563 - lr: 0.0010\n",
            "Epoch 11/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.7799 - sparse_categorical_accuracy: 0.4553 - val_loss: 1.7910 - val_sparse_categorical_accuracy: 0.4447 - lr: 0.0010\n",
            "Epoch 12/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.7674 - sparse_categorical_accuracy: 0.4560 - val_loss: 1.7099 - val_sparse_categorical_accuracy: 0.4772 - lr: 0.0010\n",
            "Epoch 13/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.7468 - sparse_categorical_accuracy: 0.4639 - val_loss: 1.7425 - val_sparse_categorical_accuracy: 0.4715 - lr: 0.0010\n",
            "Epoch 14/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.7374 - sparse_categorical_accuracy: 0.4669 - val_loss: 1.6726 - val_sparse_categorical_accuracy: 0.4915 - lr: 0.0010\n",
            "Epoch 15/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.7256 - sparse_categorical_accuracy: 0.4702 - val_loss: 1.6575 - val_sparse_categorical_accuracy: 0.4908 - lr: 0.0010\n",
            "Epoch 16/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.7189 - sparse_categorical_accuracy: 0.4728 - val_loss: 1.6338 - val_sparse_categorical_accuracy: 0.4983 - lr: 0.0010\n",
            "Epoch 17/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.6979 - sparse_categorical_accuracy: 0.4797 - val_loss: 1.6309 - val_sparse_categorical_accuracy: 0.5021 - lr: 0.0010\n",
            "Epoch 18/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.6799 - sparse_categorical_accuracy: 0.4834 - val_loss: 1.6138 - val_sparse_categorical_accuracy: 0.5048 - lr: 0.0010\n",
            "Epoch 19/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.6632 - sparse_categorical_accuracy: 0.4874 - val_loss: 1.7654 - val_sparse_categorical_accuracy: 0.4705 - lr: 0.0010\n",
            "Epoch 20/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.6416 - sparse_categorical_accuracy: 0.4941 - val_loss: 1.5938 - val_sparse_categorical_accuracy: 0.5020 - lr: 0.0010\n",
            "Epoch 21/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.6325 - sparse_categorical_accuracy: 0.4967 - val_loss: 1.6375 - val_sparse_categorical_accuracy: 0.5015 - lr: 0.0010\n",
            "Epoch 22/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.6250 - sparse_categorical_accuracy: 0.4993 - val_loss: 1.6188 - val_sparse_categorical_accuracy: 0.5105 - lr: 0.0010\n",
            "Epoch 23/40\n",
            "2649/2652 [============================>.] - ETA: 0s - loss: 1.6157 - sparse_categorical_accuracy: 0.5045\n",
            "Epoch 23: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.6157 - sparse_categorical_accuracy: 0.5045 - val_loss: 1.6366 - val_sparse_categorical_accuracy: 0.5070 - lr: 0.0010\n",
            "Epoch 24/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.4827 - sparse_categorical_accuracy: 0.5413 - val_loss: 1.4956 - val_sparse_categorical_accuracy: 0.5385 - lr: 1.0000e-04\n",
            "Epoch 25/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.4583 - sparse_categorical_accuracy: 0.5477 - val_loss: 1.4917 - val_sparse_categorical_accuracy: 0.5415 - lr: 1.0000e-04\n",
            "Epoch 26/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.4477 - sparse_categorical_accuracy: 0.5491 - val_loss: 1.4885 - val_sparse_categorical_accuracy: 0.5405 - lr: 1.0000e-04\n",
            "Epoch 27/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.4419 - sparse_categorical_accuracy: 0.5502 - val_loss: 1.4987 - val_sparse_categorical_accuracy: 0.5395 - lr: 1.0000e-04\n",
            "Epoch 28/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.4352 - sparse_categorical_accuracy: 0.5519 - val_loss: 1.4980 - val_sparse_categorical_accuracy: 0.5416 - lr: 1.0000e-04\n",
            "Epoch 29/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.4320 - sparse_categorical_accuracy: 0.5537 - val_loss: 1.4791 - val_sparse_categorical_accuracy: 0.5457 - lr: 1.0000e-04\n",
            "Epoch 30/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.4267 - sparse_categorical_accuracy: 0.5552 - val_loss: 1.4895 - val_sparse_categorical_accuracy: 0.5436 - lr: 1.0000e-04\n",
            "Epoch 31/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.4235 - sparse_categorical_accuracy: 0.5565 - val_loss: 1.4873 - val_sparse_categorical_accuracy: 0.5428 - lr: 1.0000e-04\n",
            "Epoch 32/40\n",
            "2649/2652 [============================>.] - ETA: 0s - loss: 1.4187 - sparse_categorical_accuracy: 0.5568\n",
            "Epoch 32: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.4187 - sparse_categorical_accuracy: 0.5569 - val_loss: 1.4813 - val_sparse_categorical_accuracy: 0.5427 - lr: 1.0000e-04\n",
            "Epoch 33/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.4036 - sparse_categorical_accuracy: 0.5612 - val_loss: 1.4722 - val_sparse_categorical_accuracy: 0.5478 - lr: 1.0000e-05\n",
            "Epoch 34/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.3989 - sparse_categorical_accuracy: 0.5630 - val_loss: 1.4740 - val_sparse_categorical_accuracy: 0.5474 - lr: 1.0000e-05\n",
            "Epoch 35/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.3989 - sparse_categorical_accuracy: 0.5632 - val_loss: 1.4723 - val_sparse_categorical_accuracy: 0.5481 - lr: 1.0000e-05\n",
            "Epoch 36/40\n",
            "2649/2652 [============================>.] - ETA: 0s - loss: 1.3980 - sparse_categorical_accuracy: 0.5638\n",
            "Epoch 36: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.3979 - sparse_categorical_accuracy: 0.5638 - val_loss: 1.4737 - val_sparse_categorical_accuracy: 0.5497 - lr: 1.0000e-05\n",
            "Epoch 37/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.3965 - sparse_categorical_accuracy: 0.5641 - val_loss: 1.4728 - val_sparse_categorical_accuracy: 0.5497 - lr: 1.0000e-06\n",
            "Epoch 38/40\n",
            "2652/2652 [==============================] - 42s 16ms/step - loss: 1.3952 - sparse_categorical_accuracy: 0.5643 - val_loss: 1.4720 - val_sparse_categorical_accuracy: 0.5490 - lr: 1.0000e-06\n",
            "Epoch 39/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.3955 - sparse_categorical_accuracy: 0.5643 - val_loss: 1.4719 - val_sparse_categorical_accuracy: 0.5498 - lr: 1.0000e-06\n",
            "Epoch 40/40\n",
            "2652/2652 [==============================] - 40s 15ms/step - loss: 1.3954 - sparse_categorical_accuracy: 0.5644 - val_loss: 1.4720 - val_sparse_categorical_accuracy: 0.5494 - lr: 1.0000e-06\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 40\n",
        "\n",
        "early_stop_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n",
        "\n",
        "reduce_LR = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', patience=3, verbose=1)\n",
        "\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(f'trained_models/{model_name}.h5', monitor='val_loss', save_best_only=True)\n",
        "\n",
        "history = model.fit(\n",
        "                train_dataset,\n",
        "                epochs=num_epochs,\n",
        "                steps_per_epoch=train_steps,\n",
        "                validation_data=val_dataset,\n",
        "                validation_steps=val_steps,\n",
        "                callbacks=[early_stop_callback, model_checkpoint, reduce_LR], \n",
        "                verbose=1\n",
        "                )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "g6xpCJ7Tgu2f",
        "outputId": "27efa331-572e-413e-c130-83ef0a87e6d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training history:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-880fb6c3-f614-426d-bd05-da7899001e26\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>sparse_categorical_accuracy</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_sparse_categorical_accuracy</th>\n",
              "      <th>lr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.690609</td>\n",
              "      <td>0.206589</td>\n",
              "      <td>2.245851</td>\n",
              "      <td>0.306891</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.227917</td>\n",
              "      <td>0.319181</td>\n",
              "      <td>2.080384</td>\n",
              "      <td>0.371194</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.085343</td>\n",
              "      <td>0.368660</td>\n",
              "      <td>1.974087</td>\n",
              "      <td>0.398738</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.010449</td>\n",
              "      <td>0.387220</td>\n",
              "      <td>1.926379</td>\n",
              "      <td>0.414864</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.948482</td>\n",
              "      <td>0.405802</td>\n",
              "      <td>1.917002</td>\n",
              "      <td>0.406450</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.904522</td>\n",
              "      <td>0.419931</td>\n",
              "      <td>1.856018</td>\n",
              "      <td>0.434796</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.866612</td>\n",
              "      <td>0.431431</td>\n",
              "      <td>1.858453</td>\n",
              "      <td>0.421374</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.843974</td>\n",
              "      <td>0.437064</td>\n",
              "      <td>1.780144</td>\n",
              "      <td>0.452224</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.819140</td>\n",
              "      <td>0.443038</td>\n",
              "      <td>1.752149</td>\n",
              "      <td>0.468550</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.795860</td>\n",
              "      <td>0.448918</td>\n",
              "      <td>1.758288</td>\n",
              "      <td>0.456330</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.779884</td>\n",
              "      <td>0.455281</td>\n",
              "      <td>1.791037</td>\n",
              "      <td>0.444712</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.767385</td>\n",
              "      <td>0.456000</td>\n",
              "      <td>1.709925</td>\n",
              "      <td>0.477163</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.746843</td>\n",
              "      <td>0.463907</td>\n",
              "      <td>1.742472</td>\n",
              "      <td>0.471454</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.737444</td>\n",
              "      <td>0.466912</td>\n",
              "      <td>1.672627</td>\n",
              "      <td>0.491486</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.725601</td>\n",
              "      <td>0.470235</td>\n",
              "      <td>1.657524</td>\n",
              "      <td>0.490785</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.718941</td>\n",
              "      <td>0.472780</td>\n",
              "      <td>1.633844</td>\n",
              "      <td>0.498297</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.697915</td>\n",
              "      <td>0.479732</td>\n",
              "      <td>1.630907</td>\n",
              "      <td>0.502103</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.679895</td>\n",
              "      <td>0.483362</td>\n",
              "      <td>1.613804</td>\n",
              "      <td>0.504808</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1.663159</td>\n",
              "      <td>0.487415</td>\n",
              "      <td>1.765410</td>\n",
              "      <td>0.470453</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1.641568</td>\n",
              "      <td>0.494073</td>\n",
              "      <td>1.593826</td>\n",
              "      <td>0.502003</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1.632491</td>\n",
              "      <td>0.496653</td>\n",
              "      <td>1.637460</td>\n",
              "      <td>0.501502</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1.625026</td>\n",
              "      <td>0.499305</td>\n",
              "      <td>1.618836</td>\n",
              "      <td>0.510517</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.615702</td>\n",
              "      <td>0.504478</td>\n",
              "      <td>1.636585</td>\n",
              "      <td>0.507011</td>\n",
              "      <td>0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.482652</td>\n",
              "      <td>0.541325</td>\n",
              "      <td>1.495620</td>\n",
              "      <td>0.538462</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1.458278</td>\n",
              "      <td>0.547712</td>\n",
              "      <td>1.491665</td>\n",
              "      <td>0.541466</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1.447710</td>\n",
              "      <td>0.549055</td>\n",
              "      <td>1.488539</td>\n",
              "      <td>0.540465</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1.441942</td>\n",
              "      <td>0.550186</td>\n",
              "      <td>1.498655</td>\n",
              "      <td>0.539463</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1.435170</td>\n",
              "      <td>0.551930</td>\n",
              "      <td>1.498011</td>\n",
              "      <td>0.541566</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1.432002</td>\n",
              "      <td>0.553698</td>\n",
              "      <td>1.479108</td>\n",
              "      <td>0.545673</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1.426711</td>\n",
              "      <td>0.555241</td>\n",
              "      <td>1.489517</td>\n",
              "      <td>0.543570</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1.423540</td>\n",
              "      <td>0.556549</td>\n",
              "      <td>1.487259</td>\n",
              "      <td>0.542768</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1.418711</td>\n",
              "      <td>0.556926</td>\n",
              "      <td>1.481252</td>\n",
              "      <td>0.542668</td>\n",
              "      <td>0.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1.403636</td>\n",
              "      <td>0.561157</td>\n",
              "      <td>1.472171</td>\n",
              "      <td>0.547776</td>\n",
              "      <td>0.000010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1.398862</td>\n",
              "      <td>0.562960</td>\n",
              "      <td>1.473965</td>\n",
              "      <td>0.547376</td>\n",
              "      <td>0.000010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1.398898</td>\n",
              "      <td>0.563207</td>\n",
              "      <td>1.472321</td>\n",
              "      <td>0.548077</td>\n",
              "      <td>0.000010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1.397878</td>\n",
              "      <td>0.563773</td>\n",
              "      <td>1.473663</td>\n",
              "      <td>0.549679</td>\n",
              "      <td>0.000010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1.396470</td>\n",
              "      <td>0.564067</td>\n",
              "      <td>1.472800</td>\n",
              "      <td>0.549679</td>\n",
              "      <td>0.000001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1.395187</td>\n",
              "      <td>0.564279</td>\n",
              "      <td>1.471998</td>\n",
              "      <td>0.548978</td>\n",
              "      <td>0.000001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1.395549</td>\n",
              "      <td>0.564326</td>\n",
              "      <td>1.471892</td>\n",
              "      <td>0.549780</td>\n",
              "      <td>0.000001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1.395360</td>\n",
              "      <td>0.564421</td>\n",
              "      <td>1.472015</td>\n",
              "      <td>0.549379</td>\n",
              "      <td>0.000001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-880fb6c3-f614-426d-bd05-da7899001e26')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-880fb6c3-f614-426d-bd05-da7899001e26 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-880fb6c3-f614-426d-bd05-da7899001e26');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        loss  sparse_categorical_accuracy  val_loss  \\\n",
              "0   2.690609                     0.206589  2.245851   \n",
              "1   2.227917                     0.319181  2.080384   \n",
              "2   2.085343                     0.368660  1.974087   \n",
              "3   2.010449                     0.387220  1.926379   \n",
              "4   1.948482                     0.405802  1.917002   \n",
              "5   1.904522                     0.419931  1.856018   \n",
              "6   1.866612                     0.431431  1.858453   \n",
              "7   1.843974                     0.437064  1.780144   \n",
              "8   1.819140                     0.443038  1.752149   \n",
              "9   1.795860                     0.448918  1.758288   \n",
              "10  1.779884                     0.455281  1.791037   \n",
              "11  1.767385                     0.456000  1.709925   \n",
              "12  1.746843                     0.463907  1.742472   \n",
              "13  1.737444                     0.466912  1.672627   \n",
              "14  1.725601                     0.470235  1.657524   \n",
              "15  1.718941                     0.472780  1.633844   \n",
              "16  1.697915                     0.479732  1.630907   \n",
              "17  1.679895                     0.483362  1.613804   \n",
              "18  1.663159                     0.487415  1.765410   \n",
              "19  1.641568                     0.494073  1.593826   \n",
              "20  1.632491                     0.496653  1.637460   \n",
              "21  1.625026                     0.499305  1.618836   \n",
              "22  1.615702                     0.504478  1.636585   \n",
              "23  1.482652                     0.541325  1.495620   \n",
              "24  1.458278                     0.547712  1.491665   \n",
              "25  1.447710                     0.549055  1.488539   \n",
              "26  1.441942                     0.550186  1.498655   \n",
              "27  1.435170                     0.551930  1.498011   \n",
              "28  1.432002                     0.553698  1.479108   \n",
              "29  1.426711                     0.555241  1.489517   \n",
              "30  1.423540                     0.556549  1.487259   \n",
              "31  1.418711                     0.556926  1.481252   \n",
              "32  1.403636                     0.561157  1.472171   \n",
              "33  1.398862                     0.562960  1.473965   \n",
              "34  1.398898                     0.563207  1.472321   \n",
              "35  1.397878                     0.563773  1.473663   \n",
              "36  1.396470                     0.564067  1.472800   \n",
              "37  1.395187                     0.564279  1.471998   \n",
              "38  1.395549                     0.564326  1.471892   \n",
              "39  1.395360                     0.564421  1.472015   \n",
              "\n",
              "    val_sparse_categorical_accuracy        lr  \n",
              "0                          0.306891  0.001000  \n",
              "1                          0.371194  0.001000  \n",
              "2                          0.398738  0.001000  \n",
              "3                          0.414864  0.001000  \n",
              "4                          0.406450  0.001000  \n",
              "5                          0.434796  0.001000  \n",
              "6                          0.421374  0.001000  \n",
              "7                          0.452224  0.001000  \n",
              "8                          0.468550  0.001000  \n",
              "9                          0.456330  0.001000  \n",
              "10                         0.444712  0.001000  \n",
              "11                         0.477163  0.001000  \n",
              "12                         0.471454  0.001000  \n",
              "13                         0.491486  0.001000  \n",
              "14                         0.490785  0.001000  \n",
              "15                         0.498297  0.001000  \n",
              "16                         0.502103  0.001000  \n",
              "17                         0.504808  0.001000  \n",
              "18                         0.470453  0.001000  \n",
              "19                         0.502003  0.001000  \n",
              "20                         0.501502  0.001000  \n",
              "21                         0.510517  0.001000  \n",
              "22                         0.507011  0.001000  \n",
              "23                         0.538462  0.000100  \n",
              "24                         0.541466  0.000100  \n",
              "25                         0.540465  0.000100  \n",
              "26                         0.539463  0.000100  \n",
              "27                         0.541566  0.000100  \n",
              "28                         0.545673  0.000100  \n",
              "29                         0.543570  0.000100  \n",
              "30                         0.542768  0.000100  \n",
              "31                         0.542668  0.000100  \n",
              "32                         0.547776  0.000010  \n",
              "33                         0.547376  0.000010  \n",
              "34                         0.548077  0.000010  \n",
              "35                         0.549679  0.000010  \n",
              "36                         0.549679  0.000001  \n",
              "37                         0.548978  0.000001  \n",
              "38                         0.549780  0.000001  \n",
              "39                         0.549379  0.000001  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Training history:')\n",
        "pd.DataFrame(history.history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "B3Lq3SY4gu3J",
        "outputId": "72d48cee-f68a-4403-9093-27e6eae0680d"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 576x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_utils.plot_history(history, columns=['loss', 'sparse_categorical_accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9R3FIblgu3O"
      },
      "source": [
        "Uncomment the cell below to save the weights of the trained model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "DhUE4Zc6gu3P"
      },
      "outputs": [],
      "source": [
        "# model.save_weights(f'trained_models/{model_name}.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnTj2hkAgu35"
      },
      "source": [
        "Uncomment the cell below to load some pretrained models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "2VIvbDa9gu4j"
      },
      "outputs": [],
      "source": [
        "# model_name = 'smallCnnModel'\n",
        "# model = tf.keras.models.load_model(f'{model_name}.h5')\n",
        "# model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqXQhhpvgu5P"
      },
      "source": [
        "### 4.2 - Validation metrics\n",
        "Since the dataset is unbalanced (see histograms in section 1.4) and we have more than 2 classes, the accuracy is not a good metric. To have a good understanding of the classifier performance we must rely on other metrics.  \n",
        "\n",
        "First we compute the output of the network for all data in the test set. To obtain it use:\n",
        "```python \n",
        "    model.predict(test_dataset, steps=test_steps)[:len(test_labels)].squeeze()\n",
        "```\n",
        "This is needed because the elements in `[len(val_labels):]` are not part of the dataset, but they are just added to complete the batch. \n",
        "\n",
        "Then we get the predicted label by computing the $argmax$ over the output of the network, for each input data. This is motivated by the fact that there are as many neurons as labels in the last layer of the network and we used the softmax activation function. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9qSfMZlgu5U",
        "outputId": "9b60a4c9-e3cd-4329-b15e-4e5b2b911a3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "344/344 [==============================] - 46s 134ms/step\n"
          ]
        }
      ],
      "source": [
        "# get predictions for test set\n",
        "test_labels = reference_df_test['class']\n",
        "test_pred = model.predict(test_dataset, steps=test_steps, verbose=1)[:len(test_labels)].squeeze()\n",
        "\n",
        "# estimate class\n",
        "y_pred = np.argmax(test_pred, axis=1)\n",
        "\n",
        "# true labels\n",
        "y_true = reference_df_test['class'].tolist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jinyZOKNgu7V"
      },
      "source": [
        "Then, to visualize the performance of the algorithm we plot the confusion matrix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cHIhE3c3gu7e",
        "outputId": "de9930bc-ef28-48ff-e7eb-f2d41fa5ae4b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1512x1296 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_pred, y_true)\n",
        "plot_utils.plot_confusion_matrix(cm, labels=commands, normalize=True, saveit=False, model_name='')\n",
        "# plt.savefig(f'figures/cm_{model_name}.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6K19gHq1gu7z"
      },
      "source": [
        "Moreover, we can evaluate the _precision_, _recall_ and _fscore_ metrics using some methods from the *scikit-learn* library. These metrics are calculated for each class in the dataset, so to get an a high-level view of how our model is performing we need to aggregate them. This can be done in 3 main ways:\n",
        "\n",
        "1. **Macro average**: it is the unweighted average of the selected metric; it does not take label unbalance into account.\n",
        "2. **Micro average**: it is calculated by considering all the TP, TN, FP and FN for each class, adding them up and then using those to compute the metric’s micro-average; in other words it calculates the metrics globally by counting the total TP, FP and FN.\n",
        "3. **Weighted average**: it is the average of the metric values for individual classes weighted by the support (i.e. the number of true instances) of that class. It alters _macro_ to account for label imbalance.\n",
        "\n",
        "In our case, dealing with unbalanced classes, we are more interesting in the weighted average of the considered metrics.\n",
        "\n",
        "Another interesting measure for multiclass classification is the _Cohen's Kappa score_. From [Wikipedia](https://en.wikipedia.org/wiki/Cohen%27s_kappa):\n",
        "> Cohen’s kappa coefficient (κ) is a statistic that is used to measure inter-rater reliability (and also intra-rater reliability) for qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculation, as κ takes into account the possibility of the agreement occurring by chance.\n",
        "\n",
        "It can be thought as a version of the accuracy which also integrates measurements of chance and class imbalance. Generally, a score above 0.8 is considered excellent. See [this guide](https://towardsdatascience.com/multi-class-metrics-made-simple-the-kappa-score-aka-cohens-kappa-coefficient-bdea137af09c) for more information about it. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "NPE9Z35Xgu70",
        "outputId": "1760e656-6295-481f-d2b4-63660dab5cdd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:      52.99%\n",
            "Error rate:    47.01%\n",
            "Cross-entropy: 1.5648\n",
            "Cohen's Kappa: 0.5139\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b9fad1e3-7d70-4fcb-82f2-b5da83fce87e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>macro</th>\n",
              "      <td>0.5172</td>\n",
              "      <td>0.4988</td>\n",
              "      <td>0.5041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>micro</th>\n",
              "      <td>0.5299</td>\n",
              "      <td>0.5299</td>\n",
              "      <td>0.5299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted</th>\n",
              "      <td>0.5365</td>\n",
              "      <td>0.5299</td>\n",
              "      <td>0.5302</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-b9fad1e3-7d70-4fcb-82f2-b5da83fce87e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b9fad1e3-7d70-4fcb-82f2-b5da83fce87e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          precision  recall  f1-score\n",
              "macro        0.5172  0.4988    0.5041\n",
              "micro        0.5299  0.5299    0.5299\n",
              "weighted     0.5365  0.5299    0.5302"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "accuracy = accuracy_score(y_true=y_true, y_pred=y_pred)\n",
        "logloss  = log_loss(y_true, test_pred, labels=np.arange(35))\n",
        "cohen_k  = cohen_kappa_score(y_true, y_pred)\n",
        "\n",
        "print('Accuracy:      {:.2f}%'.format(accuracy*100))\n",
        "print('Error rate:    {:.2f}%'.format((1-accuracy)*100))\n",
        "print('Cross-entropy: {:.4f}'.format(logloss))\n",
        "print(\"Cohen's Kappa: {:.4f}\".format(cohen_k))\n",
        "\n",
        "def class_report(y_true, y_pred):\n",
        "    precision_macro,    recall_macro,    fscore_macro,    _ = precision_recall_fscore_support(y_true=y_true, y_pred=y_pred, average='macro')\n",
        "    precision_micro,    recall_micro,    fscore_micro,    _ = precision_recall_fscore_support(y_true=y_true, y_pred=y_pred, average='micro')\n",
        "    precision_weighted, recall_weighted, fscore_weighted, _ = precision_recall_fscore_support(y_true=y_true, y_pred=y_pred, average='weighted')\n",
        "    \n",
        "    df_report = pd.DataFrame(\n",
        "        {\n",
        "            'precision': [precision_macro, precision_micro, precision_weighted],\n",
        "            'recall': [recall_macro, recall_micro, recall_weighted],\n",
        "            'f1-score': [fscore_macro, fscore_micro, fscore_weighted]\n",
        "        }, \n",
        "        index=['macro', 'micro', 'weighted']\n",
        "    )\n",
        "    \n",
        "    return df_report\n",
        "\n",
        "cr = class_report(y_true, y_pred)\n",
        "display(cr.round(4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "SaXtDPNwgu73",
        "outputId": "5eb79474-f85a-42fe-a65e-3a4db4a1e486"
      },
      "outputs": [
        {
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>cross-entropy</th>\n",
              "      <th>Cohen-Kappa</th>\n",
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              "      <td>0.5365</td>\n",
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              "\n",
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              "\n",
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              "  </style>\n",
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              "          document.querySelector('#df-52c1f368-ad78-4ac3-b5b8-4601d2c12fa8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-52c1f368-ad78-4ac3-b5b8-4601d2c12fa8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   precision  recall  f1-score  accuracy  cross-entropy  Cohen-Kappa\n",
              "0     0.5365  0.5299    0.5302    0.5299         1.5648       0.5139"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "weighted_metrics = cr.iloc[-1].tolist()\n",
        "\n",
        "df_metrics = pd.DataFrame(\n",
        "    [weighted_metrics + [accuracy, logloss, cohen_k]], \n",
        "    columns = ['precision', 'recall', 'f1-score', 'accuracy', 'cross-entropy', 'Cohen-Kappa'], \n",
        ")\n",
        "\n",
        "display(df_metrics.round(4))\n",
        "\n",
        "# Uncomment next lines to store the final metrics \n",
        "# if not os.path.exists(\"metrics\"):\n",
        "#   os.mkdir('metrics')\n",
        "#   \n",
        "# df_metrics.to_csv(f'metrics/metr_{model_name}_2.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "47NBCJ2yC_iA"
      },
      "outputs": [],
      "source": [
        "# tmp1 = pd.read_csv('metr_smallCnnModel_1.csv', index_col=0)\n",
        "# tmp2 = pd.read_csv('metr_smallCnnModel_2.csv', index_col=0)\n",
        "# \n",
        "# df_ = pd.concat([tmp1, tmp2], ignore_index=True, axis=0)\n",
        "# df_['Training'] = ['Without Delta', 'With Delta']\n",
        "# df_ = df_.set_index(['Training'])\n",
        "# df_"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Receiver operating characteristic (ROC) curve, averaging vertically to get the overall curve of the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.metrics import RocCurveDisplay\n",
        "\n",
        "micro_roc_auc_ovr = roc_auc_score(\n",
        "    y_true,\n",
        "    test_pred,\n",
        "    multi_class=\"ovr\",\n",
        "    average=\"macro\",\n",
        ")\n",
        "\n",
        "print(f\"Micro-averaged One-vs-Rest ROC AUC score: {micro_roc_auc_ovr:.2f}\\n\")\n",
        "\n",
        "label_binarizer = LabelBinarizer().fit(reference_df_train['class'].tolist())\n",
        "y_onehot_test = label_binarizer.transform(y_true)\n",
        "# y_onehot_test.shape  # (n_samples, n_classes)\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (14,7)\n",
        "\n",
        "RocCurveDisplay.from_predictions(\n",
        "    y_onehot_test.ravel(),\n",
        "    test_pred.ravel(),\n",
        "    name=\"micro-average OvR\",\n",
        "    color=\"darkorange\",\n",
        ")\n",
        "\n",
        "plt.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\n",
        "plt.axis(\"square\")\n",
        "plt.grid(alpha=0.5)\n",
        "plt.xlabel(\"False Positive Rate\", fontsize=18)\n",
        "plt.ylabel(\"True Positive Rate\", fontsize=18)\n",
        "plt.title(\"Micro-averaged One-vs-Rest\\nReceiver Operating Characteristic\", fontsize=20)\n",
        "plt.legend(edgecolor='black', facecolor='linen', fontsize=12, loc ='lower right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqEwef54gu74"
      },
      "source": [
        "# References\n",
        "**[Sainath15]** <br>\n",
        " Tara N. Sainath, Carolina Parada, _Convolutional Neural Networks for Small-footprint Keyword Spotting_, INTERSPEECH, Dresden, Germany, September 2015.\n",
        "\n",
        "**[Warden18]** <br>\n",
        " Pete Warden, _Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition_, arXiv:1804.03209, April 2018. \n",
        " \n",
        "**[Lyons14]** <br>\n",
        " James Lyons et al. (2020, January 14). jameslyons/python_speech_features: release v0.6.1 (Version 0.6.1). Zenodo.\n"
      ]
    }
  ],
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    "accelerator": "GPU",
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    "gpuClass": "standard",
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      "language": "python",
      "name": "python3"
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