CryBell.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jobphilipgeorge/CryBell-Enhancing-infant-bonding-and-care-through-emotion-awareness/blob/main/CryBell.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NiFLAhJ7JuNH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e5b63d7-1977-4bdd-b925-03071d8740df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting librosa==0.9.2\n",
            "  Downloading librosa-0.9.2-py3-none-any.whl (214 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/214.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/214.3 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m214.3/214.3 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: audioread>=2.1.9 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2) (3.0.1)\n",
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            "Requirement already satisfied: decorator>=4.0.10 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2) (4.4.2)\n",
            "Collecting resampy>=0.2.2 (from librosa==0.9.2)\n",
            "  Downloading resampy-0.4.3-py3-none-any.whl (3.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numba>=0.45.1 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2) (0.58.1)\n",
            "Requirement already satisfied: soundfile>=0.10.2 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2) (0.12.1)\n",
            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2) (1.8.1)\n",
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            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.0->librosa==0.9.2) (4.2.0)\n",
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            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.9.2) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.9.2) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.9.2) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.9.2) (2024.2.2)\n",
            "Installing collected packages: resampy, librosa\n",
            "  Attempting uninstall: librosa\n",
            "    Found existing installation: librosa 0.10.1\n",
            "    Uninstalling librosa-0.10.1:\n",
            "      Successfully uninstalled librosa-0.10.1\n",
            "Successfully installed librosa-0.9.2 resampy-0.4.3\n"
          ]
        }
      ],
      "source": [
        "!pip install librosa==0.9.2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/gveres/donateacry-corpus.git"
      ],
      "metadata": {
        "id": "oSAIdOddM465",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0f2fc45-a49c-4ca2-b728-273ad2e31822"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'donateacry-corpus'...\n",
            "remote: Enumerating objects: 1616, done.\u001b[K\n",
            "remote: Total 1616 (delta 0), reused 0 (delta 0), pack-reused 1616\u001b[K\n",
            "Receiving objects: 100% (1616/1616), 67.06 MiB | 26.34 MiB/s, done.\n",
            "Resolving deltas: 100% (43/43), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import librosa\n",
        "import librosa.display\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import normalize\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow\n",
        "from tensorflow.keras.layers import LSTM, Dense"
      ],
      "metadata": {
        "id": "F01F1u7WNpzU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_spectrogram(audio_file, image_file):\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(1, 1, 1)\n",
        "    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)\n",
        "\n",
        "    y, sr = librosa.load(audio_file)\n",
        "    ms = librosa.feature.melspectrogram(y, sr=sr)\n",
        "    log_ms = librosa.power_to_db(ms, ref=np.max)\n",
        "    librosa.display.specshow(log_ms, sr=sr)\n",
        "\n",
        "    fig.savefig(image_file)\n",
        "    plt.close(fig)\n",
        "\n",
        "def create_pngs_from_wavs(input_path, output_path):\n",
        "    if not os.path.exists(output_path):\n",
        "        os.makedirs(output_path)\n",
        "\n",
        "    dir = os.listdir(input_path)\n",
        "\n",
        "    for i, file in enumerate(dir):\n",
        "        input_file = os.path.join(input_path, file)\n",
        "        output_file = os.path.join(output_path, file.replace('.wav', '.png'))\n",
        "        create_spectrogram(input_file, output_file)"
      ],
      "metadata": {
        "id": "o86g_00HNveB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "create_pngs_from_wavs('/content/donateacry-corpus/donateacry_corpus_cleaned_and_updated_data/belly_pain','/content/Output/belly_pain')\n",
        "create_pngs_from_wavs('/content/donateacry-corpus/donateacry_corpus_cleaned_and_updated_data/burping', '/content/Output/burping')\n",
        "create_pngs_from_wavs('/content/donateacry-corpus/donateacry_corpus_cleaned_and_updated_data/discomfort', '/content/Output/discomfort')\n",
        "create_pngs_from_wavs('/content/donateacry-corpus/donateacry_corpus_cleaned_and_updated_data/hungry', '/content/Output/hungry')\n",
        "create_pngs_from_wavs('/content/donateacry-corpus/donateacry_corpus_cleaned_and_updated_data/tired', '/content/Output/tired')"
      ],
      "metadata": {
        "id": "4T3EzufoN0B7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = []\n",
        "y = []\n",
        "\n",
        "from keras.preprocessing import image\n",
        "\n",
        "def load_images_from_path(path, label):\n",
        "    images = []\n",
        "    labels = []\n",
        "\n",
        "    for file in os.listdir(path):\n",
        "        images.append(image.img_to_array(image.load_img(os.path.join(path, file), target_size=(224, 224, 3))))\n",
        "        labels.append((label))\n",
        "\n",
        "    return images, labels"
      ],
      "metadata": {
        "id": "SA5xb7ItQ1uT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "import shutil\n",
        "\n",
        "def move_files(source_pattern, destination_path):\n",
        "    if not os.path.exists(destination_path):\n",
        "        os.makedirs(destination_path)\n",
        "        print(f\"Directory '{destination_path}' created successfully.\")\n",
        "    else:\n",
        "        print(f\"Directory '{destination_path}' already exists.\")\n",
        "\n",
        "    files_to_move = glob.glob(source_pattern)\n",
        "    for file_path in files_to_move[:-1]:\n",
        "        shutil.move(file_path, destination_path)"
      ],
      "metadata": {
        "id": "OJNJbWHAREyw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define your directories and source patterns\n",
        "directories = {\n",
        "    '/content/Output/belly_pain_train': '/content/Output/belly_pain/*.png',\n",
        "    '/content/Output/burping_train': '/content/Output/burping/*.png',\n",
        "    '/content/Output/discomfort_train': '/content/Output/discomfort/*.png',\n",
        "    '/content/Output/hungry_train': '/content/Output/hungry/*.png',\n",
        "    '/content/Output/tired_train': '/content/Output/tired/*.png'\n",
        "}\n",
        "\n",
        "for directory, source_pattern in directories.items():\n",
        "    move_files(source_pattern, directory)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2XVJAksMRHBB",
        "outputId": "06d9ed2c-c83a-457b-fb3b-3e0e36b6c118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Directory '/content/Output/belly_pain_train' created successfully.\n",
            "Directory '/content/Output/burping_train' created successfully.\n",
            "Directory '/content/Output/discomfort_train' created successfully.\n",
            "Directory '/content/Output/hungry_train' created successfully.\n",
            "Directory '/content/Output/tired_train' created successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "images, labels = load_images_from_path('/content/Output/belly_pain_train', 0)\n",
        "\n",
        "x += images\n",
        "y += labels\n",
        "\n",
        "images, labels = load_images_from_path('/content/Output/burping_train', 1)\n",
        "\n",
        "x += images\n",
        "y += labels\n",
        "\n",
        "images, labels = load_images_from_path('/content/Output/discomfort_train', 2)\n",
        "\n",
        "x += images\n",
        "y += labels\n",
        "\n",
        "images, labels = load_images_from_path('/content/Output/hungry_train', 3)\n",
        "\n",
        "x += images\n",
        "y += labels\n",
        "\n",
        "images, labels = load_images_from_path('/content/Output/tired_train', 4)\n",
        "\n",
        "x += images\n",
        "y += labels"
      ],
      "metadata": {
        "id": "wkupa_S7TIAI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
        "\n",
        "x_train_norm = np.array(x_train) / 255\n",
        "x_test_norm = np.array(x_test) / 255\n",
        "\n",
        "y_train_encoded = to_categorical(y_train)\n",
        "y_test_encoded = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "_sblWI7rTdkj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D,Flatten, Dense, Dropout,Normalization,Resizing,InputLayer\n",
        "from tensorflow.keras.layers.experimental import RandomFourierFeatures\n",
        "from tensorflow.keras.optimizers import Adam,Adafactor,AdamW,Lion\n",
        "from tensorflow.keras.optimizers.experimental import Adadelta,Adagrad,Adamax,RMSprop,SGD,Nadam,Ftrl\n",
        "\n",
        "model = Sequential()\n",
        "model.add(InputLayer(input_shape=(224, 224, 3)))\n",
        "model.add(Resizing(64,64))\n",
        "model.add(Normalization())\n",
        "model.add(Conv2D(64, 3, strides=1, activation='relu'))\n",
        "model.add(Conv2D(128, 3, strides=1, activation='relu'))\n",
        "model.add(MaxPooling2D(2, 2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Flatten())\n",
        "model.add(RandomFourierFeatures(5))\n",
        "model.compile(optimizer=AdamW(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0wvW5F1TgHz",
        "outputId": "bddc3937-521c-4b80-dbf0-1f7f45d644bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.AdamW.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " resizing (Resizing)         (None, 64, 64, 3)         0         \n",
            "                                                                 \n",
            " normalization (Normalizati  (None, 64, 64, 3)         7         \n",
            " on)                                                             \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 62, 62, 64)        1792      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 60, 60, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 30, 30, 128)       0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 30, 30, 128)       0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 115200)            0         \n",
            "                                                                 \n",
            " random_fourier_features (R  (None, 5)                 576006    \n",
            " andomFourierFeatures)                                           \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 651661 (2.49 MB)\n",
            "Trainable params: 75648 (295.50 KB)\n",
            "Non-trainable params: 576013 (2.20 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy',\n",
        "                              factor=0.5,\n",
        "                              patience=2,\n",
        "                              min_lr=0.006,\n",
        "                              verbose=1)\n",
        "\n",
        "hist = model.fit(x_train_norm, y_train_encoded, validation_data=(x_test_norm, y_test_encoded), epochs=50,callbacks=[reduce_lr])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "maG657moTlG6",
        "outputId": "3fb1d128-ed93-405c-aea7-938a84340da1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "12/12 [==============================] - 18s 1s/step - loss: 11.2635 - accuracy: 0.0305 - val_loss: 1.0402 - val_accuracy: 0.0330 - lr: 0.0010\n",
            "Epoch 2/50\n",
            "12/12 [==============================] - 15s 1s/step - loss: 1.7075 - accuracy: 0.5291 - val_loss: 3.7250 - val_accuracy: 0.8132 - lr: 0.0010\n",
            "Epoch 3/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 8.7302 - accuracy: 0.8033 - val_loss: 12.4417 - val_accuracy: 0.8242 - lr: 0.0010\n",
            "Epoch 4/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 12.7303 - accuracy: 0.7036 - val_loss: 14.1999 - val_accuracy: 0.6593 - lr: 0.0010\n",
            "Epoch 5/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 11.8662 - accuracy: 0.4238 - val_loss: 4.8486 - val_accuracy: 0.0989 - lr: 0.0010\n",
            "Epoch 6/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 4.4211 - accuracy: 0.1163 - val_loss: 6.6010 - val_accuracy: 0.1209 - lr: 0.0010\n",
            "Epoch 7/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.5249 - accuracy: 0.1884 - val_loss: 5.2988 - val_accuracy: 0.2088 - lr: 0.0010\n",
            "Epoch 8/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.3735 - accuracy: 0.1911 - val_loss: 5.4614 - val_accuracy: 0.0769 - lr: 0.0010\n",
            "Epoch 9/50\n",
            "12/12 [==============================] - 16s 1s/step - loss: 3.8285 - accuracy: 0.1025 - val_loss: 4.2006 - val_accuracy: 0.0879 - lr: 0.0010\n",
            "Epoch 10/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 3.8514 - accuracy: 0.1247 - val_loss: 3.2396 - val_accuracy: 0.1319 - lr: 0.0010\n",
            "Epoch 11/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 3.4777 - accuracy: 0.0637 - val_loss: 2.6430 - val_accuracy: 0.0769 - lr: 0.0010\n",
            "Epoch 12/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 3.7541 - accuracy: 0.0582 - val_loss: 3.1594 - val_accuracy: 0.0659 - lr: 0.0010\n",
            "Epoch 13/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 4.7934 - accuracy: 0.0914 - val_loss: 5.1708 - val_accuracy: 0.0769 - lr: 0.0010\n",
            "Epoch 14/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.8052 - accuracy: 0.1053 - val_loss: 3.3479 - val_accuracy: 0.0769 - lr: 0.0010\n",
            "Epoch 15/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 4.9821 - accuracy: 0.1496 - val_loss: 2.8784 - val_accuracy: 0.1758 - lr: 0.0010\n",
            "Epoch 16/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.3013 - accuracy: 0.1690 - val_loss: 2.8772 - val_accuracy: 0.0879 - lr: 0.0010\n",
            "Epoch 17/50\n",
            "12/12 [==============================] - 16s 1s/step - loss: 5.2204 - accuracy: 0.2244 - val_loss: 2.2690 - val_accuracy: 0.0879 - lr: 0.0010\n",
            "Epoch 18/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.4935 - accuracy: 0.1385 - val_loss: 3.2860 - val_accuracy: 0.1319 - lr: 0.0010\n",
            "Epoch 19/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.1274 - accuracy: 0.1801 - val_loss: 6.3978 - val_accuracy: 0.1429 - lr: 0.0010\n",
            "Epoch 20/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.4252 - accuracy: 0.1939 - val_loss: 5.0353 - val_accuracy: 0.2527 - lr: 0.0010\n",
            "Epoch 21/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 4.8747 - accuracy: 0.1939 - val_loss: 4.1453 - val_accuracy: 0.1868 - lr: 0.0010\n",
            "Epoch 22/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.0084 - accuracy: 0.1690 - val_loss: 3.4119 - val_accuracy: 0.1319 - lr: 0.0010\n",
            "Epoch 23/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.1978 - accuracy: 0.1745 - val_loss: 7.3188 - val_accuracy: 0.1648 - lr: 0.0010\n",
            "Epoch 24/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.3070 - accuracy: 0.1911 - val_loss: 7.2269 - val_accuracy: 0.1538 - lr: 0.0010\n",
            "Epoch 25/50\n",
            "12/12 [==============================] - 16s 1s/step - loss: 5.7622 - accuracy: 0.1939 - val_loss: 6.4220 - val_accuracy: 0.1978 - lr: 0.0010\n",
            "Epoch 26/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.6827 - accuracy: 0.2410 - val_loss: 4.8729 - val_accuracy: 0.2308 - lr: 0.0010\n",
            "Epoch 27/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.8850 - accuracy: 0.1468 - val_loss: 5.6290 - val_accuracy: 0.1868 - lr: 0.0010\n",
            "Epoch 28/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.6716 - accuracy: 0.1884 - val_loss: 5.2198 - val_accuracy: 0.1868 - lr: 0.0010\n",
            "Epoch 29/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.7685 - accuracy: 0.1884 - val_loss: 5.0206 - val_accuracy: 0.1868 - lr: 0.0010\n",
            "Epoch 30/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.8083 - accuracy: 0.2133 - val_loss: 5.3316 - val_accuracy: 0.1429 - lr: 0.0010\n",
            "Epoch 31/50\n",
            "12/12 [==============================] - 19s 2s/step - loss: 5.5459 - accuracy: 0.2105 - val_loss: 4.5001 - val_accuracy: 0.1538 - lr: 0.0010\n",
            "Epoch 32/50\n",
            "12/12 [==============================] - 15s 1s/step - loss: 6.7208 - accuracy: 0.2133 - val_loss: 4.4394 - val_accuracy: 0.1429 - lr: 0.0010\n",
            "Epoch 33/50\n",
            "12/12 [==============================] - 15s 1s/step - loss: 6.7873 - accuracy: 0.1884 - val_loss: 6.8975 - val_accuracy: 0.2308 - lr: 0.0010\n",
            "Epoch 34/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.8532 - accuracy: 0.1939 - val_loss: 6.1920 - val_accuracy: 0.2198 - lr: 0.0010\n",
            "Epoch 35/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.6798 - accuracy: 0.1607 - val_loss: 5.7480 - val_accuracy: 0.1978 - lr: 0.0010\n",
            "Epoch 36/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.5768 - accuracy: 0.2493 - val_loss: 5.7550 - val_accuracy: 0.2857 - lr: 0.0010\n",
            "Epoch 37/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.1918 - accuracy: 0.2078 - val_loss: 6.5265 - val_accuracy: 0.1868 - lr: 0.0010\n",
            "Epoch 38/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.4193 - accuracy: 0.1939 - val_loss: 6.3311 - val_accuracy: 0.2418 - lr: 0.0010\n",
            "Epoch 39/50\n",
            "12/12 [==============================] - 16s 1s/step - loss: 5.4016 - accuracy: 0.2244 - val_loss: 6.5577 - val_accuracy: 0.1978 - lr: 0.0010\n",
            "Epoch 40/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.2424 - accuracy: 0.1634 - val_loss: 6.4907 - val_accuracy: 0.2088 - lr: 0.0010\n",
            "Epoch 41/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.4668 - accuracy: 0.1828 - val_loss: 5.9638 - val_accuracy: 0.2308 - lr: 0.0010\n",
            "Epoch 42/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.1634 - accuracy: 0.2161 - val_loss: 6.6404 - val_accuracy: 0.2198 - lr: 0.0010\n",
            "Epoch 43/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.4347 - accuracy: 0.1856 - val_loss: 6.8703 - val_accuracy: 0.2198 - lr: 0.0010\n",
            "Epoch 44/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.0689 - accuracy: 0.2022 - val_loss: 5.2404 - val_accuracy: 0.1648 - lr: 0.0010\n",
            "Epoch 45/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.0867 - accuracy: 0.1967 - val_loss: 7.0772 - val_accuracy: 0.1978 - lr: 0.0010\n",
            "Epoch 46/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.0196 - accuracy: 0.1994 - val_loss: 6.2684 - val_accuracy: 0.1978 - lr: 0.0010\n",
            "Epoch 47/50\n",
            "12/12 [==============================] - 16s 1s/step - loss: 6.2426 - accuracy: 0.1967 - val_loss: 4.8541 - val_accuracy: 0.1978 - lr: 0.0010\n",
            "Epoch 48/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 6.3107 - accuracy: 0.1856 - val_loss: 6.5621 - val_accuracy: 0.1868 - lr: 0.0010\n",
            "Epoch 49/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.4403 - accuracy: 0.2022 - val_loss: 5.8508 - val_accuracy: 0.2418 - lr: 0.0010\n",
            "Epoch 50/50\n",
            "12/12 [==============================] - 14s 1s/step - loss: 5.9716 - accuracy: 0.2133 - val_loss: 6.9805 - val_accuracy: 0.2198 - lr: 0.0010\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = hist.history['accuracy']\n",
        "val_acc = hist.history['val_accuracy']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.plot(epochs, acc, '-', label='Training Accuracy')\n",
        "plt.plot(epochs, val_acc, ':', label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "histBvuETo5-",
        "outputId": "c55af93d-a590-4e75-c608-e232c59e0588"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "import os\n",
        "\n",
        "def get_png_files(directory):\n",
        "    folders = ['belly_pain', 'burping', 'discomfort', 'hungry', 'tired']\n",
        "    png_files = []\n",
        "\n",
        "    for folder_name in folders:\n",
        "        folder_path = os.path.join(directory, folder_name)\n",
        "        if os.path.exists(folder_path):\n",
        "            png_files.extend(glob.glob(os.path.join(folder_path, '*.png')))\n",
        "        else:\n",
        "            print(f\"Folder '{folder_name}' does not exist in '{directory}'.\")\n",
        "\n",
        "    return png_files\n",
        "\n",
        "directory_path = '/content/Output'\n",
        "png_files_list = get_png_files(directory_path)\n",
        "\n",
        "results = []\n",
        "for file_path in png_files_list:\n",
        "    x = image.load_img(file_path, target_size=(224, 224))\n",
        "\n",
        "    x = image.img_to_array(x)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "\n",
        "    y = model.predict(x)\n",
        "\n",
        "    class_labels = ['belly pain','burping','discomfort','hungry','tired']\n",
        "\n",
        "    # for i, label in enumerate(class_labels):\n",
        "    #     print(f'{label}: {y[0][i]}')\n",
        "\n",
        "    results.append(f\"Original:{file_path.split('/')[3]} Predicted: {class_labels[np.argmax(y)]}\")\n",
        "\n",
        "print('\\n')\n",
        "\n",
        "for i in results:\n",
        "    print(i)\n",
        "    print('\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWeptsENWy3N",
        "outputId": "43058c33-18ff-44fe-d81b-0a7ee829f29b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 182ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 59ms/step\n",
            "\n",
            "\n",
            "Original:belly_pain Predicted: hungry\n",
            "\n",
            "\n",
            "Original:burping Predicted: burping\n",
            "\n",
            "\n",
            "Original:discomfort Predicted: discomfort\n",
            "\n",
            "\n",
            "Original:hungry Predicted: belly pain\n",
            "\n",
            "\n",
            "Original:tired Predicted: tired\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
  ]
}