binary_classification_RNN.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/HakanBayazitHabes/binary_classification_RNN/blob/master/binary_classification_RNN.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": "vmbFOvcshYtD"
      },
      "outputs": [],
      "source": [
        "# Veriyi okuma ve işleme adımında kullanılacak olan kütüphaneler\n",
        "import cv2\n",
        "import urllib\n",
        "import itertools\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import random, os, glob\n",
        "from imutils import paths\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.utils import shuffle\n",
        "import pickle\n",
        "\n",
        "# warningleri kapatmak için kullanılmaktadır.\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Model değerlendirme için kullanılacak olan kütüphaneler\n",
        "from sklearn.metrics import confusion_matrix, classification_report,roc_curve, auc,f1_score\n",
        "\n",
        "# Model için kullanılacak olan kütüphaneler\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.callbacks import History\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from keras.callbacks import ModelCheckpoint # Checkpointler eğitim aşamsında kullanıcaz\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger\n",
        "from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Dropout, SpatialDropout2D,BatchNormalization, Activation,MaxPool2D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img, array_to_img\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "\n",
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "pVLB2ul9hhM5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ccb2dd5-3b21-4499-dcbf-3b9c082f3052"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import cv2\n",
        "\n",
        "# Verilerin bulunduğu dizin\n",
        "data_directory = \"/content/drive/MyDrive/KebAI/Images\"\n",
        "\n",
        "# Dosya yollarını ve etiketleri toplama\n",
        "image_paths = []\n",
        "labels = []\n",
        "\n",
        "# Verileri yükleme\n",
        "for label in os.listdir(data_directory):\n",
        "    label_directory = os.path.join(data_directory, label)\n",
        "    for image_name in os.listdir(label_directory):\n",
        "        image_path = os.path.join(label_directory, image_name)\n",
        "        image_paths.append(image_path)\n",
        "        labels.append(label)\n",
        "\n",
        "# Etiketleri sayısallaştırma\n",
        "label_encoder = LabelEncoder()\n",
        "encoded_labels = label_encoder.fit_transform(labels)\n",
        "\n",
        "# Veri ve etiketleri eğitim ve test setlerine bölelim\n",
        "X_train, X_test, y_train, y_test = train_test_split(image_paths, encoded_labels, test_size=0.2, random_state=42)\n",
        "\n",
        "# Görüntüleri yüklemek için bir fonksiyon tanımlama\n",
        "def load_images(image_paths):\n",
        "    images = []\n",
        "    for path in image_paths:\n",
        "        img = cv2.imread(path)\n",
        "        img = cv2.resize(img, (224, 224))  # Gerekirse, boyutu ayarlayabilirsiniz\n",
        "        images.append(img)\n",
        "    return images\n",
        "\n",
        "# Görüntüleri yükleyelim\n",
        "X_train_images = load_images(X_train)\n",
        "X_test_images = load_images(X_test)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "dRJp-HHiKFMc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "# Görüntüleri Numpy dizilerine dönüştürme\n",
        "X_train_images = np.array(X_train_images)\n",
        "X_test_images = np.array(X_test_images)\n",
        "\n",
        "# Normalizasyon: Görüntü piksellerini [0, 1] aralığına ölçeklendirme\n",
        "X_train_images = X_train_images / 255.0\n",
        "X_test_images = X_test_images / 255.0\n",
        "\n"
      ],
      "metadata": {
        "id": "Iko0dZfvKF2f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_images.shape, X_test_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DWTk5GKKHqZ",
        "outputId": "31d708c4-65e1-4503-93e4-98d6540102d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((969, 224, 224, 3), (243, 224, 224, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "91hlHZsRKKFH",
        "outputId": "06ef00b0-74cb-4e4b-e17b-868d8e7dc30c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((969,), (243,))"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Flatten the height and width dimensions\n",
        "X_train_images_flattened = X_train_images.reshape(X_train_images.shape[0], -1, X_train_images.shape[3])\n",
        "X_test_images_flattened = X_test_images.reshape(X_test_images.shape[0], -1, X_test_images.shape[3])\n"
      ],
      "metadata": {
        "id": "dZwCPrmIKLoC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verileri Numpy dizilerine dönüştürme\n",
        "X_train_images = np.array(X_train_images)\n",
        "X_test_images = np.array(X_test_images)\n",
        "y_train = np.array(y_train)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "# Flatten the height, width, and channel dimensions\n",
        "X_train_images_flattened = X_train_images.reshape(X_train_images.shape[0], -1)\n",
        "X_test_images_flattened = X_test_images.reshape(X_test_images.shape[0], -1)\n",
        "\n",
        "# Reshape flattened images to include timesteps\n",
        "timesteps = 224  # Adjust this based on your data\n",
        "features = X_train_images_flattened.shape[1] // timesteps  # Calculate the number of features per timestep\n",
        "X_train_images_reshaped = X_train_images_flattened.reshape(X_train_images_flattened.shape[0], timesteps, features)\n",
        "X_test_images_reshaped = X_test_images_flattened.reshape(X_test_images_flattened.shape[0], timesteps, features)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DkMYTXTGKNDa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Modeli oluşturma ve eğitme\n",
        "model = keras.models.Sequential()\n",
        "model.add(keras.Input(shape=(timesteps, features)))  # Input shape is now (timesteps, features)\n",
        "model.add(layers.SimpleRNN(128, activation='relu', return_sequences=True))\n",
        "#model.add(layers.SimpleRNN(128, activation='relu', return_sequences=True))\n",
        "model.add(layers.SimpleRNN(128, activation='relu', return_sequences=False))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "tNO-6f7-KR4d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xynshyJqKTzT",
        "outputId": "60423663-0b9e-4293-a5e4-acb36c41860d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn_7 (SimpleRNN)    (None, 224, 128)          102528    \n",
            "                                                                 \n",
            " simple_rnn_8 (SimpleRNN)    (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 135553 (529.50 KB)\n",
            "Trainable params: 135553 (529.50 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=optimizer,\n",
        "              metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), \"acc\"])\n",
        "\n",
        "root = '/content/drive/MyDrive/KebAI_RNN/'\n",
        "\n",
        "callbacks = [\n",
        "             CSVLogger(\"/content/drive/MyDrive/KebAI_RNN/training.csv\", separator=\"-\"),\n",
        "             EarlyStopping(monitor='val_loss', patience=35, verbose=1, mode=\"min\"),\n",
        "             ModelCheckpoint(filepath= root + 'save_model_weights/kabAI_model.h5', verbose=2, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=False),\n",
        "             History()\n",
        "             ]\n",
        "#model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train_images_reshaped, y_train, epochs=100, batch_size=32,validation_split=0.2, callbacks=callbacks)\n",
        "\n",
        "model.save(root + 'data/')\n",
        "model.save_weights('/content/drive/MyDrive/KebAI_RNN/save_model_weights/')\n",
        "#model.save_weights(root + 'data/save_model_weights/kabAI_model_weights.h5')\n",
        "\n",
        "\n",
        "with open('/content/drive/MyDrive/KebAI_RNN/history.pkl', 'wb') as f:\n",
        "    pickle.dump(history.history, f)\n",
        "\n",
        "# save model to json\n",
        "model_json = model.to_json()\n",
        "with open('/content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.json', 'w') as json_file:\n",
        "  json_file.write(model_json)\n",
        "\n",
        "import json\n",
        "with open('/content/drive/MyDrive/KebAI_RNN/save_model_weights/history.json', 'w') as history_file:\n",
        "  json.dump(history.history, history_file)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0MbfY1RdKQf1",
        "outputId": "5036ec15-6605-476e-a263-5ccf56daab95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.6536 - precision_4: 0.6382 - recall_4: 0.9162 - acc: 0.6194\n",
            "Epoch 1: val_loss improved from inf to 0.56282, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 11s 321ms/step - loss: 0.6536 - precision_4: 0.6382 - recall_4: 0.9162 - acc: 0.6194 - val_loss: 0.5628 - val_precision_4: 0.7356 - val_recall_4: 1.0000 - val_acc: 0.7629\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.5477 - precision_4: 0.7429 - recall_4: 0.9693 - acc: 0.7690\n",
            "Epoch 2: val_loss improved from 0.56282 to 0.49891, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 10s 390ms/step - loss: 0.5477 - precision_4: 0.7429 - recall_4: 0.9693 - acc: 0.7690 - val_loss: 0.4989 - val_precision_4: 0.8906 - val_recall_4: 0.8906 - val_acc: 0.8557\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.4670 - precision_4: 0.8511 - recall_4: 0.9121 - acc: 0.8439\n",
            "Epoch 3: val_loss improved from 0.49891 to 0.41978, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 9s 366ms/step - loss: 0.4670 - precision_4: 0.8511 - recall_4: 0.9121 - acc: 0.8439 - val_loss: 0.4198 - val_precision_4: 0.8846 - val_recall_4: 0.8984 - val_acc: 0.8557\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.3985 - precision_4: 0.8686 - recall_4: 0.9059 - acc: 0.8542\n",
            "Epoch 4: val_loss improved from 0.41978 to 0.37280, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 9s 378ms/step - loss: 0.3985 - precision_4: 0.8686 - recall_4: 0.9059 - acc: 0.8542 - val_loss: 0.3728 - val_precision_4: 0.8592 - val_recall_4: 0.9531 - val_acc: 0.8660\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.3434 - precision_4: 0.8931 - recall_4: 0.9059 - acc: 0.8723\n",
            "Epoch 5: val_loss improved from 0.37280 to 0.32736, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 7s 266ms/step - loss: 0.3434 - precision_4: 0.8931 - recall_4: 0.9059 - acc: 0.8723 - val_loss: 0.3274 - val_precision_4: 0.8955 - val_recall_4: 0.9375 - val_acc: 0.8866\n",
            "Epoch 6/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.3072 - precision_4: 0.8847 - recall_4: 0.9100 - acc: 0.8684\n",
            "Epoch 6: val_loss did not improve from 0.32736\n",
            "25/25 [==============================] - 9s 378ms/step - loss: 0.3072 - precision_4: 0.8847 - recall_4: 0.9100 - acc: 0.8684 - val_loss: 0.3361 - val_precision_4: 0.8514 - val_recall_4: 0.9844 - val_acc: 0.8763\n",
            "Epoch 7/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.2995 - precision_4: 0.8874 - recall_4: 0.9346 - acc: 0.8839\n",
            "Epoch 7: val_loss did not improve from 0.32736\n",
            "25/25 [==============================] - 7s 269ms/step - loss: 0.2995 - precision_4: 0.8874 - recall_4: 0.9346 - acc: 0.8839 - val_loss: 0.3515 - val_precision_4: 0.9237 - val_recall_4: 0.8516 - val_acc: 0.8557\n",
            "Epoch 8/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.2436 - precision_4: 0.9209 - recall_4: 0.9284 - acc: 0.9045\n",
            "Epoch 8: val_loss improved from 0.32736 to 0.25162, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 10s 396ms/step - loss: 0.2436 - precision_4: 0.9209 - recall_4: 0.9284 - acc: 0.9045 - val_loss: 0.2516 - val_precision_4: 0.9254 - val_recall_4: 0.9688 - val_acc: 0.9278\n",
            "Epoch 9/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.2331 - precision_4: 0.9273 - recall_4: 0.9387 - acc: 0.9148\n",
            "Epoch 9: val_loss improved from 0.25162 to 0.23673, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 7s 265ms/step - loss: 0.2331 - precision_4: 0.9273 - recall_4: 0.9387 - acc: 0.9148 - val_loss: 0.2367 - val_precision_4: 0.8993 - val_recall_4: 0.9766 - val_acc: 0.9124\n",
            "Epoch 10/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.2181 - precision_4: 0.9462 - recall_4: 0.9346 - acc: 0.9252\n",
            "Epoch 10: val_loss did not improve from 0.23673\n",
            "25/25 [==============================] - 9s 379ms/step - loss: 0.2181 - precision_4: 0.9462 - recall_4: 0.9346 - acc: 0.9252 - val_loss: 0.2632 - val_precision_4: 0.8881 - val_recall_4: 0.9922 - val_acc: 0.9124\n",
            "Epoch 11/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.1791 - precision_4: 0.9384 - recall_4: 0.9652 - acc: 0.9381\n",
            "Epoch 11: val_loss did not improve from 0.23673\n",
            "25/25 [==============================] - 7s 271ms/step - loss: 0.1791 - precision_4: 0.9384 - recall_4: 0.9652 - acc: 0.9381 - val_loss: 0.2618 - val_precision_4: 0.9744 - val_recall_4: 0.8906 - val_acc: 0.9124\n",
            "Epoch 12/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.1594 - precision_4: 0.9437 - recall_4: 0.9591 - acc: 0.9381\n",
            "Epoch 12: val_loss improved from 0.23673 to 0.21600, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 10s 392ms/step - loss: 0.1594 - precision_4: 0.9437 - recall_4: 0.9591 - acc: 0.9381 - val_loss: 0.2160 - val_precision_4: 0.9137 - val_recall_4: 0.9922 - val_acc: 0.9330\n",
            "Epoch 13/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.1613 - precision_4: 0.9608 - recall_4: 0.9530 - acc: 0.9458\n",
            "Epoch 13: val_loss improved from 0.21600 to 0.20766, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 7s 275ms/step - loss: 0.1613 - precision_4: 0.9608 - recall_4: 0.9530 - acc: 0.9458 - val_loss: 0.2077 - val_precision_4: 0.9403 - val_recall_4: 0.9844 - val_acc: 0.9485\n",
            "Epoch 14/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.1453 - precision_4: 0.9610 - recall_4: 0.9571 - acc: 0.9484\n",
            "Epoch 14: val_loss did not improve from 0.20766\n",
            "25/25 [==============================] - 9s 374ms/step - loss: 0.1453 - precision_4: 0.9610 - recall_4: 0.9571 - acc: 0.9484 - val_loss: 0.2206 - val_precision_4: 0.9203 - val_recall_4: 0.9922 - val_acc: 0.9381\n",
            "Epoch 15/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.1280 - precision_4: 0.9537 - recall_4: 0.9693 - acc: 0.9510\n",
            "Epoch 15: val_loss improved from 0.20766 to 0.19055, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 7s 279ms/step - loss: 0.1280 - precision_4: 0.9537 - recall_4: 0.9693 - acc: 0.9510 - val_loss: 0.1906 - val_precision_4: 0.9323 - val_recall_4: 0.9688 - val_acc: 0.9330\n",
            "Epoch 16/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.1238 - precision_4: 0.9613 - recall_4: 0.9652 - acc: 0.9535\n",
            "Epoch 16: val_loss improved from 0.19055 to 0.17379, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 10s 381ms/step - loss: 0.1238 - precision_4: 0.9613 - recall_4: 0.9652 - acc: 0.9535 - val_loss: 0.1738 - val_precision_4: 0.9692 - val_recall_4: 0.9844 - val_acc: 0.9691\n",
            "Epoch 17/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.1085 - precision_4: 0.9773 - recall_4: 0.9693 - acc: 0.9665\n",
            "Epoch 17: val_loss did not improve from 0.17379\n",
            "25/25 [==============================] - 7s 269ms/step - loss: 0.1085 - precision_4: 0.9773 - recall_4: 0.9693 - acc: 0.9665 - val_loss: 0.1795 - val_precision_4: 0.9538 - val_recall_4: 0.9688 - val_acc: 0.9485\n",
            "Epoch 18/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.1193 - precision_4: 0.9671 - recall_4: 0.9632 - acc: 0.9561\n",
            "Epoch 18: val_loss did not improve from 0.17379\n",
            "25/25 [==============================] - 10s 407ms/step - loss: 0.1193 - precision_4: 0.9671 - recall_4: 0.9632 - acc: 0.9561 - val_loss: 0.2378 - val_precision_4: 0.9007 - val_recall_4: 0.9922 - val_acc: 0.9227\n",
            "Epoch 19/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0972 - precision_4: 0.9714 - recall_4: 0.9714 - acc: 0.9639\n",
            "Epoch 19: val_loss did not improve from 0.17379\n",
            "25/25 [==============================] - 12s 471ms/step - loss: 0.0972 - precision_4: 0.9714 - recall_4: 0.9714 - acc: 0.9639 - val_loss: 0.1767 - val_precision_4: 0.9618 - val_recall_4: 0.9844 - val_acc: 0.9639\n",
            "Epoch 20/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0653 - precision_4: 0.9856 - recall_4: 0.9816 - acc: 0.9794\n",
            "Epoch 20: val_loss improved from 0.17379 to 0.15688, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 7s 275ms/step - loss: 0.0653 - precision_4: 0.9856 - recall_4: 0.9816 - acc: 0.9794 - val_loss: 0.1569 - val_precision_4: 0.9692 - val_recall_4: 0.9844 - val_acc: 0.9691\n",
            "Epoch 21/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0497 - precision_4: 0.9938 - recall_4: 0.9857 - acc: 0.9871\n",
            "Epoch 21: val_loss did not improve from 0.15688\n",
            "25/25 [==============================] - 9s 384ms/step - loss: 0.0497 - precision_4: 0.9938 - recall_4: 0.9857 - acc: 0.9871 - val_loss: 0.1700 - val_precision_4: 0.9621 - val_recall_4: 0.9922 - val_acc: 0.9691\n",
            "Epoch 22/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0963 - precision_4: 0.9753 - recall_4: 0.9693 - acc: 0.9652\n",
            "Epoch 22: val_loss did not improve from 0.15688\n",
            "25/25 [==============================] - 6s 259ms/step - loss: 0.0963 - precision_4: 0.9753 - recall_4: 0.9693 - acc: 0.9652 - val_loss: 0.3179 - val_precision_4: 0.8828 - val_recall_4: 1.0000 - val_acc: 0.9124\n",
            "Epoch 23/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0814 - precision_4: 0.9756 - recall_4: 0.9796 - acc: 0.9716\n",
            "Epoch 23: val_loss did not improve from 0.15688\n",
            "25/25 [==============================] - 9s 382ms/step - loss: 0.0814 - precision_4: 0.9756 - recall_4: 0.9796 - acc: 0.9716 - val_loss: 0.1694 - val_precision_4: 0.9621 - val_recall_4: 0.9922 - val_acc: 0.9691\n",
            "Epoch 24/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0381 - precision_4: 0.9918 - recall_4: 0.9918 - acc: 0.9897\n",
            "Epoch 24: val_loss did not improve from 0.15688\n",
            "25/25 [==============================] - 7s 261ms/step - loss: 0.0381 - precision_4: 0.9918 - recall_4: 0.9918 - acc: 0.9897 - val_loss: 0.1771 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 25/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0330 - precision_4: 0.9939 - recall_4: 0.9939 - acc: 0.9923\n",
            "Epoch 25: val_loss did not improve from 0.15688\n",
            "25/25 [==============================] - 9s 375ms/step - loss: 0.0330 - precision_4: 0.9939 - recall_4: 0.9939 - acc: 0.9923 - val_loss: 0.1922 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 26/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0315 - precision_4: 0.9919 - recall_4: 0.9980 - acc: 0.9935\n",
            "Epoch 26: val_loss improved from 0.15688 to 0.15007, saving model to /content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0315 - precision_4: 0.9919 - recall_4: 0.9980 - acc: 0.9935 - val_loss: 0.1501 - val_precision_4: 0.9688 - val_recall_4: 0.9688 - val_acc: 0.9588\n",
            "Epoch 27/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0441 - precision_4: 0.9898 - recall_4: 0.9877 - acc: 0.9858\n",
            "Epoch 27: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 9s 383ms/step - loss: 0.0441 - precision_4: 0.9898 - recall_4: 0.9877 - acc: 0.9858 - val_loss: 0.2975 - val_precision_4: 0.9746 - val_recall_4: 0.8984 - val_acc: 0.9175\n",
            "Epoch 28/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0593 - precision_4: 0.9837 - recall_4: 0.9898 - acc: 0.9832\n",
            "Epoch 28: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 269ms/step - loss: 0.0593 - precision_4: 0.9837 - recall_4: 0.9898 - acc: 0.9832 - val_loss: 0.1829 - val_precision_4: 0.9692 - val_recall_4: 0.9844 - val_acc: 0.9691\n",
            "Epoch 29/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0486 - precision_4: 0.9857 - recall_4: 0.9877 - acc: 0.9832\n",
            "Epoch 29: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 9s 384ms/step - loss: 0.0486 - precision_4: 0.9857 - recall_4: 0.9877 - acc: 0.9832 - val_loss: 0.1924 - val_precision_4: 0.9690 - val_recall_4: 0.9766 - val_acc: 0.9639\n",
            "Epoch 30/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0404 - precision_4: 0.9959 - recall_4: 0.9877 - acc: 0.9897\n",
            "Epoch 30: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 265ms/step - loss: 0.0404 - precision_4: 0.9959 - recall_4: 0.9877 - acc: 0.9897 - val_loss: 0.2215 - val_precision_4: 0.9270 - val_recall_4: 0.9922 - val_acc: 0.9433\n",
            "Epoch 31/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0272 - precision_4: 0.9959 - recall_4: 0.9939 - acc: 0.9935\n",
            "Epoch 31: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 395ms/step - loss: 0.0272 - precision_4: 0.9959 - recall_4: 0.9939 - acc: 0.9935 - val_loss: 0.1932 - val_precision_4: 0.9412 - val_recall_4: 1.0000 - val_acc: 0.9588\n",
            "Epoch 32/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0167 - precision_4: 0.9980 - recall_4: 0.9980 - acc: 0.9974\n",
            "Epoch 32: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 270ms/step - loss: 0.0167 - precision_4: 0.9980 - recall_4: 0.9980 - acc: 0.9974 - val_loss: 0.1810 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 33/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0110 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 33: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 394ms/step - loss: 0.0110 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.1914 - val_precision_4: 0.9549 - val_recall_4: 0.9922 - val_acc: 0.9639\n",
            "Epoch 34/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0166 - precision_4: 0.9959 - recall_4: 0.9959 - acc: 0.9948\n",
            "Epoch 34: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 267ms/step - loss: 0.0166 - precision_4: 0.9959 - recall_4: 0.9959 - acc: 0.9948 - val_loss: 0.1613 - val_precision_4: 0.9615 - val_recall_4: 0.9766 - val_acc: 0.9588\n",
            "Epoch 35/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0107 - precision_4: 1.0000 - recall_4: 0.9980 - acc: 0.9987\n",
            "Epoch 35: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 401ms/step - loss: 0.0107 - precision_4: 1.0000 - recall_4: 0.9980 - acc: 0.9987 - val_loss: 0.2129 - val_precision_4: 0.9552 - val_recall_4: 1.0000 - val_acc: 0.9691\n",
            "Epoch 36/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0107 - precision_4: 1.0000 - recall_4: 0.9980 - acc: 0.9987\n",
            "Epoch 36: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 264ms/step - loss: 0.0107 - precision_4: 1.0000 - recall_4: 0.9980 - acc: 0.9987 - val_loss: 0.1879 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 37/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0067 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 37: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 394ms/step - loss: 0.0067 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.1891 - val_precision_4: 0.9615 - val_recall_4: 0.9766 - val_acc: 0.9588\n",
            "Epoch 38/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0047 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 38: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 270ms/step - loss: 0.0047 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2023 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 39/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0038 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 39: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 402ms/step - loss: 0.0038 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2043 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 40/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0035 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 40: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 268ms/step - loss: 0.0035 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2141 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 41/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0030 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 41: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 403ms/step - loss: 0.0030 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2189 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 42/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0027 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 42: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 271ms/step - loss: 0.0027 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2263 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 43/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0025 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 43: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 9s 382ms/step - loss: 0.0025 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2394 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 44/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0022 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 44: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 269ms/step - loss: 0.0022 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2311 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 45/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0019 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 45: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 388ms/step - loss: 0.0019 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2403 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 46/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0018 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 46: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 277ms/step - loss: 0.0018 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2488 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 47/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0019 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 47: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 414ms/step - loss: 0.0019 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2551 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 48/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0018 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 48: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 10s 409ms/step - loss: 0.0018 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2561 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 49/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0014 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 49: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 9s 347ms/step - loss: 0.0014 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2504 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 50/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0013 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 50: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 309ms/step - loss: 0.0013 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2597 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 51/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 0.0011 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 51: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 330ms/step - loss: 0.0011 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2660 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 52/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 9.7016e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 52: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 7s 301ms/step - loss: 9.7016e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2787 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 53/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 9.1622e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 53: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 9s 341ms/step - loss: 9.1622e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2766 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 54/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 9.1203e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 54: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 310ms/step - loss: 9.1203e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2744 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 55/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 7.8493e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 55: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 332ms/step - loss: 7.8493e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2855 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 56/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 7.1936e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 56: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 313ms/step - loss: 7.1936e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2835 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 57/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 7.0822e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 57: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 327ms/step - loss: 7.0822e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2830 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 58/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 6.4551e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 58: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 313ms/step - loss: 6.4551e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2858 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 59/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 6.0463e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 59: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 320ms/step - loss: 6.0463e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2924 - val_precision_4: 0.9545 - val_recall_4: 0.9844 - val_acc: 0.9588\n",
            "Epoch 60/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 6.0543e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 60: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 8s 307ms/step - loss: 6.0543e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2933 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 61/100\n",
            "25/25 [==============================] - ETA: 0s - loss: 5.2477e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000\n",
            "Epoch 61: val_loss did not improve from 0.15007\n",
            "25/25 [==============================] - 9s 339ms/step - loss: 5.2477e-04 - precision_4: 1.0000 - recall_4: 1.0000 - acc: 1.0000 - val_loss: 0.2968 - val_precision_4: 0.9542 - val_recall_4: 0.9766 - val_acc: 0.9536\n",
            "Epoch 61: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history.history['val_acc']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHroC_1lKYVv",
        "outputId": "781e9365-44c3-4284-deee-a6a51351a7f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8865979313850403,\n",
              " 0.8969072103500366,\n",
              " 0.907216489315033,\n",
              " 0.9020618796348572,\n",
              " 0.8865979313850403]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_images_reshaped.shape,X_test_images_reshaped.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCfHrb1kKYyM",
        "outputId": "b73b7d51-c639-49c9-f4f6-f89a12086beb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((969, 224, 672), (243, 224, 672))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVDY4wlnKaF8",
        "outputId": "86594947-206e-4a42-90aa-44cabc10779f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, 224, 128)          102528    \n",
            "                                                                 \n",
            " simple_rnn_1 (SimpleRNN)    (None, 224, 128)          32896     \n",
            "                                                                 \n",
            " simple_rnn_2 (SimpleRNN)    (None, 128)               32896     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 168449 (658.00 KB)\n",
            "Trainable params: 168449 (658.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls /content/drive/MyDrive/KebAI_RNN/data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUzGscx7Kcgd",
        "outputId": "73c8bfc8-87d0-4432-c157-96fe156a5b3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34massets\u001b[0m/  fingerprint.pb  keras_metadata.pb  saved_model.pb  \u001b[01;34mvariables\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#new_model = tf.keras.models.load_model('/content/drive/MyDrive/KebAI/data')\n",
        "new_model = tf.keras.models.load_model('/content/drive/MyDrive/KebAI_RNN/data')\n",
        "new_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUV9l4_zKfbC",
        "outputId": "9172d81f-2b9a-47f0-ebd7-ad8410d7d68d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn_7 (SimpleRNN)    (None, 224, 128)          102528    \n",
            "                                                                 \n",
            " simple_rnn_8 (SimpleRNN)    (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 135553 (529.50 KB)\n",
            "Trainable params: 135553 (529.50 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "\n",
        "# Sınıf adları ve indeksleri\n",
        "class_indices = {'cooked': 0, 'uncooked': 1}\n",
        "\n",
        "# Test veri setindeki sınıf sayılarını sayın\n",
        "class_counts_test = Counter(y_test)\n",
        "\n",
        "# Sınıf adları ve sayıları\n",
        "for class_index, class_count in class_counts_test.items():\n",
        "    class_name = [name for name, index in class_indices.items() if index == class_index][0]\n",
        "    print(f\"Class: {class_name}, Count: {class_count} samples in the test set\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yvr_ha3CKglY",
        "outputId": "e2a7754a-c054-46af-ad86-6c5ebf3014a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class: uncooked, Count: 139 samples in the test set\n",
            "Class: cooked, Count: 104 samples in the test set\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Eğitim boyunca epoch-acc ve epoch-loss değeri çıktısı\n",
        "\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# History nesnesini dosyadan yükleyin\n",
        "with open('/content/drive/MyDrive/KebAI_RNN/history.pkl', 'rb') as f:\n",
        "    loaded_history = pickle.load(f)\n",
        "\n",
        "# Eğitim sırasında elde edilen metrik değerlere erişin\n",
        "loaded_accuracy = loaded_history['acc']\n",
        "loaded_loss = loaded_history['loss']\n",
        "loaded_val_accuracy = loaded_history['val_acc']\n",
        "loaded_val_loss = loaded_history['val_loss']\n",
        "\n",
        "# Epoch-accuracy ve epoch-loss grafiklerini oluşturun\n",
        "epochs = range(1, len(loaded_accuracy) + 1)\n",
        "\n",
        "plt.figure(figsize=(12, 4))\n",
        "\n",
        "# Accuracy grafiği\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs, loaded_accuracy, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, loaded_val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# Loss grafiği\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs, loaded_loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, loaded_val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "jxD8mgxVM6ly",
        "outputId": "908f6104-de2c-404e-edf1-9b869521e6ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 1. CSV dosyasını düzenle\n",
        "csv_file_path = '/content/drive/MyDrive/KebAI_RNN/training.csv'\n",
        "new_csv_file_path = '/content/drive/MyDrive/KebAI_RNN/training_updated.csv'\n",
        "\n",
        "# CSV dosyasını oku\n",
        "df = pd.read_csv(csv_file_path, sep='-')\n",
        "\n",
        "# Başlıkları düzenle (örneğin, virgülle ayır)\n",
        "df.to_csv(new_csv_file_path, sep=',', index=False)\n",
        "\n",
        "# 2. Grafiği çizmek için kodu güncelle\n",
        "# (yeni CSV dosyasını kullanarak)\n",
        "\n",
        "# Yeni CSV dosyasını oku\n",
        "df_updated = pd.read_csv(new_csv_file_path)\n",
        "\n",
        "# Eğitim sırasında elde edilen metrik değerlere erişin\n",
        "epochs = df_updated['epoch']\n",
        "accuracy = df_updated['acc']\n",
        "val_accuracy = df_updated['val_acc']\n",
        "loss = df_updated['loss']\n",
        "val_loss = df_updated['val_loss']\n",
        "\n",
        "# Epoch-accuracy ve epoch-loss grafiklerini oluşturun\n",
        "plt.figure(figsize=(12, 4))\n",
        "\n",
        "# Accuracy grafiği\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs, accuracy, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# Loss grafiği\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "BUtSwQ1-M7VN",
        "outputId": "5001566b-3c5d-4787-95ca-df8c5713d0a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6zEplN2M9Ru",
        "outputId": "756166e1-87dc-4e94-ae36-247d7a95a459"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.918367326259613"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SavedModel dosyasını yükle\n",
        "#saved_model_path = '/content/drive/MyDrive/KebAI_RNN/save_model_weights/kabAI_model.h5'\n",
        "#loaded_model = tf.keras.models.\n",
        "saved_model_path = '/content/drive/MyDrive/KebAI_RNN/data'\n",
        "loaded_model = tf.keras.models.load_model(saved_model_path)\n"
      ],
      "metadata": {
        "id": "dVc7GazfNAM6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you already have your model and data loaded\n",
        "\n",
        "# Make binary predictions on the test set\n",
        "y_pred = model.predict(X_test_images_reshaped)\n",
        "y_pred_binary = (y_pred > 0.5).astype(int)\n",
        "\n",
        "# Convert predictions to 1D array\n",
        "y_pred_binary = y_pred_binary.flatten()\n",
        "\n",
        "# Ensure data types are integers\n",
        "y_test = y_test.astype(int)\n",
        "y_pred_binary = y_pred_binary.astype(int)\n",
        "\n",
        "# Class labels\n",
        "classes = [\"Cooked\", \"Uncooked\"]\n",
        "\n",
        "# Generate confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred_binary)\n",
        "\n",
        "# Plot confusion matrix using seaborn\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=classes, yticklabels=classes)\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "rQ_LxiAhNC33",
        "outputId": "d5d0a52d-02b8-4957-ccb4-d23d01117073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 1s 91ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
        "import numpy as np\n",
        "\n",
        "# Assuming you already have your model and data loaded\n",
        "\n",
        "# Make binary predictions on the test set\n",
        "y_pred = loaded_model.predict(X_test_images_reshaped)\n",
        "y_pred_binary = (y_pred > 0.5).astype(int)\n",
        "\n",
        "# Convert predictions to 1D array\n",
        "y_pred_binary = y_pred_binary.flatten()\n",
        "\n",
        "# Ensure data types are integers\n",
        "y_test = y_test.astype(int)\n",
        "y_pred_binary = y_pred_binary.astype(int)\n",
        "\n",
        "# Generate confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred_binary)\n",
        "\n",
        "# Calculate precision, recall, loss, and accuracy\n",
        "precision = precision_score(y_test, y_pred_binary)\n",
        "recall = recall_score(y_test, y_pred_binary)\n",
        "\n",
        "# Evaluate model on test set\n",
        "evaluation_results = model.evaluate(X_test_images_reshaped, y_test, verbose=0)\n",
        "\n",
        "# Unpack the results\n",
        "loss, accuracy = evaluation_results[0], evaluation_results[1]\n",
        "\n",
        "# Print values as percentages\n",
        "print(f'Precision: {precision * 100:.2f}%')\n",
        "print(f'Recall: {recall * 100:.2f}%')\n",
        "print(f'Loss: {loss * 100:.2f}%')\n",
        "print(f'Accuracy: {accuracy * 100:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjjoN1RrNGEF",
        "outputId": "3be1026b-3e04-464d-b7f1-1b51dc8af9f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 2s 142ms/step\n",
            "Precision: 95.10%\n",
            "Recall: 97.84%\n",
            "Loss: 23.48%\n",
            "Accuracy: 95.10%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you already have your model and data loaded\n",
        "\n",
        "# Make binary predictions on the test set\n",
        "y_pred = loaded_model.predict(X_test_images_reshaped)\n",
        "\n",
        "# Calculate fpr, tpr, and thresholds for ROC curve\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "\n",
        "# Calculate AUC (Area Under the Curve)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "#plt.xlim([0.0, 1.0])\n",
        "#plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate (FPR)')\n",
        "plt.ylabel('True Positive Rate (TPR)')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "wtA1Bnr3NKkq",
        "outputId": "fe8cc94f-841b-4bd0-da48-d1939fcb9993"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 1s 123ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}