Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_epoch_32[[:space:]](1).keras filter=lfs diff=lfs merge=lfs -text

model_epoch_32 (1).keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:461ae2465939df3c80793af3ace0937247e83476fece42b062043b28e939579d
+size 213062341

part1 (1).ipynb

@@ -0,0 +1,957 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_Q4gIvePGw85",
+        "outputId": "21debd12-2d0d-43d4-9f0b-e79430f9a832"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n",
+            "/content\n",
+            " AssemblyTask\n",
+            " BOOKS\n",
+            " car2.jpg\n",
+            " car.jpg\n",
+            "'Colab Notebooks'\n",
+            "'Copy of ⁨جدول الفرقة الثانية - عام - مج ٢.pdf⁩.pdf'\n",
+            "'Coursera N24TOG3ZARUP.pdf'\n",
+            " Grades\n",
+            "'graphicsTask (2).png'\n",
+            "'graphicsTask (2).svg'\n",
+            " IMG_4899.jpeg\n",
+            " IMG_4900.jpeg\n",
+            " IMG_4901.jpeg\n",
+            " IMG_5986.jpeg\n",
+            " InternVideo.mp4\n",
+            " LOOPS.gslides\n",
+            " NTI-PRJCT\n",
+            "'Session 6 .gdoc'\n",
+            " SWEProject.drawio\n",
+            " Task2.rar\n",
+            "\"The _Animator's_Survival_Kit.pdf\"\n",
+            "'Untitled Diagram'\n",
+            "'Untitled document (1).gdoc'\n",
+            "'Untitled document.gdoc'\n",
+            " Wahb_CV.pdf\n",
+            "'WhatsApp Image 2024-09-28 at 18.11.27_5e5ce6c9.jpg'\n",
+            "'WhatsApp Image 2024-09-28 at 18.45.13_fd438e9f.jpg'\n",
+            "'WhatsApp Image 2025-05-28 at 15.11 (1).24_84c876ae.jpg'\n",
+            "'WhatsApp Image 2025-05-28 at 15.11.24_84c876ae.jpg'\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "import os\n",
+        "print(os.getcwd())\n",
+        "\n",
+        "!ls /content/drive/MyDrive/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "files.upload()\n",
+        "\n",
+        "!pip install kaggle\n",
+        "\n",
+        "!mkdir -p ~/.kaggle\n",
+        "!cp kaggle.json ~/.kaggle/\n",
+        "!chmod 600 ~/.kaggle/kaggle.json\n",
+        "\n",
+        "!kaggle datasets download -d dagnelies/deepfake-faces\n",
+        "!unzip -q deepfake-faces.zip -d deepfake_faces\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 440
+        },
+        "id": "7Emqe4aNG7Ak",
+        "outputId": "769def2b-ae6c-491e-8cf3-57c4b1e678d4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-f634150d-7f42-49fc-8650-c73cced50986\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-f634150d-7f42-49fc-8650-c73cced50986\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving kaggle.json to kaggle.json\n",
+            "Requirement already satisfied: kaggle in /usr/local/lib/python3.11/dist-packages (1.7.4.5)\n",
+            "Requirement already satisfied: bleach in /usr/local/lib/python3.11/dist-packages (from kaggle) (6.2.0)\n",
+            "Requirement already satisfied: certifi>=14.05.14 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2025.8.3)\n",
+            "Requirement already satisfied: charset-normalizer in /usr/local/lib/python3.11/dist-packages (from kaggle) (3.4.3)\n",
+            "Requirement already satisfied: idna in /usr/local/lib/python3.11/dist-packages (from kaggle) (3.10)\n",
+            "Requirement already satisfied: protobuf in /usr/local/lib/python3.11/dist-packages (from kaggle) (5.29.5)\n",
+            "Requirement already satisfied: python-dateutil>=2.5.3 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.9.0.post0)\n",
+            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.11/dist-packages (from kaggle) (8.0.4)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.32.3)\n",
+            "Requirement already satisfied: setuptools>=21.0.0 in /usr/local/lib/python3.11/dist-packages (from kaggle) (75.2.0)\n",
+            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.11/dist-packages (from kaggle) (1.17.0)\n",
+            "Requirement already satisfied: text-unidecode in /usr/local/lib/python3.11/dist-packages (from kaggle) (1.3)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from kaggle) (4.67.1)\n",
+            "Requirement already satisfied: urllib3>=1.15.1 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.5.0)\n",
+            "Requirement already satisfied: webencodings in /usr/local/lib/python3.11/dist-packages (from kaggle) (0.5.1)\n",
+            "Dataset URL: https://www.kaggle.com/datasets/dagnelies/deepfake-faces\n",
+            "License(s): other\n",
+            "Downloading deepfake-faces.zip to /content\n",
+            " 96% 416M/433M [00:00<00:00, 447MB/s]\n",
+            "100% 433M/433M [00:00<00:00, 516MB/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import plotly.graph_objects as go\n",
+        "from plotly.offline import iplot\n",
+        "import tensorflow as tf\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import cv2"
+      ],
+      "metadata": {
+        "id": "sgQ6ibXHHCAd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "meta = pd.read_csv('/content/deepfake_faces/metadata.csv')\n",
+        "print(meta.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HRchwl81HEiU",
+        "outputId": "efb7634a-6f85-4be3-ed9e-be16005c75b4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "        videoname  original_width  original_height label        original\n",
+            "0  aznyksihgl.mp4             129              129  FAKE  xnojggkrxt.mp4\n",
+            "1  gkwmalrvcj.mp4             129              129  FAKE  hqqmtxvbjj.mp4\n",
+            "2  lxnqzocgaq.mp4             223              217  FAKE  xjzkfqddyk.mp4\n",
+            "3  itsbtrrelv.mp4             186              186  FAKE  kqvepwqxfe.mp4\n",
+            "4  ddvgrczjno.mp4             155              155  FAKE  pluadmqqta.mp4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def summary(df):\n",
+        "    summary_df = pd.DataFrame(df.dtypes, columns=['dtypes'])\n",
+        "    summary_df['count'] = df.count().values\n",
+        "    summary_df['unique'] = df.nunique().values\n",
+        "    summary_df['missing#'] = df.isna().sum()\n",
+        "    summary_df['missing%'] = df.isna().sum() / len(df)\n",
+        "    return summary_df"
+      ],
+      "metadata": {
+        "id": "8da4BT5CHGVz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(summary(meta).style.background_gradient('Purples'))\n",
+        "\n",
+        "print('Fake Images:', len(meta[meta.label=='FAKE']))\n",
+        "print('Real Images:', len(meta[meta.label=='REAL']))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SaZKTtqFHKQM",
+        "outputId": "088c301f-1b19-40c9-f210-d09baea550d8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<pandas.io.formats.style.Styler object at 0x7dae5a9e74d0>\n",
+            "Fake Images: 79341\n",
+            "Real Images: 16293\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Sample balanced dataset\n",
+        "real_df = meta[meta['label'] == 'REAL']\n",
+        "fake_df = meta[meta['label'] == 'FAKE']\n",
+        "sample_size = 16000\n",
+        "\n",
+        "real_df = real_df.sample(sample_size, random_state=42)\n",
+        "fake_df = fake_df.sample(sample_size, random_state=42)\n",
+        "\n",
+        "sample_meta = pd.concat([real_df, fake_df])"
+      ],
+      "metadata": {
+        "id": "9m8dhK0ZHP8c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample_meta['filepath'] = '/content/deepfake_faces/faces_224/' + sample_meta['videoname'].str[:-4] + '.jpg'\n",
+        "\n",
+        "# Split the data\n",
+        "Train_set, temp_df = train_test_split(sample_meta, test_size=0.2, random_state=42, stratify=sample_meta['label'])\n",
+        "Val_set, Test_set = train_test_split(temp_df, test_size=0.5, random_state=42, stratify=temp_df['label'])\n",
+        "\n",
+        "print(f'Train Set: {Train_set.shape}')\n",
+        "print(f'Validation Set: {Val_set.shape}')\n",
+        "print(f'Test Set: {Test_set.shape}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bL62yOEcHWqE",
+        "outputId": "1a8042a4-0115-4821-b4b7-077b1bcbaf35"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train Set: (25600, 6)\n",
+            "Validation Set: (3200, 6)\n",
+            "Test Set: (3200, 6)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_augmentation = tf.keras.Sequential([\n",
+        "    tf.keras.layers.RandomFlip(\"horizontal\"),\n",
+        "    tf.keras.layers.RandomRotation(0.1),\n",
+        "    tf.keras.layers.RandomZoom(0.1),\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "e4xmsSeGIDe8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "label_map = {'REAL': 0, 'FAKE': 1}\n"
+      ],
+      "metadata": {
+        "id": "2ba3DFLDIFWs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def preprocess_image(filepath, label):\n",
+        "    image = tf.io.read_file(filepath)\n",
+        "    image = tf.image.decode_jpeg(image, channels=3)\n",
+        "    image = tf.image.resize(image, [224, 224])\n",
+        "    image = tf.cast(image, tf.float32)\n",
+        "    # EfficientNet expects values in [0, 255] for its preprocess function\n",
+        "    return image, label\n",
+        "\n",
+        "# Create TensorFlow datasets\n",
+        "batch_size = 32\n",
+        "\n",
+        "train_ds = tf.data.Dataset.from_tensor_slices((\n",
+        "    Train_set['filepath'].values,\n",
+        "    Train_set['label'].map(label_map).values\n",
+        "))\n",
+        "train_ds = (train_ds\n",
+        "           .map(preprocess_image, num_parallel_calls=tf.data.AUTOTUNE)\n",
+        "           .shuffle(1000, seed=42)\n",
+        "           .batch(batch_size)\n",
+        "           .prefetch(tf.data.AUTOTUNE))\n",
+        "\n",
+        "val_ds = tf.data.Dataset.from_tensor_slices((\n",
+        "    Val_set['filepath'].values,\n",
+        "    Val_set['label'].map(label_map).values\n",
+        "))\n",
+        "val_ds = (val_ds\n",
+        "         .map(preprocess_image, num_parallel_calls=tf.data.AUTOTUNE)\n",
+        "         .batch(batch_size)\n",
+        "         .prefetch(tf.data.AUTOTUNE))\n",
+        "\n",
+        "test_ds = tf.data.Dataset.from_tensor_slices((\n",
+        "    Test_set['filepath'].values,\n",
+        "    Test_set['label'].map(label_map).values\n",
+        "))\n",
+        "test_ds = (test_ds\n",
+        "          .map(preprocess_image, num_parallel_calls=tf.data.AUTOTUNE)\n",
+        "          .batch(batch_size)\n",
+        "          .prefetch(tf.data.AUTOTUNE))"
+      ],
+      "metadata": {
+        "id": "OendSPcjIF99"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_class_counts(train_set, val_set, test_set):\n",
+        "    sets = ['Train Set', 'Validation Set', 'Test Set']\n",
+        "    colors = ['#52A666', '#C15B4E']\n",
+        "\n",
+        "    y = {\n",
+        "        'REAL': [np.sum(train_set == 'REAL'), np.sum(val_set == 'REAL'), np.sum(test_set == 'REAL')],\n",
+        "        'FAKE': [np.sum(train_set == 'FAKE'), np.sum(val_set == 'FAKE'), np.sum(test_set == 'FAKE')]\n",
+        "    }\n",
+        "\n",
+        "    trace0 = go.Bar(x=sets, y=y['REAL'], name='REAL', marker={'color': colors[0]}, opacity=0.7)\n",
+        "    trace1 = go.Bar(x=sets, y=y['FAKE'], name='FAKE', marker={'color': colors[1]}, opacity=0.7)\n",
+        "\n",
+        "    data = [trace0, trace1]\n",
+        "    layout = go.Layout(title='Count of Classes in each set:', xaxis={'title': 'Set'}, yaxis={'title': 'Count'})\n",
+        "\n",
+        "    fig = go.Figure(data, layout)\n",
+        "    iplot(fig)\n",
+        "\n",
+        "plot_class_counts(np.array(Train_set['label']), np.array(Val_set['label']), np.array(Test_set['label']))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "BUtvuXUPIL3E",
+        "outputId": "a7261012-adf3-4212-fac0-1b37f0fd8159"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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of Classes in each set:\"},\"xaxis\":{\"title\":{\"text\":\"Set\"}},\"yaxis\":{\"title\":{\"text\":\"Count\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('3aa0cb85-3812-44b8-a6a0-293506fec333');\n",
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+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tf.keras.backend.clear_session()\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "from tensorflow.keras.applications import EfficientNetB4\n",
+        "\n",
+        "# Build model with data augmentation\n",
+        "base_model = EfficientNetB4(include_top=False, weights='imagenet', input_shape=(224, 224, 3))\n",
+        "\n",
+        "# Create the full model with data augmentation\n",
+        "inputs = tf.keras.Input(shape=(224, 224, 3))\n",
+        "\n",
+        "# Apply data augmentation only during training\n",
+        "x = data_augmentation(inputs)\n",
+        "\n",
+        "# Apply EfficientNet preprocessing\n",
+        "x = tf.keras.applications.efficientnet.preprocess_input(x)\n",
+        "\n",
+        "# Pass through base model\n",
+        "x = base_model(x, training=False)  # Keep base model frozen initially\n",
+        "\n",
+        "# Add classification head\n",
+        "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
+        "x = tf.keras.layers.Dropout(0.2)(x)\n",
+        "outputs = tf.keras.layers.Dense(1, activation=\"sigmoid\")(x)\n",
+        "\n",
+        "model = tf.keras.Model(inputs, outputs)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ev8RU-JIIU4M",
+        "outputId": "7cbc8dd3-3ad7-462b-ff76-fd5f1909d11e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb4_notop.h5\n",
+            "\u001b[1m71686520/71686520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compile model\n",
+        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)  # Using Adam for better convergence\n",
+        "model.compile(\n",
+        "    optimizer=optimizer,\n",
+        "    loss=\"binary_crossentropy\",\n",
+        "    metrics=[\"accuracy\"]\n",
+        ")\n",
+        "\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "0IpweVPuIY38",
+        "outputId": "971546e4-c2c5-4d3e-d4c6-b618b5e3e58e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_1 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ sequential (\u001b[38;5;33mSequential\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ efficientnetb4 (\u001b[38;5;33mFunctional\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1792\u001b[0m)     │    \u001b[38;5;34m17,673,823\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ global_average_pooling2d        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1792\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)        │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1792\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │         \u001b[38;5;34m1,793\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ sequential (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ efficientnetb4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1792</span>)     │    <span style=\"color: #00af00; text-decoration-color: #00af00\">17,673,823</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ global_average_pooling2d        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1792</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)        │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼──────────────���┤\n",
+              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1792</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,793</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m17,675,616\u001b[0m (67.43 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">17,675,616</span> (67.43 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m17,550,409\u001b[0m (66.95 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">17,550,409</span> (66.95 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m125,207\u001b[0m (489.09 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">125,207</span> (489.09 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Setup callbacks\n",
+        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Model checkpoint\n",
+        "checkpoint_path = \"/content/drive/MyDrive/NTI-PRJCT/model_epoch_{epoch:02d}.keras\"\n",
+        "checkpoint_callback = ModelCheckpoint(\n",
+        "    filepath=checkpoint_path,\n",
+        "    monitor='val_accuracy',\n",
+        "    save_best_only=False,\n",
+        "    save_weights_only=False,\n",
+        "    verbose=1\n",
+        ")\n",
+        "\n",
+        "# Early stopping with proper patience\n",
+        "early_stopping = EarlyStopping(\n",
+        "    monitor='val_loss',\n",
+        "    patience=8,\n",
+        "    restore_best_weights=True,\n",
+        "    verbose=1\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "TUVta5W5IcTM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr_reducer = ReduceLROnPlateau(\n",
+        "    monitor='val_loss',\n",
+        "    factor=0.2,\n",
+        "    patience=5,\n",
+        "    min_lr=1e-7,\n",
+        "    verbose=1\n",
+        ")\n",
+        "\n",
+        "# Training\n",
+        "history = model.fit(\n",
+        "    train_ds,\n",
+        "    validation_data=val_ds,\n",
+        "    epochs=50,\n",
+        "    callbacks=[early_stopping, checkpoint_callback, lr_reducer],\n",
+        "    verbose=1\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3jaEVVMHInmc",
+        "outputId": "4420da4b-569b-43f3-fe36-734dab972380"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 857ms/step - accuracy: 0.7771 - loss: 0.4718\n",
+            "Epoch 1: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_01.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m717s\u001b[0m 896ms/step - accuracy: 0.7772 - loss: 0.4718 - val_accuracy: 0.8034 - val_loss: 0.4270 - learning_rate: 0.0010\n",
+            "Epoch 2/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 857ms/step - accuracy: 0.8216 - loss: 0.3945\n",
+            "Epoch 2: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_02.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m710s\u001b[0m 887ms/step - accuracy: 0.8216 - loss: 0.3945 - val_accuracy: 0.8350 - val_loss: 0.3646 - learning_rate: 0.0010\n",
+            "Epoch 3/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.8460 - loss: 0.3416\n",
+            "Epoch 3: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_03.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m740s\u001b[0m 885ms/step - accuracy: 0.8460 - loss: 0.3416 - val_accuracy: 0.8519 - val_loss: 0.3786 - learning_rate: 0.0010\n",
+            "Epoch 4/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 857ms/step - accuracy: 0.8644 - loss: 0.3068\n",
+            "Epoch 4: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_04.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m743s\u001b[0m 886ms/step - accuracy: 0.8644 - loss: 0.3068 - val_accuracy: 0.8444 - val_loss: 0.3616 - learning_rate: 0.0010\n",
+            "Epoch 5/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.8799 - loss: 0.2816\n",
+            "Epoch 5: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_05.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m740s\u001b[0m 885ms/step - accuracy: 0.8799 - loss: 0.2816 - val_accuracy: 0.8388 - val_loss: 0.4151 - learning_rate: 0.0010\n",
+            "Epoch 6/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 855ms/step - accuracy: 0.8885 - loss: 0.2567\n",
+            "Epoch 6: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_06.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m742s\u001b[0m 885ms/step - accuracy: 0.8885 - loss: 0.2567 - val_accuracy: 0.8512 - val_loss: 0.3529 - learning_rate: 0.0010\n",
+            "Epoch 7/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.8985 - loss: 0.2374\n",
+            "Epoch 7: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_07.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m743s\u001b[0m 886ms/step - accuracy: 0.8985 - loss: 0.2374 - val_accuracy: 0.8609 - val_loss: 0.3472 - learning_rate: 0.0010\n",
+            "Epoch 8/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.9127 - loss: 0.2133\n",
+            "Epoch 8: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_08.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m742s\u001b[0m 885ms/step - accuracy: 0.9127 - loss: 0.2133 - val_accuracy: 0.8087 - val_loss: 0.4966 - learning_rate: 0.0010\n",
+            "Epoch 9/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.9165 - loss: 0.2004\n",
+            "Epoch 9: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_09.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m743s\u001b[0m 887ms/step - accuracy: 0.9165 - loss: 0.2004 - val_accuracy: 0.8619 - val_loss: 0.3643 - learning_rate: 0.0010\n",
+            "Epoch 10/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.9204 - loss: 0.1923\n",
+            "Epoch 10: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_10.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m741s\u001b[0m 885ms/step - accuracy: 0.9204 - loss: 0.1923 - val_accuracy: 0.8597 - val_loss: 0.3573 - learning_rate: 0.0010\n",
+            "Epoch 11/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 855ms/step - accuracy: 0.9288 - loss: 0.1784\n",
+            "Epoch 11: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_11.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m709s\u001b[0m 885ms/step - accuracy: 0.9288 - loss: 0.1784 - val_accuracy: 0.8697 - val_loss: 0.3494 - learning_rate: 0.0010\n",
+            "Epoch 12/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 854ms/step - accuracy: 0.9316 - loss: 0.1672\n",
+            "Epoch 12: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_12.keras\n",
+            "\n",
+            "Epoch 12: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m706s\u001b[0m 881ms/step - accuracy: 0.9317 - loss: 0.1672 - val_accuracy: 0.8353 - val_loss: 0.4639 - learning_rate: 0.0010\n",
+            "Epoch 13/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 855ms/step - accuracy: 0.9546 - loss: 0.1189\n",
+            "Epoch 13: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_13.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m709s\u001b[0m 885ms/step - accuracy: 0.9546 - loss: 0.1189 - val_accuracy: 0.9131 - val_loss: 0.2431 - learning_rate: 2.0000e-04\n",
+            "Epoch 14/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 855ms/step - accuracy: 0.9681 - loss: 0.0799\n",
+            "Epoch 14: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_14.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m741s\u001b[0m 884ms/step - accuracy: 0.9681 - loss: 0.0799 - val_accuracy: 0.9187 - val_loss: 0.2386 - learning_rate: 2.0000e-04\n",
+            "Epoch 15/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.9742 - loss: 0.0689\n",
+            "Epoch 15: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_15.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m708s\u001b[0m 885ms/step - accuracy: 0.9742 - loss: 0.0689 - val_accuracy: 0.9162 - val_loss: 0.2737 - learning_rate: 2.0000e-04\n",
+            "Epoch 16/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.9768 - loss: 0.0594\n",
+            "Epoch 16: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_16.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m746s\u001b[0m 890ms/step - accuracy: 0.9768 - loss: 0.0594 - val_accuracy: 0.9106 - val_loss: 0.2691 - learning_rate: 2.0000e-04\n",
+            "Epoch 17/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━��━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 857ms/step - accuracy: 0.9784 - loss: 0.0552\n",
+            "Epoch 17: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_17.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m744s\u001b[0m 893ms/step - accuracy: 0.9784 - loss: 0.0552 - val_accuracy: 0.9147 - val_loss: 0.2741 - learning_rate: 2.0000e-04\n",
+            "Epoch 18/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 858ms/step - accuracy: 0.9805 - loss: 0.0505\n",
+            "Epoch 18: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_18.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m716s\u001b[0m 895ms/step - accuracy: 0.9805 - loss: 0.0505 - val_accuracy: 0.9184 - val_loss: 0.2681 - learning_rate: 2.0000e-04\n",
+            "Epoch 19/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 861ms/step - accuracy: 0.9843 - loss: 0.0412\n",
+            "Epoch 19: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_19.keras\n",
+            "\n",
+            "Epoch 19: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m739s\u001b[0m 891ms/step - accuracy: 0.9843 - loss: 0.0412 - val_accuracy: 0.9159 - val_loss: 0.2972 - learning_rate: 2.0000e-04\n",
+            "Epoch 20/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856ms/step - accuracy: 0.9862 - loss: 0.0367\n",
+            "Epoch 20: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_20.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m711s\u001b[0m 888ms/step - accuracy: 0.9862 - loss: 0.0367 - val_accuracy: 0.9156 - val_loss: 0.3108 - learning_rate: 4.0000e-05\n",
+            "Epoch 21/50\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 860ms/step - accuracy: 0.9897 - loss: 0.0274\n",
+            "Epoch 21: saving model to /content/drive/MyDrive/NTI-PRJCT/model_epoch_21.keras\n",
+            "\u001b[1m800/800\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m716s\u001b[0m 894ms/step - accuracy: 0.9897 - loss: 0.0274 - val_accuracy: 0.9184 - val_loss: 0.3099 - learning_rate: 4.0000e-05\n",
+            "Epoch 22/50\n",
+            "\u001b[1m150/800\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m9:21\u001b[0m 864ms/step - accuracy: 0.9879 - loss: 0.0310"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "KFBcgWXf3kLt"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}