{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceType":"competition","sourceId":70203,"databundleVersionId":8068726}],"dockerImageVersionId":31328,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install tensorflow librosa numpy pandas matplotlib scikit-learn seaborn\n!pip install soundfile","metadata":{"_uuid":"99fda293-6a8f-4e09-9873-4101d95fa31f","_cell_guid":"6b34f156-00a5-4827-86b6-3d332d0a8676","trusted":true,"collapsed":true,"execution":{"iopub.status.busy":"2026-05-18T12:33:27.561737Z","iopub.execute_input":"2026-05-18T12:33:27.562404Z","iopub.status.idle":"2026-05-18T12:33:35.108813Z","shell.execute_reply.started":"2026-05-18T12:33:27.562374Z","shell.execute_reply":"2026-05-18T12:33:35.108Z"},"jupyter":{"outputs_hidden":true}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: tensorflow in /usr/local/lib/python3.12/dist-packages (2.19.0)\nRequirement already satisfied: librosa in /usr/local/lib/python3.12/dist-packages (0.11.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\nRequirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.3.3)\nRequirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\nRequirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\nRequirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\nRequirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (1.4.0)\nRequirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (1.6.3)\nRequirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (25.12.19)\nRequirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (0.7.0)\nRequirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (0.2.0)\nRequirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (18.1.1)\nRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (3.4.0)\nRequirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from tensorflow) (26.0)\nRequirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (5.29.5)\nRequirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (2.32.4)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from tensorflow) (75.2.0)\nRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (1.17.0)\nRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (3.3.0)\nRequirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (4.15.0)\nRequirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (2.1.1)\nRequirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (1.78.1)\nRequirement already satisfied: tensorboard~=2.19.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (2.19.0)\nRequirement already satisfied: keras>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (3.10.0)\nRequirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (3.15.1)\nRequirement already satisfied: ml-dtypes<1.0.0,>=0.5.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (0.5.4)\nRequirement already satisfied: audioread>=2.1.9 in /usr/local/lib/python3.12/dist-packages (from librosa) (3.1.0)\nRequirement already satisfied: numba>=0.51.0 in /usr/local/lib/python3.12/dist-packages (from librosa) (0.60.0)\nRequirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from librosa) (1.16.3)\nRequirement already satisfied: joblib>=1.0 in /usr/local/lib/python3.12/dist-packages (from librosa) (1.5.3)\nRequirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.12/dist-packages (from librosa) (4.4.2)\nRequirement already satisfied: soundfile>=0.12.1 in /usr/local/lib/python3.12/dist-packages (from librosa) (0.13.1)\nRequirement already satisfied: pooch>=1.1 in /usr/local/lib/python3.12/dist-packages (from librosa) (1.9.0)\nRequirement already satisfied: soxr>=0.3.2 in /usr/local/lib/python3.12/dist-packages (from librosa) (1.0.0)\nRequirement already satisfied: lazy_loader>=0.1 in /usr/local/lib/python3.12/dist-packages (from librosa) (0.4)\nRequirement already satisfied: msgpack>=1.0 in /usr/local/lib/python3.12/dist-packages (from librosa) (1.1.2)\nRequirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\nRequirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\nRequirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.3)\nRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.61.1)\nRequirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\nRequirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\nRequirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from astunparse>=1.6.0->tensorflow) (0.46.3)\nRequirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from keras>=3.5.0->tensorflow) (13.9.4)\nRequirement already satisfied: namex in /usr/local/lib/python3.12/dist-packages (from keras>=3.5.0->tensorflow) (0.1.0)\nRequirement already satisfied: optree in /usr/local/lib/python3.12/dist-packages (from keras>=3.5.0->tensorflow) (0.19.0)\nRequirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.51.0->librosa) (0.43.0)\nRequirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from pooch>=1.1->librosa) (4.9.2)\nRequirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.4)\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.11)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.5.0)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow) (2026.1.4)\nRequirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.12/dist-packages (from soundfile>=0.12.1->librosa) (2.0.0)\nRequirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (3.10.2)\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (0.7.2)\nRequirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (3.1.6)\nRequirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi>=1.0->soundfile>=0.12.1->librosa) (3.0)\nRequirement already satisfied: markupsafe>=2.1.1 in /usr/local/lib/python3.12/dist-packages (from werkzeug>=1.0.1->tensorboard~=2.19.0->tensorflow) (3.0.3)\nRequirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras>=3.5.0->tensorflow) (4.0.0)\nRequirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras>=3.5.0->tensorflow) (2.19.2)\nRequirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)\nRequirement already satisfied: soundfile in /usr/local/lib/python3.12/dist-packages (0.13.1)\nRequirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.12/dist-packages (from soundfile) (2.0.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from soundfile) (2.0.2)\nRequirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi>=1.0->soundfile) (3.0)\n","output_type":"stream"}],"execution_count":47},{"cell_type":"code","source":"#Import Libraries\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport librosa\nimport librosa.display\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\nfrom tensorflow.keras.utils import to_categorical","metadata":{"_uuid":"fc148a4a-0b6b-4617-b45b-c1152b601604","_cell_guid":"54f7e48b-315d-4f3f-9152-ba3f5ceddfb0","trusted":true,"execution":{"iopub.status.busy":"2026-05-18T12:33:39.429402Z","iopub.execute_input":"2026-05-18T12:33:39.429719Z","iopub.status.idle":"2026-05-18T12:33:39.436751Z","shell.execute_reply.started":"2026-05-18T12:33:39.42969Z","shell.execute_reply":"2026-05-18T12:33:39.435683Z"}},"outputs":[],"execution_count":48},{"cell_type":"code","source":"#Load Metadata\nmetadata = pd.read_csv(\"/kaggle/input/competitions/birdclef-2024/train_metadata.csv\")\n\nselected_birds = ['comsan', 'comros', 'barswa', 'litegr', 'hoopoe']\ndf = metadata[metadata['primary_label'].isin(selected_birds)].copy()\n\nprint(\"Selected birds:\", df['primary_label'].value_counts())","metadata":{"_uuid":"b41dd54c-4d6e-4cfe-b05b-9e2a8fe1c14b","_cell_guid":"990965a7-ca6d-4946-821f-82b142d328ad","trusted":true,"execution":{"iopub.status.busy":"2026-05-18T12:33:45.6665Z","iopub.execute_input":"2026-05-18T12:33:45.66748Z","iopub.status.idle":"2026-05-18T12:33:45.800883Z","shell.execute_reply.started":"2026-05-18T12:33:45.667447Z","shell.execute_reply":"2026-05-18T12:33:45.799996Z"}},"outputs":[{"name":"stdout","text":"Selected birds: primary_label\nbarswa    500\ncomros    500\ncomsan    500\nhoopoe    500\nlitegr    405\nName: count, dtype: int64\n","output_type":"stream"}],"execution_count":49},{"cell_type":"code","source":"#converting sound to Mel spectogram Function\ndef audio_to_mel(file_path, n_mels=128, duration=5):\n    try:\n        y, sr = librosa.load(file_path, sr=22050, duration=duration, mono=True)\n        mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, \n                                             fmax=8000, hop_length=512)\n        mel_db = librosa.power_to_db(mel, ref=np.max)\n        return mel_db\n    except:\n        return None","metadata":{"_uuid":"852e7fbc-5370-4f66-8cf9-35076b1f3c2e","_cell_guid":"a3f316b3-61d7-41a7-b1f4-774fc14d0d4d","trusted":true,"collapsed":false,"execution":{"iopub.status.busy":"2026-05-18T12:33:56.787429Z","iopub.execute_input":"2026-05-18T12:33:56.788153Z","iopub.status.idle":"2026-05-18T12:33:56.794572Z","shell.execute_reply.started":"2026-05-18T12:33:56.788118Z","shell.execute_reply":"2026-05-18T12:33:56.793213Z"},"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":51},{"cell_type":"code","source":"#Load Data with Fixed Shape\nDATA_DIR = \"/kaggle/input/competitions/birdclef-2024/train_audio\"\n\nX = []\ny = []\n\nprint(\" Loading and processing audio files...\")\n\nfor _, row in df.iterrows():\n    file_path = os.path.join(DATA_DIR, row['primary_label'], row['filename'])\n    \n    if not os.path.exists(file_path):\n        file_path = os.path.join(DATA_DIR, row['filename'])\n    \n    if os.path.exists(file_path):\n        mel = audio_to_mel(file_path)\n        if mel is not None:\n            mel = mel[:, :240]\n            if mel.shape[1] < 240:\n                mel = np.pad(mel, ((0,0), (0, 240 - mel.shape[1])), mode='constant')\n            \n            mel = mel[..., np.newaxis]\n            X.append(mel)\n            y.append(row['primary_label'])\n\nX = np.array(X)\nX = (X - X.min()) / (X.max() - X.min())\nprint(\"✅ Final Data Shape:\", X.shape)\nprint(\"✅ Samples loaded:\", len(y))","metadata":{"_uuid":"e01099b0-3e48-4030-8f03-e0b88c5d8673","_cell_guid":"a18fcf50-fda1-4562-835f-bae5af47b89f","trusted":true,"execution":{"iopub.status.busy":"2026-05-18T12:43:19.030271Z","iopub.execute_input":"2026-05-18T12:43:19.03112Z","iopub.status.idle":"2026-05-18T12:44:20.21105Z","shell.execute_reply.started":"2026-05-18T12:43:19.031085Z","shell.execute_reply":"2026-05-18T12:44:20.210351Z"}},"outputs":[{"name":"stdout","text":" Loading and processing audio files...\n✅ Final Data Shape: (2405, 128, 240, 1)\n✅ Samples loaded: 2405\n","output_type":"stream"}],"execution_count":61},{"cell_type":"code","source":"#Encode Labels\nif len(y) == 0:\n    print(\" No data loaded!\")\nelse:\n    label_encoder = LabelEncoder()\n    y_encoded = label_encoder.fit_transform(y)\n    y_cat = to_categorical(y_encoded)\n\n    print(\" Classes:\", label_encoder.classes_)\n    print(\"Number of classes:\", len(label_encoder.classes_))","metadata":{"_uuid":"cc9e9b94-0868-49b8-a354-4f82cefdd778","_cell_guid":"e80165f6-41df-4021-b35e-633050aab345","trusted":true,"collapsed":false,"execution":{"iopub.status.busy":"2026-05-18T12:44:48.101106Z","iopub.execute_input":"2026-05-18T12:44:48.102208Z","iopub.status.idle":"2026-05-18T12:44:48.108809Z","shell.execute_reply.started":"2026-05-18T12:44:48.102172Z","shell.execute_reply":"2026-05-18T12:44:48.107923Z"},"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","text":"✅ Classes: ['barswa' 'comros' 'comsan' 'hoopoe' 'litegr']\nNumber of classes: 5\n","output_type":"stream"}],"execution_count":62},{"cell_type":"code","source":"#Train Test Split\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y_cat, test_size=0.2, random_state=42, stratify=y_encoded\n)\n\nprint(\"Train shape:\", X_train.shape)\nprint(\"Test shape:\", X_test.shape)","metadata":{"_uuid":"102e80af-5176-4931-af9e-002d084b3ce3","_cell_guid":"3fc444a9-df75-4a57-9f42-f36acf0bb7e2","trusted":true,"collapsed":false,"execution":{"iopub.status.busy":"2026-05-18T12:44:50.510608Z","iopub.execute_input":"2026-05-18T12:44:50.510911Z","iopub.status.idle":"2026-05-18T12:44:50.588818Z","shell.execute_reply.started":"2026-05-18T12:44:50.510889Z","shell.execute_reply":"2026-05-18T12:44:50.587999Z"},"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","text":"Train shape: (1924, 128, 240, 1)\nTest shape: (481, 128, 240, 1)\n","output_type":"stream"}],"execution_count":63},{"cell_type":"code","source":"#Build Simple CNN Model\nmodel = Sequential()\n\nmodel.add(Conv2D(32, (3,3), activation='relu', input_shape=(128, 240, 1)))\nmodel.add(MaxPooling2D(2,2))\n\nmodel.add(Conv2D(64, (3,3), activation='relu'))\nmodel.add(MaxPooling2D(2,2))\n\nmodel.add(Conv2D(128, (3,3), activation='relu'))\nmodel.add(MaxPooling2D(2,2))\n\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(len(label_encoder.classes_), activation='softmax'))\n\nmodel.summary()","metadata":{"_uuid":"02965eb0-e65f-49f8-bb11-c88ad6d6a9af","_cell_guid":"c07bae20-363f-4145-aeb1-bb7902663496","trusted":true,"collapsed":false,"execution":{"iopub.status.busy":"2026-05-18T12:44:53.262884Z","iopub.execute_input":"2026-05-18T12:44:53.263576Z","iopub.status.idle":"2026-05-18T12:44:53.357118Z","shell.execute_reply.started":"2026-05-18T12:44:53.263545Z","shell.execute_reply":"2026-05-18T12:44:53.356275Z"},"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_2\"\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: \"sequential_2\"</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│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m238\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m320\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m119\u001b[0m, \u001b[38;5;34m32\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m117\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │        \u001b[38;5;34m18,496\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m58\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50176\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m6,422,656\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │           \u001b[38;5;34m645\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│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">238</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">119</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">117</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">58</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50176</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_4 (<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\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">6,422,656</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_2 (<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\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_5 (<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\">5</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">645</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,515,973\u001b[0m (24.86 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\">6,515,973</span> (24.86 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,515,973\u001b[0m (24.86 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\">6,515,973</span> (24.86 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\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\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}}],"execution_count":64},{"cell_type":"code","source":"#Compile Model\nmodel.compile(\n    optimizer='adam',\n    loss='categorical_crossentropy',\n    metrics=['accuracy']\n)\n\nprint(\"Model compiled successfully\")","metadata":{"_uuid":"088e3805-94df-42ac-b59b-5bdd02e5fdff","_cell_guid":"765363bb-b103-4e85-bc54-3c0e4f918633","trusted":true,"collapsed":false,"execution":{"iopub.status.busy":"2026-05-18T12:44:57.990731Z","iopub.execute_input":"2026-05-18T12:44:57.991547Z","iopub.status.idle":"2026-05-18T12:44:58.001589Z","shell.execute_reply.started":"2026-05-18T12:44:57.991505Z","shell.execute_reply":"2026-05-18T12:44:58.000783Z"},"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","text":"Model compiled successfully\n","output_type":"stream"}],"execution_count":65},{"cell_type":"code","source":"#Train Model\n\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nearly_stop = EarlyStopping(monitor='val_accuracy', patience=5, restore_best_weights=True)\n\nhistory = model.fit(\n    X_train, y_train,\n    epochs=50,\n    batch_size=32,\n    validation_split=0.2,\n    callbacks=[early_stop],\n    verbose=1\n)","metadata":{"_uuid":"f4afcb6a-027b-4a2d-b293-b24e8cd7d382","_cell_guid":"c5787b11-c771-44ad-aada-85948751b8d7","trusted":true,"collapsed":false,"execution":{"iopub.status.busy":"2026-05-18T12:45:01.366466Z","iopub.execute_input":"2026-05-18T12:45:01.367171Z","iopub.status.idle":"2026-05-18T12:45:36.55206Z","shell.execute_reply.started":"2026-05-18T12:45:01.367122Z","shell.execute_reply":"2026-05-18T12:45:36.551154Z"},"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 104ms/step - accuracy: 0.2306 - loss: 1.8831 - val_accuracy: 0.2545 - val_loss: 1.6327\nEpoch 2/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.3405 - loss: 1.4958 - val_accuracy: 0.5403 - val_loss: 1.2713\nEpoch 3/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.4825 - loss: 1.3088 - val_accuracy: 0.5818 - val_loss: 1.0582\nEpoch 4/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.6236 - loss: 0.9672 - val_accuracy: 0.7429 - val_loss: 0.7074\nEpoch 5/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 0.7448 - loss: 0.7274 - val_accuracy: 0.6831 - val_loss: 0.8111\nEpoch 6/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.7535 - loss: 0.6901 - val_accuracy: 0.7766 - val_loss: 0.6080\nEpoch 7/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8009 - loss: 0.5757 - val_accuracy: 0.7844 - val_loss: 0.5738\nEpoch 8/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 0.8283 - loss: 0.5555 - val_accuracy: 0.7844 - val_loss: 0.5912\nEpoch 9/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.8634 - loss: 0.4093 - val_accuracy: 0.8364 - val_loss: 0.5172\nEpoch 10/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.8968 - loss: 0.3646 - val_accuracy: 0.8182 - val_loss: 0.5071\nEpoch 11/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.8677 - loss: 0.3980 - val_accuracy: 0.8000 - val_loss: 0.5602\nEpoch 12/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.9059 - loss: 0.2730 - val_accuracy: 0.8208 - val_loss: 0.5369\nEpoch 13/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 40ms/step - accuracy: 0.9280 - loss: 0.1918 - val_accuracy: 0.8156 - val_loss: 0.5966\nEpoch 14/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 42ms/step - accuracy: 0.9395 - loss: 0.1749 - val_accuracy: 0.8234 - val_loss: 0.6130\n","output_type":"stream"}],"execution_count":66},{"cell_type":"code","source":"#Evaluate Model\ntest_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\nprint(f\"Test Accuracy: {test_acc*100:.2f}%\")","metadata":{"_uuid":"596dbd41-0797-46c6-a665-42c871403559","_cell_guid":"3053df30-546b-4af3-8c11-11f184e22911","trusted":true,"execution":{"iopub.status.busy":"2026-05-18T12:45:42.453586Z","iopub.execute_input":"2026-05-18T12:45:42.454291Z","iopub.status.idle":"2026-05-18T12:45:43.029639Z","shell.execute_reply.started":"2026-05-18T12:45:42.454259Z","shell.execute_reply":"2026-05-18T12:45:43.028781Z"}},"outputs":[{"name":"stdout","text":"Test Accuracy: 79.63%\n","output_type":"stream"}],"execution_count":67},{"cell_type":"code","source":"#Plot Results\nplt.figure(figsize=(12, 5))\n\nplt.subplot(1, 2, 1)\nplt.plot(history.history['accuracy'], label='Train Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.title('Model Accuracy')\nplt.legend()\n\nplt.subplot(1, 2, 2)\nplt.plot(history.history['loss'], label='Train Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.title('Model Loss')\nplt.legend()\n\nplt.show()","metadata":{"_uuid":"559fcf5b-668b-4d1f-85e1-84b70e1d2050","_cell_guid":"9fa589f3-ba34-42dd-a791-fd1e74de8342","trusted":true,"collapsed":false,"execution":{"iopub.status.busy":"2026-05-18T12:45:53.819691Z","iopub.execute_input":"2026-05-18T12:45:53.820459Z","iopub.status.idle":"2026-05-18T12:45:54.080708Z","shell.execute_reply.started":"2026-05-18T12:45:53.820425Z","shell.execute_reply":"2026-05-18T12:45:54.080067Z"},"jupyter":{"outputs_hidden":false}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x500 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":68},{"cell_type":"code","source":"sample = X_test[0:1]\npred = model.predict(sample, verbose=0)\npredicted_idx = np.argmax(pred)\nbird_name = label_encoder.inverse_transform([predicted_idx])[0]\n\nprint(f\" Predicted Bird: {bird_name}\")\nprint(\"\\nProbabilities:\")\nfor i, prob in enumerate(pred[0]):\n    print(f\"  {label_encoder.classes_[i]}: {prob*100:.2f}%\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T12:46:05.528601Z","iopub.execute_input":"2026-05-18T12:46:05.529682Z","iopub.status.idle":"2026-05-18T12:46:05.642865Z","shell.execute_reply.started":"2026-05-18T12:46:05.529615Z","shell.execute_reply":"2026-05-18T12:46:05.641937Z"}},"outputs":[{"name":"stdout","text":" Predicted Bird: comros\n\nProbabilities:\n  barswa: 4.23%\n  comros: 95.26%\n  comsan: 0.17%\n  hoopoe: 0.33%\n  litegr: 0.02%\n","output_type":"stream"}],"execution_count":70}]}