{"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":"none","dataSources":[{"sourceType":"competition","sourceId":70203,"databundleVersionId":8068726}],"dockerImageVersionId":31329,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"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":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:55:28.153061Z","iopub.execute_input":"2026-05-18T17:55:28.153795Z","iopub.status.idle":"2026-05-18T17:55:35.621914Z","shell.execute_reply.started":"2026-05-18T17:55:28.153762Z","shell.execute_reply":"2026-05-18T17:55:35.62107Z"}},"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 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(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":65},{"cell_type":"code","source":"import tensorflow as tf\nimport librosa\nimport os\n\nfrom tensorflow import keras \nfrom tensorflow.keras import datasets as dt , layers as lr , models as md\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences as ps\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom tensorflow.keras.utils import to_categorical\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\nimport pandas as pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:55:35.62403Z","iopub.execute_input":"2026-05-18T17:55:35.624906Z","iopub.status.idle":"2026-05-18T17:55:35.63044Z","shell.execute_reply.started":"2026-05-18T17:55:35.624871Z","shell.execute_reply":"2026-05-18T17:55:35.629446Z"}},"outputs":[],"execution_count":66},{"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'] #COMSAN = 1 , COMROS = 2 \ndf = metadata[metadata['primary_label'].isin(selected_birds)].copy()\n\nprint(\"Selected birds:\", df['primary_label'].value_counts() )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:55:35.631655Z","iopub.execute_input":"2026-05-18T17:55:35.632031Z","iopub.status.idle":"2026-05-18T17:55:35.746242Z","shell.execute_reply.started":"2026-05-18T17:55:35.632004Z","shell.execute_reply":"2026-05-18T17:55:35.745476Z"}},"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":67},{"cell_type":"code","source":"#convert audio to img \n# n_mels = 128 by default -> number f rows \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.featuremelspectrogram. ( \n y = y , sr = sr , n_mels = n_mels , fmax = 8000 , hop_length = 512 # n_fft = 2048\n )\n mel_db = librosa.power_to_db ( mel, ref = np.max )\n return mel_db\n except:\n return None","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:55:35.748109Z","iopub.execute_input":"2026-05-18T17:55:35.748477Z","iopub.status.idle":"2026-05-18T17:55:35.75416Z","shell.execute_reply.started":"2026-05-18T17:55:35.748449Z","shell.execute_reply":"2026-05-18T17:55:35.753207Z"}},"outputs":[],"execution_count":68},{"cell_type":"code","source":"\n#Load Data with Fixed Shape\nDATA_DIR = \"/kaggle/input/competitions/birdclef-2024/train_audio\"\n\nX = [] # input \ny = [] # ans\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))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:55:35.75517Z","iopub.execute_input":"2026-05-18T17:55:35.755788Z","iopub.status.idle":"2026-05-18T17:56:35.422978Z","shell.execute_reply.started":"2026-05-18T17:55:35.755748Z","shell.execute_reply":"2026-05-18T17:56:35.422215Z"}},"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":69},{"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) # ONE-HOT ENCODING 1 --> 100 2 --> 010\n\n print(\" Classes:\", label_encoder.classes_)\n print(\" Number of classes:\", len(label_encoder.classes_))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:56:35.424Z","iopub.execute_input":"2026-05-18T17:56:35.424338Z","iopub.status.idle":"2026-05-18T17:56:35.431346Z","shell.execute_reply.started":"2026-05-18T17:56:35.424314Z","shell.execute_reply":"2026-05-18T17:56:35.430487Z"}},"outputs":[{"name":"stdout","text":" Classes: ['barswa' 'comros' 'comsan' 'hoopoe' 'litegr']\n Number of classes: 5\n","output_type":"stream"}],"execution_count":70},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:56:35.432603Z","iopub.execute_input":"2026-05-18T17:56:35.432845Z","iopub.status.idle":"2026-05-18T17:56:35.530023Z","shell.execute_reply.started":"2026-05-18T17:56:35.432817Z","shell.execute_reply":"2026-05-18T17:56:35.529321Z"}},"outputs":[{"name":"stdout","text":"Train shape: (1924, 128, 240, 1)\nTest shape: (481, 128, 240, 1)\n","output_type":"stream"}],"execution_count":71},{"cell_type":"code","source":"\ninput = keras.Input ( shape = ( 128 , 240 , 1 ) )\n\n\nl = lr.Conv2D ( 32 , ( 3 , 3 ) , activation = 'relu' , padding = 'same' ) ( input ) # 128 x 240 x 32 \nl = lr.Conv2D ( 32 , ( 3 , 3 ) , activation = 'relu' , padding = 'same' ) ( l ) # 128 x 240 x 32 \nl = lr.Conv2D ( 32 , ( 3 , 3 ) , activation = 'relu' , padding = 'same' ) ( l ) # 128 x 240 x 32 \nl = lr.MaxPooling2D ( ( 2 , 2 ) ) ( l ) # 64 x 120 x 32\n\n\nl = lr.Conv2D ( 64 , ( 3 , 3 ) , activation = 'relu' , padding = 'same' ) ( l ) # 64 x 120 x 64\nl = lr.Conv2D ( 64 , ( 3 , 3 ) , activation = 'relu' , padding = 'same' ) ( l ) # 64 x 120 x 64\nl = lr.MaxPooling2D ( ( 2 , 2 ) ) ( l ) # 32 x 60 x 64\n\n\nl = lr.Conv2D ( 128 , ( 3 , 3 ) , activation = 'relu' , padding = 'same' ) ( l ) # 32 x 60 x 128\nl = lr.MaxPooling2D ( ( 2 , 2 ) ) ( l ) # # 16 x 30 x 128\n\n\nl = lr.GlobalAveragePooling2D()(l) # l = lr.Flatten () ( l ) # 128\nl = lr.Dense ( 128 , activation = 'relu' ) ( l )\nl = lr.Dense ( 5 , activation = 'softmax' ) ( l )\nl = lr.Dropout( 0.5 ) ( l )\n\nmodel = md.Model ( input , l )\n\nmodel.summary ()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:56:35.53106Z","iopub.execute_input":"2026-05-18T17:56:35.531408Z","iopub.status.idle":"2026-05-18T17:56:35.620328Z","shell.execute_reply.started":"2026-05-18T17:56:35.531381Z","shell.execute_reply":"2026-05-18T17:56:35.619769Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_1\"\u001b[0m\n","text/html":"Model: \"functional_1\"\n
\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;34m128\u001b[0m, \u001b[38;5;34m240\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m240\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m240\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m240\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m16,512\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_3 (\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│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_1 (InputLayer) │ (None, 128, 240, 1) │ 0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_6 (Conv2D) │ (None, 128, 240, 32) │ 320 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_7 (Conv2D) │ (None, 128, 240, 32) │ 9,248 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_8 (Conv2D) │ (None, 128, 240, 32) │ 9,248 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_3 (MaxPooling2D) │ (None, 64, 120, 32) │ 0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_9 (Conv2D) │ (None, 64, 120, 64) │ 18,496 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_10 (Conv2D) │ (None, 64, 120, 64) │ 36,928 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_4 (MaxPooling2D) │ (None, 32, 60, 64) │ 0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_11 (Conv2D) │ (None, 32, 60, 128) │ 73,856 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_5 (MaxPooling2D) │ (None, 16, 30, 128) │ 0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_1 │ (None, 128) │ 0 │\n│ (GlobalAveragePooling2D) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_2 (Dense) │ (None, 128) │ 16,512 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_3 (Dense) │ (None, 5) │ 645 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_1 (Dropout) │ (None, 5) │ 0 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m165,253\u001b[0m (645.52 KB)\n","text/html":" Total params: 165,253 (645.52 KB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m165,253\u001b[0m (645.52 KB)\n","text/html":" Trainable params: 165,253 (645.52 KB)\n
\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":" Non-trainable params: 0 (0.00 B)\n
\n"},"metadata":{}}],"execution_count":72},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:56:35.621344Z","iopub.execute_input":"2026-05-18T17:56:35.6217Z","iopub.status.idle":"2026-05-18T17:56:35.636416Z","shell.execute_reply.started":"2026-05-18T17:56:35.621674Z","shell.execute_reply":"2026-05-18T17:56:35.635711Z"}},"outputs":[{"name":"stdout","text":"Model compiled successfully\n","output_type":"stream"}],"execution_count":73},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T17:56:35.638634Z","iopub.execute_input":"2026-05-18T17:56:35.638898Z","iopub.status.idle":"2026-05-18T18:00:37.276414Z","shell.execute_reply.started":"2026-05-18T17:56:35.638877Z","shell.execute_reply":"2026-05-18T18:00:37.275491Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/50\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1779126997.903269 551 service.cc:152] XLA service 0x787fd4006210 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1779126997.903304 551 service.cc:160] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\nI0000 00:00:1779126997.903308 551 service.cc:160] StreamExecutor device (1): Tesla T4, Compute Capability 7.5\nI0000 00:00:1779126998.463934 551 cuda_dnn.cc:529] Loaded cuDNN version 91002\n2026-05-18 17:56:41.639981: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n2026-05-18 17:56:41.793641: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n2026-05-18 17:56:43.791884: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n2026-05-18 17:56:44.082177: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m 1/49\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m10:30\u001b[0m 13s/step - accuracy: 0.4375 - loss: nan","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1779127008.850612 551 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m48/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.2192 - loss: nan","output_type":"stream"},{"name":"stderr","text":"2026-05-18 17:56:54.094260: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n2026-05-18 17:56:54.241051: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n2026-05-18 17:56:54.487076: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n2026-05-18 17:56:54.630059: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n2026-05-18 17:56:55.236902: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n2026-05-18 17:56:55.519350: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 249ms/step - accuracy: 0.2191 - loss: nan - val_accuracy: 0.1844 - val_loss: 1.6136\nEpoch 2/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 86ms/step - accuracy: 0.2026 - loss: nan - val_accuracy: 0.1714 - val_loss: 1.6117\nEpoch 3/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 87ms/step - accuracy: 0.2036 - loss: nan - val_accuracy: 0.2000 - val_loss: 1.6056\nEpoch 4/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 88ms/step - accuracy: 0.2167 - loss: nan - val_accuracy: 0.1948 - val_loss: 1.6035\nEpoch 5/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 88ms/step - accuracy: 0.2232 - loss: nan - val_accuracy: 0.2104 - val_loss: 1.6118\nEpoch 6/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 88ms/step - accuracy: 0.1926 - loss: nan - val_accuracy: 0.1766 - val_loss: 1.6162\nEpoch 7/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 89ms/step - accuracy: 0.2447 - loss: nan - val_accuracy: 0.1870 - val_loss: 1.6075\nEpoch 8/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 89ms/step - accuracy: 0.2301 - loss: nan - val_accuracy: 0.2052 - val_loss: 1.6051\nEpoch 9/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 90ms/step - accuracy: 0.2423 - loss: nan - val_accuracy: 0.2260 - val_loss: 1.5989\nEpoch 10/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 91ms/step - accuracy: 0.2150 - loss: nan - val_accuracy: 0.2208 - val_loss: 1.5928\nEpoch 11/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 92ms/step - accuracy: 0.2376 - loss: nan - val_accuracy: 0.2234 - val_loss: 1.6089\nEpoch 12/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 92ms/step - accuracy: 0.2374 - loss: nan - val_accuracy: 0.2104 - val_loss: 1.5853\nEpoch 13/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.2321 - loss: nan - val_accuracy: 0.2052 - val_loss: 1.5933\nEpoch 14/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.2386 - loss: nan - val_accuracy: 0.2468 - val_loss: 1.5791\nEpoch 15/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.2440 - loss: nan - val_accuracy: 0.2494 - val_loss: 1.5918\nEpoch 16/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.2287 - loss: nan - val_accuracy: 0.2701 - val_loss: 1.5696\nEpoch 17/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 96ms/step - accuracy: 0.2864 - loss: nan - val_accuracy: 0.2649 - val_loss: 1.5697\nEpoch 18/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.2501 - loss: nan - val_accuracy: 0.3065 - val_loss: 1.5475\nEpoch 19/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 96ms/step - accuracy: 0.2479 - loss: nan - val_accuracy: 0.3091 - val_loss: 1.5492\nEpoch 20/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 96ms/step - accuracy: 0.2514 - loss: nan - val_accuracy: 0.3325 - val_loss: 1.5176\nEpoch 21/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.2457 - loss: nan - val_accuracy: 0.2883 - val_loss: 1.5540\nEpoch 22/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.2471 - loss: nan - val_accuracy: 0.2961 - val_loss: 1.5389\nEpoch 23/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.2627 - loss: nan - val_accuracy: 0.3221 - val_loss: 1.5000\nEpoch 24/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.2895 - loss: nan - val_accuracy: 0.3195 - val_loss: 1.5019\nEpoch 25/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 96ms/step - accuracy: 0.2763 - loss: nan - val_accuracy: 0.3662 - val_loss: 1.4488\nEpoch 26/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.2900 - loss: nan - val_accuracy: 0.3740 - val_loss: 1.4545\nEpoch 27/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3107 - loss: nan - val_accuracy: 0.4156 - val_loss: 1.3872\nEpoch 28/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3059 - loss: nan - val_accuracy: 0.4182 - val_loss: 1.3957\nEpoch 29/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3270 - loss: nan - val_accuracy: 0.3481 - val_loss: 1.3896\nEpoch 30/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3118 - loss: nan - val_accuracy: 0.4416 - val_loss: 1.3842\nEpoch 31/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3553 - loss: nan - val_accuracy: 0.4208 - val_loss: 1.3609\nEpoch 32/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3356 - loss: nan - val_accuracy: 0.4156 - val_loss: 1.3452\nEpoch 33/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3691 - loss: nan - val_accuracy: 0.5039 - val_loss: 1.1985\nEpoch 34/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3670 - loss: nan - val_accuracy: 0.4312 - val_loss: 1.3393\nEpoch 35/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3387 - loss: nan - val_accuracy: 0.5948 - val_loss: 1.0921\nEpoch 36/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.3723 - loss: nan - val_accuracy: 0.5584 - val_loss: 1.1040\nEpoch 37/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.3420 - loss: nan - val_accuracy: 0.6026 - val_loss: 1.0314\nEpoch 38/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.3735 - loss: nan - val_accuracy: 0.6468 - val_loss: 0.9604\nEpoch 39/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.4097 - loss: nan - val_accuracy: 0.6805 - val_loss: 0.8441\nEpoch 40/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.4562 - loss: nan - val_accuracy: 0.6649 - val_loss: 0.9691\nEpoch 41/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.4137 - loss: nan - val_accuracy: 0.6623 - val_loss: 0.9315\nEpoch 42/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.4338 - loss: nan - val_accuracy: 0.6779 - val_loss: 0.8136\nEpoch 43/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.4112 - loss: nan - val_accuracy: 0.7247 - val_loss: 0.7672\nEpoch 44/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.3995 - loss: nan - val_accuracy: 0.7013 - val_loss: 0.8025\nEpoch 45/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.4136 - loss: nan - val_accuracy: 0.7039 - val_loss: 0.8120\nEpoch 46/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.4192 - loss: nan - val_accuracy: 0.7039 - val_loss: 0.7876\nEpoch 47/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 95ms/step - accuracy: 0.4304 - loss: nan - val_accuracy: 0.7169 - val_loss: 0.8071\nEpoch 48/50\n\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 94ms/step - accuracy: 0.4114 - loss: nan - val_accuracy: 0.7195 - val_loss: 0.7593\n","output_type":"stream"}],"execution_count":74},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-18T18:00:37.277463Z","iopub.execute_input":"2026-05-18T18:00:37.278196Z","iopub.status.idle":"2026-05-18T18:00:37.719455Z","shell.execute_reply.started":"2026-05-18T18:00:37.278159Z","shell.execute_reply":"2026-05-18T18:00:37.718779Z"}},"outputs":[{"name":"stdout","text":"Test Accuracy: 73.18%\n","output_type":"stream"}],"execution_count":75}]}