{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"datasetVersion","sourceId":8048710,"datasetId":4746291,"databundleVersionId":8162657},{"sourceType":"datasetVersion","sourceId":9923007,"datasetId":6098761,"databundleVersionId":10182107},{"sourceType":"datasetVersion","sourceId":7392733,"datasetId":4297749,"databundleVersionId":7483738},{"sourceType":"datasetVersion","sourceId":7402356,"datasetId":4304475,"databundleVersionId":7493457},{"sourceType":"datasetVersion","sourceId":8064454,"datasetId":4757588,"databundleVersionId":8179596},{"sourceType":"datasetVersion","sourceId":8057855,"datasetId":4752740,"databundleVersionId":8172576},{"sourceType":"datasetVersion","sourceId":8057847,"datasetId":4752734,"databundleVersionId":8172568},{"sourceType":"datasetVersion","sourceId":8057843,"datasetId":4752731,"databundleVersionId":8172564},{"sourceType":"kernelVersion","sourceId":158958765}],"dockerImageVersionId":30787,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**EFFICIENT NET**","metadata":{}},{"cell_type":"code","source":"import os, gc\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0,1\"\nimport tensorflow as tf\nimport pandas as pd, numpy as np\nimport matplotlib.pyplot as plt\nprint('TensorFlow version =',tf.__version__)\n\n# USE MULTIPLE GPUS\ngpus = tf.config.list_physical_devices('GPU')\nif len(gpus)<=1: \n    strategy = tf.distribute.OneDeviceStrategy(device=\"/gpu:0\")\n    print(f'Using {len(gpus)} GPU')\nelse: \n    strategy = tf.distribute.MirroredStrategy()\n    print(f'Using {len(gpus)} GPUs')\n\nVER = 3\n\nLOAD_MODELS_FROMb0 = '/kaggle/input/two-step-b0-hms/'\nLOAD_MODELS_FROMb1='/kaggle/input/version-10-b1-two-step-hms/'\nLOAD_MODELS_FROMb2=\"/kaggle/input/version-10-b2-two-step-hms/\"\nLOAD_MODELS_FROMb3=\"/kaggle/input/version-10-b3-two-step-hms/\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:24:18.556751Z","iopub.execute_input":"2026-04-25T10:24:18.557068Z","iopub.status.idle":"2026-04-25T10:24:33.505606Z","shell.execute_reply.started":"2026-04-25T10:24:18.557034Z","shell.execute_reply":"2026-04-25T10:24:33.504513Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# USE MIXED PRECISION\nMIX = True\nif MIX:\n    tf.config.optimizer.set_experimental_options({\"auto_mixed_precision\": True})\n    print('Mixed precision enabled')\nelse:\n    print('Using full precision')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:24:33.508026Z","iopub.execute_input":"2026-04-25T10:24:33.508586Z","iopub.status.idle":"2026-04-25T10:24:33.515299Z","shell.execute_reply.started":"2026-04-25T10:24:33.508552Z","shell.execute_reply":"2026-04-25T10:24:33.514062Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/hms-harmful-brain-activity-classification/train.csv')\nTARGETS = df.columns[-6:]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:24:33.516578Z","iopub.execute_input":"2026-04-25T10:24:33.516964Z","iopub.status.idle":"2026-04-25T10:24:33.739466Z","shell.execute_reply.started":"2026-04-25T10:24:33.516930Z","shell.execute_reply":"2026-04-25T10:24:33.738449Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import albumentations as albu\nTARS = {'Seizure':0, 'LPD':1, 'GPD':2, 'LRDA':3, 'GRDA':4, 'Other':5}\nTARS2 = {x:y for y,x in TARS.items()}\n\nclass DataGenerator(tf.keras.utils.Sequence):\n    'Generates data for Keras'\n    def __init__(self, data, batch_size=32, shuffle=False, augment=False, mode='train',\n                 specs = None, eeg_specs = None): \n\n        self.data = data\n        self.batch_size = batch_size\n        self.shuffle = shuffle\n        self.augment = augment\n        self.mode = mode\n        self.specs = specs\n        self.eeg_specs = eeg_specs\n        self.on_epoch_end()\n        \n    def __len__(self):\n        'Denotes the number of batches per epoch'\n        ct = int( np.ceil( len(self.data) / self.batch_size ) )\n        return ct\n\n    def __getitem__(self, index):\n        'Generate one batch of data'\n        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n        X, y = self.__data_generation(indexes)\n        if self.augment: X = self.__augment_batch(X) \n        return X, y\n\n    def on_epoch_end(self):\n        'Updates indexes after each epoch'\n        self.indexes = np.arange( len(self.data) )\n        if self.shuffle: np.random.shuffle(self.indexes)\n                        \n    def __data_generation(self, indexes):\n        'Generates data containing batch_size samples' \n        \n        X = np.zeros((len(indexes),128,256,8),dtype='float32')\n        y = np.zeros((len(indexes),6),dtype='float32')\n        img = np.ones((128,256),dtype='float32')\n        \n        for j,i in enumerate(indexes):\n            row = self.data.iloc[i]\n            if self.mode=='test': \n                r = 0\n            else: \n                r = int( (row['min'] + row['max'])//4 )\n\n            for k in range(4):\n                # EXTRACT 300 ROWS OF SPECTROGRAM\n                img = self.specs[row.spec_id][r:r+300,k*100:(k+1)*100].T\n                \n                # LOG TRANSFORM SPECTROGRAM\n                img = np.clip(img,0,32)\n                img = np.nan_to_num(img, nan=0.0)\n                img = img / 32\n                \n                # CROP TO 256 TIME STEPS\n                X[j,14:-14,:,k] = img[:,22:-22]\n        \n            # EEG SPECTROGRAMS\n            img = self.eeg_specs[row.eeg_id]\n            X[j,:,:,4:] = img\n                \n            if self.mode!='test':\n                y[j,] = row[TARGETS]\n            \n        return X,y\n    \n    def __random_transform(self, img):\n        composition = albu.Compose([\n            albu.HorizontalFlip(p=0.5),\n        ])\n        return composition(image=img)['image']\n            \n    def __augment_batch(self, img_batch):\n        for i in range(img_batch.shape[0]):\n            img_batch[i, ] = self.__random_transform(img_batch[i, ])\n        return img_batch","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:24:33.740752Z","iopub.execute_input":"2026-04-25T10:24:33.741081Z","iopub.status.idle":"2026-04-25T10:25:07.332469Z","shell.execute_reply.started":"2026-04-25T10:24:33.741049Z","shell.execute_reply":"2026-04-25T10:25:07.331300Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install --no-index --find-links=/kaggle/input/tf-efficientnet-whl-files /kaggle/input/tf-efficientnet-whl-files/efficientnet-1.1.1-py3-none-any.whl\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:07.334119Z","iopub.execute_input":"2026-04-25T10:25:07.334722Z","iopub.status.idle":"2026-04-25T10:25:19.469662Z","shell.execute_reply.started":"2026-04-25T10:25:07.334685Z","shell.execute_reply":"2026-04-25T10:25:19.468298Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"USE_KAGGLE_SPECTROGRAMS=True\nUSE_EEG_SPECTROGRAMS=True","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.471454Z","iopub.execute_input":"2026-04-25T10:25:19.471822Z","iopub.status.idle":"2026-04-25T10:25:19.477162Z","shell.execute_reply.started":"2026-04-25T10:25:19.471787Z","shell.execute_reply":"2026-04-25T10:25:19.476134Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import efficientnet.tfkeras as efn\n\ndef build_modelb0():\n    \n    inp = tf.keras.Input(shape=(128,256,8))\n    base_model = efn.EfficientNetB0(include_top=False, weights=None, input_shape=None)\n    base_model.load_weights('/kaggle/input/tf-efficientnet-imagenet-weights/efficientnet-b0_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5')\n    \n    # RESHAPE INPUT 128x256x8 => 512x512x3 MONOTONE IMAGE\n    # KAGGLE SPECTROGRAMS\n    x1 = [inp[:,:,:,i:i+1] for i in range(4)]\n    x1 = tf.keras.layers.Concatenate(axis=1)(x1)\n    #x1=x1-[0.45]\n    # EEG SPECTROGRAMS\n    x2 = [inp[:,:,:,i+4:i+5] for i in range(4)]\n    x2 = tf.keras.layers.Concatenate(axis=1)(x2)\n    # MAKE 512X512X3\n    if USE_KAGGLE_SPECTROGRAMS & USE_EEG_SPECTROGRAMS:\n        x = tf.keras.layers.Concatenate(axis=2)([x1,x2])\n    elif USE_EEG_SPECTROGRAMS: x = x2\n    else: x = x1\n    x = tf.keras.layers.Concatenate(axis=3)([x,x,x])\n    \n    # OUTPUT\n    x = base_model(x)\n    x = tf.keras.layers.GlobalAveragePooling2D()(x)\n    x = tf.keras.layers.Dense(6,activation='softmax', dtype='float32')(x)\n        \n    # COMPILE MODEL\n    model = tf.keras.Model(inputs=inp, outputs=x)\n    opt = tf.keras.optimizers.Adam(learning_rate = 1e-3)\n    loss = tf.keras.losses.KLDivergence()\n\n    model.compile(loss=loss, optimizer = opt) \n        \n    return model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.481811Z","iopub.execute_input":"2026-04-25T10:25:19.482200Z","iopub.status.idle":"2026-04-25T10:25:19.523868Z","shell.execute_reply.started":"2026-04-25T10:25:19.482163Z","shell.execute_reply":"2026-04-25T10:25:19.523031Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def build_modelb1():\n    \n    inp = tf.keras.Input(shape=(128,256,8))\n    base_model = efn.EfficientNetB1(include_top=False, weights=None, input_shape=None)\n    base_model.load_weights('/kaggle/input/tf-efficientnet-imagenet-weights/efficientnet-b1_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5')\n    \n    # RESHAPE INPUT 128x256x8 => 512x512x3 MONOTONE IMAGE\n    # KAGGLE SPECTROGRAMS\n    x1 = [inp[:,:,:,i:i+1] for i in range(4)]\n    x1 = tf.keras.layers.Concatenate(axis=1)(x1)\n    #x1=x1-[0.45]\n    # EEG SPECTROGRAMS\n    x2 = [inp[:,:,:,i+4:i+5] for i in range(4)]\n    x2 = tf.keras.layers.Concatenate(axis=1)(x2)\n    # MAKE 512X512X3\n    if USE_KAGGLE_SPECTROGRAMS & USE_EEG_SPECTROGRAMS:\n        x = tf.keras.layers.Concatenate(axis=2)([x1,x2])\n    elif USE_EEG_SPECTROGRAMS: x = x2\n    else: x = x1\n    x = tf.keras.layers.Concatenate(axis=3)([x,x,x])\n    \n    # OUTPUT\n    x = base_model(x)\n    x = tf.keras.layers.GlobalAveragePooling2D()(x)\n    x = tf.keras.layers.Dense(6,activation='softmax', dtype='float32')(x)\n        \n    # COMPILE MODEL\n    model = tf.keras.Model(inputs=inp, outputs=x)\n    opt = tf.keras.optimizers.Adam(learning_rate = 1e-3)\n    loss = tf.keras.losses.KLDivergence()\n\n    model.compile(loss=loss, optimizer = opt) \n        \n    return model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.524956Z","iopub.execute_input":"2026-04-25T10:25:19.525882Z","iopub.status.idle":"2026-04-25T10:25:19.535895Z","shell.execute_reply.started":"2026-04-25T10:25:19.525845Z","shell.execute_reply":"2026-04-25T10:25:19.534730Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def build_modelb2():\n    \n    inp = tf.keras.Input(shape=(128,256,8))\n    base_model = efn.EfficientNetB2(include_top=False, weights=None, input_shape=None)\n    base_model.load_weights('/kaggle/input/tf-efficientnet-imagenet-weights/efficientnet-b2_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5')\n    \n    # RESHAPE INPUT 128x256x8 => 512x512x3 MONOTONE IMAGE\n    # KAGGLE SPECTROGRAMS\n    x1 = [inp[:,:,:,i:i+1] for i in range(4)]\n    x1 = tf.keras.layers.Concatenate(axis=1)(x1)\n    #x1=x1-[0.45]\n    # EEG SPECTROGRAMS\n    x2 = [inp[:,:,:,i+4:i+5] for i in range(4)]\n    x2 = tf.keras.layers.Concatenate(axis=1)(x2)\n    # MAKE 512X512X3\n    if USE_KAGGLE_SPECTROGRAMS & USE_EEG_SPECTROGRAMS:\n        x = tf.keras.layers.Concatenate(axis=2)([x1,x2])\n    elif USE_EEG_SPECTROGRAMS: x = x2\n    else: x = x1\n    x = tf.keras.layers.Concatenate(axis=3)([x,x,x])\n    \n    # OUTPUT\n    x = base_model(x)\n    x = tf.keras.layers.GlobalAveragePooling2D()(x)\n    x = tf.keras.layers.Dense(6,activation='softmax', dtype='float32')(x)\n        \n    # COMPILE MODEL\n    model = tf.keras.Model(inputs=inp, outputs=x)\n    opt = tf.keras.optimizers.Adam(learning_rate = 1e-3)\n    loss = tf.keras.losses.KLDivergence()\n\n    model.compile(loss=loss, optimizer = opt) \n        \n    return model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.537176Z","iopub.execute_input":"2026-04-25T10:25:19.537500Z","iopub.status.idle":"2026-04-25T10:25:19.555502Z","shell.execute_reply.started":"2026-04-25T10:25:19.537471Z","shell.execute_reply":"2026-04-25T10:25:19.554426Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def build_modelb3():\n    \n    inp = tf.keras.Input(shape=(128,256,8))\n    base_model = efn.EfficientNetB3(include_top=False, weights=None, input_shape=None)\n    base_model.load_weights('/kaggle/input/tf-efficientnet-imagenet-weights/efficientnet-b3_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5')\n    \n    # RESHAPE INPUT 128x256x8 => 512x512x3 MONOTONE IMAGE\n    # KAGGLE SPECTROGRAMS\n    x1 = [inp[:,:,:,i:i+1] for i in range(4)]\n    x1 = tf.keras.layers.Concatenate(axis=1)(x1)\n    #x1=x1-[0.45]\n    # EEG SPECTROGRAMS\n    x2 = [inp[:,:,:,i+4:i+5] for i in range(4)]\n    x2 = tf.keras.layers.Concatenate(axis=1)(x2)\n    # MAKE 512X512X3\n    if USE_KAGGLE_SPECTROGRAMS & USE_EEG_SPECTROGRAMS:\n        x = tf.keras.layers.Concatenate(axis=2)([x1,x2])\n    elif USE_EEG_SPECTROGRAMS: x = x2\n    else: x = x1\n    x = tf.keras.layers.Concatenate(axis=3)([x,x,x])\n    \n    # OUTPUT\n    x = base_model(x)\n    x = tf.keras.layers.GlobalAveragePooling2D()(x)\n    x = tf.keras.layers.Dense(6,activation='softmax', dtype='float32')(x)\n        \n    # COMPILE MODEL\n    model = tf.keras.Model(inputs=inp, outputs=x)\n    opt = tf.keras.optimizers.Adam(learning_rate = 1e-3)\n    loss = tf.keras.losses.KLDivergence()\n\n    model.compile(loss=loss, optimizer = opt) \n        \n    return model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.557448Z","iopub.execute_input":"2026-04-25T10:25:19.557788Z","iopub.status.idle":"2026-04-25T10:25:19.572481Z","shell.execute_reply.started":"2026-04-25T10:25:19.557757Z","shell.execute_reply":"2026-04-25T10:25:19.571191Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"infer test for efficient net models","metadata":{}},{"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/hms-harmful-brain-activity-classification/test.csv')\nprint('Test shape',test.shape)\ntest.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.573683Z","iopub.execute_input":"2026-04-25T10:25:19.573979Z","iopub.status.idle":"2026-04-25T10:25:19.627091Z","shell.execute_reply.started":"2026-04-25T10:25:19.573944Z","shell.execute_reply":"2026-04-25T10:25:19.625850Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# READ ALL SPECTROGRAMS\nPATH2 = '/kaggle/input/hms-harmful-brain-activity-classification/test_spectrograms/'\nfiles2 = os.listdir(PATH2)\nprint(f'There are {len(files2)} test spectrogram parquets')\n    \nspectrograms2 = {}\nfor i,f in enumerate(files2):\n    if i%100==0: print(i,', ',end='')\n    tmp = pd.read_parquet(f'{PATH2}{f}')\n    name = int(f.split('.')[0])\n    spectrograms2[name] = tmp.iloc[:,1:].values\n    \n# RENAME FOR DATALOADER\ntest = test.rename({'spectrogram_id':'spec_id'},axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.628654Z","iopub.execute_input":"2026-04-25T10:25:19.629427Z","iopub.status.idle":"2026-04-25T10:25:19.837884Z","shell.execute_reply.started":"2026-04-25T10:25:19.629372Z","shell.execute_reply":"2026-04-25T10:25:19.836944Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pywt, librosa\n\nUSE_WAVELET = None \n\nNAMES = ['LL','LP','RP','RR']\n\nFEATS = [['Fp1','F7','T3','T5','O1'],\n         ['Fp1','F3','C3','P3','O1'],\n         ['Fp2','F8','T4','T6','O2'],\n         ['Fp2','F4','C4','P4','O2']]\n\n# DENOISE FUNCTION\ndef maddest(d, axis=None):\n    return np.mean(np.absolute(d - np.mean(d, axis)), axis)\n\ndef denoise(x, wavelet='haar', level=1):    \n    coeff = pywt.wavedec(x, wavelet, mode=\"per\")\n    sigma = (1/0.6745) * maddest(coeff[-level])\n\n    uthresh = sigma * np.sqrt(2*np.log(len(x)))\n    coeff[1:] = (pywt.threshold(i, value=uthresh, mode='hard') for i in coeff[1:])\n\n    ret=pywt.waverec(coeff, wavelet, mode='per')\n    \n    return ret\n\ndef spectrogram_from_eeg(parquet_path, display=False):\n    \n    # LOAD MIDDLE 50 SECONDS OF EEG SERIES\n    eeg = pd.read_parquet(parquet_path)\n    middle = (len(eeg)-10_000)//2\n    eeg = eeg.iloc[middle:middle+10_000]\n    \n    # VARIABLE TO HOLD SPECTROGRAM\n    img = np.zeros((128,256,4),dtype='float32')\n    \n    if display: plt.figure(figsize=(10,7))\n    signals = []\n    for k in range(4):\n        COLS = FEATS[k]\n        \n        for kk in range(4):\n        \n            # COMPUTE PAIR DIFFERENCES\n            x = eeg[COLS[kk]].values - eeg[COLS[kk+1]].values\n\n            # FILL NANS\n            m = np.nanmean(x)\n            if np.isnan(x).mean()<1: x = np.nan_to_num(x,nan=m)\n            else: x[:] = 0\n\n            # DENOISE\n            if USE_WAVELET:\n                x = denoise(x, wavelet=USE_WAVELET)\n            signals.append(x)\n\n            # RAW SPECTROGRAM\n            mel_spec = librosa.feature.melspectrogram(y=x, sr=200, hop_length=len(x)//256, \n                  n_fft=1024, n_mels=128, fmin=0, fmax=20, win_length=128)\n\n            # LOG TRANSFORM\n            width = (mel_spec.shape[1]//32)*32\n            mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max).astype(np.float32)[:,:width]\n\n            # STANDARDIZE TO -1 TO 1\n            mel_spec_db = (mel_spec_db+40)/40 \n            img[:,:,k] += mel_spec_db\n                \n        # AVERAGE THE 4 MONTAGE DIFFERENCES\n        img[:,:,k] /= 4.0\n        \n        if display:\n            plt.subplot(2,2,k+1)\n            plt.imshow(img[:,:,k],aspect='auto',origin='lower')\n            plt.title(f'EEG {eeg_id} - Spectrogram {NAMES[k]}')\n            \n    if display: \n        plt.show()\n        plt.figure(figsize=(10,5))\n        offset = 0\n        for k in range(4):\n            if k>0: offset -= signals[3-k].min()\n            plt.plot(range(10_000),signals[k]+offset,label=NAMES[3-k])\n            offset += signals[3-k].max()\n        plt.legend()\n        plt.title(f'EEG {eeg_id} Signals')\n        plt.show()\n        print(); print('#'*25); print()\n        \n    return img","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.839164Z","iopub.execute_input":"2026-04-25T10:25:19.839513Z","iopub.status.idle":"2026-04-25T10:25:19.961621Z","shell.execute_reply.started":"2026-04-25T10:25:19.839483Z","shell.execute_reply":"2026-04-25T10:25:19.960406Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# READ ALL EEG SPECTROGRAMS\nPATH2 = '/kaggle/input/hms-harmful-brain-activity-classification/test_eegs/'\nDISPLAY = 1\nEEG_IDS2 = test.eeg_id.unique()\nall_eegs2 = {}\n\nprint('Converting Test EEG to Spectrograms...'); print()\nfor i,eeg_id in enumerate(EEG_IDS2):\n        \n    # CREATE SPECTROGRAM FROM EEG PARQUET\n    img = spectrogram_from_eeg(f'{PATH2}{eeg_id}.parquet', i<DISPLAY)\n    all_eegs2[eeg_id] = img","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:19.962889Z","iopub.execute_input":"2026-04-25T10:25:19.963476Z","iopub.status.idle":"2026-04-25T10:25:38.312316Z","shell.execute_reply.started":"2026-04-25T10:25:19.963442Z","shell.execute_reply":"2026-04-25T10:25:38.311288Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# INFER EFFICIENTNET ON TEST\npreds = []\nmodelb0 = build_modelb0()\nmodelb1 = build_modelb1()\nmodelb2 = build_modelb2()\nmodelb3 = build_modelb3()\n\ntest_gen = DataGenerator(test, shuffle=False, batch_size=128, mode='test',\n                         specs = spectrograms2, eeg_specs = all_eegs2)\n\nfor i in range(5):\n    print(f'Fold {i+1}')\n    modelb0.load_weights(f'{LOAD_MODELS_FROMb0}EffNet_v{VER}_f{i}.h5')\n    modelb1.load_weights(f'{LOAD_MODELS_FROMb1}EffNet_v{VER}_f{i}.h5')\n    modelb2.load_weights(f'{LOAD_MODELS_FROMb2}EffNet_v{VER}_f{i}.h5')\n    modelb3.load_weights(f'{LOAD_MODELS_FROMb3}EffNet_v{VER}_f{i}.h5')\n\n        \n    pred = (modelb0.predict(test_gen, verbose=1)+modelb1.predict(test_gen, verbose=1)+modelb2.predict(test_gen, verbose=1)+modelb3.predict(test_gen, verbose=1))/4\n#     pred = modelb0.predict(test_gen, verbose=1)\n    preds.append(pred)\npred1 = np.mean(preds,axis=0)\nprint()\nprint('Test preds shape',pred1.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:25:38.313942Z","iopub.execute_input":"2026-04-25T10:25:38.314840Z","iopub.status.idle":"2026-04-25T10:26:14.065611Z","shell.execute_reply.started":"2026-04-25T10:25:38.314784Z","shell.execute_reply":"2026-04-25T10:26:14.064551Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pred1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:26:14.067097Z","iopub.execute_input":"2026-04-25T10:26:14.067433Z","iopub.status.idle":"2026-04-25T10:26:14.074572Z","shell.execute_reply.started":"2026-04-25T10:26:14.067402Z","shell.execute_reply":"2026-04-25T10:26:14.073570Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pred1.sum(axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:26:14.075913Z","iopub.execute_input":"2026-04-25T10:26:14.076314Z","iopub.status.idle":"2026-04-25T10:26:14.089603Z","shell.execute_reply.started":"2026-04-25T10:26:14.076270Z","shell.execute_reply":"2026-04-25T10:26:14.088569Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sub = pd.DataFrame({'eeg_id':test.eeg_id.values})\nsub[TARGETS] = pred\nsub.to_csv('submission.csv',index=False)\nprint('Submissionn shape',sub.shape)\nsub.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:26:14.090913Z","iopub.execute_input":"2026-04-25T10:26:14.091245Z","iopub.status.idle":"2026-04-25T10:26:14.116377Z","shell.execute_reply.started":"2026-04-25T10:26:14.091186Z","shell.execute_reply":"2026-04-25T10:26:14.115413Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sub.iloc[:,-6:].sum(axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-25T10:26:14.117547Z","iopub.execute_input":"2026-04-25T10:26:14.117842Z","iopub.status.idle":"2026-04-25T10:26:14.127339Z","shell.execute_reply.started":"2026-04-25T10:26:14.117812Z","shell.execute_reply":"2026-04-25T10:26:14.126253Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}