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plt\nprint(tf.__version__)","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"IMG_SIZE=112\nN_CHANNELS=1","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"HEIGHT = 137\nWIDTH = 236","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"preds_dict = {\n    'grapheme_root': [],\n    'vowel_diacritic': [],\n    'consonant_diacritic': []\n}","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"components = ['consonant_diacritic', 'grapheme_root', 'vowel_diacritic']\ntarget=[] # model predictions placeholder\nrow_id=[] # row_id place holder","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"models_list = []\nmodel = tf.keras.models.load_model('/kaggle/input/densenet121-fold-1/densenet121_fold_1_98906.h5', compile=False)\nmodels_list.append(model)\ndel model\ngc.collect()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class TestDataGenerator(tf.keras.utils.Sequence):\n    def __init__(self, data, height, width, n_channels, batch_size=32):\n        self._data = data\n        self._list_idx = np.arange(len(data))\n        self._batch_size = batch_size\n        self.height = height\n        self.width = width\n        self.n_channels = n_channels\n        self._indices = np.arange(len(self._list_idx))\n        \n    def __len__(self):\n        return int(np.ceil(len(self._data) / float(self._batch_size)))\n    \n    def __getitem__(self, index):\n        batch_idx = self._indices[index*self._batch_size:(index+1)*self._batch_size]\n        Data = np.empty((len(batch_idx), self.height, self.width, self.n_channels))\n        \n        for i, k in enumerate(batch_idx):\n            image = self._data[k].copy().reshape(self.height, self.width, self.n_channels).astype(np.float32) / 255            \n            Data[i,:, :, :] =  image\n        \n        return Data","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"components = ['consonant_diacritic', 'grapheme_root', 'vowel_diacritic']\ntarget=[] # model predictions placeholder\nrow_id=[] # row_id place holder\nfor i in range(4):\n    df_test_img = pd.read_parquet('/kaggle/input/bengaliai-cv19/test_image_data_{}.parquet'.format(i))\n    df_test_img.set_index('image_id', inplace=True)\n    \n    '''X_test = np.zeros((len(df_test_img), HEIGHT, WIDTH, N_CHANNELS))\n    for j in range(len(df_test_img)):\n        img = df_test_img.iloc[j].values.astype(np.float32).reshape(HEIGHT, WIDTH, N_CHANNELS) / 255\n        X_test[j] = img'''\n    \n    #X_test = df_test_img.values.reshape(-1, HEIGHT, WIDTH, N_CHANNELS).astype(np.float32) / 255\n    \n    test_datagen = TestDataGenerator(df_test_img.values, height=HEIGHT, width=WIDTH, n_channels=N_CHANNELS)\n    \n    preds_0_list = []\n    preds_1_list = []\n    preds_2_list = []\n    for model in models_list:\n        preds = model.predict_generator(test_datagen)\n        preds_0_list.append(preds[0])\n        preds_1_list.append(preds[1])\n        preds_2_list.append(preds[2])\n    mean_preds_0 = np.mean(preds_0_list, axis=0)\n    mean_preds_1 = np.mean(preds_1_list, axis=0)\n    mean_preds_2 = np.mean(preds_2_list, axis=0)\n    preds = [mean_preds_0, mean_preds_1, mean_preds_2]\n\n    for z, p in enumerate(preds_dict):\n        preds_dict[p] = np.argmax(preds[z], axis=1)\n\n    for k,_id in enumerate(df_test_img.index.values):  \n        for q,comp in enumerate(components):\n            id_sample=_id+'_'+comp\n            row_id.append(id_sample)\n            target.append(preds_dict[comp][k])\n    del df_test_img, test_datagen, preds_0_list, preds_1_list, preds_2_list, mean_preds_0, mean_preds_1, mean_preds_2, preds \n    gc.collect()\n\ndel preds_dict\ngc.collect()\n    \ndf_sample = pd.DataFrame(\n    {\n        'row_id': row_id,\n        'target':target\n    },\n    columns = ['row_id','target'] \n)\ndel row_id, target\ngc.collect()\ndf_sample.to_csv('submission.csv',index=False)\ndf_sample.head()","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}