{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.16","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"tpu1vmV38","dataSources":[{"sourceId":21154,"databundleVersionId":1243559,"sourceType":"competition"},{"sourceId":1138814,"sourceType":"datasetVersion","datasetId":601927},{"sourceId":216869094,"sourceType":"kernelVersion"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Обучение ResNet152V2, InceptionResNetV2, Xception, EfficientNetV2L\n\nhttps://www.kaggle.com/code/mashakholodenko/start-with-ensemble-v3?scriptVersionId=212208041  \n*  4 версия блокнота DenseNet201+EfficientNetB7 GPU \n*  224, 224 EPOCHS=30 BATCH_SIZE=16 adam\n*  LB нет, потому что соревнование не принимает долгие блокноты на GPU\n*  баг с TPU => запустился на tpu только в новом окружении, но возникли конфликты библиотек\n\nhttps://www.kaggle.com/code/mashakholodenko/start-with-ensemble-v3?scriptVersionId=216731772\n*  13-14 версия блокнота DenseNet201+InceptionResNetV2 TPU \n*  224, 224 EPOCHS=35 BATCH_SIZE=128 adam\n*  LB 0.93873 V13 (bestalpha умножение), 0.93124 V14 (bestalpha сред.арифм)\n\nhttps://www.kaggle.com/code/mashakholodenko/start-with-ensemble-v3/notebook?scriptVersionId=216801098\n*  15 версия блокнота DenseNet201+EfficientNetB7 noisy-student TPU 3.5.0 keras \n*  224, 224 EPOCHS=35 BATCH_SIZE=128 adam\n*  LB 0.97506 (bestalpha умножение)\n*  EPOCHS=23 EfficientNetB7 метрика уже останавливает рост\n*  обновлен датасет, правильное добавление дополнительного датасета\n\nhttps://www.kaggle.com/code/mashakholodenko/start-with-ensemble-v3?scriptVersionId=216869094\n*  17 версия блокнота DenseNet201+EfficientNetV2M TPU 3.5.0 keras \n*  224, 224 EPOCHS=35 BATCH_SIZE=128 adam\n*  LB 0.97532 \n*  EfficientNetB7 очень жирный и долгий\n\nhttps://www.kaggle.com/code/mashakholodenko/start-with-ensemble-v3\n*  18 версия блокнота ResNet152V2, InceptionResNetV2, Xception, EfficientNetV2L TPU 3.5.0 keras \n*  224, 224 EPOCHS=35 BATCH_SIZE=128 adam\n*  только обучение, без предсказания\n  \nhttps://www.kaggle.com/code/mashakholodenko/start-with-pre-train \n* DenseNet201 GPU\n* 224, 224 EPOCHS=30 BATCH_SIZE=8 adam\n* LB 0.92348\n\nhttps://www.kaggle.com/code/mashakholodenko/start-with-more-data-ensemble-v4\n* черновик для ансамбля с доп датасетом, создан отдельно из-за бага с TPU kernel died (баг из-за старой версии окружения форкнутого блокнота)\n* 15 версия Start_with_ensemble_v3 полное вычисление LB 0.97506\n* 17 версия Start_with_ensemble_v3 загрузка сабмита LB 0.97532\n* объединение 3хDenseNet201+EfficientNetB7+EfficientNetV2M+InceptionResNetV2 LB -----","metadata":{}},{"cell_type":"code","source":"# !pip cache purge\n# !pip install -q efficientnet\n# !pip install virtualenv\n# !virtualenv myenv\n# !source myenv/bin/activate\n!pip install keras==3.5.0 --no-deps\n# !pip install keras==3.5.0 tensorflow==2.16.1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:13.387890Z","iopub.execute_input":"2025-01-12T19:15:13.388095Z","iopub.status.idle":"2025-01-12T19:15:17.441056Z","shell.execute_reply.started":"2025-01-12T19:15:13.388058Z","shell.execute_reply":"2025-01-12T19:15:17.440037Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport tensorflow as tf\n# from tensorflow.keras.applications.densenet import DenseNet201\n# import efficientnet.tfkeras as efn\nfrom tensorflow.keras.applications import EfficientNetB7, EfficientNetV2M\n# import tensorflow_addons as tfa\n# from tensorflow_addons.optimizers import AdamW\nfrom tensorflow.keras.models import Model, Sequential\nfrom tensorflow.keras.layers import GlobalAveragePooling2D,Dense\nfrom sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\n#  библиотека для работы с наборами данных на Kaggle\nfrom kaggle_datasets import KaggleDatasets\nimport re\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\n%matplotlib inline \n\nimport warnings\nwarnings.filterwarnings('ignore')\n# import absl.logging\n# absl.logging.set_verbosity(absl.logging.ERROR)\n# import os\n# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Уровень 2 — только ошибки\n# # Отключение вывода логов XLA\n# os.environ['XLA_FLAGS'] = '--xla_disable_jit=1'\n\nprint(\"Tensorflow version \" + tf.__version__)\nimport keras\nprint(\"keras \" + keras.__version__)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:17.442094Z","iopub.execute_input":"2025-01-12T19:15:17.442354Z","iopub.status.idle":"2025-01-12T19:15:37.139588Z","shell.execute_reply.started":"2025-01-12T19:15:17.442327Z","shell.execute_reply":"2025-01-12T19:15:37.138186Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Определяем, какой ускоритель можем использовать","metadata":{}},{"cell_type":"code","source":"AUTO = tf.data.experimental.AUTOTUNE\n# Обнаружение оборудования, возврат соответствующей стратегии распространения: TPU, GPU, CPU\ntry:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # Обнаружение TPU. Параметры среды не требуются, если задана переменная среды TPU_NAME. На Kaggle это всегда так.\n    print('Running on TPU ', tpu.master())\nexcept ValueError:\n    tpu = None\n\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nelse:\n    strategy = tf.distribute.get_strategy() # стратегия распространения по умолчанию в Tensorflow. Работает на CPU и одном GPU.\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:37.140508Z","iopub.execute_input":"2025-01-12T19:15:37.141027Z","iopub.status.idle":"2025-01-12T19:15:45.764535Z","shell.execute_reply.started":"2025-01-12T19:15:37.141001Z","shell.execute_reply":"2025-01-12T19:15:45.763373Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Параметры","metadata":{}},{"cell_type":"code","source":"IMAGE_SIZE = [224, 224] \n# IMAGE_SIZE = [512, 512] # при таком размере графическому процессору не хватит памяти. Используйте TPU\n# 224 вытягивает на гпу с BATCH_SIZE = 16 до 30 эпох\n# EPOCHS = 35\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\nEPOCHS = 35\nif tpu:\n    BATCH_SIZE = 16 * strategy.num_replicas_in_sync\nelse:\n    BATCH_SIZE = 128 * strategy.num_replicas_in_sync\n    EPOCHS = 2\n\n\nSEED = 752\n\nrandom.seed(SEED)\nnp.random.seed(SEED)\ntf.random.set_seed(SEED)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:45.765612Z","iopub.execute_input":"2025-01-12T19:15:45.765890Z","iopub.status.idle":"2025-01-12T19:15:45.771960Z","shell.execute_reply.started":"2025-01-12T19:15:45.765860Z","shell.execute_reply":"2025-01-12T19:15:45.770984Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"BATCH_SIZE","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:45.773345Z","iopub.execute_input":"2025-01-12T19:15:45.773598Z","iopub.status.idle":"2025-01-12T19:15:45.795398Z","shell.execute_reply.started":"2025-01-12T19:15:45.773574Z","shell.execute_reply":"2025-01-12T19:15:45.794313Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Загрузка датасета и допдатасета","metadata":{}},{"cell_type":"code","source":"# GCS_DS_PATH = KaggleDatasets().get_gcs_path(\"tpu-getting-started\") #получаем путь к наборам данных\n# MORE_IMAGES_GCS_DS_PATH = KaggleDatasets().get_gcs_path('tf-flower-photo-tfrec')\nGCS_DS_PATH = \"/kaggle/input/tpu-getting-started\"\nGCS_DS_PATH_EXT  = \"/kaggle/input/tf-flower-photo-tfrec\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:45.796332Z","iopub.execute_input":"2025-01-12T19:15:45.796574Z","iopub.status.idle":"2025-01-12T19:15:45.805223Z","shell.execute_reply.started":"2025-01-12T19:15:45.796549Z","shell.execute_reply":"2025-01-12T19:15:45.804157Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"GCS_PATH_SELECT = { # available image sizes\n    192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',\n    224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',\n    331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',\n    512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'\n}\nGCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]\n\n# External data\nGCS_PATH_SELECT_EXT = {\n    192: '/tfrecords-jpeg-192x192',\n    224: '/tfrecords-jpeg-224x224',\n    331: '/tfrecords-jpeg-331x331',\n    512: '/tfrecords-jpeg-512x512'\n}\nGCS_PATH_EXT = GCS_PATH_SELECT_EXT[IMAGE_SIZE[0]]\n\nIMAGENET_FILES = tf.io.gfile.glob(GCS_DS_PATH_EXT + '/imagenet' + GCS_PATH_EXT + '/*.tfrec')\nINATURELIST_FILES = tf.io.gfile.glob(GCS_DS_PATH_EXT + '/inaturalist' + GCS_PATH_EXT + '/*.tfrec')\nOPENIMAGE_FILES = tf.io.gfile.glob(GCS_DS_PATH_EXT + '/openimage' + GCS_PATH_EXT + '/*.tfrec')\nOXFORD_FILES = tf.io.gfile.glob(GCS_DS_PATH_EXT + '/oxford_102' + GCS_PATH_EXT + '/*.tfrec')\nTENSORFLOW_FILES = tf.io.gfile.glob(GCS_DS_PATH_EXT + '/tf_flowers' + GCS_PATH_EXT + '/*.tfrec')\n\nADDITIONAL_TRAINING_FILENAMES = IMAGENET_FILES + INATURELIST_FILES + OPENIMAGE_FILES + OXFORD_FILES + TENSORFLOW_FILES\n\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')\nVALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')\n\nTRAINING_FILENAMES = TRAINING_FILENAMES + ADDITIONAL_TRAINING_FILENAMES","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:45.806219Z","iopub.execute_input":"2025-01-12T19:15:45.806454Z","iopub.status.idle":"2025-01-12T19:15:46.013135Z","shell.execute_reply.started":"2025-01-12T19:15:45.806431Z","shell.execute_reply":"2025-01-12T19:15:46.011002Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Аугментация\n\nчасть взята отсюда\nhttps://www.kaggle.com/code/luongduongminh/effnet-densenet#Random-blockout-augmentation\n\nhttps://www.kaggle.com/code/lkatran/more-data-with-densenet-v2","metadata":{}},{"cell_type":"code","source":"def random_blockout(img, sl=0.1, sh=0.2, rl=0.4):\n    p=random.random()\n    if p>=0.25:\n        w, h, c = IMAGE_SIZE[0], IMAGE_SIZE[1], 3\n        origin_area = tf.cast(h*w, tf.float32)\n\n        e_size_l = tf.cast(tf.round(tf.sqrt(origin_area * sl * rl)), tf.int32)\n        e_size_h = tf.cast(tf.round(tf.sqrt(origin_area * sh / rl)), tf.int32)\n\n        e_height_h = tf.minimum(e_size_h, h)\n        e_width_h = tf.minimum(e_size_h, w)\n\n        erase_height = tf.random.uniform(shape=[], minval=e_size_l, maxval=e_height_h, dtype=tf.int32)\n        erase_width = tf.random.uniform(shape=[], minval=e_size_l, maxval=e_width_h, dtype=tf.int32)\n\n        erase_area = tf.zeros(shape=[erase_height, erase_width, c])\n        erase_area = tf.cast(erase_area, tf.uint8)\n\n        pad_h = h - erase_height\n        pad_top = tf.random.uniform(shape=[], minval=0, maxval=pad_h, dtype=tf.int32)\n        pad_bottom = pad_h - pad_top\n\n        pad_w = w - erase_width\n        pad_left = tf.random.uniform(shape=[], minval=0, maxval=pad_w, dtype=tf.int32)\n        pad_right = pad_w - pad_left\n\n        erase_mask = tf.pad([erase_area], [[0,0],[pad_top, pad_bottom], [pad_left, pad_right], [0,0]], constant_values=1)\n        erase_mask = tf.squeeze(erase_mask, axis=0)\n        erased_img = tf.multiply(tf.cast(img,tf.float32), tf.cast(erase_mask, tf.float32))\n\n        return tf.cast(erased_img, img.dtype)\n    else:\n        return tf.cast(img, img.dtype)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:46.013831Z","iopub.execute_input":"2025-01-12T19:15:46.014120Z","iopub.status.idle":"2025-01-12T19:15:46.024155Z","shell.execute_reply.started":"2025-01-12T19:15:46.014092Z","shell.execute_reply":"2025-01-12T19:15:46.022789Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Загружаем данные\n\nЭти данные загружаются из Kaggle и автоматически сегментируются для максимального распараллеливания.","metadata":{}},{"cell_type":"code","source":"def decode_image(image_data):\n    image = tf.image.decode_jpeg(image_data, channels=3)\n    image = tf.cast(image, tf.float32) / 255.0  # convert image to floats in [0, 1] range\n    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU\n    return image\n\ndef read_labeled_tfrecord(example):\n    LABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring\n        \"class\": tf.io.FixedLenFeature([], tf.int64),  # shape [] means single element\n    }\n    example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)\n    image = decode_image(example['image'])\n    label = tf.cast(example['class'], tf.int32)\n    return image, label # returns a dataset of (image, label) pairs\n\ndef read_unlabeled_tfrecord(example):\n    UNLABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring\n        \"id\": tf.io.FixedLenFeature([], tf.string),  # shape [] means single element\n        # class is missing, this competitions's challenge is to predict flower classes for the test dataset\n    }\n    example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)\n    image = decode_image(example['image'])\n    idnum = example['id']\n    return image, idnum # returns a dataset of image(s)\n\ndef load_dataset(filenames, labeled=True, ordered=False):\n    # Read from TFRecords. For optimal performance, reading from multiple files at once and\n    # disregarding data order. Order does not matter since we will be shuffling the data anyway.\n\n    ignore_order = tf.data.Options()\n    if not ordered:\n        ignore_order.experimental_deterministic = False # disable order, increase speed\n\n    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files\n    dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order\n    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)\n    # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False\n    return dataset\n\ndef data_augment(image, label):\n    # data augmentation. Thanks to the dataset.prefetch(AUTO) statement in the next function (below),\n    # this happens essentially for free on TPU. Data pipeline code is executed on the \"CPU\" part\n    # of the TPU while the TPU itself is computing gradients.\n\n    image = tf.image.random_flip_left_right(image, seed=SEED)\n    image = random_blockout(image)\n    return image, label   \n\ndef get_training_dataset():\n    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)\n    dataset = dataset.repeat() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(2048)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_validation_dataset(ordered=False):\n    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_test_dataset(ordered=False):\n    dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef count_data_items(filenames):\n    # the number of data items is written in the name of the .tfrec files, i.e. flowers00-230.tfrec = 230 data items\n    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) for filename in filenames]\n    return np.sum(n)\n\n\n# training_dataset = get_training_dataset()\n# validation_dataset = get_validation_dataset()\n\nNUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)\nNUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\n\nVALIDATION_STEPS = -(-NUM_VALIDATION_IMAGES // BATCH_SIZE)\nTEST_STEPS = -(-NUM_TEST_IMAGES // BATCH_SIZE)  \n\nprint('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))\nprint('STEPS_PER_EPOCH', STEPS_PER_EPOCH)\nprint('TEST_STEPS  {}, VALIDATION_STEPS {}'.format(TEST_STEPS , VALIDATION_STEPS))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:46.025164Z","iopub.execute_input":"2025-01-12T19:15:46.025421Z","iopub.status.idle":"2025-01-12T19:15:46.053355Z","shell.execute_reply.started":"2025-01-12T19:15:46.025395Z","shell.execute_reply":"2025-01-12T19:15:46.052459Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"validation_size = round(NUM_VALIDATION_IMAGES / BATCH_SIZE)\ntest_size= round(NUM_TEST_IMAGES / BATCH_SIZE)\ntrain_size= round(NUM_TRAINING_IMAGES / BATCH_SIZE)\nprint(\"Train size:\", train_size)   \nprint(\"Validation size:\", validation_size)\nprint(\"Test size:\", test_size)             ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:46.054319Z","iopub.execute_input":"2025-01-12T19:15:46.054531Z","iopub.status.idle":"2025-01-12T19:15:46.074988Z","shell.execute_reply.started":"2025-01-12T19:15:46.054509Z","shell.execute_reply":"2025-01-12T19:15:46.073519Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Построить модель на TPU","metadata":{}},{"cell_type":"code","source":"# функция управляющая изменениями шага обучения в процессе тренировки нейронной сети\nLR_START = 0.00001\nLR_MAX = 0.00005 * strategy.num_replicas_in_sync#0.0001\nLR_MIN = 0.00001\nLR_RAMPUP_EPOCHS = 5\nLR_SUSTAIN_EPOCHS = 0\nLR_EXP_DECAY = .75\n\ndef lrfn(epoch):\n    if epoch < LR_RAMPUP_EPOCHS:\n        lr = (LR_MAX - LR_START) / LR_RAMPUP_EPOCHS * epoch + LR_START\n    elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:\n        lr = LR_MAX\n    else:\n        lr = (LR_MAX - LR_MIN) * LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS) + LR_MIN\n    return lr\n    \nlr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=True)\n\n# построим график изменения шага обучение в зависимости от эпох\nrng = [i for i in range(EPOCHS)]\ny = [lrfn(x) for x in rng]\nplt.plot(rng, y)\nprint(\"Learning rate schedule: {:.3g} to {:.3g} to {:.3g}\".format(y[0], max(y), y[-1]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:46.075923Z","iopub.execute_input":"2025-01-12T19:15:46.076181Z","iopub.status.idle":"2025-01-12T19:15:46.257468Z","shell.execute_reply.started":"2025-01-12T19:15:46.076156Z","shell.execute_reply":"2025-01-12T19:15:46.256352Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Проверка данных\ntrain_dataset = get_training_dataset()\nval_dataset = get_validation_dataset()\nprint(train_dataset, '\\n ', val_dataset)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:46.258314Z","iopub.execute_input":"2025-01-12T19:15:46.258590Z","iopub.status.idle":"2025-01-12T19:15:46.684044Z","shell.execute_reply.started":"2025-01-12T19:15:46.258561Z","shell.execute_reply":"2025-01-12T19:15:46.683052Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"##  ResNet152V2","metadata":{},"attachments":{"7db15f47-c4dd-48d3-9224-2b090498d844.png":{"image/png":"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"}}},{"cell_type":"code","source":"with strategy.scope():\n    resnet152 = tf.keras.applications.ResNet152V2(input_shape=[*IMAGE_SIZE, 3], \n                                                  weights='imagenet', \n                                                  include_top=False)\n    resnet152.trainable = True\n    \n    model1 = tf.keras.Sequential([\n        resnet152,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(104, activation='softmax')\n    ])\n        \n    model1.compile(\n        optimizer=tf.keras.optimizers.Adam(),\n        loss='sparse_categorical_crossentropy',\n        metrics=['sparse_categorical_accuracy']\n    )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:15:46.684997Z","iopub.execute_input":"2025-01-12T19:15:46.685404Z","iopub.status.idle":"2025-01-12T19:16:18.875139Z","shell.execute_reply.started":"2025-01-12T19:15:46.685377Z","shell.execute_reply":"2025-01-12T19:16:18.873738Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# чекпоинт\nchk_callback1 = tf.keras.callbacks.ModelCheckpoint(filepath='ResNet152V2_best.keras',\n                                                   # save_weights_only=True,\n                                                   monitor='val_sparse_categorical_accuracy',\n                                                   mode='max',\n                                                   save_best_only=True,\n                                                   verbose=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:16:18.876261Z","iopub.execute_input":"2025-01-12T19:16:18.876513Z","iopub.status.idle":"2025-01-12T19:16:18.881184Z","shell.execute_reply.started":"2025-01-12T19:16:18.876488Z","shell.execute_reply":"2025-01-12T19:16:18.879989Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"with strategy.scope(): \n    history1 = model1.fit(get_training_dataset(), \n                steps_per_epoch=STEPS_PER_EPOCH, \n                epochs=EPOCHS, \n                validation_data=get_validation_dataset(),\n                # validation_steps=VALIDATION_STEPS,\n                callbacks=[lr_callback, chk_callback1],\n                verbose=2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:16:18.882362Z","iopub.execute_input":"2025-01-12T19:16:18.882604Z","iopub.status.idle":"2025-01-12T19:21:45.985276Z","shell.execute_reply.started":"2025-01-12T19:16:18.882579Z","shell.execute_reply":"2025-01-12T19:21:45.983902Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"##  InceptionResNetV2","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    inception_resnet_v2 = tf.keras.applications.InceptionResNetV2(input_shape=[*IMAGE_SIZE, 3], \n                                                                 weights='imagenet', \n                                                                 include_top=False)\n    inception_resnet_v2.trainable = True\n    \n    model2 = tf.keras.Sequential([\n        inception_resnet_v2,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(104, activation='softmax')\n    ])\n        \n    model2.compile(\n        optimizer=tf.keras.optimizers.Adam(),\n        loss='sparse_categorical_crossentropy',\n        metrics=['sparse_categorical_accuracy']\n    )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:21:45.986860Z","iopub.execute_input":"2025-01-12T19:21:45.987179Z","iopub.status.idle":"2025-01-12T19:22:20.750301Z","shell.execute_reply.started":"2025-01-12T19:21:45.987149Z","shell.execute_reply":"2025-01-12T19:22:20.749007Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# чекпоинт\nchk_callback2 = tf.keras.callbacks.ModelCheckpoint(filepath='InceptionResNetV2_best.keras',\n                                                   # save_weights_only=True,\n                                                   monitor='val_sparse_categorical_accuracy',\n                                                   mode='max',\n                                                   save_best_only=True,\n                                                   verbose=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:22:20.751307Z","iopub.execute_input":"2025-01-12T19:22:20.751548Z","iopub.status.idle":"2025-01-12T19:22:20.755869Z","shell.execute_reply.started":"2025-01-12T19:22:20.751523Z","shell.execute_reply":"2025-01-12T19:22:20.754935Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"with strategy.scope(): \n    history2 = model2.fit(get_training_dataset(), \n                steps_per_epoch=STEPS_PER_EPOCH, \n                epochs=EPOCHS, \n                validation_data=get_validation_dataset(),\n                # validation_steps=VALIDATION_STEPS,\n                callbacks=[lr_callback, chk_callback2],\n                verbose=2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:22:20.756639Z","iopub.execute_input":"2025-01-12T19:22:20.756853Z","iopub.status.idle":"2025-01-12T19:28:20.097830Z","shell.execute_reply.started":"2025-01-12T19:22:20.756832Z","shell.execute_reply":"2025-01-12T19:28:20.096392Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Xception","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    \n    xception = tf.keras.applications.Xception(input_shape=[*IMAGE_SIZE, 3], \n                                               weights='imagenet', \n                                               include_top=False)\n    xception.trainable = True\n    \n    model3 = tf.keras.Sequential([\n        xception,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(104, activation='softmax')\n    ])\n        \n    model3.compile(\n        optimizer=tf.keras.optimizers.Adam(),\n        loss='sparse_categorical_crossentropy',\n        metrics=['sparse_categorical_accuracy']\n    )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:28:20.099682Z","iopub.execute_input":"2025-01-12T19:28:20.099942Z","iopub.status.idle":"2025-01-12T19:28:31.613514Z","shell.execute_reply.started":"2025-01-12T19:28:20.099916Z","shell.execute_reply":"2025-01-12T19:28:31.611917Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# чекпоинт\nchk_callback3 = tf.keras.callbacks.ModelCheckpoint(filepath='Xception_best.keras',\n                                                   # save_weights_only=True,\n                                                   monitor='val_sparse_categorical_accuracy',\n                                                   mode='max',\n                                                   save_best_only=True,\n                                                   verbose=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:24.748381Z","iopub.execute_input":"2025-01-12T19:29:24.748796Z","iopub.status.idle":"2025-01-12T19:29:24.752996Z","shell.execute_reply.started":"2025-01-12T19:29:24.748763Z","shell.execute_reply":"2025-01-12T19:29:24.752166Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"with strategy.scope(): \n    history3 = model3.fit(get_training_dataset(), \n                steps_per_epoch=STEPS_PER_EPOCH, \n                epochs=EPOCHS, \n                validation_data=get_validation_dataset(),\n                # validation_steps=VALIDATION_STEPS,\n                callbacks=[lr_callback, chk_callback3],\n                verbose=2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:35.890018Z","iopub.execute_input":"2025-01-12T19:29:35.890608Z","iopub.status.idle":"2025-01-12T19:31:38.567271Z","shell.execute_reply.started":"2025-01-12T19:29:35.890557Z","shell.execute_reply":"2025-01-12T19:31:38.565949Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## EfficientNetV2L","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    efficientnetv2l = tf.keras.applications.EfficientNetV2L(input_shape=[*IMAGE_SIZE, 3], \n                                                             weights='imagenet', \n                                                             include_top=False)\n    efficientnetv2l.trainable = True\n\n    model4 = tf.keras.Sequential([\n        efficientnetv2l,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(104, activation='softmax')\n    ])\n        \n    model4.compile(\n        optimizer=tf.keras.optimizers.Adam(),\n        loss='sparse_categorical_crossentropy',\n        metrics=['sparse_categorical_accuracy']\n    )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:35:20.590889Z","iopub.execute_input":"2025-01-12T19:35:20.591389Z","iopub.status.idle":"2025-01-12T19:36:19.643005Z","shell.execute_reply.started":"2025-01-12T19:35:20.591355Z","shell.execute_reply":"2025-01-12T19:36:19.641849Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# чекпоинт\nchk_callback4 = tf.keras.callbacks.ModelCheckpoint(filepath='EfficientNetV2L_best.keras',\n                                                   # save_weights_only=True,\n                                                   monitor='val_sparse_categorical_accuracy',\n                                                   mode='max',\n                                                   save_best_only=True,\n                                                   verbose=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:36:47.244667Z","iopub.execute_input":"2025-01-12T19:36:47.245138Z","iopub.status.idle":"2025-01-12T19:36:47.250062Z","shell.execute_reply.started":"2025-01-12T19:36:47.245095Z","shell.execute_reply":"2025-01-12T19:36:47.248853Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"with strategy.scope(): \n    history4 = model4.fit(get_training_dataset(), \n                steps_per_epoch=STEPS_PER_EPOCH, \n                epochs=EPOCHS, \n                validation_data=get_validation_dataset(),\n                # validation_steps=VALIDATION_STEPS,\n                callbacks=[lr_callback, chk_callback4],\n                verbose=2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:36:50.202208Z","iopub.execute_input":"2025-01-12T19:36:50.202611Z","iopub.status.idle":"2025-01-12T19:49:00.114571Z","shell.execute_reply.started":"2025-01-12T19:36:50.202578Z","shell.execute_reply":"2025-01-12T19:49:00.113338Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Графики обучения","metadata":{}},{"cell_type":"code","source":"# plt.plot(history1.history['f1_score'], \n#          label='Оценка точности на обучающем наборе')\n# plt.plot(history1.history['val_f1_score'], \n#          label='Оценка точности на проверочном наборе')\nplt.plot(history1.history['sparse_categorical_accuracy'], \n         label='Оценка точности на обучающем наборе')\nplt.plot(history1.history['val_sparse_categorical_accuracy'], \n         label='Оценка точности на проверочном наборе')\nplt.title('ResNet152V2 точноть')\nplt.xlabel('Эпоха обучения')\nplt.ylabel('Оценка точности')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:49:13.969093Z","iopub.execute_input":"2025-01-12T19:49:13.969499Z","iopub.status.idle":"2025-01-12T19:49:14.128931Z","shell.execute_reply.started":"2025-01-12T19:49:13.969467Z","shell.execute_reply":"2025-01-12T19:49:14.127508Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plt.plot(history1.history['loss'], \n#          label='Оценка потерь на обучающем наборе')\n# plt.plot(history1.history['val_loss'], \n#          label='Оценка потерь на проверочном наборе')\nplt.plot(history1.history['loss'], \n         label='Оценка потерь на обучающем наборе')\nplt.plot(history1.history['val_loss'], \n         label='Оценка потерь на проверочном наборе')\nplt.title('ResNet152V2 потери')\nplt.xlabel('Эпоха обучения')\nplt.ylabel('Оценка потерь')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:17.256753Z","iopub.status.idle":"2025-01-12T19:29:17.257282Z","shell.execute_reply.started":"2025-01-12T19:29:17.256886Z","shell.execute_reply":"2025-01-12T19:29:17.256924Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plt.plot(history2.history['f1_score'], \n#          label='Оценка точности на обучающем наборе')\n# plt.plot(history2.history['val_f1_score'], \n#          label='Оценка точности на проверочном наборе')\nplt.plot(history2.history['sparse_categorical_accuracy'], \n         label='Оценка точности на обучающем наборе')\nplt.plot(history2.history['val_sparse_categorical_accuracy'], \n         label='Оценка точности на проверочном наборе')\nplt.title('InceptionResNetV2 точноть')\nplt.xlabel('Эпоха обучения')\nplt.ylabel('Оценка точности')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:49:36.740941Z","iopub.execute_input":"2025-01-12T19:49:36.741383Z","iopub.status.idle":"2025-01-12T19:49:36.898558Z","shell.execute_reply.started":"2025-01-12T19:49:36.741350Z","shell.execute_reply":"2025-01-12T19:49:36.897296Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plt.plot(history2.history['loss'], \n#          label='Оценка потерь на обучающем наборе')\n# plt.plot(history2.history['val_loss'], \n#          label='Оценка потерь на проверочном наборе')\nplt.plot(history2.history['loss'], \n         label='Оценка потерь на обучающем наборе')\nplt.plot(history2.history['val_loss'], \n         label='Оценка потерь на проверочном наборе')\nplt.title('InceptionResNetV2 потери')\nplt.xlabel('Эпоха обучения')\nplt.ylabel('Оценка потерь')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:49:20.781202Z","iopub.execute_input":"2025-01-12T19:49:20.781602Z","iopub.status.idle":"2025-01-12T19:49:20.981821Z","shell.execute_reply.started":"2025-01-12T19:49:20.781572Z","shell.execute_reply":"2025-01-12T19:49:20.980194Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.plot(history3.history['sparse_categorical_accuracy'], \n         label='Оценка точности на обучающем наборе')\nplt.plot(history3.history['val_sparse_categorical_accuracy'], \n         label='Оценка точности на проверочном наборе')\nplt.title('Xception точноть')\nplt.xlabel('Эпоха обучения')\nplt.ylabel('Оценка точности')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:49:52.352279Z","iopub.execute_input":"2025-01-12T19:49:52.352690Z","iopub.status.idle":"2025-01-12T19:49:52.523664Z","shell.execute_reply.started":"2025-01-12T19:49:52.352659Z","shell.execute_reply":"2025-01-12T19:49:52.522179Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.plot(history3.history['loss'], \n         label='Оценка потерь на обучающем наборе')\nplt.plot(history3.history['val_loss'], \n         label='Оценка потерь на проверочном наборе')\nplt.title('Xception потери')\nplt.xlabel('Эпоха обучения')\nplt.ylabel('Оценка потерь')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:17.261708Z","iopub.status.idle":"2025-01-12T19:29:17.262104Z","shell.execute_reply.started":"2025-01-12T19:29:17.261870Z","shell.execute_reply":"2025-01-12T19:29:17.261912Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.plot(history4.history['sparse_categorical_accuracy'], \n         label='Оценка точности на обучающем наборе')\nplt.plot(history4.history['val_sparse_categorical_accuracy'], \n         label='Оценка точности на проверочном наборе')\nplt.title('EfficientNetV2L точноть')\nplt.xlabel('Эпоха обучения')\nplt.ylabel('Оценка точности')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:49:25.604702Z","iopub.execute_input":"2025-01-12T19:49:25.605120Z","iopub.status.idle":"2025-01-12T19:49:25.760856Z","shell.execute_reply.started":"2025-01-12T19:49:25.605089Z","shell.execute_reply":"2025-01-12T19:49:25.759872Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.plot(history4.history['loss'], \n         label='Оценка потерь на обучающем наборе')\nplt.plot(history4.history['val_loss'], \n         label='Оценка потерь на проверочном наборе')\nplt.title('EfficientNetV2L потери')\nplt.xlabel('Эпоха обучения')\nplt.ylabel('Оценка потерь')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:17.263957Z","iopub.status.idle":"2025-01-12T19:29:17.264552Z","shell.execute_reply.started":"2025-01-12T19:29:17.264101Z","shell.execute_reply":"2025-01-12T19:29:17.264138Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Вычислите свои прогнозы на тестовом наборе!\n\nCоздадим файл, который можно будет отправить на конкурс.","metadata":{}},{"cell_type":"markdown","source":"## Загрузка лучшего чекпоинта","metadata":{}},{"cell_type":"code","source":"# model1 = tf.keras.models.load_model('ResNet152V2_best.keras')\n# model2 = tf.keras.models.load_model('InceptionResNetV2_best.keras')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:17.265550Z","iopub.status.idle":"2025-01-12T19:29:17.265945Z","shell.execute_reply.started":"2025-01-12T19:29:17.265684Z","shell.execute_reply":"2025-01-12T19:29:17.265722Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"##   Best alpha и метрики ансамбля","metadata":{}},{"cell_type":"code","source":"# from sklearn.metrics import f1_score\n# val_dataset = get_validation_dataset()\n# images_ds = val_dataset.map(lambda image, label: image)\n# labels_ds = val_dataset.map(lambda image, label: label).unbatch()\n# val_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy() # get everything as one batch\n# m1 = model1.predict(images_ds, verbose=2)\n# m2 = model2.predict(images_ds, verbose=2)\n# scores = []\n# for alpha in np.linspace(0,1,100):\n#     val_probabilities = alpha*m1+(1-alpha)*m2\n#     val_predictions = np.argmax(val_probabilities, axis=-1)\n#     scores.append(f1_score(val_labels, val_predictions, labels=range(104), average='macro'))\n\n# best_alpha = np.argmax(scores)/100\n    \n# print('Best alpha: ' + str(best_alpha))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:17.266591Z","iopub.status.idle":"2025-01-12T19:29:17.267240Z","shell.execute_reply.started":"2025-01-12T19:29:17.266991Z","shell.execute_reply":"2025-01-12T19:29:17.267037Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# if best_alpha > 0.5:\n#     cm_probabilities = best_alpha * m2 + (1-best_alpha) * m1\n# else:\n#     cm_probabilities = best_alpha * m1 + (1-best_alpha) * m2\n    \n# val_predictions = np.argmax(cm_probabilities, axis=-1)\n# cmat = confusion_matrix(val_labels, val_predictions, labels=range(104))\n# score = f1_score(val_labels, val_predictions, labels=range(104), average='macro')\n# precision = precision_score(val_labels, val_predictions, labels=range(104), average='macro')\n# recall = recall_score(val_labels, val_predictions, labels=range(104), average='macro')\n# #cmat = (cmat.T / cmat.sum(axis=1)).T # normalized\n# # display_confusion_matrix(cmat, score, precision, recall)\n# print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:17.267894Z","iopub.status.idle":"2025-01-12T19:29:17.268402Z","shell.execute_reply.started":"2025-01-12T19:29:17.268038Z","shell.execute_reply":"2025-01-12T19:29:17.268093Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Предсказание\nPredictions using Test Time Augmentation (TTA)","metadata":{}},{"cell_type":"code","source":"# TTA_NUM = 5\n# # Поскольку мы разделяем набор данных и выполняем итерацию отдельно для изображений и идентификаторов, порядок имеет значение.\n# def predict_tta(model, n_iter):\n#     probs  = []\n#     for i in range(n_iter):\n#         test_ds = get_test_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and ids, order matters.\n#         test_images_ds = test_ds.map(lambda image, idnum: image)\n#         probs.append(model.predict(test_images_ds,verbose=2))\n        \n#     return probs\n\n# # def run_inference(model):\n# #     test_ds = get_test_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and ids, order matters.\n# #     test_images_ds = test_ds.map(lambda image, idnum: image)\n# #     preds = model.predict(test_images_ds,verbose=2)\n# #     return preds\n\n# print('Вычисляем предсказания...')\n# test_ds = get_test_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and ids, order matters.\n# test_images_ds = test_ds.map(lambda image, idnum: image)\n# # best_alpha = 0.1\n# # best_alpha = 0.48\n\n# print('предсказания модели 1...')\n# probabilities1 = np.mean(predict_tta(model1, TTA_NUM), axis=0)\n# # probabilities1 = run_inference(model1)\n# print('завершение предсказания модели 1...')\n\n# print('предсказания модели 2...')\n# probabilities2 = np.mean(predict_tta(model2, TTA_NUM), axis=0)\n# # probabilities2 = run_inference(model2)\n# print('завершение предсказания модели 2...')\n\n# print('расчет probabilities...')\n# if best_alpha > 0.5:\n#     probabilities = best_alpha * probabilities2 + (1 - best_alpha) * probabilities1\n# else:\n#     probabilities = best_alpha * probabilities1 + (1 - best_alpha) * probabilities2\n    \n# probabilities_2 = (probabilities1 + probabilities2)/2\n# probabilities_3 = np.maximum(probabilities1, probabilities2)\n\n# predictions = np.argmax(probabilities, axis=-1)\n# predictions2 = np.argmax(probabilities_2, axis=-1)\n# predictions3 = np.argmax(probabilities_3, axis=-1)\n\n# print('predictions best_alpha', predictions)\n# print('predictions2', predictions2)\n# print('predictions3', predictions3)\n\n \n# print('Создание файла submission.csv...')\n# test_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()\n# test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U') # все в одной партии\n\n# np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')\n# np.savetxt('submission2.csv', np.rec.fromarrays([test_ids, predictions2]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')\n# np.savetxt('submission3.csv', np.rec.fromarrays([test_ids, predictions3]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:17.269273Z","iopub.status.idle":"2025-01-12T19:29:17.269690Z","shell.execute_reply.started":"2025-01-12T19:29:17.269415Z","shell.execute_reply":"2025-01-12T19:29:17.269455Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Сравнение расчета предсказаний","metadata":{}},{"cell_type":"code","source":"# # Загрузка CSV файлов\n# submission = pd.read_csv('submission.csv')\n# submission2 = pd.read_csv('submission2.csv')\n# submission3 = pd.read_csv('submission3.csv')\n\n# # Объединяем DataFrame по 'id'\n# merged_1 = submission.merge(submission2, on='id', suffixes=('_1', '_2'))\n# merged_all = merged_1.merge(submission3, on='id', suffixes=('', '_3'))\n\n# # Фильтруем результаты, чтобы найти строки, где метки не совпадают\n# mismatches = merged_all[\n#     (merged_all['label_1'] != merged_all['label_2']) | \n#     (merged_all['label_1'] != merged_all['label']) | \n#     (merged_all['label_2'] != merged_all['label'])\n# ]\n\n# # Выводим id и label из всех трех DataFrame\n# output = mismatches[['id', 'label_1', 'label_2', 'label']]\n\n# # Печатаем или сохраняем результат\n# print(output)\n\n# # Сохраняем в новый CSV файл\n# output.to_csv('mismatches.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-12T19:29:17.270944Z","iopub.status.idle":"2025-01-12T19:29:17.271589Z","shell.execute_reply.started":"2025-01-12T19:29:17.271176Z","shell.execute_reply":"2025-01-12T19:29:17.271220Z"}},"outputs":[],"execution_count":null}]}