{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","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"}],"dockerImageVersionId":30628,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -q efficientnet","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-19T06:02:48.489449Z","iopub.execute_input":"2023-12-19T06:02:48.489797Z","iopub.status.idle":"2023-12-19T06:02:55.201622Z","shell.execute_reply.started":"2023-12-19T06:02:48.489768Z","shell.execute_reply":"2023-12-19T06:02:55.200629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Импорт библиотек","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\n#предварительно обученные модели\nfrom tensorflow.keras.applications import DenseNet201 \nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.layers import Dense, Flatten\nfrom tensorflow.keras.layers import Dropout, BatchNormalization, GaussianDropout\nfrom tensorflow.keras.layers import GlobalAveragePooling2D\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nfrom tensorflow.keras.applications.vgg16 import VGG16\nfrom tensorflow.keras.applications.vgg19 import VGG19\nfrom tensorflow.keras.applications.inception_v3 import InceptionV3\nfrom tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2\nfrom tensorflow.keras.applications.densenet import DenseNet121, DenseNet169, DenseNet201 \nfrom tensorflow.keras.applications.xception import Xception\nfrom tensorflow.keras.applications.resnet50 import ResNet50\nfrom tensorflow.keras.applications.resnet_v2 import ResNet50V2, ResNet101V2, ResNet152V2\nfrom tensorflow.keras.applications.nasnet import NASNetLarge\nfrom efficientnet.tfkeras import EfficientNetB7, EfficientNetL2, EfficientNetB0, EfficientNetB1\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 \nprint(\"Tensorflow version \" + tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:02:55.203245Z","iopub.execute_input":"2023-12-19T06:02:55.20372Z","iopub.status.idle":"2023-12-19T06:03:10.363875Z","shell.execute_reply.started":"2023-12-19T06:02:55.203685Z","shell.execute_reply":"2023-12-19T06:03:10.363178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Ускоритель\n","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.TPUStrategy(tpu)\nelse:\n    strategy = tf.distribute.get_strategy() # стратегия распространения по умолчанию в Tensorflow. Работает на CPU и одном GPU.\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:03:10.364704Z","iopub.execute_input":"2023-12-19T06:03:10.365109Z","iopub.status.idle":"2023-12-19T06:03:18.299155Z","shell.execute_reply.started":"2023-12-19T06:03:10.365081Z","shell.execute_reply":"2023-12-19T06:03:18.298377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Загрузка данных","metadata":{}},{"cell_type":"code","source":"from kaggle_datasets import KaggleDatasets\n\nGCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started') #получаем путь к наборам данных\nprint(GCS_DS_PATH)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:03:18.300732Z","iopub.execute_input":"2023-12-19T06:03:18.301008Z","iopub.status.idle":"2023-12-19T06:03:18.305168Z","shell.execute_reply.started":"2023-12-19T06:03:18.300978Z","shell.execute_reply":"2023-12-19T06:03:18.304509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"IMAGE_SIZE = [512, 512]\nEPOCHS = 13\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\n\nGCS_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\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\n\nSEED = 2020","metadata":{"execution":{"iopub.status.busy":"2023-12-19T07:18:36.870911Z","iopub.execute_input":"2023-12-19T07:18:36.871742Z","iopub.status.idle":"2023-12-19T07:18:36.896964Z","shell.execute_reply.started":"2023-12-19T07:18:36.871705Z","shell.execute_reply":"2023-12-19T07:18:36.895905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def decode_image(image_data):\n    \"\"\"Декодирует изображение в vyjujvthye. vfnhbwe (тензор)\n    Нормализует данные и преобразовывает изображения к указанному размеру\"\"\"\n    image = tf.image.decode_jpeg(image_data, channels=3) # Декодирование изображения в формате JPEG в тензор uint8.\n    image = tf.cast(image, tf.float32) / 255.0  # преобразовать изображение в плавающее в диапазоне [0, 1]\n    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # явный размер, необходимый для TPU\n#     image = tf.keras.applications.inception_resnet_v2.preprocess_input(image)\n    return image\n\ndef read_labeled_tfrecord(example):\n    LABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string означает байтовую строку\n        \"class\": tf.io.FixedLenFeature([], tf.int64),  # [] означает отдельный элемент\n    }\n    example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT) # парсим отдельный пример в указанном формате\n    image = decode_image(example['image']) # преобразуем изображение к нужному нам формату\n    label = tf.cast(example['class'], tf.int32)\n    return image, label # возвращает набор данных пар (изображение, метка)\n\ndef read_unlabeled_tfrecord(example):\n    UNLABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string означает байтовую строку\n        \"id\": tf.io.FixedLenFeature([], tf.string),  # [] означает отдельный элемент\n        # класс отсутствует, задача этого конкурса - предсказать классы цветов для тестового набора данных\n    }\n    example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)\n    image = decode_image(example['image']) # преобразуем изображение к нужному нам формату\n    idnum = example['id']\n    return image, idnum # returns a dataset of image(s)\n\ndef load_dataset(filenames, labeled=True, ordered=False):\n    \"\"\"Читает из TFRecords. Для оптимальной производительности одновременное чтение из нескольких\n    файлов без учета порядка данных. Порядок не имеет значения, поскольку мы все равно будем перетасовывать данные\"\"\"\n\n    ignore_order = tf.data.Options() # Представляет параметры для tf.data.Dataset.\n    if not ordered:\n        ignore_order.experimental_deterministic = False # отключить порядок, увеличить скорость\n\n    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # автоматически чередует чтение из нескольких файлов\n    dataset = dataset.with_options(ignore_order) # использует данные сразу после их поступления, а не в исходном порядке\n    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)\n    # возвращает набор данных пар (изображение, метка), если метка = Истина, или пар (изображение, идентификатор), если метка = Ложь\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    flag = random.randint(1,3)\n    if flag == 1:\n        image = tf.image.random_flip_left_right(image, seed=SEED)\n    elif flag == 2:\n        image = tf.image.random_flip_up_down(image, seed=SEED)\n    else:\n        pass\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() # набор обучающих данных должен повторяться в течение нескольких эпох\n    dataset = dataset.shuffle(2048)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) #готовим следующий набор, пока предыдущий обучается\n    return dataset\n\ndef get_validation_dataset():\n    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=False)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache() # кешируем набор\n    dataset = dataset.prefetch(AUTO) #готовим следующий набор, пока предыдущий обучается\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) #готовим следующий набор, пока предыдущий обучается\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\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\nprint('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:03:18.358656Z","iopub.execute_input":"2023-12-19T06:03:18.358994Z","iopub.status.idle":"2023-12-19T06:03:18.375959Z","shell.execute_reply.started":"2023-12-19T06:03:18.358966Z","shell.execute_reply":"2023-12-19T06:03:18.375232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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 = .8\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":{"execution":{"iopub.status.busy":"2023-12-19T06:03:18.376951Z","iopub.execute_input":"2023-12-19T06:03:18.377205Z","iopub.status.idle":"2023-12-19T06:03:18.559362Z","shell.execute_reply.started":"2023-12-19T06:03:18.377179Z","shell.execute_reply":"2023-12-19T06:03:18.558693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndef get_model(use_model, weights):\n    # noisy-student\n    base_model = use_model(weights=weights, \n                      include_top=False, pooling='avg',\n                      input_shape=(*IMAGE_SIZE, 3))\n    x = base_model.output\n    predictions = Dense(104, activation='softmax')(x)\n    return Model(inputs=base_model.input, outputs=predictions)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:03:18.560283Z","iopub.execute_input":"2023-12-19T06:03:18.560573Z","iopub.status.idle":"2023-12-19T06:03:18.564904Z","shell.execute_reply.started":"2023-12-19T06:03:18.560544Z","shell.execute_reply":"2023-12-19T06:03:18.564195Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# DenseNet121","metadata":{}},{"cell_type":"code","source":"#with strategy.scope():    \n    #dense = get_model(DenseNet121, 'imagenet')\n        \n#dense.compile(\n #   optimizer='adam',\n #   loss = 'sparse_categorical_crossentropy',\n  #  metrics=['sparse_categorical_accuracy']\n#)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:03:18.566792Z","iopub.execute_input":"2023-12-19T06:03:18.567029Z","iopub.status.idle":"2023-12-19T06:03:51.136422Z","shell.execute_reply.started":"2023-12-19T06:03:18.567004Z","shell.execute_reply":"2023-12-19T06:03:51.135485Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#history = dense.fit(get_training_dataset(), \n #         steps_per_epoch=STEPS_PER_EPOCH, \n  #        epochs=EPOCHS, \n   #       callbacks=[lr_callback, ModelCheckpoint(filepath='dense.h5', monitor='val_loss',\n    #                              save_best_only=True)],workers = 3)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:03:51.137544Z","iopub.execute_input":"2023-12-19T06:03:51.137839Z","iopub.status.idle":"2023-12-19T06:23:10.049231Z","shell.execute_reply.started":"2023-12-19T06:03:51.137813Z","shell.execute_reply":"2023-12-19T06:23:10.047851Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# InceptoinV3","metadata":{}},{"cell_type":"code","source":"#with strategy.scope():    \n    #inception = get_model(InceptionV3, 'imagenet')\n        \n#inception.compile(\n    #optimizer='adam',\n    #loss = 'sparse_categorical_crossentropy',\n    #metrics=['sparse_categorical_accuracy']\n#)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:23:10.052943Z","iopub.execute_input":"2023-12-19T06:23:10.053226Z","iopub.status.idle":"2023-12-19T06:23:39.408407Z","shell.execute_reply.started":"2023-12-19T06:23:10.053198Z","shell.execute_reply":"2023-12-19T06:23:39.407412Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#history = inception.fit(get_training_dataset(), \n          #steps_per_epoch=STEPS_PER_EPOCH, \n         # epochs=EPOCHS, \n         # callbacks=[lr_callback, ModelCheckpoint(filepath='inceptionV3.h5', monitor='val_loss',\n                                  #save_best_only=True)],workers = 3)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T06:23:39.409467Z","iopub.execute_input":"2023-12-19T06:23:39.409709Z","iopub.status.idle":"2023-12-19T06:39:53.406974Z","shell.execute_reply.started":"2023-12-19T06:23:39.409682Z","shell.execute_reply":"2023-12-19T06:39:53.405897Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EfficientNetB7","metadata":{}},{"cell_type":"code","source":"with strategy.scope():    \n    efficientnet = get_model(EfficientNetB7, 'noisy-student')\n        \nefficientnet.compile(\n    optimizer='adam',\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy'])","metadata":{"execution":{"iopub.status.busy":"2023-12-19T07:18:46.643265Z","iopub.execute_input":"2023-12-19T07:18:46.644129Z","iopub.status.idle":"2023-12-19T07:19:21.913179Z","shell.execute_reply.started":"2023-12-19T07:18:46.644097Z","shell.execute_reply":"2023-12-19T07:19:21.91203Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"history = efficientnet.fit(get_training_dataset(), \n          steps_per_epoch=STEPS_PER_EPOCH, \n          epochs=EPOCHS, \n          callbacks=[lr_callback, ModelCheckpoint(filepath='/kaggle/working/efnetb7.h5', monitor='val_loss',\n                                  save_best_only=True)],workers = 3)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T07:19:21.914695Z","iopub.execute_input":"2023-12-19T07:19:21.914977Z","iopub.status.idle":"2023-12-19T07:49:44.2703Z","shell.execute_reply.started":"2023-12-19T07:19:21.914948Z","shell.execute_reply":"2023-12-19T07:49:44.26928Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Лучшая модель","metadata":{}},{"cell_type":"code","source":"#eval_dataset = get_validation_dataset()\n\n#eval_results = {\n    #'DenseNet121': dense.evaluate(eval_dataset)[1],\n    #'InceptionV3': inception.evaluate(eval_dataset)[1],\n    #'EfficientNetB7': efficientnet.evaluate(eval_dataset)[1],\n#}\n\n#fig, ax = plt.subplots()\n#ax.bar(list(eval_results.keys()), list(eval_results.values()))\n#ax.set_ylabel('sparse_categorical_accuracy')\n#ax.set_title('Метрика моделей на валидационных данных')\n#plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T07:50:18.733127Z","iopub.execute_input":"2023-12-19T07:50:18.733984Z","iopub.status.idle":"2023-12-19T07:51:21.952244Z","shell.execute_reply.started":"2023-12-19T07:50:18.733947Z","shell.execute_reply":"2023-12-19T07:51:21.951199Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_ds = get_test_dataset(ordered=True) \n\nprint('Вычисляем предсказания...')\ntest_images_ds = test_ds.map(lambda image, idnum: image)\nprobabilities = efficientnet.predict(test_images_ds)\npredictions = np.argmax(probabilities, axis=-1)\nprint(predictions)\n\nprint('Создание файла submission.csv...')\ntest_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()\ntest_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U') # все в одной партии\nnp.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')\nprint('Создан')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T07:54:54.055068Z","iopub.execute_input":"2023-12-19T07:54:54.055923Z","iopub.status.idle":"2023-12-19T07:55:27.744062Z","shell.execute_reply.started":"2023-12-19T07:54:54.055884Z","shell.execute_reply":"2023-12-19T07:55:27.743149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Сравнение моделей можно посмотреть в других версиях ноутбука, лучший оказалась модель EfficientNetB7**","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:f75c1954-d629-4e30-8405-f6df8804df9a.png)\n","metadata":{},"attachments":{"f75c1954-d629-4e30-8405-f6df8804df9a.png":{"image/png":"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"}}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}