04-CV/04-TransferLearning/TransferLearning.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Предобученные модели и трансферное обучение\n","\n","Обучение CNN может занять много времени, и для этой задачи требуется много данных. Тем не менее, большая часть времени тратится на обучения низкоуровневых фильтров, которые сеть использует для извлечения шаблонов из изображений. Возникает закономерный вопрос – можно ли использовать нейронную сеть, обученную на одном наборе данных, и адаптировать ее к классификации разных изображений без полноценного процесса обучения?\n","\n","Такой подход называется **трансферным обучением** (**transfer learning**), потому что мы переносим некоторые знания из одной нейросетевой модели в другую. В этом случае мы обычно начинаем с предобученной модели, натренированной на некотором большом датасете, таком как **ImageNet**. Эти модели уже могут хорошо извлекать различные признаки из общих изображений общего характера, и во многих случаях просто создание классификатора поверх этих извлеченных объектов может дать хороший результат."]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["import tensorflow as tf\n","from tensorflow import keras\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import os"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import os\n","os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'"]},{"cell_type":"markdown","metadata":{},"source":["### Датасет \"Кошки против собак\"\n","\n","В этом разделе мы решим реальную задачу классификации изображений кошек и собак. Мы будем использовать [датасет Cats vs. Dogs](https://www.kaggle.com/c/dogs-vs-cats) с Kaggle, который также можно загрузить [с сайта Microsoft](https://www.microsoft.com/en-us/download/details.aspx).\n","\n","Давайте загрузим этот набор данных и извлечем его в каталог 'data' (этот процесс может занять некоторое время!):"]},{"cell_type":"code","execution_count":3,"metadata":{"tags":[]},"outputs":[],"source":["if not os.path.exists('data/kagglecatsanddogs_5340.zip'):\n","    !wget -P data https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["import zipfile\n","if not os.path.exists('data/PetImages'):\n","    with zipfile.ZipFile('data/kagglecatsanddogs_5340.zip', 'r') as zip_ref:\n","        zip_ref.extractall('data')"]},{"cell_type":"markdown","metadata":{},"source":["Этот датасет очень похож на реальные данные, в том смысле, что в наборе есть некоторые поврежденные файлы. Автоматически проверим и удалим повреждённые файлы:"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Corrupt image or wrong format: data/PetImages/Cat\\10073.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\10125.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\10404.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\10501.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\10820.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\10874.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11083.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11086.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11095.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11210.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11397.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\1151.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11565.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11729.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11864.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11874.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\11935.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\12080.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\12235.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\12269.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\1267.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\1386.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\140.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\1757.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\1914.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\1936.jpg\n","Corrupt image or wrong format: data/PetImages/Cat\\1937.jpg\n","Corrupt image or wrong format: 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or wrong format: data/PetImages/Dog\\6032.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6059.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6213.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6238.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6305.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6318.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6430.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6500.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6503.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6555.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\663.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6718.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\6855.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\7112.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\7128.jpg\n","Corrupt image or wrong format: 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image or wrong format: data/PetImages/Dog\\8563.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\8641.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\8693.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\8715.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\8730.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\8889.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9026.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9043.jpg\n"]},{"name":"stderr","output_type":"stream","text":["c:\\winapp\\conda\\lib\\site-packages\\PIL\\TiffImagePlugin.py:822: UserWarning: Truncated File Read\n","  warnings.warn(str(msg))\n"]},{"name":"stdout","output_type":"stream","text":["Corrupt image or wrong format: data/PetImages/Dog\\9145.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9188.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9367.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9414.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9500.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9556.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9640.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9643.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9851.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9961.jpg\n","Corrupt image or wrong format: data/PetImages/Dog\\9967.jpg\n"]}],"source":["import glob\n","from PIL import Image\n","\n","def check_image(fn):\n","    try:\n","        im = Image.open(fn)\n","        im.verify()\n","        return im.format=='JPEG'\n","    except:\n","        return False\n","    \n","def check_image_dir(path):\n","    for fn in glob.glob(path):\n","        if not check_image(fn):\n","            print(\"Corrupt image or wrong format: {}\".format(fn))\n","            os.remove(fn)\n","\n","check_image_dir('data/PetImages/Cat/*.jpg')\n","check_image_dir('data/PetImages/Dog/*.jpg')"]},{"cell_type":"markdown","metadata":{},"source":["## Загрузка набора данных\n","\n","В предыдущих примерах мы загружали наборы данных, встроенные в Keras. Теперь мы собираемся иметь дело с нашим собственным набором данных, который нам нужно загрузить из некоторой директории.\n","\n","Размер датасетов изображений может быть довольно большим, и нельзя полагаться на то, что все данные смогут поместиться в памяти. Для загрузки наборов данных часто используют **генераторы**, которые считывают данные с диска по мере надобности и возвращают минибатчи, используемые при обучении.\n","\n","Для задачи классификации изображений, Keras содержит функцию 'image_dataset_from_directory', которая  загружает изображения из подкаталогов, соответствующих различным классам. Эта функция также заботится о масштабировании изображений, а также может разделять набор данных на обучающие и тестовые выборки:"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 24769 files belonging to 2 classes.\n","Using 19816 files for training.\n","Found 24769 files belonging to 2 classes.\n","Using 4953 files for validation.\n"]}],"source":["data_dir = 'data/PetImages'\n","batch_size = 64\n","ds_train = keras.preprocessing.image_dataset_from_directory(\n","    data_dir,\n","    validation_split = 0.2,\n","    subset = 'training',\n","    seed = 13,\n","    image_size = (224,224),\n","    batch_size = batch_size\n",")\n","ds_test = keras.preprocessing.image_dataset_from_directory(\n","    data_dir,\n","    validation_split = 0.2,\n","    subset = 'validation',\n","    seed = 13,\n","    image_size = (224,224),\n","    batch_size = batch_size\n",")"]},{"cell_type":"markdown","metadata":{},"source":["Важно использовать одно и то же значение `seed` для обоих вызовов, так как оно влияет на разделение изображений между обучающим и тестовым набором данных.\n","\n","Keras автоматически использует имена классов в соответствии с названиями подкаталогов:"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["['Cat', 'Dog']"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["ds_train.class_names"]},{"cell_type":"markdown","metadata":{},"source":["Полученные наборы данных могут быть непосредственно переданы в функцию `fit` для обучения модели. Они содержат как соответствующие изображения, так и метки. Вот пример того, как мы можем обращаться к данным:"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Training batch shape: features=(64, 224, 224, 3), labels=(64,)\n"]}],"source":["for x,y in ds_train:\n","    print(f\"Training batch shape: features={x.shape}, labels={y.shape}\")\n","    x_sample, y_sample = x,y\n","    break"]},{"cell_type":"markdown","metadata":{},"source":["Попробуем посмотреть на изображения из датасета:"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1080x216 with 10 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["def display_dataset(dataset, labels=None, n=10, classes=None):\n","    fig,ax = plt.subplots(1,n,figsize=(15,3))\n","    for i in range(n):\n","        ax[i].imshow(dataset[i])\n","        ax[i].axis('off')\n","        if classes is not None and labels is not None:\n","            ax[i].set_title(classes[labels[i][0]])\n","    \n","display_dataset(x_sample.numpy().astype(int),np.expand_dims(y_sample,1),classes=ds_train.class_names)"]},{"cell_type":"markdown","metadata":{},"source":["> **Примечание**: Все изображения в наборе данных представлены в виде тензоров с плавающей запятой с диапазоном 0-255. Прежде чем передать их в нейронную сеть, нам нужно масштабировать эти значения в диапазоне 0-1. При отображении изображений нам нужно либо сделать то же самое, либо преобразовать значения в тип 'int' (что мы и делаем в приведенном выше коде), чтобы показать `matplotlib`, что мы хотим отобразить исходное немасштабированное изображение."]},{"cell_type":"markdown","metadata":{},"source":["## Предобученные модели\n","\n","Для многих задач классификации изображений можно найти предварительно обученные модели нейронных сетей. Многие из этих моделей доступны в пространстве имен `keras.applications`, и еще больше моделей можно найти в Интернете по ключевым словам **model zoo** (*зоопарк моделей*). Давайте посмотрим, как можно загрузить и использовать простейшую модель VGG-16:"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n","553467904/553467096 [==============================] - 17s 0us/step\n","553476096/553467096 [==============================] - 17s 0us/step\n","Most probable class = [234]\n","Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n","40960/35363 [==================================] - 0s 1us/step\n","49152/35363 [=========================================] - 0s 1us/step\n"]},{"data":{"text/plain":["[[('n02106550', 'Rottweiler', 0.80610114),\n","  ('n02089078', 'black-and-tan_coonhound', 0.071738556),\n","  ('n02107312', 'miniature_pinscher', 0.02583869),\n","  ('n02107142', 'Doberman', 0.021859147),\n","  ('n02101006', 'Gordon_setter', 0.017213248)]]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["vgg = keras.applications.VGG16()\n","inp = keras.applications.vgg16.preprocess_input(x_sample[:1])\n","\n","res = vgg(inp)\n","print(f\"Most probable class = {tf.argmax(res,1)}\")\n","\n","keras.applications.vgg16.decode_predictions(res.numpy())"]},{"cell_type":"markdown","metadata":{},"source":["Здесь есть несколько важных вещей:\n","\n","* Перед передачей входных данных в большинство предобученных сетей их необходимо предобработать  определенным образом. В Keras это делается путем вызова соответствующей функции `preprocess_input`, которая получает пакет изображений и возвращает их обработанный результат. В случае VGG-16 изображения нормализуются, и из него вычитается некоторое предопределенное значение среднего для каждого из каналов. Это связано с тем, что VGG-16 изначально был обучен на изображениях с такой предварительной обработкой.\n","* Нейронная сеть применяется к входному пакету, и в результате мы получаем пакет 1000-элементных тензоров, которые показывают вероятность каждого класса. Мы можем найти наиболее вероятный номер класса, вызвав `argmax` на этом тензоре.\n","* Полученный результат является [номером класса `ImageNet`](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a). Чтобы понять, что это за класс, мы также можем использовать функцию `decode_predictions`, которая возвращает верхние $n$ классов вместе с их именами."]},{"cell_type":"markdown","metadata":{},"source":["Давайте также посмотрим на архитектуру сети VGG-16:"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"vgg16\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n","_________________________________________________________________\n","block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n","_________________________________________________________________\n","block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n","_________________________________________________________________\n","block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n","_________________________________________________________________\n","block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n","_________________________________________________________________\n","block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n","_________________________________________________________________\n","block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n","_________________________________________________________________\n","block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n","_________________________________________________________________\n","block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n","_________________________________________________________________\n","block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n","_________________________________________________________________\n","block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n","_________________________________________________________________\n","block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n","_________________________________________________________________\n","block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n","_________________________________________________________________\n","block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n","_________________________________________________________________\n","block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n","_________________________________________________________________\n","block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n","_________________________________________________________________\n","block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n","_________________________________________________________________\n","block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n","_________________________________________________________________\n","block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n","_________________________________________________________________\n","flatten (Flatten)            (None, 25088)             0         \n","_________________________________________________________________\n","fc1 (Dense)                  (None, 4096)              102764544 \n","_________________________________________________________________\n","fc2 (Dense)                  (None, 4096)              16781312  \n","_________________________________________________________________\n","predictions (Dense)          (None, 1000)              4097000   \n","=================================================================\n","Total params: 138,357,544\n","Trainable params: 138,357,544\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["vgg.summary()"]},{"cell_type":"markdown","metadata":{},"source":["### Вычисления на GPU \n","\n","Глубокие нейронные сети, такие как VGG-16 и другие более современные архитектуры требуют довольно много вычислительной мощности для работы. Имеет смысл использовать ускорители GPU, если они доступны. К счастью, Keras автоматически ускоряет вычисления на графическом процессоре при его наличии. Мы можем проверить, доступен ли GPU, используя следующий код:"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/plain":["[]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["tf.config.list_physical_devices('GPU')"]},{"cell_type":"markdown","metadata":{},"source":["В Google Colab для поддержки GPU необходимо в свойствах ноутбука поставить тип ускорителя GPU.\n","\n","## Извлечение VGG-признаков\n","\n","Пирамидальная архитектура VGG-16 устроена таким образом, что сначала идёт несколько свёрточных слоёв с пулингом, затем - слой `Flatten`, после чего поверх получившегося вектора используется несколько полносвязных слоёв классификаторв. Мы можем использовать VGG-16 для извлечения признаков из изображений, отбросив классификатор и используя полученный после свёрточных слоёв **вектор признаков**. В этом случае нам нужна модель без окончательных классификационных слоев, которую мы можем загрузить так:"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n","58892288/58889256 [==============================] - 2s 0us/step\n","58900480/58889256 [==============================] - 2s 0us/step\n","Shape after applying VGG-16: (7, 7, 512)\n"]},{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x1dbd6ae72e0>"]},"execution_count":10,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1080x216 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["vgg = keras.applications.VGG16(include_top=False)\n","\n","inp = keras.applications.vgg16.preprocess_input(x_sample[:1])\n","res = vgg(inp)\n","print(f\"Shape after applying VGG-16: {res[0].shape}\")\n","plt.figure(figsize=(15,3))\n","plt.imshow(res[0].numpy().reshape(-1,512))"]},{"cell_type":"markdown","metadata":{},"source":["Размерность вектора признаков составляет 7x7x512, но для того, чтобы визуализировать его, нам пришлось преобразовать его в 2D-форму.\n","\n","Теперь давайте используем эти признаки для классификации изображений. Возьмем некоторую часть изображений (50 минибатчей, в нашем случае) и предварительно вычислим их векторы признаков. Для этого мы можем использовать API Tensorflow **dataset**. Функция `map` принимает набор данных и применяет для его преобразования заданную функцию. Мы используем этот механизм для создания новых наборов данных, `ds_features_train` и `ds_features_test`, которые содержат извлеченные VGG признаки:"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["(64, 7, 7, 512) (64,)\n"]}],"source":["num = batch_size*50\n","ds_features_train = ds_train.take(50).map(lambda x,y : (vgg(x),y))\n","ds_features_test = ds_test.take(10).map(lambda x,y : (vgg(x),y))\n","\n","for x,y in ds_features_train:\n","    print(x.shape,y.shape)\n","    break"]},{"cell_type":"markdown","metadata":{},"source":["Мы использовали конструкцию `.take(50)` чтобы ограничить размер набора данных и ускорить нашу демонстрацию. Конечно, вы можете выполнить этот эксперимент с полным набором данных.\n","\n","Теперь, когда у нас есть набор данных с извлеченными признаками, мы можем обучить простой полносвязный классификатор, чтобы различать кошек и собак. Эта сеть примет вектор формы 7x7x512 и выдаст один выход, который соответствует либо собаке, либо кошке. Поскольку это двоичная классификация, мы используем функцию активации сигмоид, и функцию потерь `binary_crossentropy`."]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["50/50 [==============================] - 797s 16s/step - loss: 1.5099 - acc: 0.9103 - val_loss: 1.9361 - val_acc: 0.9312\n"]}],"source":["model = keras.models.Sequential([\n","    keras.layers.Flatten(input_shape=(7,7,512)),\n","    keras.layers.Dense(1,activation='sigmoid')\n","])\n","model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n","hist = model.fit(ds_features_train, validation_data=ds_features_test)"]},{"cell_type":"markdown","metadata":{},"source":["Результат отличный, мы можем отличить кошку от собаки почти c 95% вероятностью! Тем не менее, мы протестировали этот подход только на подмножестве всех изображений, потому что ручное извлечение признаков занимает много времени.\n","\n","## Трансферное обучение с помощью единой сети VGG\n","\n","Мы также можем избежать явного предварительного вычисления векторов признаков, используя предобученную сеть VGG-16 в нашей сети в качестве первого уровня. \n","\n","Прелесть архитектуры Keras заключается в том, что модель VGG-16, которую мы определили выше, также может быть использована в качестве слоя в другой нейронной сети! Нам просто нужно построить сеть с полносвязным классификатором поверх нее, а затем обучить всю сеть с помощью обратного распространения."]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","vgg16 (Functional)           (None, 7, 7, 512)         14714688  \n","_________________________________________________________________\n","flatten (Flatten)            (None, 25088)             0         \n","_________________________________________________________________\n","dense (Dense)                (None, 1)                 25089     \n","=================================================================\n","Total params: 14,739,777\n","Trainable params: 25,089\n","Non-trainable params: 14,714,688\n","_________________________________________________________________\n"]}],"source":["model = keras.models.Sequential()\n","model.add(keras.applications.VGG16(include_top=False,input_shape=(224,224,3)))\n","model.add(keras.layers.Flatten())\n","model.add(keras.layers.Dense(1,activation='sigmoid'))\n","\n","model.layers[0].trainable = False\n","\n","model.summary()"]},{"cell_type":"markdown","metadata":{},"source":["Эта модель представляет собой единую сеть классификации, которая принимает изображение и возвращает класс. Однако сложность заключается в том, что мы хотим, чтобы VGG16 действовал как экстрактор признаков, а не переобучался. Таким образом, нам нужно **заморозить веса** сверточного экстрактора признаков. Мы можем получить доступ к первому уровню сети, вызвав `model.layers[0]`, и нам просто нужно установить для свойства `trainable` значение `False`.\n","\n","> **Примечание**: Замораживание весов экстрактора признаков необходимо, потому что в противном случае необученный слой классификатора может разрушить исходные предварительно обученные веса сверточного экстрактора.\n","\n","Вы можете заметить, что, в то время как общее количество параметров в нашей сети составляет около 15 миллионов, мы обучаем только 25 тысяч параметров. Все остальные параметры сверточных фильтров верхнего уровня предварительно обучены и зафиксированы. Это хорошо, потому что мы можем точно настроить меньшее количество параметров с меньшим количеством примеров.\n","\n","Теперь мы обучим нашу сеть и посмотрим на получившуюся точность. Время выполнения этого кода может быть значительным!"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["310/310 [==============================] - 265s 716ms/step - loss: 0.9917 - acc: 0.9512 - val_loss: 0.8156 - val_acc: 0.9671\n"]}],"source":["model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n","hist = model.fit(ds_train, validation_data=ds_test)"]},{"cell_type":"markdown","metadata":{},"source":["Похоже, мы получили достаточно точный классификатор кошек против собак! \n","\n","## Сохранение и загрузка модели\n","\n","После того, как мы обучили модель, мы можем сохранить архитектуру модели и обученные веса в файл для будущего использования:"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["INFO:tensorflow:Assets written to: data/cats_dogs.tf/assets\n"]}],"source":["model.save('data/cats_dogs.tf')"]},{"cell_type":"markdown","metadata":{},"source":["Затем мы можем загрузить модель из файла в любое время. Записывать модель полезно также и во время обучения, чтобы в случае возникновения проблем (отключения электричества) не потерять всю проделанную работу."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["model = keras.models.load_model('data/cats_dogs.tf')"]},{"cell_type":"markdown","metadata":{},"source":["## Тонкая настройка фичеризатора\n","\n","В предыдущем разделе мы обучили последний слой классификатора на нашем собственном наборе данных. Мы не переобучали экстрактор признаков VGG, и наша модель полагалась на паттерны, которым модель научилась на данных ImageNet. Если же наши объекты визуально сильно отличаются от обычных изображений ImageNet (например, мы работаем с рентгеновскими снимками), эта комбинация паттернов может работать не лучшим образом. В этом случае имеет смысл начать тренировать и сверточные слои.\n","\n","Для этого мы можем разморозить параметры сверточного фильтра, которые мы ранее заморозили. \n","\n","> **Замечание:** Важно сначала заморозить параметры и выполнить несколько эпох тренировки, чтобы стабилизировать веса в классификационном слое. Если сразу начать обучение сквозной сети с незамороженными параметрами, то большие ошибки на начальном этапе скорее всего разрушат предобученные веса в сверточных слоях.\n","\n","Наша сверточная модель VGG-16 расположена внутри первого слоя, и сама она состоит из множества слоев. Мы можем взглянуть на его структуру: "]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"vgg16\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n","_________________________________________________________________\n","block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n","_________________________________________________________________\n","block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n","_________________________________________________________________\n","block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n","_________________________________________________________________\n","block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n","_________________________________________________________________\n","block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n","_________________________________________________________________\n","block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n","_________________________________________________________________\n","block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n","_________________________________________________________________\n","block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n","_________________________________________________________________\n","block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n","_________________________________________________________________\n","block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n","_________________________________________________________________\n","block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n","_________________________________________________________________\n","block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n","_________________________________________________________________\n","block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n","_________________________________________________________________\n","block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n","_________________________________________________________________\n","block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n","_________________________________________________________________\n","block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n","_________________________________________________________________\n","block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n","_________________________________________________________________\n","block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n","=================================================================\n","Total params: 14,714,688\n","Trainable params: 0\n","Non-trainable params: 14,714,688\n","_________________________________________________________________\n"]}],"source":["model.layers[0].summary()"]},{"cell_type":"markdown","metadata":{},"source":["Разморозим все свёрточные слои:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model.layers[0].trainable = True"]},{"cell_type":"markdown","metadata":{},"source":["Размораживать сразу все слои – не самая лучшая идея. Сначала мы можем разморозить только несколько последних слоев, потому что они содержат шаблоны более высокого уровня, которые имеют отношение к нашим изображениям. Например, для начала мы можем заморозить все слои, кроме последних 4: "]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","vgg16 (Functional)           (None, 7, 7, 512)         14714688  \n","_________________________________________________________________\n","flatten (Flatten)            (None, 25088)             0         \n","_________________________________________________________________\n","dense (Dense)                (None, 1)                 25089     \n","=================================================================\n","Total params: 14,739,777\n","Trainable params: 7,104,513\n","Non-trainable params: 7,635,264\n","_________________________________________________________________\n"]}],"source":["for i in range(len(model.layers[0].layers)-4):\n","    model.layers[0].layers[i].trainable = False\n","model.summary()"]},{"cell_type":"markdown","metadata":{},"source":["Обратите внимание, что количество обучаемых параметров значительно увеличилось, но оно по-прежнему составляет около 50% от всех параметров.\n","\n","После размораживания мы можем сделать еще несколько эпох обучения (в нашем примере мы сделаем только одну). Вы также можете выбрать более низкую скорость обучения, чтобы свести к минимуму влияние на предварительно обученные веса. Тем не менее, даже при низкой скорости обучения вы можете ожидать, что точность упадет в начале тренировки, пока, наконец, не достигнет немного более высокого уровня, чем в случае фиксированных весов.\n","\n","> **Примечание:** Это обучение происходит намного медленнее, потому что нам нужно распространять градиенты обратно через много слоев сети!"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["310/310 [==============================] - 201s 645ms/step - loss: 0.5270 - acc: 0.9776 - val_loss: 1.4132 - val_acc: 0.9653\n"]}],"source":["hist = model.fit(ds_train, validation_data=ds_test)"]},{"cell_type":"markdown","metadata":{},"source":["Мы не будем тратить время и обучать модель до конца, но вы можете поэкспериментировать и посмотреть, удастся ли вам получить более высокую точность на тестовом датасете. Помните, что более мощная модель с большим количеством параметров склонна к переобучению!"]},{"cell_type":"markdown","metadata":{},"source":["## Другие модели компьютерного зрения\n","\n","VGG-16 является одной из простейших архитектур свёрточных сетей для компьютерного зрения. Keras предоставляет гораздо больше предварительно обученных сетей. Наиболее часто используемыми из них являются архитектуры **ResNet**, разработанная Microsoft, и **Inception** от Google. Например, давайте рассмотрим архитектуру простейшей модели ResNet-50.\n","\n","> ResNet — это семейство моделей с разной глубиной, можно попробовать поэкспериментировать с ResNet-152, если хотите посмотреть, как выглядит действительно глубокая модель."]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"resnet50\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_3 (InputLayer)            [(None, 224, 224, 3) 0                                            \n","__________________________________________________________________________________________________\n","conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_3[0][0]                    \n","__________________________________________________________________________________________________\n","conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n","__________________________________________________________________________________________________\n","conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n","__________________________________________________________________________________________________\n","conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n","__________________________________________________________________________________________________\n","pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n","__________________________________________________________________________________________________\n","pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n","__________________________________________________________________________________________________\n","conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n","__________________________________________________________________________________________________\n","conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n","__________________________________________________________________________________________________\n","conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n","                                                                 conv2_block1_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n","__________________________________________________________________________________________________\n","conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n","__________________________________________________________________________________________________\n","conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n","                                                                 conv2_block2_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n","__________________________________________________________________________________________________\n","conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n","                                                                 conv2_block3_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n","                                                                 conv3_block1_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n","                                                                 conv3_block2_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n","                                                                 conv3_block3_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n","                                                                 conv3_block4_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n","                                                                 conv4_block1_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n","                                                                 conv4_block2_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n","                                                                 conv4_block3_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n","                                                                 conv4_block4_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n","                                                                 conv4_block5_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n","                                                                 conv4_block6_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n","                                                                 conv5_block1_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n","                                                                 conv5_block2_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n","                                                                 conv5_block3_3_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n","__________________________________________________________________________________________________\n","avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]                   \n","==================================================================================================\n","Total params: 25,636,712\n","Trainable params: 25,583,592\n","Non-trainable params: 53,120\n","__________________________________________________________________________________________________\n"]}],"source":["resnet = keras.applications.ResNet50()\n","resnet.summary()"]},{"cell_type":"markdown","metadata":{},"source":["Как видите, модель содержит те же привычные строительные блоки: сверточные слои, пулинговые слои и конечный плотный классификатор. Мы можем использовать эту модель точно так же, как мы использовали VGG-16 для трансферного обучения. Вы можете попробовать поэкспериментировать с приведенным выше кодом, используя различные модели ResNet в качестве базовой модели, и посмотреть, как изменяется точность."]},{"cell_type":"markdown","metadata":{},"source":["## Выводы\n","\n","Используя transfer learning, мы смогли быстро обучить классификатор для нашей задачи и достичь высокой точности со сравнительно небольшим количеством примеров. Однако, этот пример был не совсем справедливым, потому что оригинальная сеть VGG-16 была предварительно обучена распознавать и кошек, и собак - и мы просто повторно использовали большинство шаблонов, которые уже присутствовали в сети. Скорее всего на более экзотических объектах, таких, как детали на производственной линии на заводе, рентгеновские снимки иди или различные листья деревьев, можно ожидать сущесвенно более низкой точности."]}],"metadata":{"kernelspec":{"display_name":"Python 3.9.12 ('base')","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12"},"vscode":{"interpreter":{"hash":"d21c24a7952cc9fa93a33c3da71b373ffe761d6ab0c9cc9b2153e31da51ffb58"}}},"nbformat":4,"nbformat_minor":4}