Загрузил все лабораторные работы
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Dogs vs. Cats"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from tensorflow import keras\n",
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"from tensorflow.keras.models import Model\n",
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"from tensorflow.keras.applications.vgg16 import VGG16\n",
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"from tensorflow.keras.applications.vgg16 import preprocess_input\n",
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"from tensorflow.keras.preprocessing.image import load_img, img_to_array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"IMG_SIZE = (224, 224) # размер входного изображения сети"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Функции загрузки данных"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import re\n",
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"from random import shuffle\n",
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"from glob import glob\n",
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"\n",
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"train_files = glob('../input/train/*.jpg')\n",
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"test_files = glob('../input/test/*.jpg')\n",
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"\n",
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"# загружаем входное изображение и предобрабатываем\n",
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"def load_image(path, target_size=IMG_SIZE):\n",
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" img = load_img(path, target_size=target_size) # загрузка и масштабирование изображения\n",
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" array = img_to_array(img)\n",
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" return preprocess_input(array) # предобработка для VGG16\n",
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"\n",
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"# генератор для последовательного чтения обучающих данных с диска\n",
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"def fit_generator(files, batch_size=32):\n",
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" while True:\n",
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" shuffle(files)\n",
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" for k in range(len(files) // batch_size):\n",
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" i = k * batch_size\n",
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" j = i + batch_size\n",
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" if j > len(files):\n",
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" j = - j % len(files)\n",
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" x = np.array([load_image(path) for path in files[i:j]])\n",
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" y = np.array([1. if re.match('.*/dog\\.\\d', path) else 0. for path in files[i:j]])\n",
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" yield (x, y)\n",
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"\n",
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"# генератор последовательного чтения тестовых данных с диска\n",
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"def predict_generator(files):\n",
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" while True:\n",
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" for path in files:\n",
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" yield np.array([load_image(path)])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Визуализируем примеры для обучения"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"from matplotlib import pyplot as plt\n",
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"fig = plt.figure(figsize=(20, 20))\n",
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"for i, path in enumerate(train_files[:10], 1):\n",
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" subplot = fig.add_subplot(i // 5 + 1, 5, i)\n",
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" plt.imshow(plt.imread(path));\n",
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" subplot.set_title('%s' % path.split('/')[-1]);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Загружаем предобученную модель"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# base_model - объект класса keras.models.Model (Functional Model)\n",
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"base_model = VGG16(include_top = False,\n",
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" weights = 'imagenet',\n",
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" input_shape = (IMG_SIZE[0], IMG_SIZE[1], 3))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# фиксируем все веса предобученной сети\n",
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"for layer in base_model.layers:\n",
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" layer.trainable = False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"base_model.summary()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Добавляем полносвязный слой"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = base_model.layers[-5].output\n",
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"x = keras.layers.Flatten()(x)\n",
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"x = keras.layers.Dense(1, # один выход\n",
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" activation='sigmoid', # функция активации \n",
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" kernel_regularizer=keras.regularizers.l1(1e-4))(x)\n",
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"model = Model(inputs=base_model.input, outputs=x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Выводим архитектуру модели"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.summary()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Компилируем модель и запускаем обучение"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.compile(optimizer='adam', \n",
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" loss='binary_crossentropy', # функция потерь binary_crossentropy (log loss\n",
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" metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"shuffle(train_files) # перемешиваем обучающую выборку\n",
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"\n",
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"train_val_split = 100 # число изображений в валидационной выборке\n",
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"\n",
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"validation_data = next(fit_generator(train_files[:train_val_split], train_val_split))\n",
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"\n",
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"# запускаем процесс обучения\n",
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"model.fit_generator(fit_generator(train_files[train_val_split:]), # данные читаем функцией-генератором\n",
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" steps_per_epoch=10, # число вызовов генератора за эпоху\n",
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" epochs=100, # число эпох обучения\n",
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" validation_data=validation_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.save('cats-dogs-vgg16.hdf5')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Предсказания на проверочной выборке"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pred = model.predict_generator(predict_generator(test_files), len(test_files), max_queue_size=500)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"from matplotlib import pyplot as plt\n",
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"fig = plt.figure(figsize=(20, 20))\n",
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"for i, (path, score) in enumerate(zip(test_files[80:][:10], pred[80:][:10]), 1):\n",
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" subplot = fig.add_subplot(i // 5 + 1, 5, i)\n",
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" plt.imshow(plt.imread(path));\n",
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" subplot.set_title('%.3f' % score);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Готовим данные для сабмита"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('submit.txt', 'w') as dst:\n",
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" dst.write('id,label\\n')\n",
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" for path, score in zip(test_files, pred):\n",
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" dst.write('%s,%f\\n' % (re.search('(\\d+)', path).group(0), score))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# LogLoss = 1.04979"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.2"
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},
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"widgets": {
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"state": {},
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"version": "1.1.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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