python - 使用自定义数据生成器拟合具有大量数据的 Keras 模型

Question

我正在尝试用大量数据拟合我的 Keras 模型。

为此，我使用自定义数据生成器和 model.fit_generator 函数。

但是，我似乎无法理解我这样做是否正确。

这是我所拥有的:

from os.path import join

import cv2
import numpy as np
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau

# The function returns a list of image names from folder
from data.preprocessing import get_list_of_images


class VGG19(object):
    def __init__(self, weights_path=None, train_folder='data/train', validation_folder='data/val'):
        self.weights_path = weights_path
        self.model = self._init_model()

        if weights_path:
            self.model.load_weights(weights_path)
        else:
            self.datagen = self._init_datagen()
            self.train_folder = train_folder
            self.validation_folder = validation_folder
            self.model.compile(
                loss='binary_crossentropy',
                optimizer='adam',
                metrics=['accuracy']
            )

    def fit(self, batch_size=32, nb_epoch=10):
        self.model.fit_generator(
            self._generate_data_from_folder(self.train_folder), 32,
            nb_epoch,
            verbose=1,
            callbacks=[
                TensorBoard(log_dir='./logs', write_images=True),
                ModelCheckpoint(filepath='weights.{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_loss'),
                ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=0.001)
            ],
            validation_data=self._generate_data_from_folder(self.validation_folder),
            nb_val_samples=32
        )

    def predict(self, X, batch_size=32, verbose=1):
        return self.model.predict(X, batch_size=batch_size, verbose=verbose)

    def predict_proba(self, X, batch_size=32, verbose=1):
        return self.model.predict_proba(X, batch_size=batch_size, verbose=verbose)

    def _init_model(self):
        model = Sequential()
        # model definition goes here...
        return model

    def _init_datagen(self):
        return ImageDataGenerator(
            featurewise_center=True,
            samplewise_center=False,
            featurewise_std_normalization=True,
            samplewise_std_normalization=False,
            zca_whitening=False,
            rotation_range=20,
            width_shift_range=0.2,
            height_shift_range=0.2,
            horizontal_flip=True,
            vertical_flip=True
        )

    def _generate_data_from_folder(self, folder_path):
        while 1:
            images = get_list_of_images(folder_path)

            for image_path in images:
                x = cv2.imread(join(folder_path, image_path))
                y = 0 if image_path.split('.')[0] == 'dog' else 1

                yield (x, y)

---

我的数据集包含名称如下的图像:

• cat.[number].jpg，即:cat.124.jpg

• dog.[number].jpg，即:dog.64.jpg

所以，基本上，我正在尝试训练一个模型来执行二元猫狗分类。

---

我的 _generate_data_from_folder 函数是否正确实现了小批量优化？

如何将 ImageDataGenerator 的用法添加到我的 _generate_data_from_folder 函数(来自 _init_datagen 函数)？

Answer 1

好的，这是我开始工作的项目最终版本的 github 链接:

https://github.com/yakovenkodenis/dogs-vs-cats-kaggle

希望对某人有帮助