mixup: Data-Dependent Data Augmentation
mixup: Data-Dependent Data Augmentation
Python 机器学习
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详细介绍
mixup generator
This is an implementation of the mixup algorithm.
Mixup
Mixup [1] is a kind of image augmentation methods, which augments training data by mixing-up both of training images and labels by linear interpolation with weight lambda
:
X = lambda * X1 + (1 - lambda) * X2,
y = lambda * y1 + (1 - lambda) * y2,
where lambda
is drawn from the Beta distribution Be(alpha, alpha)
, and alpha
is a hyperparameter.
Please check mixup_generator.py for implementation details.
Usage
Get MixupGenerator
:
from mixup_generator import MixupGenerator
training_generator = MixupGenerator(x_train, y_train, batch_size=batch_size, alpha=0.2)()
x_train
: training images (#images x h x w x c)y_train
: labels as one hot vectors (#images x #classes or list of #images x #classes for multi-task training)batch_size
: batch sizealpha
: hyper parameter;lambda
is drawn from the beta distributionBe(alpha, alpha)
.
Get mixed training batch:
x, y = next(generator)
Please refer to test_mixup.ipynb to see how it works.
Use MixupGenerator with fit_generator in Keras
It is very easy to use MixupGenerator
in training if you are using Keras; Get MixupGenerator
, and then fit model by fit_generator
:
model.fit_generator(generator=training_generator,
steps_per_epoch=x_train.shape[0] // batch_size,
validation_data=(x_test, y_test),
epochs=epochs, verbose=1,
callbacks=callbacks)
Please refer to cifar10_resnet.py for complete example, which is imported from official Keras examples.
MixupGenerator with ImageDataGenerator
The MixupGenerator
can be combined with keras.preprocessing.image.ImageDataGenerator
for further image augmentation:
datagen = ImageDataGenerator(
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True)
training_generator = MixupGenerator(x_train, y_train, batch_size=batch_size, alpha=0.2, datagen=datagen)()
In this case, the mixed-up training images are further augmented by ImageDataGenerator.
Mixup with Random Erasing
Random Erasing [2] is a kind of image augmentation methods for convolutional neural networks (CNN). It tries to regularize models using training images that are randomly masked with random values.
Please refer to this repository for the details of algorithm and its implementation.
Mixup can be combined with Random Erasing via ImageDataGenerator by:
from random_eraser import get_random_eraser
datagen = ImageDataGenerator(
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
preprocessing_function=get_random_eraser(v_l=0, v_h=255))
generator = MixupGenerator(x_train, y_train, alpha=1.0, datagen=datagen)()
The augmented images become like this:
Results
(!Only a single trial)
Without mixup:
Test loss: 0.862150103855
Test accuracy: 0.8978
With mixup alpha = 0.2:
Test loss: 0.510702615929
Test accuracy: 0.9117
With mixup alpha = 0.5:
Test loss: 0.48489781661
Test accuracy: 0.9181
With mixup alpha = 1.0:
Test loss: 0.493033925915
Test accuracy: 0.9167
References
[1] H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, "mixup: Beyond Empirical Risk Minimization," in arXiv:1710.09412, 2017.
[2] Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, "Random Erasing Data Augmentation," in arXiv:1708.04896, 2017.