data_augumentation_example.py

# -*- coding: utf-8 -*-
"""
Created on Wed Jul 25 10:52:21 2018

@author: xuwe421
"""

# Standardize images across the dataset, mean=0, stdev=1
from keras.datasets import cifar100
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot
from keras import backend as K
K.set_image_dim_ordering('th')
# load data
(X_train, y_train), (X_test, y_test) = cifar100.load_data()
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 3, 32, 32)
X_test = X_test.reshape(X_test.shape[0], 3, 32, 32)
# convert from int to float
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# define data preparation
datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True,  channel_shift_range=10)
# fit parameters from data
datagen.fit(X_train)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9):
	# create a grid of 3x3 images
	for i in range(0, 9):
		pyplot.subplot(330 + 1 + i)
		pyplot.imshow(X_batch[i].reshape( 32, 32,3), cmap=pyplot.get_cmap('gray'))
	# show the plot
	pyplot.show()
	break