82_standardize_image_pixels_keras.py

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 

# example of using ImageDataGenerator to standardize images
from keras.datasets import mnist
from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.preprocessing.image import ImageDataGenerator

# load dataset
(trainX, trainY), (testX, testY) = mnist.load_data()

# reshape dataset to have a single channel
width, height, channels = trainX.shape[1], trainX.shape[2], 1
trainX = trainX.reshape((trainX.shape[0], width, height, channels))
testX = testX.reshape((testX.shape[0], width, height, channels))

# one hot encode target values
trainY = to_categorical(trainY)
testY = to_categorical(testY)

# create generator to standardize images
datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True)

# calculate mean on training dataset
datagen.fit(trainX)

# prepare an iterators to scale images
train_iterator = datagen.flow(trainX, trainY, batch_size=64)
test_iterator = datagen.flow(testX, testY, batch_size=64)
print('Batches train=%d, test=%d' % (len(train_iterator), len(test_iterator)))

# define model
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(width, height, channels)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

# compile model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# fit model with generator
model.fit_generator(train_iterator, steps_per_epoch=len(train_iterator), epochs=5)

# evaluate model
_, acc = model.evaluate_generator(test_iterator, steps=len(test_iterator), verbose=0)
print('Test Accuracy: %.3f' % (acc * 100))