from keras.datasets import mnist
from keras.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)
# confirm scale of pixels
print('Train min=%.3f, max=%.3f' % (trainX.min(), trainX.max()))
print('Test min=%.3f, max=%.3f' % (testX.min(), testX.max()))
# create generator (1.0/255.0 = 0.003921568627451)
datagen = ImageDataGenerator(rescale=1.0/255.0)
# 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)))
# confirm the scaling works
batchX, batchy = train_iterator.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max()))
# 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))