mnist_CNN.py

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils

def analyze():

	batch_size = 128
	nb_classes = 10
	nb_epoch = 3

	# input image dimensions
	img_rows, img_cols = 28, 28
	# number of convolutional filters to use
	nb_filters = 32
	# size of pooling area for max pooling
	nb_pool = 2
	# convolution kernel size
	nb_conv = 3
	# data threshold
	train_counts = 10000
	testing_counts = 1000

	# the data, shuffled and split between train and test sets
	(X_train, y_train), (X_test, y_test) = mnist.load_data()

	# feeding smaller dataset
	X_train = np.delete(X_train, np.s_[train_counts:], axis=0)
	y_train = np.delete(y_train, np.s_[train_counts:], axis=0)
	X_test = np.delete(X_test, np.s_[testing_counts:], axis=0)
	y_test = np.delete(y_test, np.s_[testing_counts:], axis=0)

	X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
	X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
	X_train = X_train.astype('float32')
	X_test = X_test.astype('float32')
	X_train /= 255
	X_test /= 255
	print('X_train shape:', X_train.shape)
	print(X_train.shape[0], 'train samples')
	print(X_test.shape[0], 'test samples')

	# convert class vectors to binary class matrices
	Y_train = np_utils.to_categorical(y_train, nb_classes)
	Y_test = np_utils.to_categorical(y_test, nb_classes)

	model = Sequential()

	model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
	                        border_mode='valid',
	                        input_shape=(1, img_rows, img_cols)))
	model.add(Activation('relu'))
	model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
	model.add(Activation('relu'))
	model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
	model.add(Dropout(0.25))

	model.add(Flatten())
	model.add(Dense(128))
	model.add(Activation('relu'))
	model.add(Dropout(0.5))
	model.add(Dense(nb_classes))
	model.add(Activation('softmax'))

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

	model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
	          verbose=1, validation_data=(X_test, Y_test))
	score = model.evaluate(X_test, Y_test, verbose=0)
	print('Test score:', score[0])
	print('Test accuracy:', score[1])

analyze()