def load_data():
# load mnist data
(source_data, _), (test_source_data, _) = mnist.load_data()
# pad with zeros 28x28 MNIST image to become 32x32
# svhn is 32x32
source_data = np.pad(source_data, ((0, 0), (2, 2), (2, 2)),
'constant',
constant_values=0)
test_source_data = np.pad(test_source_data, ((0, 0), (2, 2), (2, 2)),
'constant',
constant_values=0)
# input image dimensions
# we assume data format "channels_last"
rows = source_data.shape[1]
cols = source_data.shape[2]
channels = 1
# reshape images to row x col x channels
# for CNN output/validation
size = source_data.shape[0]
source_data = source_data.reshape(size, rows, cols, channels)
size = test_source_data.shape[0]
test_source_data = test_source_data.reshape(size, rows, cols, channels)
# load SVHN data
target_data = loadmat("datasets/train_32x32.mat")
test_target_data = loadmat("datasets/test_32x32.mat")
# source data, target data, test_source data
data = (source_data, target_data, test_source_data, test_target_data)
filenames = ('mnist_test_source.png', 'svhn_test_target.png')
titles = ('MNIST test source images', 'SVHN test target images')
return other_utils.load_data(data, titles, filenames)
def load_cifar10():
(B_data, _), (test_B_data, _) = cifar10.load_data()
A_data = other_utils.rgb2gray(B_data)
test_A_data = other_utils.rgb2gray(test_B_data)
A_data = A_data[:, :, :, np.newaxis]
test_A_data = test_A_data[:, :, :, np.newaxis]
data = (A_data, B_data, test_A_data, test_B_data)
titles = ('CIFAR10 test_A_data images', 'CIFAR10 test_A_data images')
return other_utils.load_data(data, titles)
def load_mnist_svhn():
(A_data, _), (test_A_data, _) = mnist.load_data()
A_data = np.pad(A_data, ((0, 0), (2, 2), (2, 2)),
'constant',
constant_values=0)
test_A_data = np.pad(test_A_data, ((0, 0), (2, 2), (2, 2)),
'constant',
constant_values=0)
A_data = A_data[:, :, :, np.newaxis]
test_A_data = test_A_data[:, :, :, np.newaxis]
B_data_mat = io.loadmat(
"C:/Users/ysp/Desktop/Deep Learning/train_32x32.mat")
test_B_mat = io.loadmat(
"C:/Users/ysp/Desktop/Deep Learning/test_32x32.mat")
B_data = other_utils.loadmat(B_data_mat)
test_B_data = other_utils.loadmat(test_B_mat)
data = (A_data, B_data, test_A_data, test_B_data)
titles = ('MNIST test_A_data images', 'SVHN test_B_data images')
return other_utils.load_data(data, titles)
def load_data():
# load CIFAR10 data
(target_data, _), (test_target_data, _) = cifar10.load_data()
# input image dimensions
# we assume data format "channels_last"
rows = target_data.shape[1]
cols = target_data.shape[2]
channels = target_data.shape[3]
# convert color train and test images to gray
source_data = other_utils.rgb2gray(target_data)
test_source_data = other_utils.rgb2gray(test_target_data)
# reshape images to row x col x channel for CNN input
source_data = source_data.reshape(source_data.shape[0], rows, cols, 1)
test_source_data = test_source_data.reshape(test_source_data.shape[0],
rows, cols, 1)
# source data, target data, test_source data
data = (source_data, target_data, test_source_data, test_target_data)
filenames = ('cifar10_test_source.png', 'cifar10_test_target.png')
titles = ('CIFAR10 test source images', 'CIFAR10 test target images')
return other_utils.load_data(data, titles, filenames)