files = os.listdir()
file_list = []
for i in files:
if (i.find("idx") != -1):
file_list.append(i)
'''
##############################Check print_meta function#################################################################
'''
for name in (file_list):
mnist.print_meta(name)
'''
##############################Check load_images, load_labels, and save_images#########################################################
num_images = 20
start_pos = 0
image_file = 'train-images.idx3-ubyte'
label_file = 'train-labels.idx1-ubyte'
output_path = file_path + '\\Git_Repos\\jc2\\MNIST_Load\\Images\\test_image_'
mnist.print_meta(image_file)
image_data = mnist.load_images(image_file, num_images, start_pos)
label_data = mnist.load_labels(label_file, num_images, start_pos)
mnist.save_images(image_data, output_path)
print (label_data)
num_images = 5000 # Number of training images
num_timages = 5000 # Number of test images
#################################### Set Rozell params ################################################
lamb = 0.0
tau = 10.0
delta = 0.01
u_stop = 0.001
t_type = "S"
alpha = 0.85
############################ Load all MNIST images and labels #########################################
image_data = mnist.load_images(image_file, num_images, 5000)
label_data = mnist.load_labels(label_file, num_images, 5000)
timage_data = mnist.load_images(timage_file, num_timages)
tlabel_data = mnist.load_labels(tlabel_file, num_timages)
if len(image_data) != len(label_data):
print("TRAINING DATA ERROR: Num of images doesn't match num of labels!!!!!")
if len(timage_data) != len(tlabel_data):
print("TEST DATA ERROR: Num of images doesn't match num of labels!!!!!")
############################### Build training data ###################################
images = []
onehot_labels = []
numeric_labels = []
for i in range(len(image_data)):
image_data[i] = image_data[i].astype(float)
image_data[i] /= 255.0
