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