Ejemplo n.º 1
0
def load_valid_dataset(dataset_type):
    global valid_dataset

    if dataset_type == 'cifar-10':
        valid_dataset_filename = '..' + os.sep + 'data' + os.sep + 'cifar-10.pickle'

        (train_dataset, train_labels), \
        (valid_dataset, valid_labels), \
        (test_dataset, test_labels) = load_data.reformat_data_cifar10(valid_dataset_filename)

        del train_dataset, train_labels, test_dataset, test_labels

    elif dataset_type == 'imagenet-100':
        valid_dataset_fname = 'imagenet_small' + os.sep + 'imagenet_small_valid_dataset'
        valid_label_fname = 'imagenet_small' + os.sep + 'imagenet_small_valid_labels'

        fp1 = np.memmap(valid_dataset_fname,
                        dtype=np.float32,
                        mode='r',
                        offset=np.dtype('float32').itemsize * 0,
                        shape=(valid_size, image_size, image_size,
                               num_channels))
        fp2 = np.memmap(valid_label_fname,
                        dtype=np.int32,
                        mode='r',
                        offset=np.dtype('int32').itemsize * 0,
                        shape=(valid_size, 1))
        v_dataset = fp1[:, :, :, :]
        v_labels = fp2[:]

        v_dataset, v_labels = load_data.reformat_data_imagenet_with_memmap_array(
            v_dataset, v_labels, silent=True)

    del v_labels
    valid_dataset = v_dataset
Ejemplo n.º 2
0
    beta = 1e-3

    try:
        opts,args = getopt.getopt(
            sys.argv[1:],"",['data=',"log_suffix="])
    except getopt.GetoptError as err:
        print('<filename>.py --data= --log_suffix=')

    if len(opts)!=0:
        for opt,arg in opts:
            if opt == '--data':
                data_filename = arg
            if opt == '--log_suffix':
                log_suffix = arg
    if dataset_type=='cifar-10':
        (full_train_dataset,full_train_labels),(valid_dataset,valid_labels),(test_dataset,test_labels)=load_data.reformat_data_cifar10(data_filename)

    graph = tf.Graph()

    # Value logger will log info used to calculate policies
    test_logger = logging.getLogger('test_logger_'+log_suffix)
    test_logger.setLevel(logging.INFO)
    fileHandler = logging.FileHandler('test_logger_'+log_suffix, mode='w')
    fileHandler.setFormatter(logging.Formatter('%(message)s'))
    test_logger.addHandler(fileHandler)

    test_accuracies = []

    with tf.Session(graph=graph) as session:
        #tf.global_variables_initializer().run()
        # Input data.
Ejemplo n.º 3
0
__author__ = 'Thushan Ganegedara'

import load_data
from scipy.misc import imsave
import numpy as np
if __name__=='__main__':

    load_data.load_and_save_data_cifar10(filename='cifar-10-white.pickle',zca_whiten=True,return_original=True,separate_rgb=False)
    (tr_white_dataset,tr_labels),(v_white_dataset,v_labels),(ts_white_dataset,ts_labels) = load_data.reformat_data_cifar10(filename='cifar-10-white.pickle')
    #(tr_dataset,tr_labels),(v_dataset,v_labels),(ts_dataset,ts_labels) = load_data.reformat_data_cifar10(filename='cifar-10.pickle')
    for i in range(10):
        rand_idx = np.random.randint(0,5000)
        #imsave('test_img.png', tr_dataset[rand_idx,:,:,:])
        imsave('test_img_whitened_'+str(i)+'.png', tr_white_dataset[rand_idx,:,:,:])