Python util.load_data() Examples

The following are 2 code examples of util.load_data(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module util , or try the search function .
Example #1
Source File: train.py    From DeepLearning-OCR with Apache License 2.0 5 votes vote down vote up 
def main():
	# img_width, img_height = 48, 48
	img_width, img_height = 200, 60
	img_channels = 1 
	# batch_size = 1024
	batch_size = 32
	nb_epoch = 1000
	post_correction = False

	save_dir = 'save_model/' + str(datetime.now()).split('.')[0].split()[0] + '/' # model is saved corresponding to the datetime
	train_data_dir = 'train_data/ip_train/'
	# train_data_dir = 'train_data/single_1000000/'
	val_data_dir = 'train_data/ip_val/'
	test_data_dir = 'test_data//'
	weights_file_path = 'save_model/2016-10-27/weights.11-1.58.hdf5'
	char_set, char2idx = get_char_set(train_data_dir)
	nb_classes = len(char_set)
	max_nb_char = get_maxnb_char(train_data_dir)
	label_set = get_label_set(train_data_dir)
	# val 'char_set:', char_set
	print 'nb_classes:', nb_classes
	print 'max_nb_char:', max_nb_char
	print 'size_label_set:', len(label_set)
	model = build_shallow(img_channels, img_width, img_height, max_nb_char, nb_classes) # build CNN architecture
	# model.load_weights(weights_file_path) # load trained model

	val_data = load_data(val_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
	# val_data = None 
	train_data = load_data(train_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx) 
	train(model, batch_size, nb_epoch, save_dir, train_data, val_data, char_set)

	# train_data = load_data(train_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
	# test(model, train_data, char_set, label_set, post_correction)
	# val_data = load_data(val_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
	# test(model, val_data, char_set, label_set, post_correction)
	# test_data = load_data(test_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
	# test(model, test_data, char_set, label_set, post_correction)
Example #2
Source File: model.py    From sfcn-opi with MIT License 4 votes vote down vote up 
def data_prepare(print_image_shape=False, print_input_shape=False):
    """
    prepare data for model.
    :param print_image_shape: print image shape if set true.
    :param print_input_shape: print input shape(after categorize) if set true
    :return: list of input to model
    """
    def reshape_mask(origin, cate, num_class):
        return cate.reshape((origin.shape[0], origin.shape[1], origin.shape[2], num_class))

    train_imgs, train_det_masks, train_cls_masks = load_data(data_path=DATA_DIR, type='train')
    valid_imgs, valid_det_masks, valid_cls_masks = load_data(data_path=DATA_DIR, type='validation')
    test_imgs, test_det_masks, test_cls_masks = load_data(data_path=DATA_DIR, type='test')

    if print_image_shape:
        print('Image shape print below: ')
        print('train_imgs: {}, train_det_masks: {}, train_cls_masks: {}'.format(train_imgs.shape, train_det_masks.shape,
                                                                                train_cls_masks.shape))
        print('valid_imgs: {}, valid_det_masks: {}, validn_cls_masks: {}'.format(valid_imgs.shape, valid_det_masks.shape, valid_cls_masks.shape))
        print('test_imgs: {}, test_det_masks: {}, test_cls_masks: {}'.format(test_imgs.shape, test_det_masks.shape, test_cls_masks.shape))
        print()

    train_det = np_utils.to_categorical(train_det_masks, 2)
    train_det = reshape_mask(train_det_masks, train_det, 2)
    train_cls = np_utils.to_categorical(train_cls_masks, 5)
    train_cls = reshape_mask(train_cls_masks, train_cls, 5)

    valid_det = np_utils.to_categorical(valid_det_masks, 2)
    valid_det = reshape_mask(valid_det_masks, valid_det, 2)
    valid_cls = np_utils.to_categorical(valid_cls_masks, 5)
    valid_cls = reshape_mask(valid_cls_masks, valid_cls, 5)

    test_det = np_utils.to_categorical(test_det_masks, 2)
    test_det = reshape_mask(test_det_masks, test_det, 2)
    test_cls = np_utils.to_categorical(test_cls_masks, 5)
    test_cls = reshape_mask(test_cls_masks, test_cls, 5)

    if print_input_shape:
        print('input shape print below: ')
        print('train_imgs: {}, train_det: {}, train_cls: {}'.format(train_imgs.shape, train_det.shape, train_cls.shape))
        print('valid_imgs: {}, valid_det: {}, validn_cls: {}'.format(valid_imgs.shape, valid_det.shape, valid_cls.shape))
        print('test_imgs: {}, test_det: {}, test_cls: {}'.format(test_imgs.shape, test_det.shape, test_cls.shape))
        print()
    return [train_imgs, train_det, train_cls, valid_imgs, valid_det, valid_cls, test_imgs, test_det, test_cls]