Python images_gray_falt_version 예제들

예제 #1

파일: knn.py 프로젝트: farhad-dalirani/AUT_Neural_Networks

path_unlabeled = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\unlabeled_X.bin'
path_labels_name = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\class_names.txt'
path_save_directory = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\new\\new'

# Read images (train images)
# x_train = read_all_images(path_train_x)
# convert images to gray flatten
# x_train = images_gray_falt_version(images=x_train)
# convert type
# x_train = x_train.astype('float32') / 255.
# print(x_train.shape, type(x_train))

# Read images (test images)
x_test = read_all_images(path_test_x)
# convert images to gray flatten
x_test = images_gray_falt_version(images=x_test)
# convert type
x_test = x_test.astype('float32') / 255.
print(x_test.shape, type(x_test))

# Read images (unlabeled images)
x_unlabel = read_all_images(path_unlabeled)
# convert images to gray flatten
x_unlabel = images_gray_falt_version(images=x_unlabel)
# convert type
x_unlabel = x_unlabel.astype('float32') / 255.
print(x_unlabel.shape, type(x_unlabel))

# Read labels (train)
# y_train = read_labels(path_train_y)
# print(y_train.shape)

예제 #2

파일: auto_encoders_run_stl10.py 프로젝트: farhad-dalirani/AUT_Neural_Networks

#       Read Dataset
#################################################################################
# dataset paths
path_dataset = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary'
path_train_x = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\train_X.bin'
path_train_y = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\train_y.bin'
path_test_x = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\test_X.bin'
path_test_y = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\test_y.bin'
path_unlabeled = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\unlabeled_X.bin'
path_labels_name = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\class_names.txt'
path_save_directory = 'D:\\ANN\\dataset\\stl10_binary\\stl10_binary\\new\\new'

# Read images (train images)
x_train = read_all_images(path_train_x)
# convert images to gray flatten
x_train = images_gray_falt_version(images=x_train)
# convert type
x_train = x_train.astype('float32') / 255.
print(x_train.shape, type(x_train))

# Read images (test images)
x_test = read_all_images(path_test_x)
# convert images to gray flatten
x_test = images_gray_falt_version(images=x_test)
# convert type
x_test = x_test.astype('float32') / 255.
print(x_test.shape, type(x_test))

# Read labels (train)
y_train = read_labels(path_train_y)
print(y_train.shape)