' % set_filename ) dataset = load_letter ( folder, min_num_images_per_class ) try : with open ( set_filename, 'wb' ) as f : pickle. print ( ' %s already present - Skipping pickling.' % set_filename ) else : print ( 'Pickling %s. exists ( set_filename ) and not force : # You may override by setting force=True. std ( dataset )) return dataset def maybe_pickle ( data_folders, min_num_images_per_class, force = False ): dataset_names = for folder in data_folders : set_filename = folder '.pickle' dataset_names. mean ( dataset )) print ( 'Standard deviation:', np. shape )) dataset = image_data num_images = num_images 1 except IOError as e : print ( 'Could not read:', image_file, ':', e, '- it \' s ok, skipping.' ) dataset = dataset if num_images < min_num_images : raise Exception ( 'Many fewer images than expected: %d < %d ' % ( num_images, min_num_images )) print ( 'Full dataset tensor:', dataset. shape != ( image_size, image_size ): raise Exception ( 'Unexpected image shape: %s ' % str ( image_data. astype ( float ) - pixel_depth / 2 ) / pixel_depth if image_data. join ( folder, image ) try : image_data = ( ndimage. float32 ) print ( folder ) num_images = 0 for image in image_files : image_file = os. ndarray ( shape = ( len ( image_files ), image_size, image_size ), dtype = np. def load_letter ( folder, min_num_images ): """Load the data for a single letter label.""" image_files = os. pixel_depth = 255.0 # Number of levels per pixel. Image_size = 28 # Pixel width and height. Can you get to it with a browser?' ) return filename train_filename = maybe_download ( 'notMNIST_', 247336696 ) test_filename = maybe_download ( 'notMNIST_', 8458043 ) st_size = expected_bytes : print ( 'Found and verified', filename ) else : raise Exception ( 'Failed to verify ' filename '. exists ( filename ): print ( 'Attempting to download:', filename ) filename, _ = urlretrieve ( url filename, filename, reporthook = download_progress_hook ) print ( ' \n Download Complete!' ) statinfo = os.
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