2018-08-14 tensorflow-mnist

mnist.py provides a sort of methods to process file IO

main class

class DataSeta(object)：

main method

read_data_sets(train_dir, fake_data, one_hot, dtype=dtypes.float32,...,source_url)

if fake_data:
if not source_url:
   source_url=DEFAULT_SOURCE_URL
local_file=base.maybe_download(TRAIN_IMAGES,train_dir, source_url+TRAIN_IMAGES)
#base.maybe_download() return a filepath in which you can always find the target file
with gfile.Open(local_file,'rb') as f:
       train_images=extract_images(f)
local_file=base.maybe_download(TRAIN_LABEALS,train_dir, source_url+LABELS)
#base.maybe_download() return a filepath in which you can always find the target file
with gfile.Open(local_file,'rb') as f:
       train_images=extract_labels(f)
local_file=base.maybe_download(TEST_IMAGES,train_dir, source_url+TEST_IMAGES)
#base.maybe_download() return a filepath in which you can always find the target file
with gfile.Open(local_file,'rb') as f:
       test_images=extract_images(f)
local_file=base.maybe_download(TEST_LABELS,train_dir, source_url+TEST_LABELS)
#base.maybe_download() return a filepath in which you can always find the target file
with gfile.Open(local_file,'rb') as f:
       test_labels=extract_labels(f)
return base.Datasets(train=train, validation=validation,test=test)
# Datasets is a collections.nametuple('Datasets',['train','validation','test'])

base.maybe_download(filename,work_directory,source_url)

if not gfile.Exists(work_directory):
   gfile.MakeDirs(work_directory)
filepath=os.path.join(work_directory,filename)
if not gfile.Exists(filepath):
   temp_file_name,_=urlretrieve_with_retry(source_url)
#download the file from remote server
   gfile.Copy(temp_file_name,filepath)
    with gfile.GFile(filepath) as f:
    size=f.size()
return filepath