From de308e7d17fc59805bff35103ccd54aea0e83ef0 Mon Sep 17 00:00:00 2001 From: lg Date: Sat, 13 Oct 2018 18:32:51 +0800 Subject: [PATCH] try fix doc indent --- docs/modules/prepro.rst | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/modules/prepro.rst b/docs/modules/prepro.rst index 7dd207a2c..5285aeaa3 100644 --- a/docs/modules/prepro.rst +++ b/docs/modules/prepro.rst @@ -124,20 +124,20 @@ image augmentation often remains as a key bottleneck. ``tf.image`` has three limitations: - Real-world visual tasks such as object detection, segmentation, and pose estimation -must cope with image meta-data (e.g., coordinates). -These data are beyond ``tf.image`` -which processes images as tensors. + must cope with image meta-data (e.g., coordinates). + These data are beyond ``tf.image`` + which processes images as tensors. - ``tf.image`` operators -breaks the pure Python programing experience (i.e., users have to -use ``tf.py_func`` in order to call image functions written in Python); however, -frequent uses of ``tf.py_func`` slow down TensorFlow, -making users hard to balance flexibility and performance. + breaks the pure Python programing experience (i.e., users have to + use ``tf.py_func`` in order to call image functions written in Python); however, + frequent uses of ``tf.py_func`` slow down TensorFlow, + making users hard to balance flexibility and performance. - ``tf.image`` API is inflexible. Image operations are -performed in an order. They are hard to jointly optimize. More importantly, -sequential image operations can significantly -reduces the quality of images, thus affecting training accuracy. + performed in an order. They are hard to jointly optimize. More importantly, + sequential image operations can significantly + reduces the quality of images, thus affecting training accuracy. TensorLayer addresses these limitations by providing a