Commit 94be38c7 authored by supercourage's avatar supercourage
Browse files

Fix comment typos under models/research

Some python files under models/research, it is wrong to use "Rather then" instead of "Rather than" in  comments.
parent 059c79ac
......@@ -51,7 +51,7 @@ def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
Rather than adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
......
......@@ -166,7 +166,7 @@ def distort_color(image, thread_id=0, scope=None):
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
Rather than adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
......
......@@ -47,7 +47,7 @@ def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
Rather than adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
......
......@@ -120,7 +120,7 @@ def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
Rather than adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
image: 3-D Tensor containing single image in [0, 1].
......
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