experiments/STT-tensorflow
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Fixing typo in doc string, and improve documentation.

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Change: 142727588
This commit is contained in:
Makoto Uchida 2016-12-21 20:06:34 -08:00 committed by TensorFlower Gardener
parent 58f4bfb275
commit c1e7b1580a
1 changed files with 9 additions and 6 deletions
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15
tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
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@ -273,9 +273,10 @@ class Image(ItemHandler):
is stored. is stored.
format_key: the name of the TF-Example feature in which the image format format_key: the name of the TF-Example feature in which the image format
is stored. is stored.
shape: the output shape of the image. If provided, the image is reshaped shape: the output shape of the image as 1-D `Tensor`
accordingly. If left as None, no reshaping is done. A shape should be [height, width, channels]. If provided, the image is reshaped
supplied only if all the stored images have the same shape. accordingly. If left as None, no reshaping is done. A shape should
be supplied only if all the stored images have the same shape.
channels: the number of channels in the image. channels: the number of channels in the image.
""" """
if not image_key: if not image_key:
@ -300,11 +301,12 @@ class Image(ItemHandler):
"""Decodes the image buffer. """Decodes the image buffer.
Args: Args:
image_buffer: T tensor representing the encoded image tensor. image_buffer: The tensor representing the encoded image tensor.
image_format: The image format for the image in `image_buffer`. image_format: The image format for the image in `image_buffer`.
Returns: Returns:
A decoder image. A tensor that represents decoded image of self._shape, or
(?, ?, self._channels) if self._shape is not specified.
""" """
def decode_png(): def decode_png():
return image_ops.decode_png(image_buffer, self._channels) return image_ops.decode_png(image_buffer, self._channels)
@ -329,7 +331,8 @@ class Image(ItemHandler):
} }
default_decoder = decode_jpg default_decoder = decode_jpg
image = control_flow_ops.case(pred_fn_pairs, default=default_decoder, exclusive=True) image = control_flow_ops.case(
pred_fn_pairs, default=default_decoder, exclusive=True)
image.set_shape([None, None, self._channels]) image.set_shape([None, None, self._channels])
if self._shape is not None: if self._shape is not None: