Improve shape function of tf.sparse_concat when inputs are fully defined #24018
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This fix tries to address the issue raised in 21964 where the shape function of tf.sparse_concat always returns `TensorShape([Dimension(None), Dimension(None)])` even if the inputs are fully defined. This fix addresses the issue by finding the shape if the inputs are fully defined. This fix fixes 21964. Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
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alextp
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May 16, 2019
tensorflow/python/ops/sparse_ops.py
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| dim = sum(shape[axis] for shape in shapes_value) | ||
| output_shape = shapes_value[0] | ||
| output_shape[axis] = dim | ||
| output_shape = tensor_shape.as_shape(output_shape) |
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I think output_shape is supposed to be a tensor and not a tensorshape
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
alextp
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This fix tries to address the issue raised in #21964 where the shape function of tf.sparse_concat always returns
TensorShape([Dimension(None), Dimension(None)])even if the inputs are fully defined.This fix addresses the issue by finding the shape if the inputs are fully defined.
This fix fixes #21964.
Signed-off-by: Yong Tang yong.tang.github@outlook.com