Decoding a rank 5 tensor from tfrecords yields “CopyElementToLargerSlice Unhandled rank: 5”
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I am currently trying to decode a sequence feature of type tf.string() with the length FixedLenSequenceFeature of shape (None, None, 120, 160, 7) in TensorFlow version 1.4.0. I am using padded batches and tf.parse_single_sequence_example() but when sampling batches from tf.data.TFRecordDataset, I get the following error:
Unimplemented: CopyElementToLargerSlice Unhandled rank: 5
and further:
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[?,?,120,160,3], [?], [?], [?,?,3], [?,?,120,160,3], [?], [?], [?,?,?,120,160,7], [?,?,?,3], [?,?,?,3], [?,?,120,160]], output_types=[DT_INT16, DT_INT64, DT_INT64, DT_DOUBLE, DT_INT16, DT_INT64, DT_INT64, DT_INT16, DT_DOUBLE, DT_DOUBLE, DT_INT16], _device="/job:localhost/replica:0/task:0/device:CPU:0"](Iterator)]]
Handling rank 5 tensors in this way doesn't seem implemented. When can this be expected to be implemented?
Thank you
python tensorflow
asked Nov 10 at 0:50
Fábio Ferreira
9632623
add a comment |
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0
down vote
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I am currently trying to decode a sequence feature of type tf.string() with the length FixedLenSequenceFeature of shape (None, None, 120, 160, 7) in TensorFlow version 1.4.0. I am using padded batches and tf.parse_single_sequence_example() but when sampling batches from tf.data.TFRecordDataset, I get the following error:
Unimplemented: CopyElementToLargerSlice Unhandled rank: 5
and further:
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[?,?,120,160,3], [?], [?], [?,?,3], [?,?,120,160,3], [?], [?], [?,?,?,120,160,7], [?,?,?,3], [?,?,?,3], [?,?,120,160]], output_types=[DT_INT16, DT_INT64, DT_INT64, DT_DOUBLE, DT_INT16, DT_INT64, DT_INT64, DT_INT16, DT_DOUBLE, DT_DOUBLE, DT_INT16], _device="/job:localhost/replica:0/task:0/device:CPU:0"](Iterator)]]
Handling rank 5 tensors in this way doesn't seem implemented. When can this be expected to be implemented?
Thank you
python tensorflow
asked Nov 10 at 0:50
Fábio Ferreira
9632623
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am currently trying to decode a sequence feature of type tf.string() with the length FixedLenSequenceFeature of shape (None, None, 120, 160, 7) in TensorFlow version 1.4.0. I am using padded batches and tf.parse_single_sequence_example() but when sampling batches from tf.data.TFRecordDataset, I get the following error:
Unimplemented: CopyElementToLargerSlice Unhandled rank: 5
and further:
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[?,?,120,160,3], [?], [?], [?,?,3], [?,?,120,160,3], [?], [?], [?,?,?,120,160,7], [?,?,?,3], [?,?,?,3], [?,?,120,160]], output_types=[DT_INT16, DT_INT64, DT_INT64, DT_DOUBLE, DT_INT16, DT_INT64, DT_INT64, DT_INT16, DT_DOUBLE, DT_DOUBLE, DT_INT16], _device="/job:localhost/replica:0/task:0/device:CPU:0"](Iterator)]]
Handling rank 5 tensors in this way doesn't seem implemented. When can this be expected to be implemented?
Thank you
python tensorflow
asked Nov 10 at 0:50
Fábio Ferreira
9632623
I am currently trying to decode a sequence feature of type tf.string() with the length FixedLenSequenceFeature of shape (None, None, 120, 160, 7) in TensorFlow version 1.4.0. I am using padded batches and tf.parse_single_sequence_example() but when sampling batches from tf.data.TFRecordDataset, I get the following error:
Unimplemented: CopyElementToLargerSlice Unhandled rank: 5
and further:
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[?,?,120,160,3], [?], [?], [?,?,3], [?,?,120,160,3], [?], [?], [?,?,?,120,160,7], [?,?,?,3], [?,?,?,3], [?,?,120,160]], output_types=[DT_INT16, DT_INT64, DT_INT64, DT_DOUBLE, DT_INT16, DT_INT64, DT_INT64, DT_INT16, DT_DOUBLE, DT_DOUBLE, DT_INT16], _device="/job:localhost/replica:0/task:0/device:CPU:0"](Iterator)]]
Handling rank 5 tensors in this way doesn't seem implemented. When can this be expected to be implemented?
Thank you
python tensorflow
python tensorflow
asked Nov 10 at 0:50
Fábio Ferreira
9632623
asked Nov 10 at 0:50
Fábio Ferreira
9632623
edited Nov 10 at 1:10
asked Nov 10 at 0:50
Fábio Ferreira
9632623
asked Nov 10 at 0:50
Fábio Ferreira
9632623
asked Nov 10 at 0:50
Fábio Ferreira
9632623
9632623
add a comment |
add a comment |
1 Answer
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oldest
votes
up vote
0
down vote
accepted
It turns out, updating from TensorFlow version 1.4.0 to version 1.12.0 solved the issue.
answered Nov 10 at 0:56
Fábio Ferreira
9632623
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
It turns out, updating from TensorFlow version 1.4.0 to version 1.12.0 solved the issue.
answered Nov 10 at 0:56
Fábio Ferreira
9632623
add a comment |
up vote
0
down vote
accepted
It turns out, updating from TensorFlow version 1.4.0 to version 1.12.0 solved the issue.
answered Nov 10 at 0:56
Fábio Ferreira
9632623
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
It turns out, updating from TensorFlow version 1.4.0 to version 1.12.0 solved the issue.
answered Nov 10 at 0:56
Fábio Ferreira
9632623
It turns out, updating from TensorFlow version 1.4.0 to version 1.12.0 solved the issue.
answered Nov 10 at 0:56
Fábio Ferreira
9632623
edited Nov 10 at 3:19
answered Nov 10 at 0:56
Fábio Ferreira
9632623
answered Nov 10 at 0:56
Fábio Ferreira
9632623
answered Nov 10 at 0:56
Fábio Ferreira
9632623
9632623
add a comment |
add a comment |
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