Tensorflow: How to use boolean_mask in a way that perserves 2D tensor shape
When I use tf.boolean_mask, the result is flattened.
tensor7 = tf.constant( [[ 0, 1, 2, 3, -1],[ 2, 3, 4, -1, -1],[ 3, 6, 5, 4, 3]], tf.int64)
mask7 = tf.constant([[ True, True, True, True, False], [ True, True, True, False, False], [ True, True, True, True, True]], tf.bool)
result7=tf.boolean_mask(tensor7, mask7, axis=0)
with tf.Session() as sess:
print(sess.run([ result7 ]))
array([0, 1, 2, 3, 2, 3, 4, 3, 6, 5, 4, 3])]
Is there a way to use it that preserves the original 3 array shape? The shapes of the individual arrays should change since they're shorter now. I'm looking for something like this
[array([[ 0, 1, 2, 3 ],
[ 2, 3, 4 ],
[ 3, 6, 5, 4, 3]])
python tensorflow
asked Dec 29 '18 at 2:11
SantoshGupta7SantoshGupta7
6361514
add a comment |
When I use tf.boolean_mask, the result is flattened.
tensor7 = tf.constant( [[ 0, 1, 2, 3, -1],[ 2, 3, 4, -1, -1],[ 3, 6, 5, 4, 3]], tf.int64)
mask7 = tf.constant([[ True, True, True, True, False], [ True, True, True, False, False], [ True, True, True, True, True]], tf.bool)
result7=tf.boolean_mask(tensor7, mask7, axis=0)
with tf.Session() as sess:
print(sess.run([ result7 ]))
array([0, 1, 2, 3, 2, 3, 4, 3, 6, 5, 4, 3])]
Is there a way to use it that preserves the original 3 array shape? The shapes of the individual arrays should change since they're shorter now. I'm looking for something like this
[array([[ 0, 1, 2, 3 ],
[ 2, 3, 4 ],
[ 3, 6, 5, 4, 3]])
python tensorflow
asked Dec 29 '18 at 2:11
SantoshGupta7SantoshGupta7
6361514
add a comment |
When I use tf.boolean_mask, the result is flattened.
tensor7 = tf.constant( [[ 0, 1, 2, 3, -1],[ 2, 3, 4, -1, -1],[ 3, 6, 5, 4, 3]], tf.int64)
mask7 = tf.constant([[ True, True, True, True, False], [ True, True, True, False, False], [ True, True, True, True, True]], tf.bool)
result7=tf.boolean_mask(tensor7, mask7, axis=0)
with tf.Session() as sess:
print(sess.run([ result7 ]))
array([0, 1, 2, 3, 2, 3, 4, 3, 6, 5, 4, 3])]
Is there a way to use it that preserves the original 3 array shape? The shapes of the individual arrays should change since they're shorter now. I'm looking for something like this
[array([[ 0, 1, 2, 3 ],
[ 2, 3, 4 ],
[ 3, 6, 5, 4, 3]])
python tensorflow
asked Dec 29 '18 at 2:11
SantoshGupta7SantoshGupta7
6361514
When I use tf.boolean_mask, the result is flattened.
tensor7 = tf.constant( [[ 0, 1, 2, 3, -1],[ 2, 3, 4, -1, -1],[ 3, 6, 5, 4, 3]], tf.int64)
mask7 = tf.constant([[ True, True, True, True, False], [ True, True, True, False, False], [ True, True, True, True, True]], tf.bool)
result7=tf.boolean_mask(tensor7, mask7, axis=0)
with tf.Session() as sess:
print(sess.run([ result7 ]))
array([0, 1, 2, 3, 2, 3, 4, 3, 6, 5, 4, 3])]
Is there a way to use it that preserves the original 3 array shape? The shapes of the individual arrays should change since they're shorter now. I'm looking for something like this
[array([[ 0, 1, 2, 3 ],
[ 2, 3, 4 ],
[ 3, 6, 5, 4, 3]])
python tensorflow
python tensorflow
asked Dec 29 '18 at 2:11
SantoshGupta7SantoshGupta7
6361514
asked Dec 29 '18 at 2:11
SantoshGupta7SantoshGupta7
6361514
asked Dec 29 '18 at 2:11
SantoshGupta7SantoshGupta7
6361514
asked Dec 29 '18 at 2:11
SantoshGupta7SantoshGupta7
6361514
asked Dec 29 '18 at 2:11
SantoshGupta7SantoshGupta7
6361514
6361514
add a comment |
add a comment |
1 Answer
1
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Your expectation may not be logically sound, as pointed out further in this feature request that resembles your question. You would need to have dynamic shapes for tensors, which has only limited support in TensorFlow (e.g. Ragged Tensors).
answered Dec 29 '18 at 2:19
feliksfeliks
911115
what if we used ragged tensors? tensorflow.org/guide/ragged_tensors
– SantoshGupta7
Dec 29 '18 at 5:07
I tried with ragged tensors but that just raises another error. Dynamic shapes are possible for Tensors but just not ready for every function in Tensorflow. Marking as correct for this particular instance.
– SantoshGupta7
Dec 29 '18 at 23:04
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Your expectation may not be logically sound, as pointed out further in this feature request that resembles your question. You would need to have dynamic shapes for tensors, which has only limited support in TensorFlow (e.g. Ragged Tensors).
answered Dec 29 '18 at 2:19
feliksfeliks
911115
what if we used ragged tensors? tensorflow.org/guide/ragged_tensors
– SantoshGupta7
Dec 29 '18 at 5:07
I tried with ragged tensors but that just raises another error. Dynamic shapes are possible for Tensors but just not ready for every function in Tensorflow. Marking as correct for this particular instance.
– SantoshGupta7
Dec 29 '18 at 23:04
add a comment |
Your expectation may not be logically sound, as pointed out further in this feature request that resembles your question. You would need to have dynamic shapes for tensors, which has only limited support in TensorFlow (e.g. Ragged Tensors).
answered Dec 29 '18 at 2:19
feliksfeliks
911115
what if we used ragged tensors? tensorflow.org/guide/ragged_tensors
– SantoshGupta7
Dec 29 '18 at 5:07
I tried with ragged tensors but that just raises another error. Dynamic shapes are possible for Tensors but just not ready for every function in Tensorflow. Marking as correct for this particular instance.
– SantoshGupta7
Dec 29 '18 at 23:04
add a comment |
Your expectation may not be logically sound, as pointed out further in this feature request that resembles your question. You would need to have dynamic shapes for tensors, which has only limited support in TensorFlow (e.g. Ragged Tensors).
answered Dec 29 '18 at 2:19
feliksfeliks
911115
Your expectation may not be logically sound, as pointed out further in this feature request that resembles your question. You would need to have dynamic shapes for tensors, which has only limited support in TensorFlow (e.g. Ragged Tensors).
answered Dec 29 '18 at 2:19
feliksfeliks
911115
edited Jan 1 at 16:28
answered Dec 29 '18 at 2:19
feliksfeliks
911115
answered Dec 29 '18 at 2:19
feliksfeliks
911115
answered Dec 29 '18 at 2:19
feliksfeliks
911115
911115
what if we used ragged tensors? tensorflow.org/guide/ragged_tensors
– SantoshGupta7
Dec 29 '18 at 5:07
I tried with ragged tensors but that just raises another error. Dynamic shapes are possible for Tensors but just not ready for every function in Tensorflow. Marking as correct for this particular instance.
– SantoshGupta7
Dec 29 '18 at 23:04
add a comment |
what if we used ragged tensors? tensorflow.org/guide/ragged_tensors
– SantoshGupta7
Dec 29 '18 at 5:07
I tried with ragged tensors but that just raises another error. Dynamic shapes are possible for Tensors but just not ready for every function in Tensorflow. Marking as correct for this particular instance.
– SantoshGupta7
Dec 29 '18 at 23:04
what if we used ragged tensors? tensorflow.org/guide/ragged_tensors
– SantoshGupta7
Dec 29 '18 at 5:07
what if we used ragged tensors? tensorflow.org/guide/ragged_tensors
– SantoshGupta7
Dec 29 '18 at 5:07
I tried with ragged tensors but that just raises another error. Dynamic shapes are possible for Tensors but just not ready for every function in Tensorflow. Marking as correct for this particular instance.
– SantoshGupta7
Dec 29 '18 at 23:04
I tried with ragged tensors but that just raises another error. Dynamic shapes are possible for Tensors but just not ready for every function in Tensorflow. Marking as correct for this particular instance.
– SantoshGupta7
Dec 29 '18 at 23:04
add a comment |
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