how to calculate the euclidean distance between the vectors in one matrix？
3
I want to calculate the Euclidean distance of the vectors in the features, which is a tf.Tensor got from the network.
I tried it in the following way, but failed with error:
'Tensor' object is not iterable
So I want to calculate the distance between the rows in one matrix just through matrix，without iteration of every rows.
features, _ = mnist_net(images)
feature_matrix = np.zeros(shape=(FLAGS.batch_size,FLAGS.batch_size))
for i in range (FLAGS.batch_size):
for j in range (FLAGS.batch_size):
aa = tf.slice(features,[i,0],[1,50])
bb = tf.slice(features,[j,0],[1,50])
feature_matrix[i,j] = tf.sqrt(sum((aa-bb)**2))
python tensorflow
edited Nov 20 at 9:22
blue-phoenox
3,92181441
asked Nov 20 at 1:26
Bobo Xi
336
add a comment |
3
I want to calculate the Euclidean distance of the vectors in the features, which is a tf.Tensor got from the network.
I tried it in the following way, but failed with error:
'Tensor' object is not iterable
So I want to calculate the distance between the rows in one matrix just through matrix，without iteration of every rows.
features, _ = mnist_net(images)
feature_matrix = np.zeros(shape=(FLAGS.batch_size,FLAGS.batch_size))
for i in range (FLAGS.batch_size):
for j in range (FLAGS.batch_size):
aa = tf.slice(features,[i,0],[1,50])
bb = tf.slice(features,[j,0],[1,50])
feature_matrix[i,j] = tf.sqrt(sum((aa-bb)**2))
python tensorflow
edited Nov 20 at 9:22
blue-phoenox
3,92181441
asked Nov 20 at 1:26
Bobo Xi
336
What have you tried so far? It is better to give some code and explain your attempt.
– Banghua Zhao
Nov 20 at 1:30
What do you mean by distance here? There are a lot of distance measures
– user7374610
Nov 20 at 2:37
I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610
– Bobo Xi
Nov 20 at 4:00
add a comment |
3
3
3
0
I want to calculate the Euclidean distance of the vectors in the features, which is a tf.Tensor got from the network.
I tried it in the following way, but failed with error:
'Tensor' object is not iterable
So I want to calculate the distance between the rows in one matrix just through matrix，without iteration of every rows.
features, _ = mnist_net(images)
feature_matrix = np.zeros(shape=(FLAGS.batch_size,FLAGS.batch_size))
for i in range (FLAGS.batch_size):
for j in range (FLAGS.batch_size):
aa = tf.slice(features,[i,0],[1,50])
bb = tf.slice(features,[j,0],[1,50])
feature_matrix[i,j] = tf.sqrt(sum((aa-bb)**2))
python tensorflow
edited Nov 20 at 9:22
blue-phoenox
3,92181441
asked Nov 20 at 1:26
Bobo Xi
336
I want to calculate the Euclidean distance of the vectors in the features, which is a tf.Tensor got from the network.
I tried it in the following way, but failed with error:
'Tensor' object is not iterable
So I want to calculate the distance between the rows in one matrix just through matrix，without iteration of every rows.
features, _ = mnist_net(images)
feature_matrix = np.zeros(shape=(FLAGS.batch_size,FLAGS.batch_size))
for i in range (FLAGS.batch_size):
for j in range (FLAGS.batch_size):
aa = tf.slice(features,[i,0],[1,50])
bb = tf.slice(features,[j,0],[1,50])
feature_matrix[i,j] = tf.sqrt(sum((aa-bb)**2))
python tensorflow
python tensorflow
edited Nov 20 at 9:22
blue-phoenox
3,92181441
asked Nov 20 at 1:26
Bobo Xi
336
edited Nov 20 at 9:22
blue-phoenox
3,92181441
asked Nov 20 at 1:26
Bobo Xi
336
edited Nov 20 at 9:22
blue-phoenox
3,92181441
edited Nov 20 at 9:22
blue-phoenox
3,92181441
edited Nov 20 at 9:22
blue-phoenox
3,92181441
3,92181441
asked Nov 20 at 1:26
Bobo Xi
336
asked Nov 20 at 1:26
Bobo Xi
336
asked Nov 20 at 1:26
Bobo Xi
336
336
What have you tried so far? It is better to give some code and explain your attempt.
– Banghua Zhao
Nov 20 at 1:30
What do you mean by distance here? There are a lot of distance measures
– user7374610
Nov 20 at 2:37
I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610
– Bobo Xi
Nov 20 at 4:00
add a comment |
What have you tried so far? It is better to give some code and explain your attempt.
– Banghua Zhao
Nov 20 at 1:30
What do you mean by distance here? There are a lot of distance measures
– user7374610
Nov 20 at 2:37
I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610
– Bobo Xi
Nov 20 at 4:00
What have you tried so far? It is better to give some code and explain your attempt.
– Banghua Zhao
Nov 20 at 1:30
What have you tried so far? It is better to give some code and explain your attempt.
– Banghua Zhao
Nov 20 at 1:30
What do you mean by distance here? There are a lot of distance measures
– user7374610
Nov 20 at 2:37
What do you mean by distance here? There are a lot of distance measures
– user7374610
Nov 20 at 2:37
I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610
– Bobo Xi
Nov 20 at 4:00
I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610
– Bobo Xi
Nov 20 at 4:00
add a comment |
1 Answer
1
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oldest
votes
2
You can achieve that simply with tf.norm/tf.linalg.norm:
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
For example:
import tensorflow as tf
with tf.Session() as sess:
features = tf.placeholder(tf.float32, [None, None])
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
print(sess.run(feature_matrix, feed_dict={features: [[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]]}))
Output:
[[ 0. 5.196152 10.392304 15.588457]
[ 5.196152 0. 5.196152 10.392304]
[10.392304 5.196152 0. 5.196152]
[15.588457 10.392304 5.196152 0. ]]
EDIT:
If you cannot use tf.norm, the following is an equivalent implementation:
sqdiff = tf.squared_difference(features[:, tf.newaxis], features)
feature_matrix = tf.sqrt(tf.reduce_sum(sqdiff, axis=-1))
answered Nov 20 at 10:14
jdehesa
22.2k43150
I tried but it didn't work. The error is 'module 'tensorflow' has no attribute 'linalg'' , and the verion of Tensorflow is 1.0.1.
– Bobo Xi
Nov 20 at 10:22
@BoboXi Oh right, try withtf.norm... 1.0.1 is pretty old though, I'm not sure if that is there either.
– jdehesa
Nov 20 at 10:24
@ jdehesa The later solution works, thank you very much!
– Bobo Xi
Nov 20 at 11:40
@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.
– jdehesa
Nov 20 at 12:41
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
2
You can achieve that simply with tf.norm/tf.linalg.norm:
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
For example:
import tensorflow as tf
with tf.Session() as sess:
features = tf.placeholder(tf.float32, [None, None])
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
print(sess.run(feature_matrix, feed_dict={features: [[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]]}))
Output:
[[ 0. 5.196152 10.392304 15.588457]
[ 5.196152 0. 5.196152 10.392304]
[10.392304 5.196152 0. 5.196152]
[15.588457 10.392304 5.196152 0. ]]
EDIT:
If you cannot use tf.norm, the following is an equivalent implementation:
sqdiff = tf.squared_difference(features[:, tf.newaxis], features)
feature_matrix = tf.sqrt(tf.reduce_sum(sqdiff, axis=-1))
answered Nov 20 at 10:14
jdehesa
22.2k43150
I tried but it didn't work. The error is 'module 'tensorflow' has no attribute 'linalg'' , and the verion of Tensorflow is 1.0.1.
– Bobo Xi
Nov 20 at 10:22
@BoboXi Oh right, try withtf.norm... 1.0.1 is pretty old though, I'm not sure if that is there either.
– jdehesa
Nov 20 at 10:24
@ jdehesa The later solution works, thank you very much!
– Bobo Xi
Nov 20 at 11:40
@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.
– jdehesa
Nov 20 at 12:41
add a comment |
2
You can achieve that simply with tf.norm/tf.linalg.norm:
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
For example:
import tensorflow as tf
with tf.Session() as sess:
features = tf.placeholder(tf.float32, [None, None])
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
print(sess.run(feature_matrix, feed_dict={features: [[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]]}))
Output:
[[ 0. 5.196152 10.392304 15.588457]
[ 5.196152 0. 5.196152 10.392304]
[10.392304 5.196152 0. 5.196152]
[15.588457 10.392304 5.196152 0. ]]
EDIT:
If you cannot use tf.norm, the following is an equivalent implementation:
sqdiff = tf.squared_difference(features[:, tf.newaxis], features)
feature_matrix = tf.sqrt(tf.reduce_sum(sqdiff, axis=-1))
answered Nov 20 at 10:14
jdehesa
22.2k43150
I tried but it didn't work. The error is 'module 'tensorflow' has no attribute 'linalg'' , and the verion of Tensorflow is 1.0.1.
– Bobo Xi
Nov 20 at 10:22
@BoboXi Oh right, try withtf.norm... 1.0.1 is pretty old though, I'm not sure if that is there either.
– jdehesa
Nov 20 at 10:24
@ jdehesa The later solution works, thank you very much!
– Bobo Xi
Nov 20 at 11:40
@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.
– jdehesa
Nov 20 at 12:41
add a comment |
2
2
2
You can achieve that simply with tf.norm/tf.linalg.norm:
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
For example:
import tensorflow as tf
with tf.Session() as sess:
features = tf.placeholder(tf.float32, [None, None])
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
print(sess.run(feature_matrix, feed_dict={features: [[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]]}))
Output:
[[ 0. 5.196152 10.392304 15.588457]
[ 5.196152 0. 5.196152 10.392304]
[10.392304 5.196152 0. 5.196152]
[15.588457 10.392304 5.196152 0. ]]
EDIT:
If you cannot use tf.norm, the following is an equivalent implementation:
sqdiff = tf.squared_difference(features[:, tf.newaxis], features)
feature_matrix = tf.sqrt(tf.reduce_sum(sqdiff, axis=-1))
answered Nov 20 at 10:14
jdehesa
22.2k43150
You can achieve that simply with tf.norm/tf.linalg.norm:
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
For example:
import tensorflow as tf
with tf.Session() as sess:
features = tf.placeholder(tf.float32, [None, None])
feature_matrix = tf.linalg.norm(features[:, tf.newaxis] - features, axis=-1)
print(sess.run(feature_matrix, feed_dict={features: [[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]]}))
Output:
[[ 0. 5.196152 10.392304 15.588457]
[ 5.196152 0. 5.196152 10.392304]
[10.392304 5.196152 0. 5.196152]
[15.588457 10.392304 5.196152 0. ]]
EDIT:
If you cannot use tf.norm, the following is an equivalent implementation:
sqdiff = tf.squared_difference(features[:, tf.newaxis], features)
feature_matrix = tf.sqrt(tf.reduce_sum(sqdiff, axis=-1))
answered Nov 20 at 10:14
jdehesa
22.2k43150
edited Nov 20 at 10:33
answered Nov 20 at 10:14
jdehesa
22.2k43150
answered Nov 20 at 10:14
jdehesa
22.2k43150
answered Nov 20 at 10:14
jdehesa
22.2k43150
22.2k43150
I tried but it didn't work. The error is 'module 'tensorflow' has no attribute 'linalg'' , and the verion of Tensorflow is 1.0.1.
– Bobo Xi
Nov 20 at 10:22
@BoboXi Oh right, try withtf.norm... 1.0.1 is pretty old though, I'm not sure if that is there either.
– jdehesa
Nov 20 at 10:24
@ jdehesa The later solution works, thank you very much!
– Bobo Xi
Nov 20 at 11:40
@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.
– jdehesa
Nov 20 at 12:41
add a comment |
I tried but it didn't work. The error is 'module 'tensorflow' has no attribute 'linalg'' , and the verion of Tensorflow is 1.0.1.
– Bobo Xi
Nov 20 at 10:22
@BoboXi Oh right, try withtf.norm... 1.0.1 is pretty old though, I'm not sure if that is there either.
– jdehesa
Nov 20 at 10:24
@ jdehesa The later solution works, thank you very much!
– Bobo Xi
Nov 20 at 11:40
@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.
– jdehesa
Nov 20 at 12:41
I tried but it didn't work. The error is 'module 'tensorflow' has no attribute 'linalg'' , and the verion of Tensorflow is 1.0.1.
– Bobo Xi
Nov 20 at 10:22
I tried but it didn't work. The error is 'module 'tensorflow' has no attribute 'linalg'' , and the verion of Tensorflow is 1.0.1.
– Bobo Xi
Nov 20 at 10:22
@BoboXi Oh right, try with
tf.norm ... 1.0.1 is pretty old though, I'm not sure if that is there either.– jdehesa
Nov 20 at 10:24
@BoboXi Oh right, try with
tf.norm ... 1.0.1 is pretty old though, I'm not sure if that is there either.– jdehesa
Nov 20 at 10:24
@ jdehesa The later solution works, thank you very much!
– Bobo Xi
Nov 20 at 11:40
@ jdehesa The later solution works, thank you very much!
– Bobo Xi
Nov 20 at 11:40
@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.
– jdehesa
Nov 20 at 12:41
@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.
– jdehesa
Nov 20 at 12:41
add a comment |
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What have you tried so far? It is better to give some code and explain your attempt.
– Banghua Zhao
Nov 20 at 1:30
What do you mean by distance here? There are a lot of distance measures
– user7374610
Nov 20 at 2:37
I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610
– Bobo Xi
Nov 20 at 4:00