how to calculate the euclidean distance between the vectors in one matrix？

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))

edited Nov 20 '18 at 9:22

blue-phoenox

4,226101745

asked Nov 20 '18 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 '18 at 1:30

What do you mean by distance here? There are a lot of distance measures

– zrelova
Nov 20 '18 at 2:37

I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610

– Bobo Xi
Nov 20 '18 at 4:00

add a comment |

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))

edited Nov 20 '18 at 9:22

blue-phoenox

4,226101745

asked Nov 20 '18 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 '18 at 1:30

What do you mean by distance here? There are a lot of distance measures

– zrelova
Nov 20 '18 at 2:37

I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610

– Bobo Xi
Nov 20 '18 at 4:00

add a comment |

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))

edited Nov 20 '18 at 9:22

blue-phoenox

4,226101745

asked Nov 20 '18 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

edited Nov 20 '18 at 9:22

blue-phoenox

4,226101745

asked Nov 20 '18 at 1:26

Bobo Xi

336

edited Nov 20 '18 at 9:22

blue-phoenox

4,226101745

asked Nov 20 '18 at 1:26

Bobo Xi

336

edited Nov 20 '18 at 9:22

blue-phoenox

4,226101745

edited Nov 20 '18 at 9:22

blue-phoenox

4,226101745

edited Nov 20 '18 at 9:22

blue-phoenox

4,226101745

asked Nov 20 '18 at 1:26

Bobo Xi

336

asked Nov 20 '18 at 1:26

Bobo Xi

336

asked Nov 20 '18 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 '18 at 1:30

What do you mean by distance here? There are a lot of distance measures

– zrelova
Nov 20 '18 at 2:37

I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610

– Bobo Xi
Nov 20 '18 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 '18 at 1:30

What do you mean by distance here? There are a lot of distance measures

– zrelova
Nov 20 '18 at 2:37

I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610

– Bobo Xi
Nov 20 '18 at 4:00

What have you tried so far? It is better to give some code and explain your attempt.

– Banghua Zhao
Nov 20 '18 at 1:30

What do you mean by distance here? There are a lot of distance measures

– zrelova
Nov 20 '18 at 2:37

I have fixed the question, can you help me solve it?@Banghua Zhao@user7374610

– Bobo Xi
Nov 20 '18 at 4:00

add a comment |

1 Answer
1

active

oldest

votes

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))

edited Nov 20 '18 at 10:33

answered Nov 20 '18 at 10:14

jdehesa

24.7k43554

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 '18 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 '18 at 10:24

@ jdehesa The later solution works, thank you very much!

– Bobo Xi
Nov 20 '18 at 11:40

@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.

– jdehesa
Nov 20 '18 at 12:41

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

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))

edited Nov 20 '18 at 10:33

answered Nov 20 '18 at 10:14

jdehesa

24.7k43554

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 '18 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 '18 at 10:24

@ jdehesa The later solution works, thank you very much!

– Bobo Xi
Nov 20 '18 at 11:40

@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.

– jdehesa
Nov 20 '18 at 12:41

add a comment |

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))

edited Nov 20 '18 at 10:33

answered Nov 20 '18 at 10:14

jdehesa

24.7k43554

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 '18 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 '18 at 10:24

@ jdehesa The later solution works, thank you very much!

– Bobo Xi
Nov 20 '18 at 11:40

@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.

– jdehesa
Nov 20 '18 at 12:41

add a comment |

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))

edited Nov 20 '18 at 10:33

answered Nov 20 '18 at 10:14

jdehesa

24.7k43554

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))

edited Nov 20 '18 at 10:33

answered Nov 20 '18 at 10:14

jdehesa

24.7k43554

edited Nov 20 '18 at 10:33

answered Nov 20 '18 at 10:14

jdehesa

24.7k43554

answered Nov 20 '18 at 10:14

jdehesa

24.7k43554

answered Nov 20 '18 at 10:14

jdehesa

24.7k43554

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 '18 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 '18 at 10:24

@ jdehesa The later solution works, thank you very much!

– Bobo Xi
Nov 20 '18 at 11:40

@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.

– jdehesa
Nov 20 '18 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 '18 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 '18 at 10:24

@ jdehesa The later solution works, thank you very much!

– Bobo Xi
Nov 20 '18 at 11:40

@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.

– jdehesa
Nov 20 '18 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 '18 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 '18 at 10:24

@ jdehesa The later solution works, thank you very much!

– Bobo Xi
Nov 20 '18 at 11:40

@BoboXi Glad it worked. Please considering marking the answer as accepted if you feel it solved your problem.

– jdehesa
Nov 20 '18 at 12:41

add a comment |

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