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How to perform cartesian product with Tensorflow?


How to perform cartesian product with Tensorflow?

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How to perform cartesian product with Tensorflow?
Tag : python , By : Mikael
Date : November 25 2020, 07:27 PM

hop of those help? I'm trying to cross stack two tensors, for example , I think this does what you need:
import tensorflow as tf

a = tf.constant([0, 1, 2])
b = tf.constant([2, 3])
c = tf.stack(tf.meshgrid(a, b, indexing='ij'), axis=-1)
c = tf.reshape(c, (-1, 2))
with tf.Session() as sess:
    print(sess.run(c))
[[0 2]
 [0 3]
 [1 2]
 [1 3]
 [2 2]
 [2 3]]

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How do I perform this type of cartesian product in spark 2.01


Tag : apache-spark , By : can
Date : March 29 2020, 07:55 AM
hop of those help? In Python we can use combinations() function from the package itertools inside mapValues():
from itertools import combinations
rdd.mapValues(lambda x: list(combinations(x, 2)))
rdd.mapValues(_.toSeq.combinations(2).toArray.map{case Seq(x,y) => (x,y)})

Cartesian Product in Tensorflow


Tag : python , By : Revision17
Date : March 29 2020, 07:55 AM
this will help I'm going to assume here that both a and b are 1-D tensors.
To get the cartesian product of the two, I would use a combination of tf.expand_dims and tf.tile:
a = tf.constant([1,2,3]) 
b = tf.constant([4,5,6,7]) 

tile_a = tf.tile(tf.expand_dims(a, 1), [1, tf.shape(b)[0]])  
tile_a = tf.expand_dims(tile_a, 2) 
tile_b = tf.tile(tf.expand_dims(b, 0), [tf.shape(a)[0], 1]) 
tile_b = tf.expand_dims(tile_b, 2) 

cartesian_product = tf.concat([tile_a, tile_b], axis=2) 

cart = tf.Session().run(cartesian_product) 

print(cart.shape) 
print(cart) 

Tensorflow: how to subtract 2 tensors in a 'Cartesian product' way?


Tag : python , By : Meg
Date : March 29 2020, 07:55 AM
Hope this helps I want to do something like this: , Making use of broadcasting:
import tensorflow as tf

N = ...
v = tf.get_variable('v', [N])
p = tf.placeholder(shape=[None])
ret = p[:, tf.newaxis] - v[tf.newaxis, :]

Tensorflow- Cartesian product of two 2-D tensors


Tag : python , By : Sinisa Ruzin
Date : March 29 2020, 07:55 AM
hope this fix your issue Here is one way. Repeat elements a and b along the second and first dimension respectively, further reshape repeated a and then concatenate the two repeated tensors.
a_ = tf.reshape(tf.tile(a, [1, b.shape[0]]), (a.shape[0] * b.shape[0], a.shape[1]))
b_ = tf.tile(b, [a.shape[0], 1])

tf.concat([a_, b_], 1).eval()
#array([[ 1,  2,  3,  7,  8],
#       [ 1,  2,  3,  9, 10],
#       [ 4,  5,  6,  7,  8],
#       [ 4,  5,  6,  9, 10]])

How to perform cartesian product in PDL


Tag : perl , By : Gianluca Riccardi
Date : March 29 2020, 07:55 AM
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