Cartesian Product in Tensorflow

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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)

You end up with a len(a) * len(b) * 2 tensor where each combination of the elements of a and b is represented in the last dimension.

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