Tensorflow python : Accessing individual elements in a tensor

Question

This question is with respect to accessing individual elements in a tensor, say [[1,2,3]]. I need to access the inner element [1,2,3] (This can be performed using .eval() or sess.run()) but it takes longer when the size of the tensor is huge)

Is there any method to do the same faster?

Thanks in Advance.

Answer 1

There are two main ways to access subsets of the elements in a tensor, either of which should work for your example.

1. Use the indexing operator (based on tf.slice()) to extract a contiguous slice from the tensor.

   input = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]])  output = input[0, :] print sess.run(output)  # ==> [1 2 3] 

   The indexing operator supports many of the same slice specifications as NumPy does.

2. Use the tf.gather() op to select a non-contiguous slice from the tensor.

   input = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]])  output = tf.gather(input, 0) print sess.run(output)  # ==> [1 2 3]  output = tf.gather(input, [0, 2]) print sess.run(output)  # ==> [[1 2 3] [7 8 9]] 

   Note that tf.gather() only allows you to select whole slices in the 0th dimension (whole rows in the example of a matrix), so you may need to tf.reshape() or tf.transpose() your input to obtain the appropriate elements.

Answer 2

I hope I understood your question well. You can access elements in a tensor in TensorFlow 2 via .numpy().

import tensorflow as tf t = tf.constant([[1,2,3]])  print(t.numpy()[0][1]) # This will print 2 

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>>> 2