In TensorFlow, how can I get nonzero values and their indices from a tensor with python?

Question

I want to do something like this.
Let's say we have a tensor A.

A = [[1,0],[0,4]] 

And I want to get nonzero values and their indices from it.

Nonzero values: [1,4]   Nonzero indices: [[0,0],[1,1]] 

There are similar operations in Numpy.
np.flatnonzero(A) return indices that are non-zero in the flattened A.
x.ravel()[np.flatnonzero(x)] extract elements according to non-zero indices.
Here's a link for these operations.

How can I do somthing like above Numpy operations in Tensorflow with python?
(Whether a matrix is flattened or not doesn't really matter.)

Answer 1

You can achieve same result in Tensorflow using not_equal and where methods.

zero = tf.constant(0, dtype=tf.float32) where = tf.not_equal(A, zero) 

where is a tensor of the same shape as A holding True or False, in the following case

[[True, False],  [False, True]] 

This would be sufficient to select zero or non-zero elements from A. If you want to obtain indices you can use wheremethod as follows:

indices = tf.where(where) 

where tensor has two True values so indices tensor will have two entries. where tensor has rank of two, so entries will have two indices:

[[0, 0],  [1, 1]] 

Answer 2

#assume that an array has 0, 3.069711,  3.167817. mask = tf.greater(array, 0) non_zero_array = tf.boolean_mask(array, mask)