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


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.)

Asked By: ByungSoo Ko



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]]
Answered By: Sergii Gryshkevych
#assume that an array has 0, 3.069711,  3.167817.
mask = tf.greater(array, 0)
non_zero_array = tf.boolean_mask(array, mask)
Answered By: user1098761

What about using sparse tensors.

>>> A = [[1,0],[0,4]]
>>> sparse = tf.sparse.from_dense(A)
>>> sparse.values.numpy(), sparse.indices.numpy()
(array([1, 4], dtype=int32), array([[0, 0],
        [1, 1]]))
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