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.)
zero = tf.constant(0, dtype=tf.float32) where = tf.not_equal(A, zero)
where is a tensor of the same shape as
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]]
#assume that an array has 0, 3.069711, 3.167817. mask = tf.greater(array, 0) non_zero_array = tf.boolean_mask(array, mask)
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]]))