tf.compat.v1.count_nonzero

Computes number of nonzero elements across dimensions of a tensor. (deprecated arguments) (deprecated arguments)

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Compat aliases for migration

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tf.compat.v1.math.count_nonzero

tf.compat.v1.count_nonzero(
    input_tensor=None, axis=None, keepdims=None, dtype=tf.dtypes.int64, name=None,
    reduction_indices=None, keep_dims=None, input=None
)

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1.

If axis has no entries, all dimensions are reduced, and a tensor with a single element is returned.

Note: Floating point comparison to zero is done by exact floating point equality check. Small values are not rounded to zero for purposes of the nonzero check.

For example:

x = tf.constant([[0, 1, 0], [1, 1, 0]])
tf.math.count_nonzero(x)  # 3
tf.math.count_nonzero(x, 0)  # [1, 2, 0]
tf.math.count_nonzero(x, 1)  # [1, 2]
tf.math.count_nonzero(x, 1, keepdims=True)  # [[1], [2]]
tf.math.count_nonzero(x, [0, 1])  # 3

Note: Strings are compared against zero-length empty string "". Any string with a size greater than zero is already considered as nonzero.

For example:

x = tf.constant(["", "a", "  ", "b", ""])
tf.math.count_nonzero(x) # 3, with "a", "  ", and "b" as nonzero strings.