tf.count_nonzero( input_tensor, axis=None, keepdims=None, dtype=tf.int64, name=None, reduction_indices=None, keep_dims=None )
See the guide: Math > Reduction
Computes number of nonzero elements across dimensions of a tensor. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead
input_tensor along the dimensions given in
keepdims is true, the rank of the tensor is reduced by 1 for each
keepdims is true, the reduced dimensions
are retained with length 1.
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.
x = tf.constant([[0, 1, 0], [1, 1, 0]]) tf.count_nonzero(x) # 3 tf.count_nonzero(x, 0) # [1, 2, 0] tf.count_nonzero(x, 1) # [1, 2] tf.count_nonzero(x, 1, keepdims=True) # [, ] tf.count_nonzero(x, [0, 1]) # 3
input_tensor: The tensor to reduce. Should be of numeric type, or
axis: The dimensions to reduce. If
None(the default), reduces all dimensions. Must be in the range
keepdims: If true, retains reduced dimensions with length 1.
dtype: The output dtype; defaults to
name: A name for the operation (optional).
reduction_indices: The old (deprecated) name for axis.
keep_dims: Deprecated alias for
The reduced tensor (number of nonzero values).