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Counts the number of occurrences of each value in an integer array.
tf.math.bincount(
arr,
weights=None,
minlength=None,
maxlength=None,
dtype=tf.dtypes.int32
,
name=None,
axis=None,
binary_output=False
)
If minlength
and maxlength
are not given, returns a vector with length
tf.reduce_max(arr) + 1
if arr
is non-empty, and length 0 otherwise.
If weights
are non-None, then index i
of the output stores the sum of the
value in weights
at each index where the corresponding value in arr
is
i
.
values = tf.constant([1,1,2,3,2,4,4,5])
tf.math.bincount(values) #[0 2 2 1 2 1]
Vector length = Maximum element in vector values
is 5. Adding 1, which is 6
will be the vector length.
Each bin value in the output indicates number of occurrences of the particular
index. Here, index 1 in output has a value 2. This indicates value 1 occurs
two times in values
.
values = tf.constant([1,1,2,3,2,4,4,5])
weights = tf.constant([1,5,0,1,0,5,4,5])
tf.math.bincount(values, weights=weights) #[0 6 0 1 9 5]
Bin will be incremented by the corresponding weight instead of 1.
Here, index 1 in output has a value 6. This is the summation of weights
corresponding to the value in values
.
Bin-counting on a certain axis
This example takes a 2 dimensional input and returns a Tensor
with
bincounting on each sample.
data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
tf.math.bincount(data, axis=-1)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[1, 1, 1, 1],
[2, 1, 1, 0]], dtype=int32)>
Bin-counting with binary_output
This example gives binary output instead of counting the occurrence.
data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
tf.math.bincount(data, axis=-1, binary_output=True)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[1, 1, 1, 1],
[1, 1, 1, 0]], dtype=int32)>
Returns | |
---|---|
A vector with the same dtype as weights or the given dtype . The bin
values.
|
Raises | |
---|---|
InvalidArgumentError if negative values are provided as an input.
|