Compute the weighted average along the specified axis.
tf.keras.ops.average(
x, axis=None, weights=None
)
Args |
x
|
Input tensor.
|
axis
|
Integer along which to average x . The default, axis=None ,
will average over all of the elements of the input tensor. If axis
is negative it counts from the last to the first axis.
|
weights
|
Tensor of wieghts associated with the values in x . Each
value in x contributes to the average according to its
associated weight. The weights array can either be 1-D (in which
case its length must be the size of a along the given axis) or of
the same shape as x . If weights=None (default), then all data
in x are assumed to have a weight equal to one.
The 1-D calculation is: avg = sum(a * weights) / sum(weights) .
The only constraint on weights is that sum(weights) must not be 0.
|
Returns |
Return the average along the specified axis.
|
Examples:
data = keras.ops.arange(1, 5)
data
array([1, 2, 3, 4], dtype=int32)
keras.ops.average(data)
array(2.5, dtype=float32)
keras.ops.average(
keras.ops.arange(1, 11),
weights=keras.ops.arange(10, 0, -1)
)
array(4., dtype=float32)
data = keras.ops.arange(6).reshape((3, 2))
data
array([[0, 1],
[2, 3],
[4, 5]], dtype=int32)
keras.ops.average(
data,
axis=1,
weights=keras.ops.array([1./4, 3./4])
)
array([0.75, 2.75, 4.75], dtype=float32)
keras.ops.average(
data,
weights=keras.ops.array([1./4, 3./4])
)
Traceback (most recent call last):
ValueError: Axis must be specified when shapes of a and weights differ.