keras-team/keras · error · ValueError
`x` and `weights` BCOOs must have the same indices
Error message
`x` and `weights` BCOOs must have the same indices
What it means
Error "`x` and `weights` BCOOs must have the same indices" thrown in keras-team/keras.
Source
Thrown at keras/src/backend/jax/numpy.py:97
def kaiser(x, beta):
x = convert_to_tensor(x)
return cast(jnp.kaiser(x, beta), config.floatx())
def bincount(x, weights=None, minlength=0, sparse=False):
# Note: bincount is never traceable / jittable because the output shape
# depends on the values in x.
if sparse or isinstance(x, jax_sparse.BCOO):
if isinstance(x, jax_sparse.BCOO):
if weights is not None:
if not isinstance(weights, jax_sparse.BCOO):
raise ValueError("`x` and `weights` must both be BCOOs")
if x.indices is not weights.indices:
# This test works in eager mode only
if not jnp.all(jnp.equal(x.indices, weights.indices)):
raise ValueError(
"`x` and `weights` BCOOs must have the same indices"
)
weights = weights.data
x = x.data
reduction_axis = 1 if len(x.shape) > 1 else 0
maxlength = jnp.maximum(jnp.max(x) + 1, minlength)
one_hot_encoding = nn.one_hot(x, maxlength, sparse=True)
if weights is not None:
expanded_weights = jnp.expand_dims(weights, reduction_axis + 1)
one_hot_encoding = one_hot_encoding * expanded_weights
outputs = jax_sparse.bcoo_reduce_sum(
one_hot_encoding,
axes=(reduction_axis,),
)
return outputs
if len(x.shape) == 2:
if weights is None:View on GitHub (pinned to 7a34a03db6)
When it happens
Trigger: Thrown at keras/src/backend/jax/numpy.py:97 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25).
Data as JSON: /api/errors/222294f745ca6fe5.
Report an issue: GitHub.