keras-team/keras · error · ValueError
Array inputs to associative_scan must have the same first di
Error message
Array inputs to associative_scan must have the same first dimension. (saw: {}) What it means
Error "Array inputs to associative_scan must have the same first dimension. (saw: {})" thrown in keras-team/keras.
Source
Thrown at keras/src/backend/numpy/core.py:233
def associative_scan(f, elems, reverse=False, axis=0):
# Ref: jax.lax.associative_scan
if not callable(f):
raise TypeError(f"`f` should be a callable. Received: f={f}")
elems_flat = tree.flatten(elems)
elems_flat = [convert_to_tensor(elem) for elem in elems_flat]
if reverse:
elems_flat = [np.flip(elem, (axis,)) for elem in elems_flat]
def _combine(a_flat, b_flat):
a = tree.pack_sequence_as(elems, a_flat)
b = tree.pack_sequence_as(elems, b_flat)
c = f(a, b)
c_flat = tree.flatten(c)
return c_flat
num_elems = int(elems_flat[0].shape[axis])
if not all(int(elem.shape[axis]) == num_elems for elem in elems_flat[1:]):
raise ValueError(
"Array inputs to associative_scan must have the same "
"first dimension. (saw: {})".format(
[elem.shape for elem in elems_flat]
)
)
def _interleave(a, b, axis):
"""Given two Tensors of static shape, interleave them along axis."""
if not (
a.shape[axis] == b.shape[axis] or a.shape[axis] == b.shape[axis] + 1
):
raise ValueError(
"Shapes are incompatible for associative_scan interleaving. "
f"a.shape[{axis}]={a.shape[axis]}, "
f"b.shape[{axis}]={b.shape[axis]}"
)
# we want to get a: [a1, a2], b: [b1, b2]View on GitHub (pinned to 7a34a03db6)
When it happens
Trigger: Thrown at keras/src/backend/numpy/core.py:233 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/9f451339e67acfaa.
Report an issue: GitHub.