keras-team/keras · error · ValueError
Arguments `target` and `output` must have the same shape. Re
Error message
Arguments `target` and `output` must have the same shape. Received: target.shape={target.shape}, output.shape={output.shape} What it means
Error "Arguments `target` and `output` must have the same shape. Received: target.shape={target.shape}, output.shape={output.shape}" thrown in keras-team/keras.
Source
Thrown at keras/src/backend/jax/ops/nn.py:971
values = jnp.greater_equal(result.data, 0).astype(dtype)
return jax_sparse.BCOO(
(values, result.indices),
shape=result.shape,
indices_sorted=True,
unique_indices=True,
)
return jnp.max(
one_hot(cast(x, "int32"), num_classes, axis=axis, dtype=dtype),
axis=reduction_axis,
)
def categorical_crossentropy(target, output, from_logits=False, axis=-1):
target = jnp.array(target)
output = jnp.array(output)
if target.shape != output.shape:
raise ValueError(
"Arguments `target` and `output` must have the same shape. "
"Received: "
f"target.shape={target.shape}, output.shape={output.shape}"
)
if len(target.shape) < 1:
raise ValueError(
"Arguments `target` and `output` must be at least rank 1. "
"Received: "
f"target.shape={target.shape}, output.shape={output.shape}"
)
if from_logits:
log_prob = jax.nn.log_softmax(output, axis=axis)
else:
output = output / jnp.sum(output, axis, keepdims=True)
output = jnp.clip(output, backend.epsilon(), 1.0 - backend.epsilon())
log_prob = jnp.log(output)
return -jnp.sum(target * log_prob, axis=axis)View on GitHub (pinned to 7a34a03db6)
When it happens
Trigger: Thrown at keras/src/backend/jax/ops/nn.py:971 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/22ce2debd32ec525.
Report an issue: GitHub.