keras-team/keras · error · TypeError

{a.ndim}-dimensional array given. Array must be two-dimensio

Error message

{a.ndim}-dimensional array given. Array must be two-dimensional

What it means

Error "{a.ndim}-dimensional array given. Array must be two-dimensional" thrown in keras-team/keras.

Source

Thrown at keras/src/backend/tensorflow/linalg.py:218

def svd(x, full_matrices=True, compute_uv=True):
    if compute_uv is False:
        return tf.linalg.svd(x, full_matrices=full_matrices, compute_uv=False)
    s, u, v = tf.linalg.svd(
        x, full_matrices=full_matrices, compute_uv=compute_uv
    )
    return u, s, tf.linalg.adjoint(v)


def lstsq(a, b, rcond=None):
    a = convert_to_tensor(a)
    b = convert_to_tensor(b)
    if a.shape[0] != b.shape[0]:
        raise ValueError("Leading dimensions of input arrays must match")
    b_orig_ndim = b.ndim
    if b_orig_ndim == 1:
        b = b[:, None]
    if a.ndim != 2:
        raise TypeError(
            f"{a.ndim}-dimensional array given. Array must be two-dimensional"
        )
    if b.ndim != 2:
        raise TypeError(
            f"{b.ndim}-dimensional array given. "
            "Array must be one or two-dimensional"
        )
    m, n = a.shape
    dtype = a.dtype
    eps = tf.experimental.numpy.finfo(dtype).eps
    if a.shape == ():
        s = tf.zeros(0, dtype=a.dtype)
        x = tf.zeros((n, *b.shape[1:]), dtype=a.dtype)
    else:
        if rcond is None:
            rcond = eps * max(n, m)
        else:
            rcond = tf.where(rcond < 0, eps, rcond)

View on GitHub (pinned to 7a34a03db6)

When it happens

Trigger: Thrown at keras/src/backend/tensorflow/linalg.py:218 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/f2df43582b5b02f8. Report an issue: GitHub.