keras-team/keras · error · ValueError

Invalid value for argument `interpolation`. Expected of one

Error message

Invalid value for argument `interpolation`. Expected of one {AFFINE_TRANSFORM_INTERPOLATIONS}. Received: interpolation={interpolation}

What it means

Raised by the input-validation guard at the top of keras/src/backend/tensorflow/image.py affine_transform when the interpolation string is not one of the module-level AFFINE_TRANSFORM_INTERPOLATIONS constants. The interpolation argument controls the resampling kernel used by the affine warp; an unrecognized string means the caller passed a method name that this TensorFlow backend implementation does not support (e.g. a JAX-only name or a typo like 'bilineer').

Solutions

1. Pass one of the supported interpolation names, e.g. interpolation="bilinear"
2. Check AFFINE_TRANSFORM_INTERPOLATIONS in keras.src.backend.tensorflow.image for the exact accepted set
3. Fix typos or backend-specific interpolation names in the calling code

When it happens

Trigger: Thrown at keras/src/backend/tensorflow/image.py:352 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/57b4385e69e9c79d. Report an issue: GitHub.

Appendix: source

Thrown at keras/src/backend/tensorflow/image.py:352

    if data_format == "channels_first":
        if len(images.shape) == 4:
            resized = tf.transpose(resized, (0, 3, 1, 2))
        elif len(images.shape) == 3:
            resized = tf.transpose(resized, (2, 0, 1))
    return resized


def affine_transform(
    images,
    transform,
    interpolation="bilinear",
    fill_mode="constant",
    fill_value=0,
    data_format=None,
):
    data_format = backend.standardize_data_format(data_format)
    if interpolation not in AFFINE_TRANSFORM_INTERPOLATIONS:
        raise ValueError(
            "Invalid value for argument `interpolation`. Expected of one "
            f"{AFFINE_TRANSFORM_INTERPOLATIONS}. Received: "
            f"interpolation={interpolation}"
        )
    if fill_mode not in AFFINE_TRANSFORM_FILL_MODES:
        raise ValueError(
            "Invalid value for argument `fill_mode`. Expected of one "
            f"{AFFINE_TRANSFORM_FILL_MODES}. Received: fill_mode={fill_mode}"
        )
    if len(images.shape) not in (3, 4):
        raise ValueError(
            "Invalid images rank: expected rank 3 (single image) "
            "or rank 4 (batch of images). Received input with shape: "
            f"images.shape={images.shape}"
        )
    if len(transform.shape) not in (1, 2):
        raise ValueError(
            "Invalid transform rank: expected rank 1 (single transform) "

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