keras-team/keras · error · ValueError

Invalid channel size: expected 3 (RGB) or 1 (Grayscale)…

Error message

Invalid channel size: expected 3 (RGB) or 1 (Grayscale). Received input with shape: images.shape={images.shape}

What it means

Channel-size guard in `rgb_to_grayscale` (NumPy backend): after the rank check passes, it inspects the channels axis (last axis for channels_last, third-from-last for channels_first) and only accepts 3 (RGB) or 1 (already grayscale). Any other channel count (e.g. 4-channel RGBA or an accidental feature axis) triggers this error with the full shape shown.

Solutions

  1. Drop the alpha channel from RGBA input: images[..., :3]
  2. Confirm the data_format argument matches where channels actually are, so a non-channel axis is not being inspected
  3. Convert inputs with more than 3 channels (e.g. satellite imagery) before calling rgb_to_grayscale
Defensive patterns

Strategy: validation

When it happens

Trigger: Thrown at keras/src/backend/numpy/ops/image.py:58 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/12dabc5d434e8031. Report an issue: GitHub.

Appendix: source

Thrown at keras/src/backend/numpy/ops/image.py:58

    "bicubic",
    "tricubic",
    "lanczos3",
    "lanczos5",
}


def rgb_to_grayscale(images, data_format=None):
    images = convert_to_tensor(images)
    data_format = standardize_data_format(data_format)
    channels_axis = -1 if data_format == "channels_last" else -3
    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 images.shape[channels_axis] not in (1, 3):
        raise ValueError(
            "Invalid channel size: expected 3 (RGB) or 1 (Grayscale). "
            f"Received input with shape: images.shape={images.shape}"
        )
    if images.shape[channels_axis] == 1:
        return images.copy()
    # Convert to floats
    original_dtype = images.dtype
    compute_dtype = backend.result_type(images.dtype, float)
    images = images.astype(compute_dtype)

    # Ref: tf.image.rgb_to_grayscale
    rgb_weights = np.array([0.2989, 0.5870, 0.1140], dtype=images.dtype)
    grayscales = np.tensordot(images, rgb_weights, axes=(channels_axis, -1))
    grayscales = np.expand_dims(grayscales, axis=channels_axis)
    return grayscales.astype(original_dtype)


def rgb_to_hsv(images, data_format=None):

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