{"record":{"id":"f745500c3a61f44a","repo":"keras-team/keras","slug":"the-number-of-dimensions-in-inputs-must-match-th","errorCode":null,"errorMessage":"The number of dimensions in `inputs` must match the number of dimensions in `shape`. Received inputs.shape={inputs.shape} and shape={self.shape}","messagePattern":"The number of dimensions in `inputs` must match the number of dimensions in `shape`\\. Received inputs\\.shape=(.+?) and shape=(.+?)","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"keras/src/ops/core.py","lineNumber":440,"sourceCode":"        return ScatterUpdate(reduction=reduction).symbolic_call(\n            inputs, indices, updates\n        )\n    return backend.core.scatter_update(\n        inputs, indices, updates, reduction=reduction\n    )\n\n\nclass Slice(Operation):\n    def __init__(self, shape, *, name=None):\n        super().__init__(name=name)\n        self.shape = shape\n\n    def call(self, inputs, start_indices):\n        return backend.core.slice(inputs, start_indices, self.shape)\n\n    def compute_output_spec(self, inputs, start_indices):\n        if len(self.shape) != len(inputs.shape):\n            raise ValueError(\n                \"The number of dimensions in `inputs` must match the number of \"\n                f\"dimensions in `shape`. Received inputs.shape={inputs.shape} \"\n                f\"and shape={self.shape}\"\n            )\n        if hasattr(start_indices, \"__len__\") and len(start_indices) != len(\n            inputs.shape\n        ):\n            raise ValueError(\n                \"The number of dimensions in `start_indices` must match the \"\n                \"number of dimensions in `inputs`. Received \"\n                f\"start_indices={start_indices} and inputs.shape={inputs.shape}\"\n            )\n\n        final_shape = []\n        for i, (input_dim, slice_dim) in enumerate(\n            zip(inputs.shape, self.shape)\n        ):\n            if slice_dim != -1:","sourceCodeStart":422,"sourceCodeEnd":458,"githubUrl":"https://github.com/keras-team/keras/blob/7a34a03db60bf60042242d6a556fc3be119046a5/keras/src/ops/core.py#L422-L458","documentation":"The slice op (used by keras.ops.slice) requires len(shape) == inputs.ndim because it replaces whole axes of the output. A 2D shape against a 3D tensor, for example, is ambiguous and rejected.","triggerScenarios":"keras.ops.slice(x, start, shape) with len(shape) != x.ndim","commonSituations":"Slicing fixed-size patches from images/sequences; refactoring shapes (channels-last changes) without updating slice shapes","solutions":["Provide one entry per dimension in shape","Derive the slice shape from inputs.shape programmatically"],"exampleFix":"# before\nkeras.ops.slice(x3d, (0, 0), (5, 5))  # x3d.ndim == 3\n\n# after\nkeras.ops.slice(x3d, (0, 0, 0), (5, 5, 3))","handlingStrategy":"validation","validationCode":"assert len(shape) == len(x.shape), (\n    f'shape {shape} vs inputs ndim {len(x.shape)}')","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Match len(shape) with inputs.ndim when slicing","Compute shape from inputs.shape dynamically instead of hardcoding"],"tags":["keras","ops","slice","shape"],"backgroundTag":"shape-mismatch-between-tensors","analyzedSha":"7a34a03db60bf60042242d6a556fc3be119046a5","analyzedAt":"2026-08-25T21:25:25.994Z","schemaVersion":2},"datasetVersion":"2026-08-26T02:17:13.382Z"}