{"record":{"id":"41e5d1b99cbd4c1d","repo":"keras-team/keras","slug":"the-number-of-input-channels-must-be-evenly-divisi-41e5d1","errorCode":null,"errorMessage":"The number of input channels must be evenly divisible by kernel's in_channels. Received input channels {channels} and kernel in_channels {kernel_in_channels}. ","messagePattern":"The number of input channels must be evenly divisible by kernel's in_channels\\. Received input channels (.+?) and kernel in_channels (.+?)\\. ","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"keras/src/backend/jax/ops/nn.py","lineNumber":757,"sourceCode":"    strides = _convert_to_spatial_operand(\n        strides,\n        num_spatial_dims,\n        data_format,\n        include_batch_and_channels=False,\n    )\n    dilation_rate = _convert_to_spatial_operand(\n        dilation_rate,\n        num_spatial_dims,\n        data_format,\n        include_batch_and_channels=False,\n    )\n    if data_format == \"channels_last\":\n        channels = inputs.shape[-1]\n    else:\n        channels = inputs.shape[1]\n    kernel_in_channels = kernel.shape[-2]\n    if channels % kernel_in_channels > 0:\n        raise ValueError(\n            \"The number of input channels must be evenly divisible by \"\n            f\"kernel's in_channels. Received input channels {channels} and \"\n            f\"kernel in_channels {kernel_in_channels}. \"\n        )\n    feature_group_count = channels // kernel_in_channels\n    kernel = convert_to_tensor(kernel)\n    inputs = convert_to_tensor(inputs, dtype=kernel.dtype)\n    result = jax.lax.conv_general_dilated(\n        inputs,\n        kernel,\n        strides,\n        padding,\n        rhs_dilation=dilation_rate,\n        dimension_numbers=dimension_numbers,\n        feature_group_count=feature_group_count,\n    )\n    if result.size == 0 and inputs.size != 0:\n        raise ValueError(","sourceCodeStart":739,"sourceCodeEnd":775,"githubUrl":"https://github.com/keras-team/keras/blob/7a34a03db60bf60042242d6a556fc3be119046a5/keras/src/backend/jax/ops/nn.py#L739-L775","documentation":"Pre-check in the JAX backend's convolution op: it reads the input's channel dimension (last axis for channels_last, axis 1 for channels_first) and the kernel's in_channels axis (kernel.shape[-2]) and requires the former to be a multiple of the latter, because the channels are split into feature_group_count equal groups for jax.lax.conv_general_dilated.","triggerScenarios":"Thrown at keras/src/backend/jax/ops/nn.py:757 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":["For grouped convolution, make input channels an exact multiple of the kernel's in_channels (e.g. in_channels=64, kernel in_channels=32 gives 2 groups)","For a normal convolution, set the kernel's in_channels equal to the input's channel count","Add a 1x1 conv or channel-padding layer upstream to align the channel counts","Check data_format — a mismatched channels_first/channels_last setting reads the wrong axis as channels"],"exampleFix":null,"handlingStrategy":"validation","validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"7a34a03db60bf60042242d6a556fc3be119046a5","analyzedAt":"2026-08-25T21:25:25.994Z","contentChangedAt":null,"schemaVersion":2},"datasetVersion":"2026-09-23T08:17:48.524Z"}