{"record":{"id":"f5d678ce2783899b","repo":"keras-team/keras","slug":"the-weights-argument-should-be-either-none-ra-f5d678","errorCode":null,"errorMessage":"The `weights` argument should be either `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Received weights={weights}","messagePattern":"The `weights` argument should be either `None` \\(random initialization\\), 'imagenet' \\(pre-training on ImageNet\\), or the path to the weights file to be loaded\\. Received weights=(.+?)","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"keras/src/applications/mobilenet.py","lineNumber":111,"sourceCode":"                will be applied to the output of the\n                last convolutional block, and thus\n                the output of the model will be a 2D tensor.\n            - `max` means that global max pooling will be applied.\n        classes: Optional number of classes to classify images into,\n            only to be specified if `include_top` is `True`, and if\n            no `weights` argument is specified. Defaults to `1000`.\n        classifier_activation: A `str` or callable. The activation function\n            to use on the \"top\" layer. Ignored unless `include_top=True`.\n            Set `classifier_activation=None` to return the logits of the \"top\"\n            layer. When loading pretrained weights, `classifier_activation`\n            can only be `None` or `\"softmax\"`.\n        name: String, the name of the model.\n\n    Returns:\n        A model instance.\n    \"\"\"\n    if not (weights in {\"imagenet\", None} or file_utils.exists(weights)):\n        raise ValueError(\n            \"The `weights` argument should be either \"\n            \"`None` (random initialization), 'imagenet' \"\n            \"(pre-training on ImageNet), \"\n            \"or the path to the weights file to be loaded. \"\n            f\"Received weights={weights}\"\n        )\n\n    if weights == \"imagenet\" and include_top and classes != 1000:\n        raise ValueError(\n            \"If using `weights='imagenet'` with `include_top=True`, \"\n            \"`classes` should be 1000.  \"\n            f\"Received classes={classes}\"\n        )\n\n    # Determine proper input shape and default size.\n    if input_shape is None:\n        default_size = 224\n    else:","sourceCodeStart":93,"sourceCodeEnd":129,"githubUrl":"https://github.com/keras-team/keras/blob/7a34a03db60bf60042242d6a556fc3be119046a5/keras/src/applications/mobilenet.py#L93-L129","documentation":"Error \"The `weights` argument should be either `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Received weights={weights}\" thrown in keras-team/keras.","triggerScenarios":"Thrown at keras/src/applications/mobilenet.py:111 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":[],"exampleFix":null,"handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"7a34a03db60bf60042242d6a556fc3be119046a5","analyzedAt":"2026-08-25T21:25:25.994Z","schemaVersion":2},"datasetVersion":"2026-08-26T02:17:13.382Z"}