{"record":{"id":"51f2000a193a6b0f","repo":"keras-team/keras","slug":"the-weights-argument-should-be-either-none-ra-51f200","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_v2.py","lineNumber":113,"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` '\n            f\"as true, `classes` should be 1000. Received `classes={classes}`\"\n        )\n\n    # Determine proper input shape and default size.\n    # If both input_shape and input_tensor are used, they should match\n    if input_shape is not None and input_tensor is not None:\n        try:\n            is_input_t_tensor = backend.is_keras_tensor(input_tensor)","sourceCodeStart":95,"sourceCodeEnd":131,"githubUrl":"https://github.com/keras-team/keras/blob/7a34a03db60bf60042242d6a556fc3be119046a5/keras/src/applications/mobilenet_v2.py#L95-L131","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_v2.py:113 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"}