{"record":{"id":"a442bc4ab75241a8","repo":"keras-team/keras","slug":"the-weights-argument-should-be-either-none-ra-a442bc","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.","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\\.","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"keras/src/applications/inception_resnet_v2.py","lineNumber":98,"sourceCode":"                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\n            into, only to be specified if `include_top` is `True`,\n            and if no `weights` argument is specified.\n        classifier_activation: A `str` or callable.\n            The activation function to use on the \"top\" layer.\n            Ignored unless `include_top=True`.\n            Set `classifier_activation=None` to return the logits\n            of the \"top\" layer. When loading pretrained weights,\n            `classifier_activation` can only be `None` or `\"softmax\"`.\n        name: The name of the model (string).\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        )\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\n    input_shape = imagenet_utils.obtain_input_shape(\n        input_shape,\n        default_size=299,\n        min_size=75,","sourceCodeStart":80,"sourceCodeEnd":116,"githubUrl":"https://github.com/keras-team/keras/blob/7a34a03db60bf60042242d6a556fc3be119046a5/keras/src/applications/inception_resnet_v2.py#L80-L116","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.\" thrown in keras-team/keras.","triggerScenarios":"Thrown at keras/src/applications/inception_resnet_v2.py:98 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"}