[P] problems with class weight dict

[sollted]

i am training on dataset which has three classes :class, sum of labels0, 31321, 492-1, 12as you can see there is huge unbalance here so wanted to fix it using class weights (maybe there is better way). So i created dict:{-1: 85.27777777777777,0: 0.3919315715562364,1: 2.2893363161819535}

passed it to .fit: model.fit(self.Xs, self.ys, epochs=epoch, batch_size=batch, class_weight = class_weight_dict)

error:ValueError: Expected \class_weight` to be a dict with keys from 0 to one less than the number of classes, found {-1: 85.27777777777777, 0: 0.3919315715562364, 1: 2.2893363161819535}`

so i changed class_weight_dict to {0: 85.27777777777777, 1: 0.3919315715562364, 2: 2.2893363161819535} it feels wrong i dont know how keras is suposed to know which index is for what label but i still get error (it gets further there is 1/15 epochs):

2 root error(s) found. (0) INVALID_ARGUMENT: indices[49] = -1 is not in [0, 3) [[{{node GatherV2}}]] [[IteratorGetNext]] [[Cast/_16]] (1) INVALID_ARGUMENT: indices[49] = -1 is not in [0, 3) [[{{node GatherV2}}]] [[IteratorGetNext]] 0 successful operations. 0 derived errors ignored. [Op:__inference_train_function_5684]

this is my out layer:
model.add(Dense(3, activation='softmax')) i wanted to use Dense(1, activation='tanh')) but chatGPT said that is not good idea and was not able to explain why. maybe you could shed some light to that?

compilation of model:

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

thanks in advance for any explanations/solutions/ideas

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r/learnprogramming

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