How to do cross validation for multi-label classification task (using cnn or lstm)

[Zhixue Zhao]

I am trying to use cross-validation for my CNN model.

I tried cross_val_scores, i guess because the output is probability instead of the label, this method is not workable. 

Also, I tried Kfold, it is kind of like a mess. 

I am thinking if it is possible that the NN output is the label(zero or one) directly instead of a probability.

Thanks.

[Sergey O.]

I'm guessing the data is one-hot encoded, and you do softmax for the last activation along with categorical_crossentropy for loss?

If so, you can prob repurpose the categorical_accuracy calculation in keras, where the label is selected based on the index of the max value in both the predicted and input data:
def categorical_accuracy(y_true, y_pred):
  return K.cast(K.equal(K.argmax(y_true, axis=-1),
  K.argmax(y_pred, axis=-1)),
  K.floatx())

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