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Usually you first split your dataset into train/test set, and then if your model training process requires a validation set, you can further split your train-set into the final train-set and the validation-set. A simple rule is that the test set never shows up in your model development process, including when you develop your data preprocessing steps (such ...


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Make each feature have a mean of 0 and standard deviation of 1. In ML modeling we usually (not always) need this to make sure all features have a comparable size so that one feature will not be playing a bigger role just because of its larger scale. And additional need of NN in this is to avoid gradient explosion. Reference. You can use model.summary() to ...


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Then the issue is likely that the output shape of your model does not align with the shape of your y_train dataset. The model expects a matrix of shape (None, 32), whereas you are providing a matrix of shape (None, 2, 32), meaning an extra dimension of two values. In addition, if you want the outputs of your model to sum up to 1 you should use a softmax ...


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