I have a multi-label classification task I am solving. I have done hyperparameter tuning to determine the best configuration for my neural network.

Is it valid to do this (determine the best hyper-parameters) and then do cross-validation to get a more accurate test estimation of the dataset?

I don't see how this would be invalid, given that the cross-validation examples I have seen already have network architectures known a priori, presumably because this is what they chose or feel is the best way of proceeding.

**Data used is as follows: for hyperparameter tuning: all data is split in to train and test set - the train set is further split when fitting the model for a 10% validation set - the optimal model is then used to predict on the test set. for cross validation:

all data (same as above) is used, but split to train and test according to fold number - no validation split is used on training, and performance is measured only on the test set in order to determine model performance at each h k-fold -0 the model parameters used are those determined by hyperparameter tuning**

I have a multi-label classification task I am solving. I have done hyperparameter tuning (with Keras Tuner) to determine the best configuration for my neural network.

Is it valid to do this (determine the best hyper-parameters) and then do cross-validation to get a more accurate test estimation of the dataset?

I don't see how this would be invalid, given that the cross-validation examples I have seen already have network architectures known a priori, presumably because this is what they chose or feel is the best way of proceeding.

For hyperparameter tuning, all data is split into training and test sets - the training set is further split, when fitting the model, for a 10% validation set - the optimal model is then used to predict on the test set.

For k-fold cross-validation, all data (same as above) is used, but I just split (with sklearn) the data into training and test datasets (so no validation dataset). The test set is used to determine the model performance at each iteration of k-fold cross-validation.

I have a multi-label classification task I am solving. I have done hyperparameter tuning to determine the best configuration for my neural network.

Is it valid to do this (determine the best hyper-parameters) and then do cross-validation to get a more accurate test estimation of the dataset?

I don't see how this would be invalid, given that the cross-validation examples I have seen already have network architectures known a priori, presumably because this is what they chose or feel is the best way of proceeding.

I have a multi-label classification task I am solving. I have done hyperparameter tuning to determine the best configuration for my neural network.

Is it valid to do this (determine the best hyper-parameters) and then do cross-validation to get a more accurate test estimation of the dataset?

I don't see how this would be invalid, given that the cross-validation examples I have seen already have network architectures known a priori, presumably because this is what they chose or feel is the best way of proceeding.

**Data used is as follows: for hyperparameter tuning: all data is split in to train and test set - the train set is further split when fitting the model for a 10% validation set - the optimal model is then used to predict on the test set. for cross validation:

all data (same as above) is used, but split to train and test according to fold number - no validation split is used on training, and performance is measured only on the test set in order to determine model performance at each h k-fold -0 the model parameters used are those determined by hyperparameter tuning**

I have a multi-label classification task I am implementing. I have done a hyper-parameter tuning to determine the best configuration for my neural network.

Is it valid to do this (determine the best hyper-parameters) and then do a cross validation to get a more accurate test estimation of the dataset? I don't see how this would be invalid as cross validation examples I have seen already have network architectures known a-priori, presumably because this is what they chose or feel is the best way of proceeding.

thank you

I have a multi-label classification task I am solving. I have done hyperparameter tuning to determine the best configuration for my neural network.

Is it valid to do this (determine the best hyper-parameters) and then do cross-validation to get a more accurate test estimation of the dataset? 

I don't see how this would be invalid, given that the cross-validation examples I have seen already have network architectures known a priori, presumably because this is what they chose or feel is the best way of proceeding.