Is there a keras method to split data?

Question

I think the title is self explanatory but to ask it in details, there's sklearn's method train_test_split() which works like: X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, stratify = Y) It means: the method will split data with 0.3 : 0.7 ratio and will try to make percentage of labels in both data equal. Is there a keras equivalent of this?

Answer 1

Now there is using the keras Dataset class. I'm running keras-2.2.4-tf along with the new tensorflow release.

Basically, load all the data into a Dataset using something like tf.data.Dataset.from_tensor_slices. Then split the data into new datasets for training and validation. For example, shuffle all the records in the dataset. Then use all but the first 400 as training and the first 400 as validation.

ds = ds_in.shuffle(buffer_size=rec_count)
ds_train = ds.skip(400)
ds_validate = ds.take(400)

An instance of the Dataset class is a natural container to pass around for the Keras models. I copied the concept from a tensorflow or keras training example but can't seem to find it again.

The canned datasets using the load_data method create numpy.ndarray classes so they are a little different but can be easily converted to a keras Dataset. I suspect this hasn't been done because so much existing code would break.