Specifying feature_columns in tf.keras.experimental.WideDeepModel

Research & Models
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#1

Hi, in TF1, we can specify the columns to be used by both wide and deep parts of the algorithm using the specified parameters:

combined_estimator = tf.estimator.DNNLinearCombinedEstimator(
    head=tf.estimator.BinaryClassHead(),
    # Wide settings
    linear_feature_columns=feature_columns,
    linear_optimizer=optimizer,
    # Deep settings
    dnn_feature_columns=feature_columns,
    dnn_hidden_units=[128],
    dnn_optimizer=optimizer)

In TF2, tf.estimator.DNNLinearCombinedEstimator was replaced by tf.keras.experimental.WideDeepModel, which only accepts 2 arguments: linear part of the model and deep part of the model, without the ability to specify what kind of features these different parts should use.

combined_model = tf.keras.experimental.WideDeepModel(linear_model, dnn_model)

How do I indicate that only a subset of original features (and some derived ones) should be used by wide part, and the rest of the features (including some derived/preprocessed ones) by the deep part?

Thanks