Trjtdtk

I have the following code to average embeddings for list of item-ids.
(Embedding is trained on review_meta_id_input, and used as look up for pirors_input and for getting average embedding)

 review_meta_id_input = tf.keras.layers.Input(shape=(1,), dtype='int32', name='review_meta_id')
 priors_input = tf.keras.layers.Input(shape=(None,), dtype='int32', name='priors') # array of ids
 item_embedding_layer = tf.keras.layers.Embedding(
 input_dim=100, # max number
 output_dim=self.item_embedding_size,
 name='item')

 review_meta_id_embedding = item_embedding_layer(review_meta_id_input)
 selected = tf.nn.embedding_lookup(review_meta_id_embedding, priors_input)
 non_zero_count = tf.cast(tf.math.count_nonzero(priors_input, axis=1), tf.float32)
 embedding_sum = tf.reduce_sum(selected, axis=1)
 item_average = tf.math.divide(embedding_sum, non_zero_count)

I also have some feature columns such as..
(I just thought feature_column looked cool, but not many documents to look for..)

 kid_youngest_month = feature_column.numeric_column("kid_youngest_month")
 kid_age_youngest_buckets = feature_column.bucketized_column(kid_youngest_month, boundaries=[12, 24, 36, 72, 96])

I'd like to define [review_meta_id_iput, priors_input, (tensors from feature_columns)] as an input to keras Model.

something like:

 inputs = [review_meta_id_input, priors_input] + feature_layer
 model = tf.keras.models.Model(inputs=inputs, outputs=o)

In order to get tensors from feature columns, the closest lead I have now is

fc_to_tensor = fc: input_layer(features, [fc]) for fc in feature_columns

from https://github.com/tensorflow/tensorflow/issues/17170

However I'm not sure what the features are in the code.

There's no clear example on https://www.tensorflow.org/api_docs/python/tf/feature_column/input_layer either.

How should I construct the features variable for fc_to_tensor ?

Or is there a way to use keras.layers.Input and feature_column at the same time?

Or is there an alternative than tf.feature_column to do the bucketing as above? then I'll just drop the feature_column for now;