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;
