Mix dense and sparse tensors inside tf.data.Dataset api - tensorflow

Question

Imagine, that i want to train model, which minimizes distance between image and query. From one side i have image features from CNN, from other side i have mappings from word to embedded vector(w2v for example):
def raw_data_generator():
for row in network_data:
yield (row["cnn"], row["w2v_indices"])
dataset = tf.data.Dataset.from_generator(raw_data_generator, (tf.float32, tf.int32))
dataset = dataset.prefetch(1000)
here i want to create batch, but i want to create dense batch for cnn features, and sparse batch for w2v, cause obviously it has variable length(and i want to use safe_embeddings_lookup_sparse). There is batch function for dense, and .apply(tf.contrib.data.dense_to_sparse_batch(..)) function for sparse, but how to use them simultaneously?

Answer 1

You could try creating two data sets (one for each feature), applying the appropriate batching to each and then zipping them together with tf.data.Dataset.zip.
#staticmethod
zip(datasets)
Creates a Dataset by zipping together the given datasets.
This method has similar semantics to the built-in zip() function in
Python, with the main difference being that the datasets argument can
be an arbitrary nested structure of Dataset objects. For example:
# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { 4, 5, 6 }
c = { (7, 8), (9, 10), (11, 12) }
d = { 13, 14 }
# The nested structure of the `datasets` argument determines the
# structure of elements in the resulting dataset.
Dataset.zip((a, b)) == { (1, 4), (2, 5), (3, 6) }
Dataset.zip((b, a)) == { (4, 1), (5, 2), (6, 3) }
# The `datasets` argument may contain an arbitrary number of
# datasets.
Dataset.zip((a, b, c)) == { (1, 4, (7, 8)),
(2, 5, (9, 10)),
(3, 6, (11, 12)) }
# The number of elements in the resulting dataset is the same as
# the size of the smallest dataset in `datasets`.
Dataset.zip((a, d)) == { (1, 13), (2, 14) }