Reputation: 1011
Suppose I have a matrix of N users, and each user is associated with a vector of words (translated to integers). So for example for N = 2 I'd have:
user 0 corresponds to words['20','56']
user 1 corresponds to words ['58','10','105']
So I have a list
user_words = [['20','56'],['58','10','105']]
Suppose further I created a 100-column embedding matrix (word_emb) for these words. I'd like to look up the (mean) embeddings of each of the user vectors and create a new Tensor, whose shape I would expect to be [2,100]. I tried doing this:
word_vec = []
for word_sequence_i in tf.map_fn(lambda x: x, user_words):
all_word_vecs = tf.nn.embedding_lookup(word_emb, word_sequence_i)
word_vec.append( tf.reduce_mean(all_word_vecs, 1))
But this gives me an error:
TypeError: `Tensor` objects are not iterable when eager execution is not enabled. To iterate over this tensor use `tf.map_fn`.
I thought I already was using tf.map_fn above! So what is Tensorflow complaining about? Is there even a way to do what I am trying to do?
Thanks so much!
Upvotes: 0
Views: 1038
Reputation: 6751
tf.map_fn
returns a Tensor
object itself, which is a symbolic reference to a value that will be computed at Session.run()
time. You can see this with type(tf.map_fn(lambda x: x, user_words))
. So, it's the iteration implied in for word_sequence_i in tf.map_fn(...)
that is generating the error.
Perhaps what you're looking for is something like:
all_word_vecs = tf.map_fn(lambda x: tf.nn.embedding_lookup(word_emb, x), user_words)
word_vec = tf.reduce_mean(all_word_vecs, axis=1)
On a related note, if this distinction between graph construction and execution is getting bothersome, you might want to give TensorFlow's eager execution a spin. See getting started and the programmer's guide.
Hope that helps.
Upvotes: 1