What does tf.nn.embedding_lookup function do?
tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None)
I cannot understand the duty of this function. Is it like a lookup table? Which means to return the parameters corresponding to each id (in ids)?
For instance, in the
skip-gram model if we use
tf.nn.embedding_lookup(embeddings, train_inputs), then for each
train_input it finds the correspond embedding?
embedding_lookup function retrieves rows of the
params tensor. The behavior is similar to using indexing with arrays in numpy. E.g.
matrix = np.random.random([1024, 64]) # 64-dimensional embeddings ids = np.array([0, 5, 17, 33]) print matrix[ids] # prints a matrix of shape [4, 64]
params argument can be also a list of tensors in which case the
ids will be distributed among the tensors. For example, given a list of 3 tensors
[2, 64], the default behavior is that they will represent
partition_strategy controls the way how the
ids are distributed among the list. The partitioning is useful for larger scale problems when the matrix might be too large to keep in one piece.