tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None)
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. E.g. given a list of 3 [2, 64] tensors the default behavior is that they will represent ids: [0, 3], [1, 4], [2, 5].
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.