http://stackoverflow.com/questions/34870614/what-does-tf-nn-embedding-lookup-function-do
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.
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Yes, this function is hard to understand, until you get the point.
In its simplest form, it is similar to tf.gather. It returns the elements of params according to the indexes specified by ids.
For example (assuming you are inside tf.InteractiveSession())
params = tf.constant([10,20,30,40]) ids = tf.constant([0,1,2,3]) print tf.nn.embedding_lookup(params,ids).eval()would return [10 20 30 40], because the first element (index 0) of params is 10, the second element of params (index 1) is 20, etc.
Similarly,
params = tf.constant([10,20,30,40]) ids = tf.constant([1,1,3]) print tf.nn.embedding_lookup(params,ids).eval()would return: [20 20 40]
But embedding_lookup is more than that. The params argument can be a list of tensors, rather than a single tensor.
params1 = tf.constant([1,2]) params2 = tf.constant([10,20]) ids = tf.constant([2,0,2,1,2,3]) result = tf.nn.embedding_lookup([params1, params2], ids)In such a case, the indexes, specified in ids, correspond to elements of tensors according to apartition strategy, where the default partition strategy is 'mod'.
In the 'mod' strategy, index 0 corresponds to the first element of the first tensor in the list. Index 1 corresponds to the first element of the second tensor. Index 2 corresponds to the first element of the third tensor, and so on. Simply index i corresponds to the first element of the (i+1)th tensor , for all the indexes 0..(n-1), assuming params is a list of n tensors.
Now, index n cannot correspond to tensor n+1, because the list params contains only ntensors. So index n corresponds to the second element of the first tensor. Similarly, index n+1corresponds to the second element of the second tensor, etc.
So, in the code
params1 = tf.constant([1,2]) params2 = tf.constant([10,20]) ids = tf.constant([2,0,2,1,2,3]) result = tf.nn.embedding_lookup([params1, params2], ids)index 0 corresponds to the first element of the first tensor: 1
index 1 corresponds to the first element of the second tensor: 10
index 2 corresponds to the second element of the first tensor: 2
index 3 corresponds to the second element of the second tensor: 20
Thus, the result would be:
[ 2 1 2 10 2 20]