Poorya Pzm - 7 months ago 100

Python Question

`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 return the param corresponding for each id (in ids)?

For instance, in the

`skip-gram`

`tf.nn.embedding_lookup(embeddings, train_inputs)`

`train_input`

Answer

`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.

Source (Stackoverflow)