# 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 `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.

From: stackoverflow.com/q/34870614

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