# tf.contrib.rnn.EmbeddingWrapper

### class tf.contrib.rnn.core_rnn_cell.EmbeddingWrapper

Operator adding input embedding to the given cell.

## Methods

### __init__(cell, embedding_classes, embedding_size, initializer=None)

Create a cell with an added input embedding.

#### Args:

• cell: an RNNCell, an embedding will be put before its inputs.
• embedding_classes: integer, how many symbols will be embedded.
• embedding_size: integer, the size of the vectors we embed into.
• initializer: an initializer to use when creating the embedding; if None, the initializer from variable scope or a default one is used.

#### Raises:

• TypeError: if cell is not an RNNCell.
• ValueError: if embedding_classes is not positive.

### zero_state(batch_size, dtype)

Return zero-filled state tensor(s).

#### Args:

• batch_size: int, float, or unit Tensor representing the batch size.
• dtype: the data type to use for the state.

#### Returns:

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size x s] for each s in state_size.