# tf.compat.v1.nn.rnn_cell.RNNCell

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.14.0/keras/layers/rnn/legacy_cells.py#L147-L338) |

Abstract object representing an RNN cell.

Inherits From: [`Layer`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/layers/Layer), [`Layer`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Module)

    tf.compat.v1.nn.rnn_cell.RNNCell(
        trainable=True, name=None, dtype=None, **kwargs
    )

Every `RNNCell` must have the properties below and implement `call` with
the signature `(output, next_state) = call(input, state)`. The optional
third input argument, `scope`, is allowed for backwards compatibility
purposes; but should be left off for new subclasses.

This definition of cell differs from the definition used in the literature.
In the literature, 'cell' refers to an object with a single scalar output.
This definition refers to a horizontal array of such units.

An RNN cell, in the most abstract setting, is anything that has
a state and performs some operation that takes a matrix of inputs.
This operation results in an output matrix with `self.output_size` columns.
If `self.state_size` is an integer, this operation also results in a new
state matrix with `self.state_size` columns. If `self.state_size` is a
(possibly nested tuple of) TensorShape object(s), then it should return a
matching structure of Tensors having shape `[batch_size].concatenate(s)`
for each `s` in `self.batch_size`.

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| ## Attributes ||
|---|---|
| `graph` | <br /> <br /> |
| `output_size` | Integer or TensorShape: size of outputs produced by this cell. |
| `scope_name` | <br /> <br /> |
| `state_size` | size(s) of state(s) used by this cell. <br /> It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. |

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## Methods

### `apply`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/legacy_tf_layers/base.py#L239-L240)

    apply(
        *args, **kwargs
    )

### `get_initial_state`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/layers/rnn/legacy_cells.py#L254-L290)

    get_initial_state(
        inputs=None, batch_size=None, dtype=None
    )

### `get_losses_for`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/engine/base_layer_v1.py#L1467-L1484)

    get_losses_for(
        inputs
    )

Retrieves losses relevant to a specific set of inputs.

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| Args ||
|---|---|
| `inputs` | Input tensor or list/tuple of input tensors. |

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| Returns ||
|---|---|
| List of loss tensors of the layer that depend on `inputs`. ||

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### `get_updates_for`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/engine/base_layer_v1.py#L1448-L1465)

    get_updates_for(
        inputs
    )

Retrieves updates relevant to a specific set of inputs.

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| Args ||
|---|---|
| `inputs` | Input tensor or list/tuple of input tensors. |

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| Returns ||
|---|---|
| List of update ops of the layer that depend on `inputs`. ||

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### `zero_state`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/layers/rnn/legacy_cells.py#L292-L328)

    zero_state(
        batch_size, dtype
    )

Return zero-filled state tensor(s).

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| Args ||
|---|---|
| `batch_size` | int, float, or unit Tensor representing the batch size. |
| `dtype` | the data type to use for the state. |

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| Returns ||
|---|---|
| If `state_size` is an int or TensorShape, then the return value is a `N-D` tensor of shape `[batch_size, state_size]` filled with zeros. <br /> 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, s]` for each s in `state_size`. ||

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