# tf.nn.rnn_cell.GRUCell

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/rnn_cell_impl.py#L484-L609) |

Gated Recurrent Unit cell (cf.

Inherits From: [`LayerRNNCell`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/rnn/LayerRNNCell)

#### View aliases


**Main aliases**

\`tf.contrib.rnn.GRUCell\`
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.nn.rnn_cell.GRUCell`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/nn/rnn_cell/GRUCell)

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    tf.nn.rnn_cell.GRUCell(
        num_units, activation=None, reuse=None, kernel_initializer=None,
        bias_initializer=None, name=None, dtype=None, **kwargs
    )

<http://arxiv.org/abs/1406.1078>).

Note that this cell is not optimized for performance. Please use
[`tf.contrib.cudnn_rnn.CudnnGRU`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/cudnn_rnn/CudnnGRU) for better performance on GPU, or
[`tf.contrib.rnn.GRUBlockCellV2`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/rnn/GRUBlockCellV2) for better performance on CPU.

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| ## Args ||
|---|---|
| `num_units` | int, The number of units in the GRU cell. |
| `activation` | Nonlinearity to use. Default: `tanh`. |
| `reuse` | (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. |
| `kernel_initializer` | (optional) The initializer to use for the weight and projection matrices. |
| `bias_initializer` | (optional) The initializer to use for the bias. |
| `name` | String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. |
| `dtype` | Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. |
| `**kwargs` | Dict, keyword named properties for common layer attributes, like `trainable` etc when constructing the cell from configs of get_config(). |

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| ## Attributes ||
|---|---|
| `graph` | DEPRECATED FUNCTION <br /> > [!WARNING] > **Warning:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their graph. |
| `output_size` | Integer or TensorShape: size of outputs produced by this cell. |
| `scope_name` | <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

### `get_initial_state`

[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/rnn_cell_impl.py#L281-L309)

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

### `zero_state`

[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/rnn_cell_impl.py#L311-L340)

    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 ||
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| 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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