# tf.compat.v1.nn.rnn_cell.BasicLSTMCell

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/layers/rnn/legacy_cells.py#L669-L854) |

> [!WARNING]
> **Deprecated:** Please use [`tf.compat.v1.nn.rnn_cell.LSTMCell`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1/nn/rnn_cell/LSTMCell) instead.

Inherits From: [`RNNCell`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1/nn/rnn_cell/RNNCell), [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1/layers/Layer), [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Module)

    tf.compat.v1.nn.rnn_cell.BasicLSTMCell(
        num_units,
        forget_bias=1.0,
        state_is_tuple=True,
        activation=None,
        reuse=None,
        name=None,
        dtype=None,
        **kwargs
    )

Basic LSTM recurrent network cell.

The implementation is based on

We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.

It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.

For advanced models, please use the full [`tf.compat.v1.nn.rnn_cell.LSTMCell`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1/nn/rnn_cell/LSTMCell)
that follows.

Note that this cell is not optimized for performance. Please use
[`tf.compat.v1.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1/keras/layers/CuDNNLSTM) for better performance on GPU, or
[`tf.raw_ops.LSTMBlockCell`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/raw_ops/LSTMBlockCell) for better performance on CPU.

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| ## Args ||
|---|---|
| `num_units` | int, The number of units in the LSTM cell. |
| `forget_bias` | float, The bias added to forget gates (see above). Must set to `0.0` manually when restoring from CudnnLSTM-trained checkpoints. |
| `state_is_tuple` | If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`. If False, they are concatenated along the column axis. The latter behavior will soon be deprecated. |
| `activation` | Activation function of the inner states. Default: `tanh`. It could also be string that is within Keras activation function names. |
| `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. |
| `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(). When restoring from CudnnLSTM-trained checkpoints, must use `CudnnCompatibleLSTMCell` instead. |

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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/v2.13.1/keras/legacy_tf_layers/base.py#L239-L240)

    apply(
        *args, **kwargs
    )

### `get_initial_state`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/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/v2.13.1/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/v2.13.1/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/v2.13.1/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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