|---|
| [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/rnn/lstm.py#L15-L328) |
Cell class for the LSTM layer.
Inherits From: [`Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/Layer.md.txt), [`Operation`](https://www.tensorflow.org/api_docs/python/tf/keras/Operation.md.txt)
```python
tf.keras.layers.LSTMCell(
units,
activation='tanh',
recurrent_activation='sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.0,
recurrent_dropout=0.0,
seed=None,
**kwargs
)
```
### Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
| - [Effective Tensorflow 2](https://www.tensorflow.org/guide/effective_tf2.md.txt) | - [Time series forecasting](https://www.tensorflow.org/tutorials/structured_data/time_series.md.txt) |
This class processes one step within the whole time sequence input, whereas
`keras.layer.LSTM` processes the whole sequence.
| ## Args ||
|---|---|
| `units` | Positive integer, dimensionality of the output space. |
| `activation` | Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). |
| `recurrent_activation` | Activation function to use for the recurrent step. Default: sigmoid (`sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). |
| `use_bias` | Boolean, (default `True`), whether the layer should use a bias vector. |
| `kernel_initializer` | Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `"glorot_uniform"`. |
| `recurrent_initializer` | Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `"orthogonal"`. |
| `bias_initializer` | Initializer for the bias vector. Default: `"zeros"`. |
| `unit_forget_bias` | Boolean (default `True`). If `True`, add 1 to the bias of the forget gate at initialization. Setting it to `True` will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.](https://github.com/mlresearch/v37/blob/gh-pages/jozefowicz15.pdf) |
| `kernel_regularizer` | Regularizer function applied to the `kernel` weights matrix. Default: `None`. |
| `recurrent_regularizer` | Regularizer function applied to the `recurrent_kernel` weights matrix. Default: `None`. |
| `bias_regularizer` | Regularizer function applied to the bias vector. Default: `None`. |
| `kernel_constraint` | Constraint function applied to the `kernel` weights matrix. Default: `None`. |
| `recurrent_constraint` | Constraint function applied to the `recurrent_kernel` weights matrix. Default: `None`. |
| `bias_constraint` | Constraint function applied to the bias vector. Default: `None`. |
| `dropout` | Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0. |
| `recurrent_dropout` | Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0. |
| `seed` | Random seed for dropout. |
| ## Call arguments ||
|---|---|
| `inputs` | A 2D tensor, with shape `(batch, features)`. |
| `states` | A 2D tensor with shape `(batch, units)`, which is the state from the previous time step. |
| `training` | Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when `dropout` or `recurrent_dropout` is used. |
#### Example:
```python
inputs = np.random.random((32, 10, 8))
rnn = keras.layers.RNN(keras.layers.LSTMCell(4))
output = rnn(inputs)
output.shape
(32, 4)
rnn = keras.layers.RNN(
keras.layers.LSTMCell(4),
return_sequences=True,
return_state=True)
whole_sequence_output, final_state = rnn(inputs)
whole_sequence_output.shape
(32, 10, 4)
final_state.shape
(32, 4)
```
| ## Attributes ||
|---|---|
| `input` | Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the *first time* the operation was called. |
| `output` | Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the *first time* the operation was called. |
## Methods
### `from_config`
[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/ops/operation.py#L322-L362)
```python
@classmethod
from_config(
config
)
```
Creates an operation from its config.
This method is the reverse of `get_config`, capable of instantiating the
same operation from the config dictionary.
> [!NOTE]
> **Note:** If you override this method, you might receive a serialized dtype config, which is a `dict`. You can deserialize it as follows:
```python
if "dtype" in config and isinstance(config["dtype"], dict):
policy = dtype_policies.deserialize(config["dtype"])
```
| Args ||
|---|---|
| `config` | A Python dictionary, typically the output of `get_config`. |
| Returns ||
|---|---|
| An operation instance. ||
### `get_dropout_mask`
[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/rnn/dropout_rnn_cell.py#L37-L44)
```python
get_dropout_mask(
step_input
)
```
### `get_initial_state`
[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/rnn/lstm.py#L324-L328)
```python
get_initial_state(
batch_size=None
)
```
### `get_recurrent_dropout_mask`
[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/rnn/dropout_rnn_cell.py#L46-L53)
```python
get_recurrent_dropout_mask(
step_input
)
```
### `reset_dropout_mask`
[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/rnn/dropout_rnn_cell.py#L55-L63)
```python
reset_dropout_mask()
```
Reset the cached dropout mask if any.
The RNN layer invokes this in the `call()` method
so that the cached mask is cleared after calling `cell.call()`. The
mask should be cached across all timestep within the same batch, but
shouldn't be cached between batches.
### `reset_recurrent_dropout_mask`
[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/rnn/dropout_rnn_cell.py#L65-L66)
```python
reset_recurrent_dropout_mask()
```