# tf.lite.experimental.nn.TFLiteLSTMCell
|---| | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/lite/experimental/examples/lstm/rnn_cell.py#L160-L528) | Long short-term memory unit (LSTM) recurrent network cell. Inherits From: [`LayerRNNCell`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/rnn/LayerRNNCell) #### View aliases **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.lite.experimental.nn.TFLiteLSTMCell`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/lite/experimental/nn/TFLiteLSTMCell)
tf.lite.experimental.nn.TFLiteLSTMCell( num_units, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=None, num_proj_shards=None, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, name=None, dtype=None ) This is used only for TfLite, it provides hints and it also makes the variables in the desired for the tflite ops (transposed and seaparated). The default non-peephole implementation is based on: Felix Gers, Jurgen Schmidhuber, and Fred Cummins. "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999. The peephole implementation is based on: Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014. The class uses optional peep-hole connections, optional cell clipping, and an optional projection layer. Note that this cell is not optimized for performance. Please use [`tf.contrib.cudnn_rnn.CudnnLSTM`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/cudnn_rnn/CudnnLSTM) for better performance on GPU, or [`tf.contrib.rnn.LSTMBlockCell`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/rnn/LSTMBlockCell) and [`tf.contrib.rnn.LSTMBlockFusedCell`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/rnn/LSTMBlockFusedCell) for better performance on CPU.


| ## Args || |---|---| | `num_units` | int, The number of units in the LSTM cell. | | `use_peepholes` | bool, set True to enable diagonal/peephole connections. | | `cell_clip` | (optional) A float value, if provided the cell state is clipped by this value prior to the cell output activation. | | `initializer` | (optional) The initializer to use for the weight and projection matrices. | | `num_proj` | (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed. | | `proj_clip` | (optional) A float value. If `num_proj > 0` and `proj_clip` is provided, then the projected values are clipped elementwise to within `[-proj_clip, proj_clip]`. | | `num_unit_shards` | Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead. | | `num_proj_shards` | Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead. | | `forget_bias` | Biases of the forget gate are initialized by default to 1 in order to reduce the scale of forgetting at the beginning of the training. Must set it manually to `0.0` 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. This latter behavior will soon be deprecated. | | `activation` | Activation function of the inner states. 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. | | `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`. When restoring from CudnnLSTM-trained checkpoints, use `CudnnCompatibleLSTMCell` instead. |



| ## Attributes || |---|---| | `graph` | DEPRECATED FUNCTION
> [!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` |
| | `state_size` | size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. |
## 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).


| 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, 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, s]` for each s in `state_size`. ||