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DEPRECATED: Please use tf.compat.v1.nn.rnn_cell.LSTMCell instead.

    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 that follows.

Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU, or tf.contrib.rnn.LSTMBlockCell and tf.contrib.rnn.LSTMBlockFusedCell for better performance on CPU.


  • 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.



  • 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.



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    inputs=None, batch_size=None, dtype=None


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    batch_size, dtype

Return zero-filled state tensor(s).


  • batch_size: int, float, or unit Tensor representing the batch size.
  • dtype: the data type to use for the state.


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.