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tf.compat.v1.nn.rnn_cell.LSTMCell

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Long short-term memory unit (LSTM) recurrent network cell.

tf.compat.v1.nn.rnn_cell.LSTMCell(
    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,
    **kwargs
)

The default non-peephole implementation is based on (Gers et al., 1999). The peephole implementation is based on (Sak et al., 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 for better performance on GPU, or tf.contrib.rnn.LSTMBlockCell and tf.contrib.rnn.LSTMBlockFusedCell for better performance on CPU. References: Long short-term memory recurrent neural network architectures for large scale acoustic modeling: Sak et al., 2014 (pdf) Learning to forget: Gers et al., 1999 (pdf) Long Short-Term Memory: Hochreiter et al., 1997 (pdf)

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. 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, use CudnnCompatibleLSTMCell instead.

Attributes:

  • graph: DEPRECATED FUNCTION

  • 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

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

zero_state

View source

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.