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tf.contrib.rnn.LSTMBlockCell

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Class LSTMBlockCell

Basic LSTM recurrent network cell.

Inherits From: LayerRNNCell

The implementation is based on: http://arxiv.org/abs/1409.2329.

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.

Unlike rnn_cell_impl.LSTMCell, this is a monolithic op and should be much faster. The weight and bias matrices should be compatible as long as the variable scope matches.

__init__

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__init__(
    num_units,
    forget_bias=1.0,
    cell_clip=None,
    use_peephole=False,
    dtype=None,
    reuse=None,
    name='lstm_cell'
)

Initialize the basic LSTM cell.

Args:

  • num_units: int, The number of units in the LSTM cell.
  • forget_bias: float, The bias added to forget gates (see above).
  • cell_clip: An optional float. Defaults to -1 (no clipping).
  • use_peephole: Whether to use peephole connections or not.
  • dtype: the variable dtype of this layer. Default to tf.float32.
  • reuse: (optional) 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. By default this is "lstm_cell", for variable-name compatibility with tf.compat.v1.nn.rnn_cell.LSTMCell.

When restoring from CudnnLSTM-trained checkpoints, must use CudnnCompatibleLSTMBlockCell instead.

Properties

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

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

zero_state

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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.