contrib.rnn.LSTMBlockFusedCell

tf.contrib.rnn.LSTMBlockFusedCell

class tf.contrib.rnn.LSTMBlockFusedCell

Defined in tensorflow/contrib/rnn/python/ops/lstm_ops.py.

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

FusedRNNCell implementation of LSTM.

This is an extremely efficient LSTM implementation, that uses a single TF op for the entire LSTM. It should be both faster and more memory-efficient than LSTMBlockCell defined above.

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.

The variable naming is consistent with rnn_cell_impl.LSTMCell.

Properties

num_units

Number of units in this cell (output dimension).

Methods

__init__

__init__(
    num_units,
    forget_bias=1.0,
    cell_clip=None,
    use_peephole=False
)

Initialize the 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: clip the cell to this value. Defaults to 3.
• use_peephole: Whether to use peephole connections or not.

__call__

__call__(
    inputs,
    initial_state=None,
    dtype=None,
    sequence_length=None,
    scope=None
)

Run this LSTM on inputs, starting from the given state.

Args:

• inputs: 3-D tensor with shape [time_len, batch_size, input_size] or a list of time_len tensors of shape [batch_size, input_size].
• initial_state: a tuple (initial_cell_state, initial_output) with tensors of shape [batch_size, self._num_units]. If this is not provided, the cell is expected to create a zero initial state of type dtype.
• dtype: The data type for the initial state and expected output. Required if initial_state is not provided or RNN state has a heterogeneous dtype.
• sequence_length: Specifies the length of each sequence in inputs. An int32 or int64 vector (tensor) size [batch_size], values in [0, time_len). Defaults to time_len for each element.
• scope: VariableScope for the created subgraph; defaults to class name.

Returns:

A pair containing:

• Output: A 3-D tensor of shape [time_len, batch_size, output_size] or a list of time_len tensors of shape [batch_size, output_size], to match the type of the inputs.
• Final state: a tuple (cell_state, output) matching initial_state.

Raises:

• ValueError: in case of shape mismatches