contrib.rnn.LSTMBlockWrapper

tf.contrib.rnn.LSTMBlockWrapper

class tf.contrib.rnn.LSTMBlockWrapper

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)

This is a helper class that provides housekeeping for LSTM cells.

This may be useful for alternative LSTM and similar type of cells. The subclasses must implement _call_cell method and num_units property.

Properties

num_units

Number of units in this cell (output dimension).

Methods

__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