tf.nn.rnn_cell.OutputProjectionWrapper.__call__(inputs, state, scope=None) Run the cell and output projection on inputs, starting from state.

tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=True) Create a RNN cell composed sequentially of a number of RNNCells. Args: cells: list of RNNCells that will be composed in this order. state_is_tuple: If True, accepted and returned states are n-tuples, where n = len(cells). If False, the states are all concatenated along the column axis. This latter behavior will soon be deprecated. Raises: ValueError: if cells is empty (not allowed), or at least one of the cells returns a sta

tf.nn.rnn_cell.MultiRNNCell.__call__(inputs, state, scope=None) Run this multi-layer cell on inputs, starting from state.

class tf.nn.rnn_cell.OutputProjectionWrapper Operator adding an output projection to the given cell. Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your outputs in time, do the projection on this batch-concatenated sequence, then split it if needed or directly feed into a softmax.

tf.nn.rnn_cell.OutputProjectionWrapper.state_size

tf.nn.rnn_cell.OutputProjectionWrapper.output_size

tf.nn.rnn_cell.OutputProjectionWrapper.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 x 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 wit

class tf.nn.rnn_cell.MultiRNNCell RNN cell composed sequentially of multiple simple cells.

tf.nn.rnn_cell.LSTMStateTuple.__repr__() Return a nicely formatted representation string

tf.nn.rnn_cell.MultiRNNCell.state_size