tensorlayer/layers/utils.py
def initialize_rnn_state(state, feed_dict=None):
    """Returns the initialized RNN state.
    The inputs are `LSTMStateTuple` or `State` of `RNNCells`, and an optional `feed_dict`.

    Parameters
    ----------
    state : RNN state.
        The TensorFlow's RNN state.
    feed_dict : dictionary
        Initial RNN state; if None, returns zero state.

    Returns
    -------
    RNN state
        The TensorFlow's RNN state.

    """
    if isinstance(state, LSTMStateTuple):
        c = state.c.eval(feed_dict=feed_dict)
        h = state.h.eval(feed_dict=feed_dict)
        return c, h
    else:
        new_state = state.eval(feed_dict=feed_dict)
        return new_state
def initialize_rnn_state(state, feed_dict=None):
    """Returns the initialized RNN state.
    The inputs are `LSTMStateTuple` or `State` of `RNNCells`, and an optional `feed_dict`.

    Parameters
    ----------
    state : RNN state.
        The TensorFlow's RNN state.
    feed_dict : dictionary
        Initial RNN state; if None, returns zero state.

    Returns
    -------
    RNN state
        The TensorFlow's RNN state.

    """
    if isinstance(state, LSTMStateTuple):
        c = state.c.eval(feed_dict=feed_dict)
        h = state.h.eval(feed_dict=feed_dict)
        return c, h
    else:
        new_state = state.eval(feed_dict=feed_dict)
        return new_state
def initialize_rnn_state ( state , feed_dict = None ) : if isinstance ( state , LSTMStateTuple ) : c = state . c . eval ( feed_dict = feed_dict ) h = state . h . eval ( feed_dict = feed_dict ) return c , h else : new_state = state . eval ( feed_dict = feed_dict ) return new_state
Returns the initialized RNN state.
    The inputs are `LSTMStateTuple` or `State` of `RNNCells`, and an optional `feed_dict`.

    Parameters
    ----------
    state : RNN state.
        The TensorFlow's RNN state.
    feed_dict : dictionary
        Initial RNN state; if None, returns zero state.

    Returns
    -------
    RNN state
        The TensorFlow's RNN state.
Returns the initialized RNN state . The inputs are LSTMStateTuple or State of RNNCells and an optional feed_dict .