tf.nn.rnn_cell.DropoutWrapper.state_size

tf.nn.rnn_cell.GRUCell.state_size

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

tf.nn.rnn_cell.InputProjectionWrapper.output_size

tf.nn.rnn_cell.RNNCell.state_size size(s) of state(s) used by this cell. It can be represented

tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tanh) Initialize

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

tf.nn.rnn_cell.BasicLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args:

tf.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None) Most basic RNN: output = new_state = activation(W * input + U

class tf.nn.rnn_cell.DropoutWrapper Operator adding dropout to inputs and outputs of the given cell.