tf.contrib.rnn.GridLSTMCell.__init__(num_units, use_peepholes=False, share_time_frequency_weights=False, cell_clip=None, initializer=None, num_unit_shards=1, forget_bias=1

class tf.contrib.rnn.GRUBlockCell Block GRU cell implementation. The implementation is based on:

tf.contrib.rnn.GRUBlockCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args:

tf.contrib.rnn.GRUBlockCell.__call__(x, h_prev, scope=None) GRU cell.

tf.contrib.rnn.LSTMBlockCell.__init__(num_units, forget_bias=1.0, use_peephole=False) Initialize the basic LSTM cell.

tf.contrib.rnn.LayerNormBasicLSTMCell.state_size

tf.contrib.rnn.AttentionCellWrapper.state_size

tf.contrib.rnn.CoupledInputForgetGateLSTMCell.state_size

tf.contrib.rnn.GRUBlockCell.output_size

tf.contrib.rnn.LayerNormBasicLSTMCell.__call__(inputs, state, scope=None) LSTM cell with layer normalization and recurrent dropout