tf.contrib.rnn.LayerNormBasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, activation=tanh, layer_norm=True, norm_gain=1.0, norm_shift=0.0, dropout_keep_prob=1.0, dropout_prob_seed=None) Initializes the basic LSTM cell. Args: num_units: int, The number of units in the LSTM cell. forget_bias: float, The bias added to forget gates (see above). input_size: Deprecated and unused. activation: Activation function of the inner states. layer_norm: If True, layer normalization wil

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

tf.contrib.rnn.LayerNormBasicLSTMCell.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 with

tf.contrib.rnn.LayerNormBasicLSTMCell.state_size

tf.contrib.rnn.LayerNormBasicLSTMCell.output_size

class tf.contrib.rnn.LayerNormBasicLSTMCell LSTM unit with layer normalization and recurrent dropout. This class adds layer normalization and recurrent dropout to a basic LSTM unit. Layer normalization implementation is based on: https://arxiv.org/abs/1607.06450. "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton and is applied before the internal nonlinearities. Recurrent dropout is base on: https://arxiv.org/abs/1603.05118 "Recurrent Dropout without Memory Loss" Stanisl

tf.contrib.rnn.GRUBlockCell.__init__(cell_size) Initialize the Block GRU cell. Args: cell_size: int, GRU cell size.

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

tf.contrib.rnn.GRUBlockCell.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 with the shape

tf.contrib.rnn.GRUBlockCell.state_size