class tf.nn.rnn_cell.BasicRNNCell The most basic RNN cell.

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

tf.nn.rnn_cell.BasicRNNCell.state_size

tf.nn.rnn_cell.BasicRNNCell.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.nn.rnn_cell.BasicRNNCell.__init__(num_units, input_size=None, activation=tanh)

class tf.nn.rnn_cell.BasicLSTMCell Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training. It does not allow cell clipping, a projection layer, and does not use peep-hole connections: it is the basic baseline. For advanced models, please use the full LSTMCell that follows.

tf.nn.rnn_cell.BasicLSTMCell.__call__(inputs, state, scope=None) Long short-term memory cell (LSTM).

tf.nn.rnn_cell.BasicLSTMCell.state_size

tf.nn.rnn_cell.BasicLSTMCell.output_size

tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tanh) Initialize 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. state_is_tuple: If True, accepted and returned states are 2-tuples of the c_state and m_state. If False, they are concatenated along the column axis. The latter behavior will soon be