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.
__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tf.tanh)
Initialize the basic LSTM cell.
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
m_state. If False, they are concatenated along the column axis. The latter behavior will soon be deprecated.
activation: Activation function of the inner states.
Return zero-filled state tensor(s).
batch_size: int, float, or unit Tensor representing the batch size.
dtype: the data type to use for the state.
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.
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
[batch_size x s] for each s in