# tf.contrib.rnn.CoupledInputForgetGateLSTMCell

### class tf.contrib.rnn.CoupledInputForgetGateLSTMCell

Long short-term memory unit (LSTM) recurrent network cell.

The default non-peephole implementation is based on:

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf

S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.

The peephole implementation is based on:

Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.

The coupling of input and forget gate is based on:

http://arxiv.org/pdf/1503.04069.pdf

Greff et al. "LSTM: A Search Space Odyssey"

The class uses optional peep-hole connections, and an optional projection layer.

## Methods

### __init__(num_units, use_peepholes=False, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=False, activation=tf.tanh)

Initialize the parameters for an LSTM cell.

#### Args:

• num_units: int, The number of units in the LSTM cell
• use_peepholes: bool, set True to enable diagonal/peephole connections.
• initializer: (optional) The initializer to use for the weight and projection matrices.
• num_proj: (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
• proj_clip: (optional) A float value. If num_proj > 0 and proj_clip is provided, then the projected values are clipped elementwise to within [-proj_clip, proj_clip].
• num_unit_shards: How to split the weight matrix. If >1, the weight matrix is stored across num_unit_shards.
• num_proj_shards: How to split the projection matrix. If >1, the projection matrix is stored across num_proj_shards.
• forget_bias: Biases of the forget gate are initialized by default to 1 in order to reduce the scale of forgetting at the beginning of the training.
• state_is_tuple: If True, accepted and returned states are 2-tuples of the c_state and m_state. By default (False), they are concatenated along the column axis. This default behavior will soon be deprecated.
• activation: Activation function of the inner states.

### 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 shapes [batch_size x s] for each s in state_size.