tf.nn.rnn_cell.LSTMCell.__init__()

tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh)

Initialize the parameters for an LSTM cell.

Args:

• num_units: int, The number of units in the LSTM cell
• input_size: Deprecated and unused.
• use_peepholes: bool, set True to enable diagonal/peephole connections.
• cell_clip: (optional) A float value, if provided the cell state is clipped by this value prior to the cell output activation.
• 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. If False, they are concatenated along the column axis. This latter behavior will soon be deprecated.

• activation: Activation function of the inner states.