tf.contrib.rnn.GridLSTMCell.__init__()

tf.contrib.rnn.GridLSTMCell.__init__(num_units, use_peepholes=False, share_time_frequency_weights=False, cell_clip=None, initializer=None, num_unit_shards=1, forget_bias=1.0, feature_size=None, frequency_skip=None, num_frequency_blocks=1, couple_input_forget_gates=False, state_is_tuple=False)

Initialize the parameters for an LSTM cell.

Args:

• num_units: int, The number of units in the LSTM cell
• use_peepholes: bool, default False. Set True to enable diagonal/peephole connections.
• share_time_frequency_weights: bool, default False. Set True to enable shared cell weights between time and frequency LSTMs.
• 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_unit_shards: int, How to split the weight matrix. If >1, the weight matrix is stored across num_unit_shards.
• forget_bias: float, 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.
• feature_size: int, The size of the input feature the LSTM spans over.
• frequency_skip: int, The amount the LSTM filter is shifted by in frequency.
• num_frequency_blocks: int, The total number of frequency blocks needed to cover the whole input feature.
• couple_input_forget_gates: bool, Whether to couple the input and forget gates, i.e. f_gate = 1.0 - i_gate, to reduce model parameters and computation cost.
• 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.