tf.contrib.rnn.LayerNormBasicLSTMCell.__init__()

tf.contrib.rnn.LayerNormBasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, activation=tanh, layer_norm=True, norm_gain=1.0, norm_shift=0.0, dropout_keep_prob=1.0, dropout_prob_seed=None)

Initializes 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.
• activation: Activation function of the inner states.
• layer_norm: If True, layer normalization will be applied.
• norm_gain: float, The layer normalization gain initial value. If layer_norm has been set to False, this argument will be ignored.
• norm_shift: float, The layer normalization shift initial value. If layer_norm has been set to False, this argument will be ignored.
• dropout_keep_prob: unit Tensor or float between 0 and 1 representing the recurrent dropout probability value. If float and 1.0, no dropout will be applied.
• dropout_prob_seed: (optional) integer, the randomness seed.