INNER CODE UNIT · Python

LSTMCell

ematvey/hierarchical-attention-networks · bn_lstm.py:13

class LSTMCell(RNNCell):
    """Vanilla LSTM implemented with same initializations as BN-LSTM"""
    def __init__(self, num_units):
        self.num_units = num_units

    @property
    def state_size(self):
        return (self.num_units, self.num_units)

    @property
    def output_size(self):
        return self.num_units

    def __call__(self, x, state, scope=None):
        with tf.variable_scope(scope or type(self).__name__):
            c, h = state

            # Keep W_xh and W_hh separate here as well to reuse initialization methods

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