INNER CODE UNIT · Python

hidden

ematvey/hierarchical-attention-networks · bn_lstm.py:44

            hidden = tf.matmul(concat, W_both) + bias

            i, j, f, o = tf.split(hidden, 4, axis=1)

            new_c = c * tf.sigmoid(f) + tf.sigmoid(i) * tf.tanh(j)
            new_h = tf.tanh(new_c) * tf.sigmoid(o)

            return new_h, (new_c, new_h)

class BNLSTMCell(RNNCell):
    """Batch normalized LSTM as described in http://arxiv.org/abs/1603.09025"""
    def __init__(self, num_units, training):
        self.num_units = num_units
        self.training = training

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

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