deep_gru_lstm__init__.py

class Model:
    def __init__(self,
                 vocabulary: list,
                 inter_time_step_size: int,
                 unit_type: str,
                 depth: int):
        
        # unit_type can be one of: 'gru' or 'lstm'
        # depth >= 1
        
        self._init(vocabulary, inter_time_step_size, unit_type, depth)
        
        self.weights = (self._init_gru() if unit_type == 'gru'
                                         else self._init_lstm())
        
        # weights and bias used to compute y (the softmax predictions)
        self.wy = tf.Variable(tf.random.normal(
            stddev=sqrt(2.0/(self.inter_time_step_size+self.vocab_size)),
            shape=(self.inter_time_step_size, self.vocab_size),
            dtype=tf.double))
        self.by = tf.Variable(tf.random.normal(
            stddev=sqrt(2.0/(1+self.vocab_size)),
            shape=(1, self.vocab_size),
            dtype=tf.double))
        
        self.weights.extend([self.wy, self.by])