deep_gru_lstm_load.py

def load(self, name: str) -> None:
    with open(f'./{name}/vocabulary.txt', 'r') as f:
        vocabulary = f.read().split('[separator]')
    with open(f'./{name}/inter_time_step_size.txt', 'r') as f:
        inter_time_step_size = int(f.read())
    with open(f'./{name}/unit_type.txt', 'r') as f:
        unit_type = f.read()
    with open(f'./{name}/depth.txt', 'r') as f:
        depth = int(f.read())
    
    self._init(vocabulary, inter_time_step_size,
                unit_type, depth)
    
    weights_names = []
    filenames = listdir(f'./{name}/weights')
    filenames.sort()
    for filename in filenames:
        if filename in ['wy.npy', 'by.npy']:
            continue
        
        attr_name, index = filename.replace('.npy', '').split('_')
        index = int(index)
        
        if index == 0:
            setattr(self, attr_name, [])
            weights_names.append(attr_name)
        getattr(self, attr_name).append(
            tf.Variable(np.load(f'./{name}/weights/{filename}')))
    
    self.wy = tf.Variable(np.load(f'./{name}/weights/wy.npy'))
    self.by = tf.Variable(np.load(f'./{name}/weights/by.npy'))
    
    self.weights = [getattr(self, weight_name)
                    for weight_name in weights_names]
    self.weights.extend([self.wy, self.by])