def __init__(self, config):
self.vocab_size = int(config['vocabulary_size'])
self.emb_size = int(config['embedding_dim'])
self.rnn_hidden_size = int(config['rnn_hidden_size'])
self.hidden_size = int(config['hidden_size'])
self.left_name, self.seq_len1 = config['left_slots'][0]
self.right_name, self.seq_len2 = config['right_slots'][0]
self.task_mode = config['training_mode']
self.emb_layer = layers.EmbeddingEnhancedLayer(self.vocab_size,
self.emb_size,
zero_pad=True,
scale=False)
self.rnn = layers.LSTMLayer(self.rnn_hidden_size)
self.extract = layers.ExtractLastLayer()
if self.task_mode == "pointwise":
self.n_class = int(config['n_class'])
self.fc1_layer = layers.FCLayer(self.rnn_hidden_size * 2,
self.hidden_size)
self.fc2_layer = layers.FCLayer(self.hidden_size, self.n_class)
elif self.task_mode == "pairwise":
self.fc1_layer = layers.FCLayer(self.rnn_hidden_size * 1,
self.hidden_size)
self.cos_layer = layers.CosineLayer()
else:
logging.error("training mode not supported")
def __init__(self, config):
self.vocab_size = int(config['vocabulary_size'])
self.emb_size = int(config['embedding_dim'])
self.rnn_hidden_size = int(config['rnn_hidden_size'])
self.hidden_size = int(config['hidden_size'])
self.left_name, self.seq_len1 = config['left_slots'][0]
self.right_name, self.seq_len2 = config['right_slots'][0]
self.emb_layer = layers.EmbeddingEnhancedLayer(self.vocab_size,
self.emb_size,
zero_pad=True,
scale=False)
self.rnn = layers.LSTMLayer(self.rnn_hidden_size)
self.extract = layers.ExtractLastLayer()
self.n_class = int(config['n_class'])
self.fc1_layer = layers.FCLayer(self.rnn_hidden_size * 2,
self.hidden_size)
self.fc2_layer = layers.FCLayer(self.hidden_size, self.n_class)