class HAN:
def __init__(self, config):
self.config = config
self.layers = Layers(config)
def build_HAN_net(self):
X_id = self.layers.X_input()
senti_Y = self.layers.senti_Y_input()
table = self.layers.word_embedding_table()
mask = self.layers.padded_word_mask(X_id)
X = self.layers.lookup(X_id, table, mask)
seq_len = self.layers.sequence_length(X_id)
sent_repr_ls = []
for i in range(self.config['model']['sentAtt_num']):
name = '_layer%d' % i
X = self.layers.biLSTM(X, seq_len, name)
graph = tf.get_default_graph()
tf.add_to_collection(
'reg',
tf.contrib.layers.l2_regularizer(
self.config['model']['reg_rate'])(graph.get_tensor_by_name(
'biLSTM%s/bidirectional_rnn/fw/lstm_cell/kernel:0' %
name)))
tf.add_to_collection(
'reg',
tf.contrib.layers.l2_regularizer(
self.config['model']['reg_rate'])(graph.get_tensor_by_name(
'biLSTM%s/bidirectional_rnn/bw/lstm_cell/kernel:0' %
name)))
sent_att = self.layers.sent_attention(X, X_id)
# (batch size, max sent len)
sent_repr = self.layers.sent_repr(sent_att, X)
sent_repr_ls.append(sent_repr)
# (batch size, sentAtt_num * max sent len)
sent_repr = tf.concat(sent_repr_ls, axis=1)
senti_score = self.layers.score(sent_repr)
pred = self.layers.senti_prediction(senti_score)
loss = self.layers.senti_loss(senti_score, senti_Y)
train_step = tf.train.AdamOptimizer(
self.config['model']['lr']).minimize(loss)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=2)
return {
'loss': loss,
'pred': pred,
'graph': tf.get_default_graph(),
'train_step': train_step,
'saver': saver
}
class Model:
def __init__(self, config):
self.config = config
self.layers = Layers(config)
def build_senti_net(self):
X_id = self.layers.X_input()
senti_Y = self.layers.senti_Y_input()
table = self.layers.word_embedding_table()
mask = self.layers.padded_word_mask(X_id)
X = self.layers.lookup(X_id, table, mask)
seq_len = self.layers.sequence_length(X_id)
bisru_name = 'share'
for i in range(self.config['model']['biSRU']['shared_layers_num']):
# (batch size, max sent len, rnn_dim)
X = self.layers.biSRU(X, seq_len, name=bisru_name)
graph = tf.get_default_graph()
tf.add_to_collection(
'reg',
tf.contrib.layers.l2_regularizer(self.config['model']['reg_rate'])(
graph.get_tensor_by_name(
'biSRU_%s/bidirectional_rnn/fw/sru_cell/kernel:0' %
bisru_name)))
tf.add_to_collection(
'reg',
tf.contrib.layers.l2_regularizer(self.config['model']['reg_rate'])(
graph.get_tensor_by_name(
'biSRU_%s/bidirectional_rnn/bw/sru_cell/kernel:0' %
bisru_name)))
# (batch size, rnn dim)
sent_repr_ls = []
for i in range(self.config['model']['biSRU']['separated_layers_num']):
bisru_name = 'sep_layer' + str(i)
X = self.layers.biSRU(X, seq_len, name=bisru_name)
tf.add_to_collection(
'reg',
tf.contrib.layers.l2_regularizer(
self.config['model']['reg_rate'])(graph.get_tensor_by_name(
'biSRU_%s/bidirectional_rnn/fw/sru_cell/kernel:0' %
bisru_name)))
tf.add_to_collection(
'reg',
tf.contrib.layers.l2_regularizer(
self.config['model']['reg_rate'])(graph.get_tensor_by_name(
'biSRU_%s/bidirectional_rnn/bw/sru_cell/kernel:0' %
bisru_name)))
# (attr num, rnn dim)
A = self.layers.attr_matrix(name=bisru_name)
# (batch size, attr num, max sent len)
att = self.layers.attention(A, X, X_id)
# (batch size, attr num, rnn dim)
sent_repr = self.layers.sent_repr(att, X)
sent_repr_ls.append(sent_repr)
# (batch size, attr num, sep bisru layers num * rnn dim)
sent_repr = tf.concat(sent_repr_ls, axis=2)
senti_score = self.layers.senti_score(sent_repr)
pred = self.layers.senti_prediction(senti_score)
loss = self.layers.senti_loss(senti_score, senti_Y)
train_step = tf.train.AdamOptimizer(
self.config['model']['lr']).minimize(loss)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=2)
return {
'loss': loss,
'pred': pred,
'graph': tf.get_default_graph(),
'train_step': train_step,
'saver': saver
}