def _build_model_op(self):
with tf.variable_scope('bidirectional_rnn'):
if self.cfg.mode_type == 'stack': # n-layers stacked bidirectional rnn
rnns = StackBiRNN(self.cfg.num_layers,
self.cfg.num_units,
scope='stack_mode')
output = rnns(self.word_embeddings, self.seq_lengths)
output = dropout(output,
keep_prob=self.keep_prob,
is_train=self.is_train)
elif self.cfg.mode_type == 'densely_connected':
rnns = DenseConnectBiRNN(self.cfg.num_layers_dc,
self.cfg.num_units_list)
output = rnns(self.word_embeddings, self.seq_lengths)
output = dropout(output,
keep_prob=self.keep_prob,
is_train=self.is_train)
else: # default single model
rnns = BiRNN(self.cfg.num_units, scope='single_mode')
output = rnns(self.word_embeddings, self.seq_lengths)
output = dropout(output,
keep_prob=self.keep_prob,
is_train=self.is_train)
self.logits = dense(output,
self.cfg.tag_vocab_size,
use_bias=True,
scope='project')
def _build_embeddings_op(self):
tf.summary.scalar('dropout_keep_probability', self.keep_prob)
tf.summary.scalar("learning_rate", self.lr)
with tf.variable_scope('words'):
if self.cfg.use_pretrained:
_word_embeddings = tf.Variable(self.cfg.glove_embeddings, name='_word_embeddings', dtype=tf.float32,
trainable=self.cfg.finetune_emb)
else:
_word_embeddings = tf.get_variable(name='_word_embeddings', dtype=tf.float32, trainable=True,
shape=[self.cfg.word_vocab_size, self.cfg.word_dim])
word_embeddings = tf.nn.embedding_lookup(_word_embeddings, self.word_ids, name="word_embeddings")
with tf.variable_scope('char_rep_method'):
if self.cfg.use_char_emb:
_char_embeddings = tf.get_variable(name='_char_embeddings', dtype=tf.float32, trainable=True,
shape=[self.cfg.char_vocab_size, self.cfg.char_dim])
char_embeddings = tf.nn.embedding_lookup(_char_embeddings, self.char_ids, name="char_embeddings")
if self.cfg.char_rep_method == 'rnn':
char_rnn = BiRNN(self.cfg.num_units_char)
char_output = char_rnn(char_embeddings, self.word_lengths, return_last_state=True)
else: # cnn model for char representation
char_output = multi_conv1d(char_embeddings, self.cfg.filter_sizes, self.cfg.heights, "VALID",
self.is_train, self.keep_prob)
word_embeddings = tf.concat([word_embeddings, char_output], axis=-1)
if self.cfg.use_highway:
with tf.variable_scope("highway"):
self.word_embeddings = highway_network(word_embeddings, self.cfg.highway_num_layers, bias=True,
is_train=self.is_train, keep_prob=self.keep_prob)
else: # directly dropout before model_op
self.word_embeddings = dropout(word_embeddings, keep_prob=self.keep_prob, is_train=self.is_train)
print("embeddings shape: {}".format(self.word_embeddings.get_shape().as_list()))
def _build_embeddings_op(self):
tf.summary.scalar('dropout_keep_probability', self.keep_prob)
tf.summary.scalar("learning_rate", self.lr)
with tf.variable_scope('words'):
if self.cfg.use_pretrained:
_word_embeddings = tf.Variable(self.cfg.glove_embeddings,
name='_word_embeddings',
dtype=tf.float32,
trainable=self.cfg.finetune_emb)
else:
_word_embeddings = tf.get_variable(
name='_word_embeddings',
dtype=tf.float32,
trainable=True,
shape=[self.cfg.word_vocab_size, self.cfg.word_dim])
word_embeddings = tf.nn.embedding_lookup(_word_embeddings,
self.word_ids,
name="word_embeddings")
with tf.variable_scope('char_represent'):
if self.cfg.use_char_emb:
_char_embeddings = tf.get_variable(
name='_char_embeddings',
dtype=tf.float32,
trainable=True,
shape=[self.cfg.char_vocab_size, self.cfg.char_dim])
char_embeddings = tf.nn.embedding_lookup(
_char_embeddings, self.char_ids, name="char_embeddings")
s = tf.shape(
char_embeddings
) # [batch size, max length of sentence, max length of word, char_dim]
output = multi_conv1d(char_embeddings,
self.cfg.filter_sizes,
self.cfg.heights,
"VALID",
self.is_train,
self.keep_prob,
scope="char_cnn")
# shape = (batch size, max sentence length, char representation size)
self.char_output = tf.reshape(
output, [s[0], s[1], self.cfg.char_out_size])
word_embeddings = tf.concat(
[word_embeddings, self.char_output], axis=-1)
if self.cfg.use_highway:
with tf.variable_scope("highway"):
self.word_embeddings = highway_network(
word_embeddings,
self.cfg.highway_num_layers,
bias=True,
is_train=self.is_train,
keep_prob=self.keep_prob)
else: # directly dropout before model_op
self.word_embeddings = dropout(word_embeddings,
keep_prob=self.keep_prob,
is_train=self.is_train)
print('word embeddings shape: {}'.format(
self.word_embeddings.get_shape().as_list()))
def _build_model_op(self):
with tf.variable_scope('bidirectional_rnn'):
if self.cfg.mode_type == 'stack': # n-layers stacked bidirectional rnn
rnns = StackBiRNN(self.cfg.num_layers, self.cfg.num_units, scope='stack_mode')
output = rnns(self.word_embeddings, self.seq_lengths)
output = dropout(output, keep_prob=self.keep_prob, is_train=self.is_train)
elif self.cfg.mode_type == 'densely_connected':
rnns = DenseConnectBiRNN(self.cfg.num_layers_dc, self.cfg.num_units_list)
output = rnns(self.word_embeddings, self.seq_lengths)
output = dropout(output, keep_prob=self.keep_prob, is_train=self.is_train)
else: # default single model
rnns = BiRNN(self.cfg.num_units, scope='single_mode')
output = rnns(self.word_embeddings, self.seq_lengths)
output = dropout(output, keep_prob=self.keep_prob, is_train=self.is_train)
print("rnn output shape: {}".format(output.get_shape().as_list()))
with tf.variable_scope("project"):
self.logits = tf.layers.dense(output, self.cfg.tag_vocab_size, use_bias=True)
print("logits shape: {}".format(self.logits.get_shape().as_list()))
self.variable_summaries(self.logits)