def _add_embedding_lookup(self):
with tf.variable_scope('word_embeddings'):
if self.cfg.use_word_emb:
_word_emb = tf.Variable(self.cfg.word_emb, name='_word_emb', trainable=self.cfg.finetune_emb,
dtype=tf.float32)
else:
_word_emb = tf.get_variable(name='_word_emb', shape=[self.cfg.vocab_size, self.cfg.word_dim],
trainable=True, dtype=tf.float32)
word_emb = tf.nn.embedding_lookup(_word_emb, self.word_ids, name='word_emb')
if self.cfg.use_char_emb: # use cnn to generate chars representation
with tf.variable_scope('char_embeddings'):
_char_emb = tf.get_variable(name='_char_emb', dtype=tf.float32, trainable=True,
shape=[self.cfg.char_vocab_size, self.cfg.char_dim])
char_emb = tf.nn.embedding_lookup(_char_emb, self.char_ids, name='char_emb')
char_emb_shape = tf.shape(char_emb)
char_rep = multi_conv1d(char_emb, self.cfg.filter_sizes, self.cfg.heights, "VALID", self.is_train,
self.cfg.keep_prob, scope="char_cnn")
char_rep = tf.reshape(char_rep, [char_emb_shape[0], char_emb_shape[1], self.cfg.char_rep_dim])
word_emb = tf.concat([word_emb, char_rep], axis=-1) # concat word emb and corresponding char rep
if self.cfg.use_highway:
self.word_emb = highway_network(word_emb, self.cfg.highway_num_layers, bias=True, is_train=self.is_train,
keep_prob=self.cfg.keep_prob)
else:
self.word_emb = dropout(word_emb, keep_prob=self.cfg.keep_prob, is_train=self.is_train)
print('word embedding shape: {}'.format(self.word_emb.get_shape().as_list()))
def _build_embedding_op(self):
with tf.variable_scope("embeddings"):
if not self.cfg["use_pretrained"]:
self.word_embeddings = tf.get_variable(name="emb", dtype=tf.float32, trainable=True,
shape=[self.word_vocab_size, self.cfg["emb_dim"]])
else:
word_emb_1 = tf.Variable(np.load(self.cfg["pretrained_emb"])["embeddings"], name="word_emb_1",
dtype=tf.float32, trainable=self.cfg["tuning_emb"])
word_emb_2 = tf.get_variable(name="word_emb_2", shape=[3, self.cfg["emb_dim"]], dtype=tf.float32,
trainable=True) # For UNK, NUM and END
self.word_embeddings = tf.concat([word_emb_1, word_emb_2], axis=0)
word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.words, name="word_emb")
print("word embedding shape: {}".format(word_emb.get_shape().as_list()))
if self.cfg["use_chars"]:
self.char_embeddings = tf.get_variable(name="c_emb", dtype=tf.float32, trainable=True,
shape=[self.char_vocab_size, self.cfg["char_emb_dim"]])
char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.chars, name="chars_emb")
# train char representation
if self.cfg["char_represent_method"] == "rnn":
char_bi_rnn = BiRNN(self.cfg["char_num_units"], cell_type=self.cfg["cell_type"], scope="c_bi_rnn")
char_represent = char_bi_rnn(char_emb, self.char_seq_len, use_last_state=True)
else:
char_represent = multi_conv1d(char_emb, self.filter_sizes, self.channel_sizes,
drop_rate=self.drop_rate,
is_train=self.is_train)
print("chars representation shape: {}".format(char_represent.get_shape().as_list()))
word_emb = tf.concat([word_emb, char_represent], axis=-1)
if self.cfg["use_highway"]:
self.word_emb = highway_network(word_emb, self.cfg["highway_layers"], use_bias=True, bias_init=0.0,
keep_prob=self.keep_prob, is_train=self.is_train)
else:
self.word_emb = tf.layers.dropout(word_emb, rate=self.drop_rate, training=self.is_train)
print("word and chars concatenation shape: {}".format(self.word_emb.get_shape().as_list()))
def _build_embeddings_op(self):
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 _add_embedding_lookup(self):
with tf.variable_scope('word_embeddings'):
if self.cfg.use_word_emb:
_word_emb = tf.Variable(self.cfg.word_emb,
name='_word_emb',
trainable=self.cfg.finetune_emb,
dtype=tf.float32)
else:
_word_emb = tf.get_variable(
name='_word_emb',
shape=[self.cfg.vocab_size, self.cfg.word_dim],
trainable=True,
dtype=tf.float32)
word_emb = tf.nn.embedding_lookup(_word_emb,
self.word_ids,
name='word_emb')
if self.cfg.use_char_emb: # use cnn to generate chars representation
with tf.variable_scope('char_embeddings'):
_char_emb = tf.get_variable(
name='_char_emb',
dtype=tf.float32,
trainable=True,
shape=[self.cfg.char_vocab_size, self.cfg.char_dim])
char_emb = tf.nn.embedding_lookup(_char_emb,
self.char_ids,
name='char_emb')
char_emb_shape = tf.shape(char_emb)
char_rep = multi_conv1d(char_emb,
self.cfg.filter_sizes,
self.cfg.heights,
"VALID",
self.is_train,
self.cfg.keep_prob,
scope="char_cnn")
char_rep = tf.reshape(char_rep, [
char_emb_shape[0], char_emb_shape[1], self.cfg.char_rep_dim
])
word_emb = tf.concat(
[word_emb, char_rep],
axis=-1) # concat word emb and corresponding char rep
if self.cfg.use_highway:
self.word_emb = highway_network(word_emb,
self.cfg.highway_num_layers,
bias=True,
is_train=self.is_train,
keep_prob=self.cfg.keep_prob)
else:
self.word_emb = dropout(word_emb,
keep_prob=self.cfg.keep_prob,
is_train=self.is_train)
print('word embedding shape: {}'.format(
self.word_emb.get_shape().as_list()))
def _build_embeddings_op(self):
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")
s = tf.shape(
char_embeddings
) # [batch size, max length of sentence, max length of word, char_dim]
if self.cfg.char_rep_method == 'rnn':
char_embeddings = tf.reshape(
char_embeddings,
shape=[s[0] * s[1], s[-2], self.cfg.char_dim])
word_lengths = tf.reshape(self.word_lengths,
shape=[s[0] * s[1]])
char_bi_rnn = BiRNN(self.cfg.num_units_char,
scope='char_rnn')
output = char_bi_rnn(char_embeddings,
word_lengths,
return_last_state=True)
else: # cnn model for char representation
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)
def _add_embedding_lookup(self):
with tf.variable_scope('word_embeddings'):
if self.cfg.use_word_emb:
#注意这种用法 声明具有初始值的变量
_word_emb = tf.Variable(self.cfg.word_emb,
name='_word_emb',
trainable=self.cfg.finetune_emb,
dtype=tf.float32)
else:
#声明一般变量
_word_emb = tf.get_variable(
name='_word_emb',
shape=[self.cfg.vocab_size, self.cfg.word_dim],
trainable=True,
dtype=tf.float32)
word_emb = tf.nn.embedding_lookup(_word_emb,
self.word_ids,
name='word_emb')
if self.cfg.use_char_emb: # use cnn to generate chars representation
with tf.variable_scope('char_embeddings'):
_char_emb = tf.get_variable(
name='_char_emb',
dtype=tf.float32,
trainable=True,
shape=[self.cfg.char_vocab_size, self.cfg.char_dim])
#在理解的时候采用一个样本进行理解
char_emb = tf.nn.embedding_lookup(
_char_emb, self.char_ids, name='char_emb'
) # 这里我要搞清楚这个更！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
print("许海明的测试-------》char_emb的形状",
char_emb.get_shape().as_list())
#这一步类似于一个图片 不过不是RGB 而是一句话最长的单词的数目
char_emb_shape = tf.shape(char_emb) #这里需要注意一下 这里的形状要搞清楚
# [-1,max_len_sen,char_out_size*len([fileter])]
char_rep = multi_conv1d(char_emb,
self.cfg.filter_sizes,
self.cfg.heights,
"VALID",
self.is_train,
self.cfg.keep_prob,
scope="char_cnn")
char_rep = tf.reshape(char_rep, [
char_emb_shape[0], char_emb_shape[1], self.cfg.char_rep_dim
])
word_emb = tf.concat(
[word_emb, char_rep],
axis=-1) # concat word emb and corresponding char rep
if self.cfg.use_highway:
self.word_emb = highway_network(word_emb,
self.cfg.highway_num_layers,
bias=True,
is_train=self.is_train,
keep_prob=self.cfg.keep_prob)
else:
self.word_emb = dropout(word_emb,
keep_prob=self.cfg.keep_prob,
is_train=self.is_train)
print('word embedding shape: {}'.format(
self.word_emb.get_shape().as_list()))