def biLSTM_layer(self, lstm_inputs,lstm_dim,lengths,num_layers,keep_prob=1.):
"""
:param lstm_inputs: [batch_size, num_steps, emb_size]
:return: [batch_size, num_steps, 2*lstm_dim]
"""
batch_size=shape(lstm_inputs,0)
with tf.variable_scope("BiLSTM"):
for layer in range(num_layers):
with tf.variable_scope("layer_{}".format(layer)):
with tf.variable_scope("forward"):
cell_fw = CustomLSTMCell(lstm_dim,batch_size,keep_prob)
with tf.variable_scope("backward"):
cell_bw = CustomLSTMCell(lstm_dim,batch_size,keep_prob)
state_fw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_fw.initial_state.c, [batch_size, 1]),
tf.tile(cell_fw.initial_state.h, [batch_size, 1]))
state_bw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_bw.initial_state.c, [batch_size, 1]),
tf.tile(cell_bw.initial_state.h, [batch_size, 1]))
(fw_outputs, bw_outputs), _ = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
inputs=lstm_inputs,
sequence_length=lengths,
initial_state_fw=state_fw,
initial_state_bw=state_bw
)
text_outputs = tf.concat([fw_outputs, bw_outputs], 2) # [num_sentences, max_sentence_length, emb]
text_outputs = tf.nn.dropout(text_outputs,keep_prob)
if layer > 0:
highway_gates = tf.sigmoid(projection(text_outputs,shape(text_outputs, 2))) # [num_sentences, max_sentence_length, emb]
text_outputs = highway_gates * text_outputs + (1 - highway_gates) * lstm_inputs
lstm_inputs=text_outputs
return lstm_inputs
def _shift(BD): #选元素
"""
convert:
-3 -2 -1 0 1 2
-3 -2 -1 0 1 2
-3 -2 -1 0 1 2
to:
0 1 2
-1 0 1
-2 -1 0
"""
bsz = shape(BD, 0)
n_head = shape(BD, 1)
max_len = shape(BD, 2)
zero_pad = tf.zeros(shape=(bsz, n_head, max_len, 1))
BD = tf.reshape(tf.concat([BD, zero_pad], axis=-1),
shape=(bsz, n_head, -1, max_len))
BD = tf.reshape(BD[:, :, :-1], shape=(bsz, n_head, max_len, -1))
BD = BD[:, :, :, max_len:]
return BD
def project_layer(self, lstm_outputs):
"""
hidden layer between lstm layer and logits
:param lstm_outputs: [batch_size, num_steps, emb_size]
:return: [batch_size, num_steps, num_tags]
"""
num_steps = shape(lstm_outputs, 1)
hidden_size = shape(lstm_outputs, 2)
with tf.variable_scope("project"):
output = tf.reshape(lstm_outputs, shape=[-1, hidden_size])
with tf.variable_scope("logits"):
W = tf.get_variable("W",
shape=[hidden_size, self.num_tags],
dtype=tf.float32,
initializer=self.initializer)
b = tf.get_variable("b",
shape=[self.num_tags],
dtype=tf.float32,
initializer=tf.zeros_initializer())
pred = tf.nn.xw_plus_b(output, W, b)
return tf.reshape(pred, [-1, num_steps, self.num_tags])
def adapting_transformer_layer(self,
batch_input,
mask,
ffnn_size,
num_heads=8,
attn_blocks_num=2,
attention_keep_prob=1.0,
ffnn_keep_prob=1.0):
attn_outs = batch_input
attention_size = shape(attn_outs, -1)
local_bias = get_local_bias(shape(attn_outs, 1), 1)
for block_id in range(attn_blocks_num):
with tf.variable_scope("num_blocks_{}".format(block_id)):
if block_id == 0:
attn_outs = local_multi_head_attention(
attn_outs,
mask,
attention_size,
num_heads,
attention_keep_prob,
reuse=False,
local_bias=local_bias)
else:
attn_outs = relative_multi_head_attention(
attn_outs,
mask,
attention_size,
num_heads,
attention_keep_prob,
reuse=False)
attn_outs = feedforward(attn_outs, [ffnn_size, attention_size],
ffnn_keep_prob,
reuse=False)
return attn_outs
def relative_multi_head_attention(x,
mask,
attention_size,
num_heads,
drop_keep_rate=1.0,
reuse=None):
# borrowed from: https://github.com/Kyubyong/transformer/blob/master/modules.py
with tf.variable_scope("relative_multi_head_attention", reuse=reuse):
# attention size must consistent with queries（keys）'s -1 dim
batch_size = shape(x, 0)
# attention_size = x.get_shape().as_list()[-1]
max_time = shape(x, 1)
pos_embed = relative_positional_encoding(max_time,
attention_size // num_heads,
True)
# linear projections, shape=(batch_size, max_time, attention_size)
query = tf.layers.dense(
x,
attention_size,
use_bias=False,
name="query_project",
kernel_initializer=tf.contrib.layers.xavier_initializer())
# query = tf.layers.dense(x, attention_size, activation=tf.nn.relu, name="query_project",
# kernel_initializer=tf.contrib.layers.xavier_initializer())
# key do not dense in this model
key = x
# value = tf.layers.dense(x, attention_size, activation=tf.nn.relu, name="value_project",
# kernel_initializer=tf.contrib.layers.xavier_initializer())
value = tf.layers.dense(
x,
attention_size,
use_bias=False,
name="value_project",
kernel_initializer=tf.contrib.layers.xavier_initializer())
# split and concatenation, shape=(batch_size, num_heads, max_time, attention_size / num_heads)
query_ = tf.stack(tf.split(query, num_heads, axis=2), axis=1)
key_ = tf.stack(tf.split(key, num_heads, axis=2), axis=1)
# value_ = tf.concat(tf.split(value, num_heads, axis=2), axis=0)
value_ = tf.stack(tf.split(value, num_heads, axis=2), axis=1)
# shape =(num_heads, attention_size / num_heads)
u = tf.get_variable('var_u',
shape=[num_heads, attention_size // num_heads],
initializer=tf.glorot_normal_initializer())
v = tf.get_variable('var_v',
shape=[num_heads, attention_size // num_heads],
initializer=tf.glorot_normal_initializer())
Qu = query_ + u[:, None]
QKuK = tf.einsum('bnqd,bnkd->bnqk', Qu, key_)
vR = tf.einsum('nd,ld->nl', v, pos_embed)[None, :, None]
QR = tf.einsum('bnqd,ld->bnql', query_, pos_embed)
QRvR = QR + vR
QRvR = _shift(QRvR) #
attn_outs = QKuK + QRvR #[batch_size,num_heads,max_time,max_time]
# attn_outs = tf.reshape(attn_outs, shape=(batch_size*num_heads, max_time, max_time))
# attn_outs = tf.concat(tf.unstack(attn_outs, axis=1), axis=0)
# activation
#apply talking heads before softmax
# pre_softmax_weight=tf.get_variable('pre_softmax_weight',shape=[num_heads,num_heads],initializer=tf.glorot_normal_initializer())
# attn_outs=tf.einsum("BNFT,NL->BLFT", attn_outs,pre_softmax_weight)
#print(mask)
ret = (1.0 - tf.cast(mask, tf.float32)) * -1e9
bias = tf.expand_dims(tf.expand_dims(ret, 1), 1)
attn_outs += bias
attn_outs = tf.nn.softmax(attn_outs)
#apply talking heads after softmax
# post_softmax_weight=tf.get_variable('post_softmax_weight',shape=[num_heads,num_heads],initializer=tf.glorot_normal_initializer())
# attn_outs=tf.einsum("BNFT,NL->BLFT", attn_outs,post_softmax_weight)
# dropout
attn_outs = tf.nn.dropout(attn_outs, drop_keep_rate)
# attn_outs = tf.concat(tf.unstack(attn_outs, axis=1), axis=0)
# weighted sum
outputs = tf.matmul(attn_outs, value_)
# restore shape
# outputs = tf.concat(tf.split(outputs, num_heads, axis=0), axis=2)
outputs = _combine_heads(outputs)
# outputs = tf.layers.dense(outputs,attention_size,use_bias=False,name="output_project",
# kernel_initializer=tf.contrib.layers.xavier_initializer())
# residual connection
outputs += x
outputs = tf.keras.layers.LayerNormalization(epsilon=1e-6,
dtype="float32")(outputs)
return outputs
def __init__(self, config, data):
self.config = config
self.data = data
self.num_tags = data.label_alphabet_size
self.gaz_emb_dim = data.gaz_emb_dim
self.word_emb_dim = data.word_emb_dim
self.biword_emb_dim = data.biword_emb_dim
# 参数初始化
self.initializer = initializers.xavier_initializer()
# add placeholders for the model
self.is_train = tf.placeholder(dtype=tf.bool,
shape=[],
name='is_train')
self.word_inputs = tf.placeholder(dtype=tf.int32,
shape=[None, None],
name="word_inputs")
self.biword_inputs = tf.placeholder(dtype=tf.int32,
shape=[None, None],
name='biword_inputs')
self.mask = tf.placeholder(dtype=tf.int32,
shape=[None, None],
name='mask')
self.word_seq_lengths = tf.placeholder(dtype=tf.int32,
shape=[None],
name="word_seq_lengths")
self.batch_label = tf.placeholder(dtype=tf.int32,
shape=[None, None],
name="batch_label")
self.layer_gaz = tf.placeholder(dtype=tf.int32,
shape=[None, None, 4, None],
name="layer_gaz")
self.gaz_mask_input = tf.placeholder(dtype=tf.int32,
shape=[None, None, 4, None],
name="gaz_mask_input")
self.gaz_count = tf.placeholder(dtype=tf.int32,
shape=[None, None, 4, None],
name="gaz_count")
self.embedding_keep_prob = self.get_keep_rate(
self.config['embedding_dropout'], self.is_train)
self.fc_keep_prob = self.get_keep_rate(self.config['fc_dropout'],
self.is_train)
self.attention_keep_prob = self.get_keep_rate(
self.config['attention_dropout'], self.is_train)
self.ffnn_keep_prob = self.get_keep_rate(self.config['ffnn_dropout'],
self.is_train)
batch_size = shape(self.word_inputs, 0)
seq_len = shape(self.word_inputs, 1)
#word
word_embs = self.word_embedding_layer(self.word_inputs,
data.word_alphabet.size(),
self.word_emb_dim)
word_embs = word_embs * tf.expand_dims(
tf.cast(self.mask, dtype=tf.float32), -1)
#biword
biword_embs = self.biword_embedding_layer(self.biword_inputs,
data.biword_alphabet.size(),
self.biword_emb_dim)
biword_embs = biword_embs * tf.expand_dims(
tf.cast(self.mask, dtype=tf.float32), -1)
word_inputs_d = tf.concat([word_embs, biword_embs], -1)
word_inputs_d = tf.nn.dropout(word_inputs_d, self.embedding_keep_prob)
#gaz
gaz_embeds = self.gaz_embedding_layer(self.layer_gaz,
data.gaz_alphabet.size(),
self.gaz_emb_dim)
gaz_embeds = tf.nn.dropout(gaz_embeds, self.embedding_keep_prob)
gaz_embeds = gaz_embeds * (1.0 - tf.expand_dims(
tf.cast(self.gaz_mask_input, dtype=tf.float32), -1)) #
#dropout
count_sum = tf.reduce_sum(self.gaz_count, 3,
keepdims=True) #(b,l,4,gn)每个位置的单词总数
count_sum = tf.reduce_sum(count_sum, 2,
keepdims=True) #(b,l,1,1)#4个位置也要算？
weights = tf.divide(self.gaz_count,
count_sum) #(b,l,4,g)tf.int32/tf.int32=tf.float64
weights = weights * 4
weights = tf.cast(tf.expand_dims(weights, -1), tf.float32)
gaz_embeds = weights * gaz_embeds #(b,l,4,g,e)
gaz_embeds = tf.reduce_sum(gaz_embeds, 3) #(b,l,4,e)
gaz_embeds_cat = tf.reshape(
gaz_embeds,
(batch_size, seq_len, 4 * self.gaz_emb_dim)) #(b,l,4*ge) l=length
word_input_cat = tf.concat([word_inputs_d, gaz_embeds_cat],
-1) #(b,l,we+4*ge)
# inputs = tf.layers.dense(word_input_cat,self.config['attention_size'], name='input_fc',
# kernel_initializer=self.initializer)
#on-lstm
inputs = self.onLSTM_layer(word_input_cat,
self.config['attention_size'],
self.word_seq_lengths, 1, 16)
outputs = self.adapting_transformer_layer(
inputs, self.mask, self.config['ffnn_size'],
self.config['num_heads'], self.config['attn_blocks_num'],
self.attention_keep_prob, self.ffnn_keep_prob)
# fc_dropout
outputs = tf.nn.dropout(outputs, self.fc_keep_prob)
# 分类
self.logits = self.project_layer(outputs)
# crf计算损失
self.loss, self.trans = self.loss_layer(self.logits,
self.word_seq_lengths)
num_train_steps = math.ceil(
self.config['train_examples_len'] /
self.config["batch_size"]) * self.config["epochs"]
num_warmup_steps = int(num_train_steps *
self.config['warmup_proportion'])
self.global_step = tf.train.get_or_create_global_step()
trainable_params = tf.trainable_variables()
for var in trainable_params:
print(" trainable_params name = %s, shape = %s" %
(var.name, var.shape))
self.train_op = optimization.create_optimizer(
trainable_params, self.loss, self.config['other_learning_rate'],
self.config['crf_learning_rate'], num_train_steps,
num_warmup_steps, self.global_step)
# saver of the model
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
def get_predictions(self,input_ids,input_mask,input_lens,segment_ids,a_input_ids,a_labels_ids,a_input_mask,a_input_lens,a_segment_ids,is_train):
self.keep_prob = self.get_keep_rate(self.config['dropout_rate'],is_train)
# self.lstm_keep_prob = self.get_keep_rate(self.config['lstm_dropout'],is_train)
self.attention_keep_prob = self.get_keep_rate(self.config['attention_dropout'],is_train)
model = modeling.BertModel(
config=self.bert_config,
is_training=is_train,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=False
)
output_layer = model.get_sequence_output()
seq_length = shape(output_layer, -2)
#辅助
detect_batch_size = shape(a_input_ids, 0)
detect_a_batch_size = shape(a_input_ids, 1)
a_input_ids = tf.reshape(a_input_ids, [detect_batch_size * detect_a_batch_size, -1])
a_input_mask = tf.reshape(a_input_mask, [detect_batch_size * detect_a_batch_size, -1])
a_labels_ids = tf.reshape(a_labels_ids, [detect_batch_size * detect_a_batch_size, -1])
a_segment_ids =tf.reshape(a_segment_ids,[detect_batch_size * detect_a_batch_size, -1])
a_model = modeling.BertModel(
config=self.bert_config,
is_training=is_train,
input_ids=a_input_ids,
input_mask=a_input_mask,
token_type_ids=a_segment_ids,
use_one_hot_embeddings=False
)
a_output_layer=a_model.get_sequence_output()
a_labels_emb = self.label_embedding_layer(a_labels_ids,True)
label_emb_size = shape(a_labels_emb, -1)
a_labels_emb = tf.reshape(a_labels_emb, [detect_batch_size, detect_a_batch_size, -1, label_emb_size])
a_hidden_size = shape(a_output_layer, -1)
a_hidden_input = tf.reshape(a_output_layer, [detect_batch_size, detect_a_batch_size, -1, a_hidden_size])
hidden_input = tf.expand_dims(output_layer, 1)
a_hidden_input = tf.transpose(a_hidden_input, [0, 1, 3, 2])
temp_feature = tf.matmul(hidden_input, a_hidden_input)
prob_feature = tf.nn.softmax(temp_feature)
aug_represent = tf.matmul(prob_feature, a_labels_emb)
aug_represent = tf.reduce_mean(aug_represent, axis=1)
final_represent = tf.concat([output_layer, aug_represent], 2)
final_represent = tf.nn.dropout(final_represent,keep_prob=self.keep_prob)
attention_outputs = self.self_attention(final_represent,input_mask,keep_prob=self.attention_keep_prob)
with tf.variable_scope("logits"):
final_size=shape(attention_outputs,-1)
output_weight = tf.get_variable(
"output_weights", [self.num_tags,final_size],
initializer=tf.truncated_normal_initializer(stddev=0.02)
)
output_bias = tf.get_variable(
"output_bias", [self.num_tags], initializer=tf.zeros_initializer()
)
output_layer = tf.reshape(attention_outputs, [-1, final_size])
logits = tf.matmul(output_layer,output_weight, transpose_b=True)
logits = tf.reshape(logits,[-1, seq_length, self.num_tags])
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits, shape=(-1, seq_length, self.num_tags))
return logits
