def build_model(self):
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC' # A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc.
if self.memory_fraction:
config.gpu_options.per_process_gpu_memory_fraction = self.memory_fraction
config.gpu_options.allow_growth = False
else:
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
# 补充输入
subject_labels = Input(shape=(None, 2), name='Subject-Labels')
subject_ids = Input(shape=(2, ), name='Subject-Ids')
object_labels = Input(shape=(None, self.num_classes, 2),
name='Object-Labels')
# 加载预训练模型
bert = build_transformer_model(
config_path=self.bert_config_path,
checkpoint_path=self.bert_checkpoint_path,
return_keras_model=False,
)
# 预测subject
output = Dense(units=2,
activation='sigmoid',
kernel_initializer=bert.initializer)(bert.model.output)
subject_preds = Lambda(lambda x: x**2)(output)
self.subject_model = Model(bert.model.inputs, subject_preds)
# 传入subject，预测object
# 通过Conditional Layer Normalization将subject融入到object的预测中
output = bert.model.layers[-2].get_output_at(-1)
subject = Lambda(self.extrac_subject)([output, subject_ids])
output = LayerNormalization(conditional=True)([output, subject])
output = Dense(units=self.num_classes * 2,
activation='sigmoid',
kernel_initializer=bert.initializer)(output)
output = Lambda(lambda x: x**4)(output)
object_preds = Reshape((-1, self.num_classes, 2))(output)
self.object_model = Model(bert.model.inputs + [subject_ids],
object_preds)
# 训练模型
self.model = Model(
bert.model.inputs + [subject_labels, subject_ids, object_labels],
[subject_preds, object_preds])
mask = bert.model.get_layer('Embedding-Token').output_mask
mask = K.cast(mask, K.floatx())
subject_loss = K.binary_crossentropy(subject_labels, subject_preds)
subject_loss = K.mean(subject_loss, 2)
subject_loss = K.sum(subject_loss * mask) / K.sum(mask)
object_loss = K.binary_crossentropy(object_labels, object_preds)
object_loss = K.sum(K.mean(object_loss, 3), 2)
object_loss = K.sum(object_loss * mask) / K.sum(mask)
self.model.add_loss(subject_loss + object_loss)
AdamEMA = extend_with_exponential_moving_average(Adam, name='AdamEMA')
self.optimizer = AdamEMA(lr=1e-4)
def build_model():
bert_model = build_transformer_model(
config_path=Config.config_path,
checkpoint_path=Config.checkpoint_path,
return_keras_model=False)
# 补充输入
subject_labels = Input(shape=(None, 2))
subject_ids = Input(shape=(2, ))
object_labels = Input(shape=(None, len(predicate2id), 2))
# 预测subject
output = Dense(units=2,
activation='sigmoid',
kernel_initializer=bert_model.initializer)(
bert_model.model.output)
subject_preds = Lambda(lambda x: x**2)(output)
subject_model = Model(bert_model.inputs, subject_preds)
# 传入subject，预测object
output = bert_model.model.layers[-2].get_output_at(-1)
subject = Lambda(extrac_subject)([output, subject_ids])
output = LayerNormalization(conditional=True)([output, subject])
output = Dense(units=len(predicate2id) * 2,
activation='sigmoid',
kernel_initializer=bert_model.initializer)(output)
output = Lambda(lambda x: x**4)(output)
object_preds = Reshape((-1, len(predicate2id), 2))(output)
object_model = Model(bert_model.model.inputs + [subject_ids], object_preds)
# 训练模型
train_model = Model(
bert_model.model.inputs + [subject_labels, subject_ids, object_labels],
[subject_preds, object_preds])
mask = bert_model.model.get_layer('Embedding-Token').output_mask
mask = K.cast(mask, K.floatx())
subject_loss = K.binary_crossentropy(subject_labels, subject_preds)
subject_loss = K.mean(subject_loss, 2)
subject_loss = K.sum(subject_loss * mask) / K.sum(mask)
object_loss = K.binary_crossentropy(object_labels, object_preds)
object_loss = K.sum(K.mean(object_loss, 3), 2)
object_loss = K.sum(object_loss * mask) / K.sum(mask)
train_model.add_loss(subject_loss + object_loss)
optimizer = Adam(Config.learning_rate)
train_model.compile(optimizer=optimizer)
return train_model, subject_model, object_model
def build(self, input_shape):
super(ResidualGatedConv1D, self).build(input_shape)
self.conv1d = Conv1D(
filters=self.filters * 2,
kernel_size=self.kernel_size,
dilation_rate=self.dilation_rate,
padding='same',
)
self.layernorm = LayerNormalization()
if self.filters != input_shape[-1]:
self.dense = Dense(self.filters, use_bias=False)
self.alpha = self.add_weight(
name='alpha', shape=[1], initializer='zeros'
)
def build_model():
"""
搭建模型主体。
:return: 模型对象
"""
with SESS.as_default():
with SESS.graph.as_default():
# 构建bert模型主体
bert_model = build_transformer_model(
config_path=bert_config.config_path,
checkpoint_path=bert_config.checkpoint_path,
return_keras_model=False,
model=bert_config.model_type
)
# l为模型内部的层名，格式为--str
for l in bert_model.layers:
bert_model.model.get_layer(l).trainable = True
# 动词起始，终止下标，keras会自动补充batch这一维度
# [batch_size, 1]
trigger_start_index = Input(shape=(1,))
trigger_end_index = Input(shape=(1,))
# 将动词下标对应位置的子向量抽取出来并计算均值
k1v = Lambda(seq_gather)([bert_model.model.output, trigger_start_index])
k2v = Lambda(seq_gather)([bert_model.model.output, trigger_end_index])
kv = Average()([k1v, k2v])
# 融合动词词向量的句子张量
t = LayerNormalization(conditional=True)([bert_model.model.output, kv])
# 取出[CLS]对应的向量用来做分类
t = Lambda(lambda x: x[:, 0])(t)
# 预测事件状态
state_out_put = Dense(3, activation='softmax')(t)
# 构建状态预测模型
state_model = Model(bert_model.model.inputs + [trigger_start_index, trigger_end_index], state_out_put)
# 设置学习率、优化器、损失函数以及每个批次的评估指标
state_model.compile(loss='sparse_categorical_crossentropy',
optimizer=Adam(state_train_config.learning_rate),
metrics=['accuracy'])
state_model.summary()
return state_model
def get_state_model():
"""
构建事件状态模型，加载模型参数，返回模型对象
使用bert输出融合动词下标预测事件状态
:return: state_model
"""
with state_sess.as_default():
with state_sess.graph.as_default():
# 构建bert模型主体
bert_model = build_transformer_model(
config_path=bert_config.config_path,
return_keras_model=False,
model=bert_config.model_type
)
# 动词下标输入
trigger_start_index = Input(shape=(1,))
trigger_end_index = Input(shape=(1,))
# 获取动词向量
k1v = Lambda(seq_gather)([bert_model.model.output, trigger_start_index])
k2v = Lambda(seq_gather)([bert_model.model.output, trigger_end_index])
kv = Average()([k1v, k2v])
# 将动词向量与bert模型输出也就是句子张量进行融合
t = LayerNormalization(conditional=True)([bert_model.model.output, kv])
# 取出[CLS]对应的向量用来做分类
t = Lambda(lambda x: x[:, 0])(t)
# 预测事件状态
state_out_put = Dense(3, activation='softmax')(t)
# 主模型
state_model = Model(bert_model.model.inputs + [trigger_start_index, trigger_end_index], state_out_put)
# 加载模型
logger.info("开始加载事件状态模型参数。。。")
state_model.load_weights(pre_config.event_state_model_path)
logger.info("事件状态模型参数加载完成！")
return state_model
return_keras_model=False,
)
# 预测subject
output = Dense(units=2,
activation='sigmoid',
kernel_initializer=bert.initializer)(bert.model.output)
subject_preds = Lambda(lambda x: x**2)(output)
subject_model = Model(bert.model.inputs, subject_preds)
# 传入subject，预测object
# 通过Conditional Layer Normalization将subject融入到object的预测中
output = bert.model.layers[-2].get_output_at(-1)
subject = Lambda(extrac_subject)([output, subject_ids])
output = LayerNormalization(conditional=True)([output, subject])
output = Dense(units=len(predicate2id) * 2,
activation='sigmoid',
kernel_initializer=bert.initializer)(output)
output = Lambda(lambda x: x**4)(output)
object_preds = Reshape((-1, len(predicate2id), 2))(output)
object_model = Model(bert.model.inputs + [subject_ids], object_preds)
# 训练模型
train_model = Model(
bert.model.inputs + [subject_labels, subject_ids, object_labels],
[subject_preds, object_preds])
# train_model.summary()
def build_model():
"""
调用模型参数，搭建事件抽取模型主体，先搭建触发词模型，然后围绕触发词下标搭建其他论元模型。
:return: 各个论元模型对象
"""
with SESS.as_default():
with SESS.graph.as_default():
# 构建bert模型主体
bert_model = build_transformer_model(
config_path=bert_config.config_path,
checkpoint_path=bert_config.checkpoint_path,
return_keras_model=False,
model=bert_config.model_type)
# l为模型内部的层名，格式为--str
for l in bert_model.layers:
bert_model.model.get_layer(l).trainable = True
# 搭建模型
# keras会自动对所有的占位张量添加batch_size维度
# 动词输入 (batch_size, seq_len)
trigger_start_in = Input(shape=(None, ))
trigger_end_in = Input(shape=(None, ))
# 动词下标输入 (batch_size, seq_len)
trigger_index_start_in = Input(shape=(1, ))
trigger_index_end_in = Input(shape=(1, ))
# 宾语输入 (batch_size, seq_len)
object_start_in = Input(shape=(None, ))
object_end_in = Input(shape=(None, ))
# 主语输入 (batch_size, seq_len)
subject_start_in = Input(shape=(None, ))
subject_end_in = Input(shape=(None, ))
# 地点输入 (batch_size, seq_len)
loc_start_in = Input(shape=(None, ))
loc_end_in = Input(shape=(None, ))
# 时间输入 (batch_size, seq_len)
time_start_in = Input(shape=(None, ))
time_end_in = Input(shape=(None, ))
# 否定词输入 (batch_size, seq_len)
negative_start_in = Input(shape=(None, ))
negative_end_in = Input(shape=(None, ))
# 将输入的占位符赋值给相应的变量(此处只是在使用时方便，没有其他的模型结构意义)
# 动词输入
trigger_start, trigger_end = trigger_start_in, trigger_end_in
# 动词下标
trigger_index_start, trigger_index_end = trigger_index_start_in, trigger_index_end_in
# 宾语输入
object_start, object_end = object_start_in, object_end_in
# 主语输入
subject_start, subject_end = subject_start_in, subject_end_in
# 地点输入
loc_start, loc_end = loc_start_in, loc_end_in
# 时间输入
time_start, time_end = time_start_in, time_end_in
# 否定词输入
negative_start, negative_end = negative_start_in, negative_end_in
# bert_model.model.inputs为列表格式，含有两个张量，[token_ids(batch, seq_len), segment_ids(batch, seq_len)]
# mask操作，将bert模型的输入的token_ids序列，进行维度扩充[batch_size, seq_len, 1],
# 然后将初始填充为0的地方全部都用0代替，非0的地方都用1占位，
# 这是为后边计算损失做准备，防止计算损失时，前期填充为0的位置也进行反向传播
mask = Lambda(lambda x: K.cast(
K.greater(K.expand_dims(x[0], 2), 0), 'float32'))(
bert_model.model.inputs)
# 计算动词输出的起始终止标签，bert_model.model.output [batch_size, seq_len, 768]
trigger_start_out = Dense(1, activation='sigmoid')(
bert_model.model.output)
trigger_end_out = Dense(1, activation='sigmoid')(
bert_model.model.output)
# 预测trigger动词的模型
trigger_model = Model(bert_model.model.inputs,
[trigger_start_out, trigger_end_out])
# 将动词下标对应位置的子向量抽取出来并计算均值
k1v = Lambda(seq_gather)(
[bert_model.model.output, trigger_index_start])
k2v = Lambda(seq_gather)(
[bert_model.model.output, trigger_index_end])
kv = Average()([k1v, k2v])
# 融合动词词向量的句子张量，用来作为预测其它论元部分的向量
t = LayerNormalization(conditional=True)(
[bert_model.model.output, kv])
# 宾语模型输出
object_start_out = Dense(1, activation='sigmoid')(t)
object_end_out = Dense(1, activation='sigmoid')(t)
# 主语模型输出
subject_start_out = Dense(1, activation='sigmoid')(t)
subject_end_out = Dense(1, activation='sigmoid')(t)
# 地点模型输出
loc_start_out = Dense(1, activation='sigmoid')(t)
loc_end_out = Dense(1, activation='sigmoid')(t)
# 时间模型输出
time_start_out = Dense(1, activation='sigmoid')(t)
time_end_out = Dense(1, activation='sigmoid')(t)
# 否定词模型输出
negative_start_out = Dense(1, activation='sigmoid')(t)
negative_end_out = Dense(1, activation='sigmoid')(t)
# 输入text和trigger，预测object
object_model = Model(
bert_model.model.inputs +
[trigger_index_start_in, trigger_index_end_in],
[object_start_out, object_end_out])
# 输入text和trigger，预测subject
subject_model = Model(
bert_model.model.inputs +
[trigger_index_start_in, trigger_index_end_in],
[subject_start_out, subject_end_out])
# 输入text和trigger，预测loc
loc_model = Model(
bert_model.model.inputs +
[trigger_index_start_in, trigger_index_end_in],
[loc_start_out, loc_end_out])
# 输入text和trigger，预测time
time_model = Model(
bert_model.model.inputs +
[trigger_index_start_in, trigger_index_end_in],
[time_start_out, time_end_out])
# 否定词模型
negative_model = Model(
bert_model.model.inputs +
[trigger_index_start_in, trigger_index_end_in],
[negative_start_out, negative_end_out])
# 主模型
train_model = Model(
bert_model.model.inputs + [
trigger_start_in, trigger_end_in, trigger_index_start_in,
trigger_index_end_in, object_start_in, object_end_in,
subject_start_in, subject_end_in, loc_start_in, loc_end_in,
time_start_in, time_end_in, negative_start_in,
negative_end_in
], [
trigger_start_out, trigger_end_out, object_start_out,
object_end_out, subject_start_out, subject_end_out,
loc_start_out, loc_end_out, time_start_out, time_end_out,
negative_start_out, negative_end_out
])
# 扩充维度, 构造成与mask矩阵相同的结构，方便后续计算模型各部分损失
trigger_start = K.expand_dims(trigger_start, 2)
trigger_end = K.expand_dims(trigger_end, 2)
object_start = K.expand_dims(object_start, 2)
object_end = K.expand_dims(object_end, 2)
subject_start = K.expand_dims(subject_start, 2)
subject_end = K.expand_dims(subject_end, 2)
loc_start = K.expand_dims(loc_start, 2)
loc_end = K.expand_dims(loc_end, 2)
time_start = K.expand_dims(time_start, 2)
time_end = K.expand_dims(time_end, 2)
negative_start = K.expand_dims(negative_start, 2)
negative_end = K.expand_dims(negative_end, 2)
# 构造模型损失函数，使用mask矩阵将前期填充为0的位置全部掩掉，不进行反向传播。
# 动词损失
trigger_start_loss = K.binary_crossentropy(trigger_start,
trigger_start_out)
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
trigger_start_loss = K.sum(trigger_start_loss * mask) / K.sum(mask)
trigger_end_loss = K.binary_crossentropy(trigger_end,
trigger_end_out)
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
trigger_end_loss = K.sum(trigger_end_loss * mask) / K.sum(mask)
# 宾语损失
object_start_loss = K.sum(
K.binary_crossentropy(object_start, object_start_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
object_start_loss = K.sum(object_start_loss * mask) / K.sum(mask)
object_end_loss = K.sum(
K.binary_crossentropy(object_end, object_end_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
object_end_loss = K.sum(object_end_loss * mask) / K.sum(mask)
# 主语损失
subject_start_loss = K.sum(
K.binary_crossentropy(subject_start, subject_start_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
subject_start_loss = K.sum(subject_start_loss * mask) / K.sum(mask)
subject_end_loss = K.sum(
K.binary_crossentropy(subject_end, subject_end_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
subject_end_loss = K.sum(subject_end_loss * mask) / K.sum(mask)
# 地点损失
loc_start_loss = K.sum(
K.binary_crossentropy(loc_start, loc_start_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
loc_start_loss = K.sum(loc_start_loss * mask) / K.sum(mask)
loc_end_loss = K.sum(K.binary_crossentropy(loc_end, loc_end_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
loc_end_loss = K.sum(loc_end_loss * mask) / K.sum(mask)
# 时间损失
time_start_loss = K.sum(
K.binary_crossentropy(time_start, time_start_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
time_start_loss = K.sum(time_start_loss * mask) / K.sum(mask)
time_end_loss = K.sum(K.binary_crossentropy(
time_end, time_end_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
time_end_loss = K.sum(time_end_loss * mask) / K.sum(mask)
# 否定词损失
negative_start_loss = K.sum(
K.binary_crossentropy(negative_start, negative_start_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
negative_start_loss = K.sum(
negative_start_loss * mask) / K.sum(mask)
negative_end_loss = K.sum(
K.binary_crossentropy(negative_end, negative_end_out))
# 使用mask矩阵，将前期填充为0的位置掩掉，不进行反向传播
negative_end_loss = K.sum(negative_end_loss * mask) / K.sum(mask)
# 合并损失
loss = (trigger_start_loss + trigger_end_loss) + (
object_start_loss +
object_end_loss) + (subject_start_loss + subject_end_loss) + (
loc_start_loss +
loc_end_loss) + (time_start_loss + time_end_loss) + (
negative_start_loss + negative_end_loss)
train_model.add_loss(loss)
train_model.compile(
optimizer=Adam(extract_train_config.learning_rate))
train_model.summary()
return trigger_model, subject_model, object_model, time_model, loc_model, negative_model, train_model
# 预测subject
output = Dense(units=2,
activation='sigmoid',
kernel_initializer=bert.initializer)(
bert.model.output) #[? ? 768]->[? ? 2]
subject_preds = Lambda(lambda x: x**2)(output)
subject_model = Model(bert.model.inputs, subject_preds) # text -> sub
# 传入subject，预测object
# 通过Conditional Layer Normalization将subject融入到object的预测中
output = bert.model.layers[-2].get_output_at(-1)
subject = Lambda(extrac_subject)(
[output, subject_ids]) # output[? ? 768] subid[? 2] ->[? 768*2]
output = LayerNormalization(conditional=True)(
[output, subject]) #[? ? 768] mean std 依赖sub-hid
output = Dense(units=len(predicate2id) * 2,
activation='sigmoid',
kernel_initializer=bert.initializer)(output)
output = Reshape(
(-1, len(predicate2id), 2))(output) #[? ? predicate49*2]->[? ? 49 2]
object_preds = Lambda(lambda x: x**4)(output)
object_model = Model(bert.model.inputs + [subject_ids],
object_preds) #sub,text -> obj,predicate
# 训练模型
train_model = Model(
bert.model.inputs + [subject_labels, subject_ids, object_labels],
[subject_preds, object_preds])
def get_extract_model():
"""
构建事件抽取模型结构，加载模型参数，返回模型对象
1、使用bert输出预测动词下标
2、使用bert输出融合动词下标预测事件时间、地点、主语、宾语、否定词
:return: 各个部分的模型对象
"""
with extract_sess.as_default():
with extract_sess.graph.as_default():
# 构建bert模型主体
bert_model = build_transformer_model(
config_path=bert_config.config_path,
return_keras_model=False,
model=bert_config.model_type
)
# 搭建模型
# 动词输入
trigger_start_in = Input(shape=(None,))
trigger_end_in = Input(shape=(None,))
# 动词下标输入
trigger_index_start_in = Input(shape=(1,))
trigger_index_end_in = Input(shape=(1,))
# 宾语输入
object_start_in = Input(shape=(None,))
object_end_in = Input(shape=(None,))
# 主语输入
subject_start_in = Input(shape=(None,))
subject_end_in = Input(shape=(None,))
# 地点输入
loc_start_in = Input(shape=(None,))
loc_end_in = Input(shape=(None,))
# 时间输入
time_start_in = Input(shape=(None,))
time_end_in = Input(shape=(None,))
# 否定词输入
negative_start_in = Input(shape=(None,))
negative_end_in = Input(shape=(None,))
# 将模型外传入的下标赋值给模型内部变量(只是为了将模型中应用与构建Model的输入区分开来)
trigger_index_start, trigger_index_end = trigger_index_start_in, trigger_index_end_in
trigger_start_out = Dense(1, activation='sigmoid')(bert_model.model.output)
trigger_end_out = Dense(1, activation='sigmoid')(bert_model.model.output)
# 预测trigger动词的模型
trigger_model = Model(bert_model.model.inputs, [trigger_start_out, trigger_end_out])
# 按照动词下标采集字向量
k1v = Lambda(seq_gather)([bert_model.model.output, trigger_index_start])
k2v = Lambda(seq_gather)([bert_model.model.output, trigger_index_end])
kv = Average()([k1v, k2v])
# 使用归一化融合动词词向量与句子张量
t = LayerNormalization(conditional=True)([bert_model.model.output, kv])
# 宾语模型输出
object_start_out = Dense(1, activation='sigmoid')(t)
object_end_out = Dense(1, activation='sigmoid')(t)
# 主语模型输出
subject_start_out = Dense(1, activation='sigmoid')(t)
subject_end_out = Dense(1, activation='sigmoid')(t)
# 地点模型输出
loc_start_out = Dense(1, activation='sigmoid')(t)
loc_end_out = Dense(1, activation='sigmoid')(t)
# 时间模型输出
time_start_out = Dense(1, activation='sigmoid')(t)
time_end_out = Dense(1, activation='sigmoid')(t)
# 否定词模型输出
negative_start_out = Dense(1, activation='sigmoid')(t)
negative_end_out = Dense(1, activation='sigmoid')(t)
# 输入text和trigger，预测object
object_model = Model(bert_model.model.inputs + [trigger_index_start_in, trigger_index_end_in],
[object_start_out, object_end_out])
# 输入text和trigger，预测subject
subject_model = Model(bert_model.model.inputs + [trigger_index_start_in, trigger_index_end_in],
[subject_start_out, subject_end_out])
# 输入text和trigger，预测loc
loc_model = Model(bert_model.model.inputs + [trigger_index_start_in, trigger_index_end_in],
[loc_start_out, loc_end_out])
# 输入text和trigger，预测time
time_model = Model(bert_model.model.inputs + [trigger_index_start_in, trigger_index_end_in],
[time_start_out, time_end_out])
# 输入text和trigger，预测否定词negative
negative_model = Model(bert_model.model.inputs + [trigger_index_start_in, trigger_index_end_in],
[negative_start_out, negative_end_out])
# 主模型
train_model = Model(
bert_model.model.inputs + [trigger_start_in, trigger_end_in, trigger_index_start_in, trigger_index_end_in,
object_start_in, object_end_in, subject_start_in, subject_end_in, loc_start_in,
loc_end_in, time_start_in, time_end_in, negative_start_in, negative_end_in],
[trigger_start_out, trigger_end_out, object_start_out, object_end_out, subject_start_out, subject_end_out,
loc_start_out, loc_end_out, time_start_out, time_end_out, negative_start_out, negative_end_out])
# 加载事件抽取模型参数
logger.info("开始加载事件抽取模型参数。。。")
train_model.load_weights(pre_config.event_extract_model_path)
logger.info("事件抽取模型参数加载完成！")
return trigger_model, object_model, subject_model, loc_model, time_model, negative_model
## embed predicate
predicate_n = 49
emb_l = Embedding(predicate_n, 32,
name='p_emb') # not tf.keras.layer, | keras.layer.xx
predicate_emb = emb_l(predicate_id)
output = bert.model.layers[-2].get_output_at(-1)
subject = Lambda(extrac_subject)(
[output, subject_ids]) # output[? ? 768] subid[? 2] ->subject[? 768*2]
###
#subject_predicate=K.concatenate([subject,predicate_emb[:,0,:]],axis=-1)
#subject_predicate=Concatenate(-1)([subject,predicate_emb[:,0,:]])
subject_predicate = Lambda(concat)([subject, predicate_emb])
output = LayerNormalization(conditional=True, name='specialNorm')(
[output, subject_predicate]) #[? ? 768] mean std 依赖sub-hid
output = Dense(
units=2,
#units=len(predicate2id) * 2,
activation='sigmoid',
kernel_initializer=bert.initializer)(output)
#output = Reshape((-1, 2))(output) #[? ? 2]
object_preds = Lambda(lambda x: x**4)(output)
#object_model = Model(bert.model.inputs + [subject_ids], object_preds) #sub,text -> obj,predicate
# 训练模型
# train_model = Model(bert.model.inputs + [subject_labels, subject_ids, object_labels],
# [subject_preds, object_preds])
train_model = Model(
bert.model.inputs + [subject_ids, predicate_id, object_labels],
subject_preds = Lambda(lambda x: x**2)(output) # 把概率平方一下,缓解不平衡问题
# subject_model对应的model
# bert.model.inputs是Input-Token和Input-Segment：0 = {Tensor} Tensor("Input-Token:0", shape=(?, ?), dtype=float32)， 1 = {Tensor} Tensor("Input-Segment:0", shape=(?, ?), dtype=float32)
subject_model = Model(bert.model.inputs, subject_preds)
# 3.2 传入subject_ids，通过指针来影响object的预测
# 通过Conditional Layer Normalization将subject融入到object的预测中
output = bert.model.layers[-2].get_output_at(
-1
) # (?, ?, 768) bert中最后LN不要做，取出来自己用CLN做 获取某一个网络层的输出；get_output_at：keras函数专用共享编码层
subject = Lambda(extrac_subject)(
[output,
subject_ids]) # shape=(?, 1536) 根据subject_ids从output中取出subject首尾的向量表征
output = LayerNormalization(conditional=True)(
[output, subject]
) # 带上subject的预测 本质是用subject(?, 1536)通过Dense变成（？，1,768）去影响归一化中的beta和gamma，就是影响缩放偏移的那个就可以了
# 输入s_ids 加上对应的p 去预测出o 那这里其实跟p的具体值没啥关系
output = Dense(
units=len(predicate2id) * 2, # dense全连接层输出概率
activation='sigmoid',
kernel_initializer=bert.initializer)(output)
output = Lambda(lambda x: x**4)(output)
object_preds = Reshape(
(-1, len(predicate2id),
2))(output) # (?, ?, 49, 2) -1表示不知道第一维的数字的多少，根据后面确定好了之后自动计算出来
# object_model对应的model
object_model = Model(bert.model.inputs + [subject_ids], object_preds)
# 定义loss，把subject和object的预测loss相加即可
