def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
# cinn = tf.keras.layers.Input(shape=(self.clen,), name='context_input')
# qinn = tf.keras.layers.Input(shape=(self.qlen,), name='question_input')
#
# embedding_layer = tf.keras.layers.Embedding(self.max_features,
# self.emb_size,
# embeddings_initializer='uniform',
# )
# cemb = embedding_layer(cinn)
# qemb = embedding_layer(qinn)
cinn = tf.keras.layers.Input(shape=(2, ), name='context_input')
qinn = tf.keras.layers.Input(shape=(2, ), name='question_input')
#context部分的Glove与char卷积拼接
Glove_c_matrix = cinn[0]
c_char_dict = cinn[1]
#将字符的向量矩阵卷积池化
cemb = np.zeros((Glove_c_matrix.shape))
for key in c_char_dict:
char_embedding_matrx = c_char_dict[key]
#char_embedding_matrx 30*100的矩阵
char_embedding = tf.layers.conv1d(char_embedding_matrx, 5, 3)
#5 28*1D
char_embedding = tf.layers.max_pooling1d(char_embedding,
pool_size=2,
strides=2)
#将多通道合成一个通道
char_embedding = tf.concat(0, char_embedding)
#将词对应的GLove跟char矩阵融合
cemb[key] = tf.concat(0, [Glove_c_matrix[key], char_embedding])
# question部分的Glove与char卷积拼接
Glove_q_matrix = qinn[0]
q_char_dict = qinn[1]
# 将字符的向量矩阵卷积池化
qemb = np.zeros((Glove_q_matrix.shape))
for key in q_char_dict:
char_embedding_matrx = q_char_dict[key]
# char_embedding_matrx 30*100的矩阵
char_embedding = tf.layers.conv1d(char_embedding_matrx, 5, 3)
# 5 28*1D
char_embedding = tf.layers.max_pooling1d(char_embedding,
pool_size=2,
strides=2)
# 将多通道合成一个通道
char_embedding = tf.concat(0, char_embedding)
# 将词对应的GLove跟char矩阵融合
cemb[key] = tf.concat(0, [Glove_q_matrix[key], char_embedding])
for i in range(self.num_highway_layers):
"""
使用两层高速神经网络
"""
highway_layer = layers.Highway(name=f'Highway{i}')
chighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'QHighway{i}')
cemb = chighway(cemb)
qemb = qhighway(qemb)
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [cinn, qinn]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
###################模型定义#######################
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
w_cinn = tf.keras.layers.Input(shape=(self.clen,), name='context_word_input') #单词,shape不含batch_size
w_qinn = tf.keras.layers.Input(shape=(self.qlen,), name='question__word_input') #单词
#加载glove向量
vocab_size=123252 #vocab_size
embedding_matrix=preprocess.load_glove()
word_embedding_layer=tf.keras.layers.Embedding(vocab_size,300,weights=[embedding_matrix],trainable=False)
wc_emb=word_embedding_layer(w_cinn)
wq_emb=word_embedding_layer(w_qinn)
#CharCnn
c_cinn = tf.keras.layers.Input(shape=(self.clen, 20), name='context_word_input') # char
c_qinn = tf.keras.layers.Input(shape=(self.qlen, 20), name='question__word_input') # char
char_embedding_layer=tf.keras.layers.Embedding(self.max_features,self.emb_size,embeddings_initializer='uniform')
cc_emb=char_embedding_layer(c_cinn)
cq_emb=char_embedding_layer(c_qinn)
cc_emb=tf.reshape(cc_emb,shape=[None,20,self.emb_size])
cq_emb=tf.reshape(cq_emb,shape=[None,20,self.emb_size])
conv1d=tf.keras.layers.Conv1D(filters=6,kernel_size=4,padding='same',activation="relu") #input_shape
cc_emb=tf.transpose(cc_emb,perm=[0,2,1])
cq_emb=tf.transpose(cq_emb,perm=[0,2,1])
cc_emb=conv1d(cc_emb) #[b*seq_len,6,xx]
cq_emb=conv1d(cq_emb)
#最大池化
cc_emb=tf.transpose(cc_emb,perm=[0,2,1])
cq_emb=tf.transpose(cq_emb,perm=[0,2,1])
max_pool_1d=tf.keras.layers.GlobalMaxPooling1D()
cc_emb=tf.reshape(max_pool_1d(cc_emb),shape=[None,self.clen,6])
cq_emb=tf.reshape(max_pool_1d(cq_emb),shape=[None,self.qlen,6])
#concat
cemb=tf.concat([wc_emb,cc_emb],axis=-1)
qemb=tf.concat([wq_emb,cq_emb],axis=-1)
#全连接
dense_1=tf.keras.layers.Dense(self.emb_size,activation=tf.keras.activations.softmax)
cemb = dense_1(cemb)
qemb = dense_1(qemb)
# cinn = tf.keras.layers.Input(shape=(self.clen,), name='context_input') #可看作placeholder
# qinn = tf.keras.layers.Input(shape=(self.qlen,), name='question_input')
# embedding_layer = tf.keras.layers.Embedding(self.max_features,
# self.emb_size,
# embeddings_initializer='uniform',
# )
# cemb = embedding_layer(cinn) #看作tf.nn.embedding_lookup()
# qemb = embedding_layer(qinn) # Model方式，下一层在call中包住上一层
for i in range(self.num_highway_layers):
"""
使用两层高速神经网络
"""
highway_layer = layers.Highway(name=f'Highway{i}') #自定义网络：Layer
chighway = tf.keras.layers.TimeDistributed(highway_layer, name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer, name=f'QHighway{i}')
cemb = chighway(cemb) #输入进入
qemb = qhighway(qemb)
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'
), name='BiRNNEncoder'
)
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer') #相似度
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode) #代码需补充
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'
), name=f'BiRNNDecoder{i}'
)
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer([cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob]) #最终输出
# inn = [cinn, qinn] #输入
inn = [c_cinn,w_cinn, c_qinn,w_qinn] #输入
self.model = tf.keras.models.Model(inn, out) #固定：输入、输出（fit时数据要对应,多任务out也可以是list），代替Sequential
self.model.summary(line_length=128) #输出各层参数状况：类似tf 1.x的summary
###############模型编译######################
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(
optimizer=optimizer, #优化器
loss=negative_avg_log_error, #计算loss,多任务可以用list,可以设置loss_weights=[loss1权重，loss2权重。。。]
metrics=[accuracy] #评估指标
)
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
# cinn_c = tf.keras.layers.Input(shape=(self.clen,self.max_char_len), name='context_input_char')
# qinn_c = tf.keras.layers.Input(shape=(self.qlen,self.max_char_len), name='question_input_char')
# embedding_layer_char = tf.keras.layers.Embedding(self.max_features, self.emb_size, embeddings_initializer='uniform')
#
# emb_cc = embedding_layer_char(cinn_c)
# emb_qc = embedding_layer_char(qinn_c)
#
# c_conv_out = []
# filter_sizes = sum(list(np.array(self.conv_layers).T[0]))
# assert filter_sizes==self.emb_size
# for filters, kernel_size in self.conv_layers:
# conv = tf.keras.layers.Conv2D(filters=filters,kernel_size=[kernel_size,self.emb_size],strides=1,activation='relu',padding='same')(emb_cc)
# conv = tf.reduce_max(conv, 2)
# c_conv_out.append(conv)
# c_conv_out = tf.keras.layers.concatenate(c_conv_out)
#
# q_conv_out = []
# for filters, kernel_size in self.conv_layers:
# conv = tf.keras.layers.Conv2D(filters=filters,kernel_size=[kernel_size,self.emb_size],strides=1,activation='relu',padding='same')(emb_qc)
# conv = tf.reduce_max(conv, 2)
# q_conv_out.append(conv)
# q_conv_out = tf.keras.layers.concatenate(q_conv_out)
cinn_w = tf.keras.layers.Input(shape=(self.clen,), name='context_input_word')
qinn_w = tf.keras.layers.Input(shape=(self.qlen,), name='question_input_word')
embedding_layer_word = tf.keras.layers.Embedding(self.vocab_size, self.emb_size,
embeddings_initializer=tf.constant_initializer(np.array(self.embedding_matrix)), trainable=False)
emb_cw = embedding_layer_word(cinn_w)
emb_qw = embedding_layer_word(qinn_w)
print('emb_cw',emb_cw.shape)
# cemb = tf.concat([emb_cw, c_conv_out], axis=2)
# qemb = tf.concat([emb_qw, q_conv_out], axis=2)
cemb = emb_cw
qemb = emb_qw
print('cemb',cemb.shape)
for i in range(self.num_highway_layers):
"""
使用两层高速神经网络
"""
highway_layer = layers.Highway(name=f'Highway{i}')
chighway = tf.keras.layers.TimeDistributed(highway_layer, name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer, name=f'QHighway{i}')
cemb = chighway(cemb)
qemb = qhighway(qemb)
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'
), name='BiRNNEncoder'
)
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'
), name=f'BiRNNDecoder{i}'
)
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer([cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [ cinn_w, qinn_w]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(
optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy]
)
def build_model(self):
cinn = tf.keras.layers.Input(shape=(self.clen, ), name='CInn')
qinn = tf.keras.layers.Input(shape=(self.qlen, ), name='QInn')
embedding_layer = tf.keras.layers.Embedding(self.max_features,
self.emb_size)
cemb = embedding_layer(cinn)
qemb = embedding_layer(qinn)
for i in range(self.num_highway_layers):
highway_layer = layers.Highway(name=f'Highway{i}')
chighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'QHighway{i}')
cemb = chighway(cemb)
qemb = qhighway(qemb)
encoder_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
cencode = encoder_layer(cemb)
qencode = encoder_layer(qemb)
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [cinn, qinn]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
'''
layers.Input(
shape=None,
batch_size=None,
name=None,
dtype=None,
sparse=False,
tensor=None,
ragged=False,
**kwargs,
)
# Word Embedding Layer
# 用GloVe初始化 word embedding
'''
cinn_c = tf.keras.layers.Input(shape=(self.clen,), self.max_char_len,name='context_input_char')
qinn_c = tf.keras.layers.Input(shape=(self.qlen,), self.max_char_lenname='question_input_char')
embedding_layer_char = tf.keras.layers.Embedding(self.max_features, # 词汇表最大数量
self.emb_size, # 词向量维度
embeddings_initializer='uniform',
)
'''
input_dim 词汇表的维度(总共有多少个不相同的词)
output_dim 嵌入词空间的维度
input_length 输入语句的长度
embeddings_initializer
embeddings_regularizer
embeddings_constraint
mask_zero
输入形状:二维张量(batch_size,input_length)
输出形状:三维张量(batch_size,input_length,output_dim)
'''
# 经过embedding得到emb_cc ，emb_cq
# Char Embedding Layer：通过character-level的CNNs，把每个词映射到向量空间
emb_cc = embedding_layer_char(cinn_c)
emb_cq = embedding_layer_char(qinn_c)
# 对c,q分别进行卷积和池化
c_conv_out = []
q_conv_out = []
filter_sizes = sum(list(np.array(self.conv_layers).T[0]))
assert filter_sizes == self.emb_size
# 卷积
# filters：卷积过滤器的数量,对应输出的维数
# kernel_size：整数,过滤器的大小,如果为一个整数，则宽和高相同
# strides：横向和纵向的步长,如果为一个整数，则横向和纵向相同
# activation：激活函数,None是线性函数
# padding：same表示不够卷积核大小的块就补0,所以输出和输入形状相同。（valid:表示不够卷积核大小的块,则丢弃）
for filters, kernel_size in self.conv_layers:
conv = tf.keras.layers.Conv2D(filters=filters, kernel_size=[kernel_size, self.emb_size], strides=1,
activation='relu',padding='same')(emb_cc)
conv = tf.reduce_max(conv, 2) # 池化
c_conv_out.append(conv)
conv = tf.keras.layers.Conv2D(filters=filters, kernel_size=[kernel_size, self.emb_size], strides=1,
activation='relu',padding='same')(emb_cq)
conv = tf.reduce_max(conv, 2) # 池化
q_conv_out.append(conv)
c_conv_out = tf.keras.layers.concatanate(c_conv_out)
q_conv_out = tf.keras.layers.concatanate(q_conv_out)
cinn_w = tf.keras.layers.Input(shape=(self.clen,), name='context_input_word')
qinn_w = tf.keras.layers.Input(shape=(self.qlen,), name='question_input_word')
# Word Embedding Layer：通过训练好的word embedding将每个词映射到一个向量空间。
embedding_layer_word = tf.keras.layers.Embedding(
self.vocab_size, # 在此是词典大小
self.emb_size,
embeddings_initializer=tf.keras.layers_initializer(self.embedding_matrix), trainable=False
)
emb_cw = embedding_layer_word(cinn_w)
emb_qw = embedding_layer_word(qinn_w)
cemb = tf.concat([emb_cw, c_conv_out], axis = 2) # tf.concatt把多个array沿着某一个维度拼接在一起，成100dim
qemb = tf.concat([emb_qw, q_conv_out], axis = 2)
for i in range(self.num_highway_layers): # 高速神经网络的个数 2
"""
使用两层高速神经网络
"""
highway_layer = layers.Highway(name=f'Highway{i}')
chighway = tf.keras.layers.TimeDistributed(highway_layer, name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer, name=f'QHighway{i}')
cemb = chighway(cemb)
qemb = qhighway(qemb)
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'
), name='BiRNNEncoder'
)
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层:结合query和context向量获得具有context中每个词具有query-aware特征的向量。
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention') # Context-to-query 注意力计算
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention') # Query-to-context 注意力计算
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层:使用RNN扫描context
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'
), name=f'BiRNNDecoder{i}'
)
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer([cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [cinn, qinn]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,loss=negative_avg_log_error,metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
############添加 获取词向量
embedding_matrix = load_embedding_matrix()
cinn = tf.keras.layers.Input(shape=(self.clen, ), name='context_input')
qinn = tf.keras.layers.Input(shape=(self.qlen, ),
name='question_input')
############添加 获取word embedding
word_embedding = tf.keras.layers.Embedding(
self.max_features,
self.emb_size,
weights=[embedding_matrix],
trainable=False,
)
############添加 获取cnn char embedding
cnn_char_embedding = tf.keras.layers.Conv1D(self.max_features,
5,
activation='tanh',
trainable=True)
# cemb = embedding_layer(cinn)
# qemb = embedding_layer(qinn)
############添加 俩embedding 结合
cemb = tf.keras.layers.Concatenate(axis=-1)(
[word_embedding, cnn_char_embedding])
qemb = tf.keras.layers.Concatenate(axis=-1)(
[word_embedding, cnn_char_embedding])
for i in range(self.num_highway_layers):
"""
使用两层高速神经网络
"""
highway_layer = layers.Highway(name=f'Highway{i}')
chighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'QHighway{i}')
cemb = chighway(cemb)
qemb = qhighway(qemb)
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [cinn, qinn]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
# 定义字符级的context, question, 词级的context,question的输入
cemb = tf.keras.layers.Input(shape=(self.word_clen,
self.word_emb_size),
name='word_context_input')
qemb = tf.keras.layers.Input(shape=(self.word_qlen,
self.word_emb_size),
name='word_question_input')
# # 词向量的embedding层
# word_embedding_layer = tf.keras.layers.Embedding(self.max_word_features, self.word_emb_size, weights=[self.glove_w2vec_matrix])
# # 字符级向量的embedding层
# char_embedding_layer = tf.keras.layers.Embedding(self.max_char_features,
# self.char_emb_size,
# embeddings_initializer='uniform',
# )
# # 输入到各层中
# char_cemb = char_embedding_layer(char_cinn)
# char_qemb = char_embedding_layer(char_qinn)
# word_cemb = word_embedding_layer(word_cinn)
# word_qemb = word_embedding_layer(word_qinn)
print(cemb.shape)
print(qemb.shape)
# print('cemb{} = []'.format(self.num_highway_layers))
# exec('cemb{} = []'.format(self.num_highway_layers))
# print(cemb2)
# exec('qemb{} = []'.format(self.num_highway_layers))
# chighway_inputs = []
# qhighway_inputs = []
# chighway_inputs.append(cemb)
# qhighway_inputs.append(qemb)
highway_layer0 = layers.Highway(name='Highway0')
chighway0 = tf.keras.layers.TimeDistributed(highway_layer0,
name='CHighway0')
qhighway0 = tf.keras.layers.TimeDistributed(highway_layer0,
name='QHighway0')
cemb1 = chighway0(cemb)
qemb1 = qhighway0(qemb)
highway_layer1 = layers.Highway(name='Highway1')
chighway1 = tf.keras.layers.TimeDistributed(highway_layer1,
name='CHighway1')
qhighway1 = tf.keras.layers.TimeDistributed(highway_layer1,
name='QHighway1')
cemb2 = chighway1(cemb1)
qemb2 = qhighway1(qemb1)
# for i in range(self.num_highway_layers):
# """
# 使用两层高速神经网络
# """
# highway_layer = layers.Highway(name=f'Highway{i}')
# chighway = tf.keras.layers.TimeDistributed(highway_layer, name=f'CHighway{i}')
# qhighway = tf.keras.layers.TimeDistributed(highway_layer, name=f'QHighway{i}')
# chighway_inputs.append(chighway(chighway_inputs[i]))
# qhighway_inputs.append(qhighway(qhighway_inputs[i]))
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
self.word_emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
# cemb_highway = chighway_inputs[-1]
# qemb_highway = qhighway_inputs[-1]
cencode = encoder_layer(cemb2) # 编码context
qencode = encoder_layer(qemb2) # 编码question
# cencode = encoder_layer(exec('cemb{}'.format(self.num_highway_layers))) # 编码context
# qencode = encoder_layer(exec('qemb{}'.format(self.num_highway_layers))) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(self.word_emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
# inn = [char_cinn, word_cinn, char_qinn, word_qinn]
inn = [cemb, qemb]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# 定义字符级的context, question, 词级的context,question的输入
cemb = tf.keras.layers.Input(shape=(self.word_clen,
self.word_emb_size),
name='word_context_input')
qemb = tf.keras.layers.Input(shape=(self.word_qlen,
self.word_emb_size),
name='word_question_input')
highway_layer0 = layers.Highway(name='Highway0')
chighway0 = tf.keras.layers.TimeDistributed(highway_layer0,
name='CHighway0')
qhighway0 = tf.keras.layers.TimeDistributed(highway_layer0,
name='QHighway0')
cemb1 = chighway0(cemb)
qemb1 = qhighway0(qemb)
highway_layer1 = layers.Highway(name='Highway1')
chighway1 = tf.keras.layers.TimeDistributed(highway_layer1,
name='CHighway1')
qhighway1 = tf.keras.layers.TimeDistributed(highway_layer1,
name='QHighway1')
cemb2 = chighway1(cemb1)
qemb2 = qhighway1(qemb1)
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
self.word_emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
cencode = encoder_layer(exec('cemb{}'.format(
self.num_highway_layers))) # 编码context
qencode = encoder_layer(exec('qemb{}'.format(
self.num_highway_layers))) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(self.word_emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
# inn = [char_cinn, word_cinn, char_qinn, word_qinn]
inn = [cemb, qemb]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
# 定义字符级的context, question, 词级的context,question的输入
char_cinn = tf.keras.layers.Input(shape=(
self.word_clen,
self.char_clen,
),
name='char_context_input')
char_qinn = tf.keras.layers.Input(shape=(
self.word_qlen,
self.char_qlen,
),
name='char_question_input')
word_cinn = tf.keras.layers.Input(shape=(self.word_clen, ),
name='word_context_input')
word_qinn = tf.keras.layers.Input(shape=(self.word_qlen, ),
name='word_question_input')
# 词向量的embedding层
word_embedding_layer = tf.keras.layers.Embedding(
self.max_word_features,
self.word_emb_size,
weights=[self.glove_w2vec_matrix])
# 字符级向量的embedding层
char_embedding_layer = tf.keras.layers.Embedding(
self.max_char_features,
self.char_emb_size,
embeddings_initializer='uniform',
)
# 输入到各层中
char_cemb = char_embedding_layer(char_cinn)
char_qemb = char_embedding_layer(char_qinn)
word_cemb = word_embedding_layer(word_cinn)
word_qemb = word_embedding_layer(word_qinn)
# context char embedding经过cnn后作为字符级别的embedding
char_c_convolution_output = []
for num_filters, filter_width in self.conv_layers:
conv = tf.keras.layers.Conv1D(filters=num_filters,
kernel_size=filter_width,
activation='relu',
name='Conv1D_C_{}_{}'.format(
num_filters,
filter_width))(char_cemb)
# print(conv.shape)
pool = tf.keras.layers.MaxPool2D(
data_format='channels_first',
pool_size=(conv.shape[2], 1),
name='MaxPoolingOverTime_C_{}_{}'.format(
num_filters, filter_width))(conv)
# print(pool.shape)
char_c_convolution_output.append(pool)
char_cemb = tf.keras.layers.concatenate(char_c_convolution_output,
axis=-1)
char_cemb = tf.squeeze(char_cemb, axis=2)
# question char embedding经过cnn后作为字符级别的embedding
char_q_convolution_output = []
for num_filters, filter_width in self.conv_layers:
conv = tf.keras.layers.Convolution1D(filters=num_filters,
kernel_size=filter_width,
activation='relu',
name='Conv1D_Q_{}_{}'.format(
num_filters,
filter_width))(char_qemb)
pool = tf.keras.layers.MaxPool2D(
data_format='channels_first',
pool_size=(conv.shape[2], 1),
name='MaxPoolingOverTime_Q_{}_{}'.format(
num_filters, filter_width))(conv)
char_q_convolution_output.append(pool)
char_qemb = tf.keras.layers.concatenate(char_q_convolution_output,
axis=-1)
char_qemb = tf.squeeze(char_qemb, axis=2)
# word级别和char级别的concat
cemb = tf.keras.layers.concatenate([word_cemb, char_cemb])
qemb = tf.keras.layers.concatenate([word_qemb, char_qemb])
print(cemb.shape)
print(qemb.shape)
for i in range(self.num_highway_layers):
"""
使用两层高速神经网络
"""
highway_layer = layers.Highway(name=f'Highway{i}')
chighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'QHighway{i}')
cemb = chighway(cemb)
qemb = qhighway(qemb)
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(
130,
# self.word_emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(
130,
# self.word_emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [char_cinn, word_cinn, char_qinn, word_qinn]
# inn = [char_cinn, char_qinn]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
cemb = tf.keras.layers.Input(shape=(self.clen, 768),
name='word_context_input')
qemb = tf.keras.layers.Input(shape=(self.qlen, 768),
name='word_question_input')
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [cemb, qemb]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
word_embedding_layer = tf.keras.layers.Embedding(
self.word_vocab_size,
self.emb_size,
weights=[self.glove_weight],
trainable=False)
char_embedding_layer = tf.keras.layers.Embedding(
self.char_vocab_size,
self.emb_size,
embeddings_initializer='uniform')
# char
# (None, 30, 10)
cinn_char = tf.keras.layers.Input(shape=(
self.clen,
max_char_len,
),
name='context_input_char')
qinn_char = tf.keras.layers.Input(shape=(
self.qlen,
max_char_len,
),
name='question_input_char')
# word
# (None, 30)
cinn_word = tf.keras.layers.Input(shape=(self.clen, ),
name='context_input_word')
qinn_word = tf.keras.layers.Input(shape=(self.qlen, ),
name='question_input_word')
# word
# (None, 30, 50)
cemb = word_embedding_layer(cinn_word)
# (None, 30, 50)
qemb = word_embedding_layer(qinn_word)
# char feature
# (None, 30, 10, 50)
c_char_emb = char_embedding_layer(cinn_char)
# (None, 30, 10, 50)
q_char_emb = char_embedding_layer(qinn_char)
# (None, 30, 6)
cemb_c = self.multi_conv1d(c_char_emb)
qemb_q = self.multi_conv1d(q_char_emb)
# (None, 30, 56)
cemb = tf.concat([cemb, cemb_c], axis=2)
qemb = tf.concat([qemb, qemb_q], axis=2)
for i in range(self.num_highway_layers):
"""
使用两层高速神经网络
"""
highway_layer = layers.Highway(name=f'Highway{i}')
chighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'QHighway{i}')
cemb = chighway(cemb)
qemb = qhighway(qemb)
# 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [cinn_word, qinn_word, cinn_char, qinn_char]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
def build_model(self):
"""
构建模型
:return:
"""
# 1 embedding 层
# TODO：homework：使用glove word embedding（或自己训练的w2v） 和 CNN char embedding
cinn = tf.keras.layers.Input(shape=(self.clen, ), name='context_input')
qinn = tf.keras.layers.Input(shape=(self.qlen, ),
name='question_input')
# embedding_layer = tf.keras.layers.Embedding(self.max_features,
# self.emb_size,
# embeddings_initializer='uniform')
# word embedding layer
word_embed_layer = tf.keras.layers.Embedding(
self.vocab_size,
self.word_embedding_dim,
weights=[self.embedding_matrix],
trainable=False)
c_w_emb = word_embedding_layer(cinn)
q_w_emb = word_embedding_layer(qinn)
#char embedding layer
c_embed_layer = tf.keras.layers.Embedding(
self.max_features, self.emb_size, embeddings_initializer='uniform')
filter_sizes = list(map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
dco = config.char_out_size
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
for i in range(self.num_highway_layers):
"""
使用两层高速神经网络
"""
highway_layer = layers.Highway(name=f'Highway{i}')
chighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'CHighway{i}')
qhighway = tf.keras.layers.TimeDistributed(highway_layer,
name=f'QHighway{i}')
cemb = chighway(cemb)
qemb = qhighway(qemb)
## 2. 上下文嵌入层
# 编码器 双向LSTM
encoder_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
self.emb_size,
recurrent_dropout=self.encoder_dropout,
return_sequences=True,
name='RNNEncoder'),
name='BiRNNEncoder')
cencode = encoder_layer(cemb) # 编码context
qencode = encoder_layer(qemb) # 编码question
# 3.注意流层
similarity_layer = layers.Similarity(name='SimilarityLayer')
similarity_matrix = similarity_layer([cencode, qencode])
c2q_att_layer = layers.C2QAttention(name='C2QAttention')
q2c_att_layer = layers.Q2CAttention(name='Q2CAttention')
c2q_att = c2q_att_layer(similarity_matrix, qencode)
q2c_att = q2c_att_layer(similarity_matrix, cencode)
# 上下文嵌入向量的生成
merged_ctx_layer = layers.MergedContext(name='MergedContext')
merged_ctx = merged_ctx_layer(cencode, c2q_att, q2c_att)
# 4.模型层
modeled_ctx = merged_ctx
for i in range(self.num_decoders):
decoder_layer = tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(self.emb_size,
recurrent_dropout=self.decoder_dropout,
return_sequences=True,
name=f'RNNDecoder{i}'),
name=f'BiRNNDecoder{i}')
modeled_ctx = decoder_layer(merged_ctx)
# 5. 输出层
span_begin_layer = layers.SpanBegin(name='SpanBegin')
span_begin_prob = span_begin_layer([merged_ctx, modeled_ctx])
span_end_layer = layers.SpanEnd(name='SpanEnd')
span_end_prob = span_end_layer(
[cencode, merged_ctx, modeled_ctx, span_begin_prob])
output_layer = layers.Combine(name='CombineOutputs')
out = output_layer([span_begin_prob, span_end_prob])
inn = [cinn, qinn]
self.model = tf.keras.models.Model(inn, out)
self.model.summary(line_length=128)
optimizer = tf.keras.optimizers.Adadelta(lr=1e-2)
self.model.compile(optimizer=optimizer,
loss=negative_avg_log_error,
metrics=[accuracy])
