基于MRPC的BERT模型实战
目录
本项目根据MRPC数据集，首先对数据进行处理(包括对每句话进行分词操作和编码操作)，然后创建BERT模型，接着根据Transformer结构（包括self-attention机制，attention_mask等），最终是二分类任务：判断两句话是否相连（这两句话是否可判断为同一句话），再连全连接层，加入偏置参数，定义损失函数。由此对模型进行训练，最终预测准确率约83%。
优点：
- 无需像RNN那样等待上一步的结果，可并行计算，层数多，速度快。
- 利用self-attention机制，不同词的重要性不同。
- 表示词向量时考虑了上下文，不同语境相同词表达的意思不同。
1.数据处理
(1)对每句话进行分词操作
- 特殊字符：[CLS]-句子开始(放在序列第一个位置，由于矩阵乘法Q*K，[CLS]整合了全局特征，最后进行分类任务时，只考虑[CLS]的向量即可)，[SEP]-句子结束标志
label_map = {}
for (i, label) in enumerate(label_list): #构建标签
label_map[label] = i#0和1两种标签
tokens_a = tokenizer.tokenize(example.text_a) #第一句话分词
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b) #第二句话分词
if tokens_b:#有b
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3" #保留3个特殊字符，3个特殊字符表示两句话，2个特殊字符表示1句话
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) #如果这俩太长了就截断操作
else:#无b
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
第一句话分词结果
(2)对每句话进行编码操作
- eg:两句话：tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
编码：
token:加入标识符的第一句话和第二句话
type_ids: 0 0 0 0 0 0 0 01 1 1 1 1 1 #表示来自哪句话，0表示第一句，1表示第二句
input_ids:该句话根据vacab.txt翻译成对于的序号
input_mask:是内容填1，共128位，不足补0,后续self attention只关注1的位置
segment_ids:第一句为0，第二句为1，不足128位补0
tokens = []#每个词
segment_ids = []#编码
tokens.append("[CLS]")
segment_ids.append(0)#CLS编码为0
for token in tokens_a:#遍历tokens_a每一个词
tokens.append(token)#拿到每一个词
segment_ids.append(0)#编码均为0
tokens.append("[SEP]")#第一句话结束后添加连接符
segment_ids.append(0)#编码为0
if tokens_b:
for token in tokens_b:#遍历tokens_b每一个词
tokens.append(token)#拿到每一个词
segment_ids.append(1)#编码均为1
tokens.append("[SEP]")#第二句话结束后添加结束符
segment_ids.append(1)#编码为1
input_ids = tokenizer.convert_tokens_to_ids(tokens) #转换成ID，根据语料库的大表vacab.txt
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids) #由于后续可能会有补齐操作，设置了一个mask目的是让attention只能放到mask为1的位置，不关注补的0的位置
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length: #PAD的长度取决于设置的最大长度
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
结果
继续遍历每一个样本
2.创建模型
(1)定义模型
model = modeling.BertModel(#创建模型
config=bert_config,
is_training=is_training,
input_ids=input_ids,#（8,128）8表示bichsize
input_mask=input_mask,#（8,128）
token_type_ids=segment_ids,#（8,128）
use_one_hot_embeddings=use_one_hot_embeddings)#TPU考虑
(2)构建BERT模型embedding层
with tf.variable_scope(scope, default_name="bert"):#构建BERT模型
with tf.variable_scope("embeddings"):#embedding层
# Perform embedding lookup on the word ids.
(self.embedding_output, self.embedding_table) = embedding_lookup(
input_ids=input_ids,
vocab_size=config.vocab_size,
embedding_size=config.hidden_size,#把词映射成768维
initializer_range=config.initializer_range,#初始化取值范围
word_embedding_name="word_embeddings",
use_one_hot_embeddings=use_one_hot_embeddings)
(3)词根据uncased_L-12_H-768_A-12预训练模型把8*128=1024个词映射成768维向量
if input_ids.shape.ndims == 2:
input_ids = tf.expand_dims(input_ids, axis=[-1])#把8*128转换成8*128*1
embedding_table = tf.get_variable( #词映射矩阵，30522, 768
name=word_embedding_name,
shape=[vocab_size, embedding_size],
initializer=create_initializer(initializer_range))
flat_input_ids = tf.reshape(input_ids, [-1])
if use_one_hot_embeddings:
one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
output = tf.matmul(one_hot_input_ids, embedding_table)
else:
output = tf.gather(embedding_table, flat_input_ids) #CPU,GPU运算1024, 768 一个batch里所有的映射768维结果
input_shape = get_shape_list(input_ids)
output = tf.reshape(output,
input_shape[0:-1] + [input_shape[-1] * embedding_size]) #(8, 128, 768)
return (output, embedding_table)
(4)加入额外编码特征（type_id）
- 因为词所在的位置会对结构产生影响，所以需要加入额外编码特征和位置编码特征。
input_shape = get_shape_list(input_tensor, expected_rank=3)#输入是8*128*768
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor#加入了位置编码信息不改变shape值8*128*768
if use_token_type:#对于第一句话为0第二句为1的type进行编码
if token_type_ids is None:
raise ValueError("`token_type_ids` must be specified if"
"`use_token_type` is True.")
token_type_table = tf.get_variable(#(2, 768)只有第一句和第二句两种不同的可能性
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# This vocab will be small so we always do one-hot here, since it is always
# faster for a small vocabulary.
flat_token_type_ids = tf.reshape(token_type_ids, [-1])#(1024)
one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)#1024，2，两种可能性，加速
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)#矩阵乘法（1024*2）（2*768）=1024，768
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width]) #8, 128, 768
output += token_type_embeddings#加法type id信息融入到原始编码
(5)加入位置编码特征
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(#512*768 位置限制最大512
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1]) #位置编码给的挺大，为了加速只需要取出有用部分就可以 128, 768
num_dims = len(output.shape.as_list())
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width]) # [1, 128, 768] 表示位置编码跟输入啥数据无关，因为原始的embedding是有batchsize当做第一个维度，这里为了计算也得加入
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
output += position_embeddings#8*128*768
output = layer_norm_and_dropout(output, dropout_prob)#最后的output融入了typeid的信息以及位置编码的信息
return output
3.transformer结构
(1)self-attention：计算机根据上下文语境自己调整每个词的权重的分配
def transpose_for_scores(input_tensor, batch_size, num_attention_heads,
seq_length, width):
output_tensor = tf.reshape(
input_tensor, [batch_size, seq_length, num_attention_heads, width])
output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])
return output_tensor
from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])#[1024, 768]
to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])#[1024, 768]
if len(from_shape) != len(to_shape):
raise ValueError(
"The rank of `from_tensor` must match the rank of `to_tensor`.")
if len(from_shape) == 3:
batch_size = from_shape[0]
from_seq_length = from_shape[1]
to_seq_length = to_shape[1]
elif len(from_shape) == 2:
if (batch_size is None or from_seq_length is None or to_seq_length is None):
raise ValueError(
"When passing in rank 2 tensors to attention_layer, the values "
"for `batch_size`, `from_seq_length`, and `to_seq_length` "
"must all be specified.")
(2)构建QKV矩阵
- Q：query,要去查询的
- K：key,等着被查的
- V：value,实际的特征信息
# Scalar dimensions referenced here:
# B = batch size (number of sequences) 8
# F = `from_tensor` sequence length 128
# T = `to_tensor` sequence length 128
# N = `num_attention_heads` 12
# H = `size_per_head` 64
from_tensor_2d = reshape_to_matrix(from_tensor)#(1024, 768)
to_tensor_2d = reshape_to_matrix(to_tensor)
#B:batchsize F:`from_tensor` T:`to_tensor` N:`num_attention_heads` H:`size_per_head`
# `query_layer` = [B*F, N*H]，8*128，12*64
query_layer = tf.layers.dense(#构建查询矩阵1024*768
from_tensor_2d,#Q矩阵由from_tensor_2d而来
num_attention_heads * size_per_head,#12*64
activation=query_act,
name="query",
kernel_initializer=create_initializer(initializer_range))
# `key_layer` = [B*T, N*H]8*128，12*64
key_layer = tf.layers.dense(
to_tensor_2d,#K矩阵由to_tensor_2d而来
num_attention_heads * size_per_head,
activation=key_act,
name="key",
kernel_initializer=create_initializer(initializer_range))
# `value_layer` = [B*T, N*H]8*128，12*64
value_layer = tf.layers.dense(#帮助得到实际特征是什么
to_tensor_2d,#V矩阵由to_tensor_2d而来
num_attention_heads * size_per_head,
activation=value_act,
name="value",
kernel_initializer=create_initializer(initializer_range))
(3)计算内积+softmax
- q与k的内积表示有多匹配，无关时内积为0，内积越大相关性越高
- 最终的得分值经过softmax，将分值转换成概率。其中(q*K)/✓dk，使得分值不随向量维度的增大而增加
- 每个词的q会跟整个序列中每一个k计算得分，然后基于得分再分配特征。softmax（(qK)/✓dk）v
示意图如下：
# `query_layer` = [B, N, F, H] #为了加速计算内积
query_layer = transpose_for_scores(query_layer, batch_size,
num_attention_heads, from_seq_length,
size_per_head)#8,128,12,64->8，12，128，64,便于做内积
# `key_layer` = [B, N, T, H] #为了加速计算内积
key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads,
to_seq_length, size_per_head)
# Take the dot product between "query" and "key" to get the raw
# attention scores.
# `attention_scores` = [B, N, F, T]
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) #，内积结果值，结果为(8, 12, 128, 128)
attention_scores = tf.multiply(attention_scores,
1.0 / math.sqrt(float(size_per_head))) #除以根号dk,64，消除维度对结果的影响
if attention_mask is not None:#加入attention_mask,对于填充的位置不考虑
# `attention_mask` = [B, 1, F, T]
attention_mask = tf.expand_dims(attention_mask, axis=[1])#8，1，128，128。1：每个位置相同
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0 #mask为1的时候结果为0 mask为0的时候结果为非常大的负数，在做softmax是所映射的概率近乎为0
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_scores += adder #把这个加入到原始的得分里相当于mask为1的就不变，mask为0的就会变成非常大的负数
# Normalize the attention scores to probabilities.
# `attention_probs` = [B, N, F, T]
attention_probs = tf.nn.softmax(attention_scores) #再做softmax此时负数做softmax相当于结果为0了就相当于不考虑了，结果为概率值（权重），8，12，128，128
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = dropout(attention_probs, attention_probs_dropout_prob)
# `value_layer` = [B, T, N, H]
value_layer = tf.reshape(
value_layer,
[batch_size, to_seq_length, num_attention_heads, size_per_head])#(8, 128, 12, 64)
# `value_layer` = [B, N, T, H]
value_layer = tf.transpose(value_layer, [0, 2, 1, 3]) #(8, 12, 128, 64)转换成可以与权重矩阵进行乘法计算的维度，transpose操作便于矩阵计算，加速
# `context_layer` = [B, N, F, H]
context_layer = tf.matmul(attention_probs, value_layer)#计算最终结果特征 (8, 12, 128, 64)
# `context_layer` = [B, F, N, H]
context_layer = tf.transpose(context_layer, [0, 2, 1, 3])#转换回[8, 128, 12, 64]
if do_return_2d_tensor:
# `context_layer` = [B*F, N*H]
context_layer = tf.reshape(
context_layer,
[batch_size * from_seq_length, num_attention_heads * size_per_head])
else:
# `context_layer` = [B, F, N*H]
context_layer = tf.reshape(
context_layer,
[batch_size, from_seq_length, num_attention_heads * size_per_head]) #(1024, 768)
return context_layer # (1024, 768)
(4)attention_mask
- 对矩阵的每一个元素再分一个维度，比如第一个位置添加维度（1111有几个1表示attention时该跟几个计算，其余为0（modeling.py）
with tf.variable_scope("encoder"):
# This converts a 2D mask of shape [batch_size, seq_length] to a 3D（2D转3D）
# mask of shape [batch_size, seq_length, seq_length] which is used
# for the attention scores.
attention_mask = create_attention_mask_from_input_mask(#对矩阵的每一个元素再分一个维度，比如第一个位置添加维度（1111有几个1表示attention时该跟哪几个计算，其余为0）
input_ids, input_mask)#输入8*128输出8*128*128
if attention_mask is not None:#加入attention_mask,对于填充的位置不考虑
# `attention_mask` = [B, 1, F, T]
attention_mask = tf.expand_dims(attention_mask, axis=[1])#8，1，128，128。1：每个位置相同
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0 #mask为1的时候结果为0 mask为0的时候结果为非常大的负数，在做softmax是所映射的概率近乎为0
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_scores += adder #把这个加入到原始的得分里相当于mask为1的就不变，mask为0的就会变成非常大的负数
# Normalize the attention scores to probabilities.
# `attention_probs` = [B, N, F, T]
attention_probs = tf.nn.softmax(attention_scores) #再做softmax此时负数做softmax相当于结果为0了就相当于不考虑了，结果为概率值（权重），8，12，128，128
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = dropout(attention_probs, attention_probs_dropout_prob)
# `value_layer` = [B, T, N, H]
value_layer = tf.reshape(
value_layer,
[batch_size, to_seq_length, num_attention_heads, size_per_head])#(8, 128, 12, 64)
# `value_layer` = [B, N, T, H]
value_layer = tf.transpose(value_layer, [0, 2, 1, 3]) #(8, 12, 128, 64)转换成可以与权重矩阵进行乘法计算的维度，transpose操作便于矩阵计算，加速
# `context_layer` = [B, N, F, H]
context_layer = tf.matmul(attention_probs, value_layer)#计算最终结果特征 (8, 12, 128, 64)
# `context_layer` = [B, F, N, H]
context_layer = tf.transpose(context_layer, [0, 2, 1, 3])#转换回[8, 128, 12, 64]
if do_return_2d_tensor:
# `context_layer` = [B*F, N*H]
context_layer = tf.reshape(
context_layer,
[batch_size * from_seq_length, num_attention_heads * size_per_head])
else:
# `context_layer` = [B, F, N*H]
context_layer = tf.reshape(
context_layer,
[batch_size, from_seq_length, num_attention_heads * size_per_head]) #(1024, 768)
return context_layer # (1024, 768)
(5)连接全连接层，加入残差连接
- 由于transforme的multi-headed机制：通过构建不同的QKV矩阵得到多个特征表达，最后将所有特征拼接在一起，所以最后可以使用全连接层来降维。
示意图如下：
with tf.variable_scope("output"): #1024, 768 残差连接
attention_output = tf.layers.dense(#全连接层
attention_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
attention_output = dropout(attention_output, hidden_dropout_prob)
attention_output = layer_norm(attention_output + layer_input)#残差连接
# The activation is only applied to the "intermediate" hidden layer.
with tf.variable_scope("intermediate"): #全连接层 (1024, 3072)
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=create_initializer(initializer_range))
# Down-project back to `hidden_size` then add the residual.
with tf.variable_scope("output"): #再变回一致的维度，1024, 768
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
layer_output = dropout(layer_output, hidden_dropout_prob)
layer_output = layer_norm(layer_output + attention_output)
prev_output = layer_output
all_layer_outputs.append(layer_output)
if do_return_all_layers:
final_outputs = []
for layer_output in all_layer_outputs:
final_output = reshape_from_matrix(layer_output, input_shape)
final_outputs.append(final_output)
return final_outputs
else:
final_output = reshape_from_matrix(prev_output, input_shape)
return final_output
4.完整网络结构
- 拿到向量，最终是二分类任务，再连全连接层，加入偏置参数，定义损失函数。
# In the demo, we are doing a simple classification task on the entire
# segment.
#
# If you want to use the token-level output, use model.get_sequence_output()
# instead.
output_layer = model.get_pooled_output()#取句子时只需要取第一个cls标识符
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(#权重参数2*768
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(#偏置参数2*768
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):#定义损失函数
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)#输出*权重
logits = tf.nn.bias_add(logits, output_bias)#加上偏置项
probabilities = tf.nn.softmax(logits, axis=-1)#softmax
log_probs = tf.nn.log_softmax(logits, axis=-1)#交叉商计算损失
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, logits, probabilities)
5.结果
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