1-3,文本数据建模流程范例.md

1-3,文本数据建模流程范例

一，准备数据

imdb数据集的目标是根据电影评论的文本内容预测评论的情感标签。

训练集有20000条电影评论文本，测试集有5000条电影评论文本，其中正面评论和负面评论都各占一半。

文本数据预处理较为繁琐，包括中文切词（本示例不涉及），构建词典，编码转换，序列填充，构建数据管道等等。

在tensorflow中完成文本数据预处理的常用方案有两种，第一种是利用tf.keras.preprocessing中的Tokenizer词典构建工具和tf.keras.utils.Sequence构建文本数据生成器管道。

第二种是使用tf.data.Dataset搭配.keras.layers.experimental.preprocessing.TextVectorization预处理层。

第一种方法较为复杂，其使用范例可以参考以下文章。

https://zhuanlan.zhihu.com/p/67697840

第二种方法为TensorFlow原生方式，相对也更加简单一些。

我们此处介绍第二种方法。

import numpy as np 
import pandas as pd 
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow.keras import models,layers,preprocessing,optimizers,losses,metrics
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import re,string

train_data_path = "./data/imdb/train.csv"
test_data_path =  "./data/imdb/test.csv"

MAX_WORDS = 10000  # 仅考虑最高频的10000个词
MAX_LEN = 200  # 每个样本保留200个词的长度
BATCH_SIZE = 20 


#构建管道
def split_line(line):
    arr = tf.strings.split(line,"\t")
    label = tf.expand_dims(tf.cast(tf.strings.to_number(arr[0]),tf.int32),axis = 0)
    text = tf.expand_dims(arr[1],axis = 0)
    return (text,label)

ds_train_raw =  tf.data.TextLineDataset(filenames = [train_data_path]) \
   .map(split_line,num_parallel_calls = tf.data.experimental.AUTOTUNE) \
   .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \
   .prefetch(tf.data.experimental.AUTOTUNE)

ds_test_raw = tf.data.TextLineDataset(filenames = [test_data_path]) \
   .map(split_line,num_parallel_calls = tf.data.experimental.AUTOTUNE) \
   .batch(BATCH_SIZE) \
   .prefetch(tf.data.experimental.AUTOTUNE)


#构建词典
def clean_text(text):
    lowercase = tf.strings.lower(text)
    stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')
    cleaned_punctuation = tf.strings.regex_replace(stripped_html,
         '[%s]' % re.escape(string.punctuation),'')
    return cleaned_punctuation

vectorize_layer = TextVectorization(
    standardize=clean_text,
    split = 'whitespace',
    max_tokens=MAX_WORDS-1, #有一个留给占位符
    output_mode='int',
    output_sequence_length=MAX_LEN)

ds_text = ds_train_raw.map(lambda text,label: text)
vectorize_layer.adapt(ds_text)
print(vectorize_layer.get_vocabulary()[0:100])


#单词编码
ds_train = ds_train_raw.map(lambda text,label:(vectorize_layer(text),label)) \
    .prefetch(tf.data.experimental.AUTOTUNE)
ds_test = ds_test_raw.map(lambda text,label:(vectorize_layer(text),label)) \
    .prefetch(tf.data.experimental.AUTOTUNE)

[b'the', b'and', b'a', b'of', b'to', b'is', b'in', b'it', b'i', b'this', b'that', b'was', b'as', b'for', b'with', b'movie', b'but', b'film', b'on', b'not', b'you', b'his', b'are', b'have', b'be', b'he', b'one', b'its', b'at', b'all', b'by', b'an', b'they', b'from', b'who', b'so', b'like', b'her', b'just', b'or', b'about', b'has', b'if', b'out', b'some', b'there', b'what', b'good', b'more', b'when', b'very', b'she', b'even', b'my', b'no', b'would', b'up', b'time', b'only', b'which', b'story', b'really', b'their', b'were', b'had', b'see', b'can', b'me', b'than', b'we', b'much', b'well', b'get', b'been', b'will', b'into', b'people', b'also', b'other', b'do', b'bad', b'because', b'great', b'first', b'how', b'him', b'most', b'dont', b'made', b'then', b'them', b'films', b'movies', b'way', b'make', b'could', b'too', b'any', b'after', b'characters']

二，定义模型

使用Keras接口有以下3种方式构建模型：使用Sequential按层顺序构建模型，使用函数式API构建任意结构模型，继承Model基类构建自定义模型。

此处选择使用继承Model基类构建自定义模型。

# 演示自定义模型范例，实际上应该优先使用Sequential或者函数式API
tf.keras.backend.clear_session()

class CnnModel(models.Model):
    def __init__(self):
        super(CnnModel, self).__init__()
        
    def build(self,input_shape):
        self.embedding = layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN)
        self.conv_1 = layers.Conv1D(16, kernel_size= 5,name = "conv_1",activation = "relu")
        self.pool_1 = layers.MaxPool1D(name = "pool_1")
        self.conv_2 = layers.Conv1D(128, kernel_size=2,name = "conv_2",activation = "relu")
        self.pool_2 = layers.MaxPool1D(name = "pool_2")
        self.flatten = layers.Flatten()
        self.dense = layers.Dense(1,activation = "sigmoid")
        super(CnnModel,self).build(input_shape)
    
    def call(self, x):
        x = self.embedding(x)
        x = self.conv_1(x)
        x = self.pool_1(x)
        x = self.conv_2(x)
        x = self.pool_2(x)
        x = self.flatten(x)
        x = self.dense(x)
        return(x)
    
    # 用于显示Output Shape
    def summary(self):
        x_input = layers.Input(shape = MAX_LEN)
        output = self.call(x_input)
        model = tf.keras.Model(inputs = x_input,outputs = output)
        model.summary()
    
model = CnnModel()
model.build(input_shape =(None,MAX_LEN))
model.summary()

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 200)]             0         
_________________________________________________________________
embedding (Embedding)        (None, 200, 7)            70000     
_________________________________________________________________
conv_1 (Conv1D)              (None, 196, 16)           576       
_________________________________________________________________
pool_1 (MaxPooling1D)        (None, 98, 16)            0         
_________________________________________________________________
conv_2 (Conv1D)              (None, 97, 128)           4224      
_________________________________________________________________
pool_2 (MaxPooling1D)        (None, 48, 128)           0         
_________________________________________________________________
flatten (Flatten)            (None, 6144)              0         
_________________________________________________________________
dense (Dense)                (None, 1)                 6145      
=================================================================
Total params: 80,945
Trainable params: 80,945
Non-trainable params: 0
_________________________________________________________________

三，训练模型

训练模型通常有3种方法，内置fit方法，内置train_on_batch方法，以及自定义训练循环。此处我们通过自定义训练循环训练模型。

#打印时间分割线
@tf.function
def printbar():
    today_ts = tf.timestamp()%(24*60*60)
    
    hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24)
    minite = tf.cast((today_ts%3600)//60,tf.int32)
    second = tf.cast(tf.floor(today_ts%60),tf.int32)
    
    def timeformat(m):
        if tf.strings.length(tf.strings.format("{}",m))==1:
            return(tf.strings.format("0{}",m))
        else:
            return(tf.strings.format("{}",m))
    
    timestring = tf.strings.join([timeformat(hour),timeformat(minite),
                timeformat(second)],separator = ":")
    tf.print("=========="*8+timestring)

    

optimizer = optimizers.Nadam()
loss_func = losses.BinaryCrossentropy()

train_loss = metrics.Mean(name='train_loss')
train_metric = metrics.BinaryAccuracy(name='train_accuracy')

valid_loss = metrics.Mean(name='valid_loss')
valid_metric = metrics.BinaryAccuracy(name='valid_accuracy')


@tf.function
def train_step(model, features, labels):
    with tf.GradientTape() as tape:
        predictions = model(features,training = True)
        loss = loss_func(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss.update_state(loss)
    train_metric.update_state(labels, predictions)
    

@tf.function
def valid_step(model, features, labels):
    predictions = model(features,training = False)
    batch_loss = loss_func(labels, predictions)
    valid_loss.update_state(batch_loss)
    valid_metric.update_state(labels, predictions)


def train_model(model,ds_train,ds_valid,epochs):
    for epoch in tf.range(1,epochs+1):
        
        for features, labels in ds_train:
            train_step(model,features,labels)

        for features, labels in ds_valid:
            valid_step(model,features,labels)
        
        #此处logs模板需要根据metric具体情况修改
        logs = 'Epoch={},Loss:{},Accuracy:{},Valid Loss:{},Valid Accuracy:{}' 
        
        if epoch%1==0:
            printbar()
            tf.print(tf.strings.format(logs,
            (epoch,train_loss.result(),train_metric.result(),valid_loss.result(),valid_metric.result())))
            tf.print("")
        
        train_loss.reset_states()
        valid_loss.reset_states()
        train_metric.reset_states()
        valid_metric.reset_states()

train_model(model,ds_train,ds_test,epochs = 6)

================================================================================13:54:08
Epoch=1,Loss:0.442317516,Accuracy:0.7695,Valid Loss:0.323672801,Valid Accuracy:0.8614

================================================================================13:54:20
Epoch=2,Loss:0.245737702,Accuracy:0.90215,Valid Loss:0.356488883,Valid Accuracy:0.8554

================================================================================13:54:32
Epoch=3,Loss:0.17360799,Accuracy:0.93455,Valid Loss:0.361132562,Valid Accuracy:0.8674

================================================================================13:54:44
Epoch=4,Loss:0.113476314,Accuracy:0.95975,Valid Loss:0.483677238,Valid Accuracy:0.856

================================================================================13:54:57
Epoch=5,Loss:0.0698405355,Accuracy:0.9768,Valid Loss:0.607856631,Valid Accuracy:0.857

================================================================================13:55:15
Epoch=6,Loss:0.0366807655,Accuracy:0.98825,Valid Loss:0.745884955,Valid Accuracy:0.854

四，评估模型

通过自定义训练循环训练的模型没有经过编译，无法直接使用model.evaluate(ds_valid)方法

def evaluate_model(model,ds_valid):
    for features, labels in ds_valid:
         valid_step(model,features,labels)
    logs = 'Valid Loss:{},Valid Accuracy:{}' 
    tf.print(tf.strings.format(logs,(valid_loss.result(),valid_metric.result())))
    
    valid_loss.reset_states()
    train_metric.reset_states()
    valid_metric.reset_states()

    

evaluate_model(model,ds_test)

Valid Loss:0.745884418,Valid Accuracy:0.854

五，使用模型

可以使用以下方法:

• model.predict(ds_test)
• model(x_test)
• model.call(x_test)
• model.predict_on_batch(x_test)

推荐优先使用model.predict(ds_test)方法，既可以对Dataset，也可以对Tensor使用。

model.predict(ds_test)

array([[0.7864823 ],
       [0.9999901 ],
       [0.99944776],
       ...,
       [0.8498302 ],
       [0.13382755],
       [1.        ]], dtype=float32)

for x_test,_ in ds_test.take(1):
    print(model(x_test))
    #以下方法等价：
    #print(model.call(x_test))
    #print(model.predict_on_batch(x_test))

tf.Tensor(
[[7.8648227e-01]
 [9.9999011e-01]
 [9.9944776e-01]
 [3.7153201e-09]
 [9.4462049e-01]
 [2.3522753e-04]
 [1.2044354e-04]
 [9.3752089e-07]
 [9.9996352e-01]
 [9.3435925e-01]
 [9.8746723e-01]
 [9.9908626e-01]
 [4.1563155e-08]
 [4.1808244e-03]
 [8.0184749e-05]
 [8.3910513e-01]
 [3.5167937e-05]
 [7.2113985e-01]
 [4.5228912e-03]
 [9.9942589e-01]], shape=(20, 1), dtype=float32)

六，保存模型

推荐使用TensorFlow原生方式保存模型。

model.save('./data/tf_model_savedmodel', save_format="tf")
print('export saved model.')

model_loaded = tf.keras.models.load_model('./data/tf_model_savedmodel')
model_loaded.predict(ds_test)

array([[0.7864823 ],
       [0.9999901 ],
       [0.99944776],
       ...,
       [0.8498302 ],
       [0.13382755],
       [1.        ]], dtype=float32)

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