TensorFlow 学习笔记--基础文本分类

电影评论文本分类

官网的教程代码有一些问题：1.调用文件夹时，官网的调用方式有错。2.调用vectorize_layer 没有返回，3.模型编译时 ，metics写错了。4.最后新的数据要转为张量才能用来预测。
这笔记里代码里都改过来了

导入库

re library 引入新的字符串表达方式

shutil — 高级文件操作 提供一些支持文件拷贝和删除的函数

os — 多种操纵系统接口

string — 常见字符串操作

import matplotlib.pyplot as plt
import os 
import re
import shutil
import string
import tensorflow as tf

from tensorflow.keras import layers
from tensorflow.keras import losses
os.getcwd()

'd:\\Desktop files\\Python_projects\\TensFlow_tutorial_personal'

下载IMDB数据集

浏览目录结构

这篇训练的目的是对电影评论进行正向或者负向分类 二元分类

tf.keras.utils.get_file()

Downloads a file from a URL if it not already in the cache.

tf.keras.utils.get_file(
fname=None,
origin=None,
untar=False,
md5_hash=None,
file_hash=None,
cache_subdir=‘datasets’,
hash_algorithm=‘auto’,
extract=False,
archive_format=‘auto’,
cache_dir=None,
force_download=False
)

os.path.join() 连接文件,并用’\“分隔开各个文件

url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
dataset = tf.keras.utils.get_file("aclImdb_v1", url, untar=True, cache_dir='.', cache_subdir='')

Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
[1m84125825/84125825[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 0us/step

dataset_dir = os.path.join('.\\aclImdb_v1', 'aclImdb')
os.listdir(dataset_dir)

文件夹格式.//aclImdb_v1//aclImdb或者 .\aclImdb_v1\aclImdb 都可以，官网给的教程找不到文件夹，自己找到文件夹改就行

train_dir = os.path.join(dataset_dir,'train')
os.listdir(trian_dir)
# print(trian_dir)

['labeledBow.feat',
 'neg',
 'pos',
 'unsup',
 'unsupBow.feat',
 'urls_neg.txt',
 'urls_pos.txt',
 'urls_unsup.txt']

aclImdb/train/pos和aclImdb/train.neg目录包含许多文本文件，每个文件都是一条电影评论

sample_file = os.path.join(train_dir,'pos/1181_9.txt')
with open(sample_file) as f:
    print(f.read())

Rachel Griffiths writes and directs this award winning short film. A heartwarming story about coping with grief and cherishing the memory of those we've loved and lost. Although, only 15 minutes long, Griffiths manages to capture so much emotion and truth onto film in the short space of time. Bud Tingwell gives a touching performance as Will, a widower struggling to cope with his wife's death. Will is confronted by the harsh reality of loneliness and helplessness as he proceeds to take care of Ruth's pet cow, Tulip. The film displays the grief and responsibility one feels for those they have loved and lost. Good cinematography, great direction, and superbly acted. It will bring tears to all those who have lost a loved one, and survived.

加载数据集

加载数据并将其准备为适合训练的格式，使用tf.keras.preprocessing.text_dataset_from_directory 构建数据目录为以下格式

main_directory/
...class_a/
......a_text_1.txt
......a_text_2.txt
...class_b/
......b_text_1.txt
......b_text_2.txt

移除其他的文件夹

shutil. rmtree() allows you to delete a directory and all of its files and subdirectories.

remove_dir = os.path.join(train_dir,'unsup')
shutil.rmtree(remove_dir)

os.listdir(train_dir)

['labeledBow.feat',
 'neg',
 'pos',
 'unsupBow.feat',
 'urls_neg.txt',
 'urls_pos.txt',
 'urls_unsup.txt']

将数据集分成三份：训练，测试和验证

已有测试集情况下，将训练集拆分为训练集和验证集

使用text_dataset_from_directory 工具进行拆分

batch_size = 32
seed = 42
raw_train_ds = tf.keras.utils.text_dataset_from_directory(
    'aclImdb_v1/aclImdb/train',
    batch_size=batch_size,
    validation_split=0.2,
    subset='training',
    seed=seed
)

Found 25000 files belonging to 2 classes.
Using 20000 files for training.

for text_batch,label_batch in raw_train_ds.take(1):
    for i in range(5):
        print("Review", text_batch[i])
        print(" ", label_batch[i])

Review tf.Tensor(b'"Pandemonium" is a horror movie spoof that comes off more stupid than funny. Believe me when I tell you, I love comedies. Especially comedy spoofs. "Airplane", "The Naked Gun" trilogy, "Blazing Saddles", "High Anxiety", and "Spaceballs" are some of my favorite comedies that spoof a particular genre. "Pandemonium" is not up there with those films. Most of the scenes in this movie had me sitting there in stunned silence because the movie wasn\'t all that funny. There are a few laughs in the film, but when you watch a comedy, you expect to laugh a lot more than a few times and that\'s all this film has going for it. Geez, "Scream" had more laughs than this film and that was more of a horror film. How bizarre is that?

*1/2 (out of four)', shape=(), dtype=string)
  tf.Tensor(0, shape=(), dtype=int32)
Review tf.Tensor(b"David Mamet is a very interesting and a very un-equal director. His first movie 'House of Games' was the one I liked best, and it set a series of films with characters whose perspective of life changes as they get into complicated situations, and so does the perspective of the viewer.

So is 'Homicide' which from the title tries to set the mind of the viewer to the usual crime drama. The principal characters are two cops, one Jewish and one Irish who deal with a racially charged area. The murder of an old Jewish shop owner who proves to be an ancient veteran of the Israeli Independence war triggers the Jewish identity in the mind and heart of the Jewish detective.

This is were the flaws of the film are the more obvious. The process of awakening is theatrical and hard to believe, the group of Jewish militants is operatic, and the way the detective eventually walks to the final violent confrontation is pathetic. The end of the film itself is Mamet-like smart, but disappoints from a human emotional perspective.

Joe Mantegna and William Macy give strong performances, but the flaws of the story are too evident to be easily compensated.", shape=(), dtype=string)
  tf.Tensor(0, shape=(), dtype=int32)
Review tf.Tensor(b'Great documentary about the lives of NY firefighters during the worst terrorist attack of all time.. That reason alone is why this should be a must see collectors item.. What shocked me was not only the attacks, but the"High Fat Diet" and physical appearance of some of these firefighters. I think a lot of Doctors would agree with me that,in the physical shape they were in, some of these firefighters would NOT of made it to the 79th floor carrying over 60 lbs of gear. Having said that i now have a greater respect for firefighters and i realize becoming a firefighter is a life altering job. The French have a history of making great documentary\'s and that is what this is, a Great Documentary.....', shape=(), dtype=string)
  tf.Tensor(1, shape=(), dtype=int32)
Review tf.Tensor(b"It's boggles the mind how this movie was nominated for seven Oscars and won one. Not because it's abysmal or because given the collective credentials of the creative team behind it really ought to deserve them but because in every category it was nominated Prizzi's Honor disappoints. Some would argue that old Hollywood pioneer John Huston had lost it by this point in his career but I don't buy it. Only the previous year he signed the superb UNDER THE VOLCANO, a dark character study set in Mexico, that ranks among the finest he ever did. Prizzi's Honor on the other hand, a film loaded with star power, good intentions and a decent script, proves to be a major letdown.

The overall tone and plot of a gangster falling in love with a female hit-man prefigures the quirky crimedies that caught Hollywood by storm in the early 90's but the script is too convoluted for its own sake, the motivations are off and on the whole the story seems unsure of what exactly it's trying to be: a romantic comedy, a crime drama, a gangster saga etc. Jack Nicholson (doing a Brooklyn accent that works perfectly for De Niro but sounds unconvincing coming from Jack) and Kathleen Turner in the leading roles seem to be in paycheck mode, just going through the motions almost sleepwalking their way through some parts. Anjelica Huston on the other hand fares better but her performance is sabotaged by her character's motivations: she starts out the victim of her bigot father's disdain, she proves to be supportive to her ex-husband, then becomes a vindictive bitch that wants his head on a plate.

The colours of the movie have a washed-up quality like it was made in the early 70's and Huston's direction is as uninteresting as everything else. There's promise behind the story and perhaps in the hands of a director hungry to be recognized it could've been morphed to something better but what's left looks like a film nobody was really interested in making.", shape=(), dtype=string)
  tf.Tensor(0, shape=(), dtype=int32)
Review tf.Tensor(b'The concept of the legal gray area in Love Crimes contributes to about 10% of the movie\'s appeal; the other 90% can be attributed to it\'s flagrant bad-ness. To say that Sean Young\'s performance as a so-called district attorney is wooden is a gross understatement. With her bland suits and superfluous hair gel, Young does a decent job at convincing the audience of her devout hatred for men. Why else would she ask her only friend to pose as a prostitute just so she can arrest cops who try to pick up on them? This hatred is also the only reason why she relentlessly pursues a perverted photographer who gives women a consensual thrill and the driving force behind this crappy movie. Watching Young go from frigid to full-frontal nudity does little to raise interest, but the temper tantrum she throws standing next to a fire by a lake does. Watching her rant and rave about her self-loathing and sexual frustration makes Love Crimes worth the rental fee, but it\'s all downhill to and from there. Despite her urge to bring Patrick Bergin\'s character to justice, her policing skills completely escape her in the throes of her own tired lust and passion. Patrick Bergin does a decent enough job as a slimy sociopath; if it worked in Sleeping With the Enemy it sure as hell can work in this. But I can\'t help but wonder if the noticeable lack of energy Young brings to the film conflicts with his sliminess. I\'m guessing it does and the result is a "thriller" with thrills that are thoroughly bad and yet comedic.', shape=(), dtype=string)
  tf.Tensor(0, shape=(), dtype=int32)

评论包含原始文本，标点符号和html代码，之后会进行处理

标签为 0或1需要对应 正向或反向评论

print("Label 0:", raw_train_ds.class_names[0])
print("Lable 1 :", raw_train_ds.class_names[1])

Label 0: neg
Lable 1 : pos

使用seed 保证拆分后的验证和训练集不重叠

raw_val_ds = tf.keras.utils.text_dataset_from_directory(
    'aclImdb_v1/aclImdb/train',
    batch_size = batch_size,
    validation_split=0.2,
    subset='validation',
    seed=seed   
)

Found 25000 files belonging to 2 classes.
Using 5000 files for validation.

raw_test_ds = tf.keras.utils.text_dataset_from_directory(
    'aclImdb_v1/aclImdb/test',
    batch_size = batch_size
)

Found 25000 files belonging to 2 classes.

对训练数据进行预处理

使用tf.keras.layers.TextVectorization 层对数据进行标准化,词例化和向量化。

标准化： 对文本预处理，移除标点符号和html元素，简化数据集

词例化：将字符串分割为词例

向量化：将词例转化为数字，方便神经网络处理

评论中的html 如 TextVectorization 并不能处理，需要自定义标准化函数移除文本中的html

def cosutom_standardization(input_data):
    lowercase = tf.strings.lower(input_data)
    stripped_html = tf.strings.regex_replace(lowercase,'',' ')
    cleaned_text = tf.strings.regex_replace(stripped_html,
                            '[%s]'%re.escape(string.punctuation),
                            '')
    return cleaned_text

创建 TextVectorization层，对数据进行标准化，设置output_mode为int方便为每个词例创建整数索引

定义一些常量，最大句子长度等

max_features = 10000
sequence_length = 250

vectorize_layer = layers.TextVectorization(
    standardize=cosutom_standardization,
    max_tokens=max_features,
    output_mode='int',
    output_sequence_length=sequence_length
)

使用adapt处理数据集，构建字符串到整数的索引

train_text = raw_train_ds.map(lambda x, y:x)
vectorize_layer.adapt(train_text)

def vectorize_text(text,label):
    text = tf.expand_dims(text,-1)
    return vectorize_layer(text),label
    

text_batch, label_batch = next(iter(raw_train_ds))
first_review, first_label = text_batch[0], label_batch[0]
print("review",first_review)
print("label", first_label)
print("vectorized review:",vectorize_text(first_review,first_label))

review tf.Tensor(b'"Emma" was a product of what might be called by the First Great Jane Austen Cycle of the mid-nineties, and it was recently shown on British television, doubtless because of the interest in the author created by the Second Great Jane Austen Cycle which started with "Pride and Prejudice" two years ago. We currently have in the cinemas the Austen biopic "Becoming Jane", and ITV have recently produced three TV movies based on Austen novels. These include "Northanger Abbey", the only one of the six major novels not to have been filmed previously, so the cycle should now be complete. No doubt, however, there will be more to come in the near future. (There is, after all, her juvenile "Love and Freindship" (sic), the short novella "Lady Susan", and someone, somewhere, has doubtless supplied endings to her two unfinished fragments "The Watsons" and "Sanditon". Then there are all those Austen sequels churned out by modern writers\xc2\x85\xc2\x85\xc2\x85).

The main character is Emma Woodhouse, a young lady from an aristocratic family in Regency England. (Not, as some reviewers have assumed, Victorian England- Austen died before Queen Victoria was even born). Emma is, financially, considerably better off than most Austen heroines such as Elizabeth Bennett or Fanny Price, and has no need to find herself a wealthy husband. Instead, her main preoccupation seems to be finding husbands for her friends. She persuades her friend Harriet to turn down a proposal of marriage from a young farmer, Robert Martin, believing that Harriet should be setting her sights on the ambitious clergyman Mr Elton. This scheme goes disastrously wrong, however, as Elton has no interest in Harriet, but has fallen in love with Emma herself. The speed with which Emma rejects his proposal makes one wonder just why she was so keen to match her friend with a man she regards (with good reason) as an unsuitable marriage partner for herself. This being a Jane Austen plot, Emma turns out to be less of a committed spinster than she seems, and she too finds herself falling in love, leading to further complications.

Emma always insists that she will not marry without affection, and when she does find a partner, the handsome Mr Knightley, we feel that this will indeed be an affectionate marriage. It does not, however, seem likely to be a very passionate one (unlike, say, that of Elizabeth Bennett and Mr Darcy). Knightley, who is sixteen years older than Emma (she is 21, he 37), and related to her by marriage, is more like a father-figure than a lover. Much more of a father-figure, in fact, than her actual father, a querulous and selfish old hypochondriac who seems more like her grandfather. When Emma is rude to her unbearably garrulous and tedious friend Miss Bates, it is Knightley who chides her for her lack of manners. (His surname is probably meant to indicate his gentlemanly nature- nineteenth-century gentlemen liked to think of themselves as the modern equivalent of mediaeval knights with their elaborate codes of chivalry). Both Gwyneth Paltrow and Jeremy Northam play their parts very well, but this is not really one of the great screen romances.

Of the other characters, I liked Juliet Stephenson\'s vulgar Mrs Elton and Toni Collette\'s Harriet. I know that in the novel Harriet was a na\xc3\xafve young teenager, whereas here she is more like the character Collette played in "Muriel\'s Wedding"- a gauche, slightly overweight twentysomething, fretting about her chances of finding a man. Nevertheless, I felt that this characterisation worked well in the context of the film and did not detract from Austen\'s themes.

"Emma" is one of Austen\'s more light-hearted works, without the darker overtones of "Mansfield Park" or even "Pride and Prejudice", and this is reflected on screen. We see a world of beauty and grace, full of stately homes and elegant costumes and fine manners. Apart from the ruffianly gypsies, who make a very brief appearance, the only "poor" people we see are Mrs Bates and her daughter, and, as they live in the sort of picturesque rose-strewn thatched cottage which today would change hands for over \xc2\xa3500,000, we can be sure that their poverty is relative, not absolute. In Emma\'s world, poverty is defined as not having your own stately home. This is, of course, not a comprehensive picture of early nineteenth-century life, but nobody has ever claimed Austen as the Regency equivalent of a kitchen-sink realist. Sophisticated romantic comedy, combined with a keen eye for analysing human character, was more in her line.

I would not rate this film quite as highly as the 1994 "Sense and Sensibility" or the recent "Pride and Prejudice"- it tends to drag a bit in the middle, although it has a strong beginning and strong ending- but it is, in the main, a highly enjoyable Austen adaptation. 7/10', shape=(), dtype=string)
label tf.Tensor(1, shape=(), dtype=int32)
vectorized review: (, )

每一个词例都被一个整数替换了，通过使用vectorize.layer.get_vocabulary() 来查找每个整数对应的词例

print("111--->",vectorize_layer.get_vocabulary()[111])

111---> little

将之前创建的TextVectorization 层用于训练集，测试集和验证集

train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)
test_ds = raw_test_ds.map(vectorize_text)

配置数据集提高性能

.cahce()会将数据保存在内存中，确保数据集训练时不会卡壳，数据集过大时也可以通过这种方法创建高性能你磁盘缓存，比小文件读取效率更高

pretetch()在训练时同时执行数据预处理和模型执行

AUTOTUNE = tf.data.AUTOTUNE

train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)

创建神经网络模型

embedding_dim = 16

model = tf.keras.Sequential([    
    layers.Embedding(max_features + 1, embedding_dim),
    layers.Dropout(0.2),
    layers.GlobalAveragePooling1D(),
    layers.Dense(16),
    layers.Dropout(0.2),
    layers.Dense(1)] 
)
# Provide a sample input to build the model
# model(tf.constant([[1, 2, 3, 4, 5]]))  # Example input
model.summary()

Model: "sequential_11"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ embedding_12 (Embedding)        │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_22 (Dropout)            │ ?                      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d_12     │ ?                      │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_12 (Dense)                │ ?                      │   0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_23 (Dropout)            │ ?                      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_13 (Dense)                │ ?                      │   0 (unbuilt) │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 0 (0.00 B)

 Trainable params: 0 (0.00 B)

 Non-trainable params: 0 (0.00 B)

1.第一层是 Embedding 层。此层采用整数编码的评论，并查找每个单词索引的嵌入向量。这些向量是通过模型训练学习到的。向量向输出数组增加了一个维度。得到的维度为：(batch, sequence, embedding)。

2.GlobalAveragePooling1D 将通过对序列维度求平均值来为每个样本返回一个定长输出向量。这允许模型以尽可能最简单的方式处理变长输入。

3.该定长输出向量通过一个有 16 个隐层单元的全连接（Dense）层传输。

4.最后一层与单个输出结点密集连接。使用 Sigmoid 激活函数，其函数值为介于 0 与 1 之间的浮点数，表示概率或置信度

这里模型没有计算出参数数量，是没有问题的，需要输入数据才能确定参数数量。

编译模型

选择损失函数与优化器

model.compile(loss = losses.BinaryCrossentropy(from_logits=True),
              optimizer='adam',
              metrics=[tf.metrics.BinaryAccuracy(threshold=0.0)])

训练模型

epochs = 10
history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs
)

Epoch 1/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 5ms/step - binary_accuracy: 0.5921 - loss: 0.6628 - val_binary_accuracy: 0.8348 - val_loss: 0.4097
Epoch 2/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step - binary_accuracy: 0.8334 - loss: 0.3825 - val_binary_accuracy: 0.8674 - val_loss: 0.3241
Epoch 3/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step - binary_accuracy: 0.8815 - loss: 0.2888 - val_binary_accuracy: 0.8746 - val_loss: 0.3051
Epoch 4/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step - binary_accuracy: 0.9036 - loss: 0.2446 - val_binary_accuracy: 0.8746 - val_loss: 0.3022
Epoch 5/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 5ms/step - binary_accuracy: 0.9151 - loss: 0.2160 - val_binary_accuracy: 0.8756 - val_loss: 0.3079
Epoch 6/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 5ms/step - binary_accuracy: 0.9272 - loss: 0.1911 - val_binary_accuracy: 0.8720 - val_loss: 0.3264
Epoch 7/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 5ms/step - binary_accuracy: 0.9326 - loss: 0.1785 - val_binary_accuracy: 0.8732 - val_loss: 0.3293
Epoch 8/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 5ms/step - binary_accuracy: 0.9419 - loss: 0.1606 - val_binary_accuracy: 0.8726 - val_loss: 0.3496
Epoch 9/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step - binary_accuracy: 0.9473 - loss: 0.1470 - val_binary_accuracy: 0.8710 - val_loss: 0.3648
Epoch 10/10
[1m625/625[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step - binary_accuracy: 0.9513 - loss: 0.1345 - val_binary_accuracy: 0.8704 - val_loss: 0.3864

评估模型

loss, accuracy = model.evaluate(test_ds)
print("Loss:", loss)
print("Accuracy:", accuracy)

[1m782/782[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - binary_accuracy: 0.8505 - loss: 0.4580
Loss: 0.4551878273487091
Accuracy: 0.8504800200462341

创建准确率和损失随训练次数变化图

history_dict = history.history
history_dict.keys()

dict_keys(['binary_accuracy', 'loss', 'val_binary_accuracy', 'val_loss'])

acc = history_dict['binary_accuracy']
val_acc = history_dict['val_binary_accuracy']
loss = history_dict['loss']
val_loss = history_dict['val_loss']

epochs = range(1,len(acc)+1)
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')

plt.show()

有 过拟合现象出现 训练集准确率逐渐上升，但在测试集上准确率达到顶点后开始下降，
通过设置EarlyStopping ，DropOut等方式来防止过拟合，之后再学习。

导出模型

export_model = tf.keras.Sequential([
    vectorize_layer,
    model,
    layers.Activation('sigmoid')
])
export_model.compile(
    loss  = losses.BinaryCrossentropy(from_logits=False),
    optimizer = 'adam',
    metrics = ['accuracy']
)

loss, accuracy = export_model.evaluate(raw_test_ds)
print(accuracy)

[1m782/782[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 7ms/step - accuracy: 0.8506 - loss: 0.4584
0.8504800200462341

对新数据进行预测

examples = [
  "The movie was great!",
  "The movie was okay.",
  "The movie was terrible..."
]
examples_tensor = tf.convert_to_tensor(examples)
# 必须要将新的数据类型转化为张量，模型才能进行处理
# Ensure the model can handle the data by passing the tensor through the export model
predictions = export_model.predict(examples_tensor)

print(predictions)

[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 81ms/step
[[0.7450576 ]
 [0.47260225]
 [0.26826346]]

下次提交文本的多类分类

参考：基本文本分类