从零开始的文本分类
作者：Mark Omernick，Francois Chollet
创建日期 2019/11/06
上次修改日期 2020/05/17
描述：从原始文本文件开始的文本情感分类。
简介
此示例展示了如何从原始文本（作为磁盘上的一组文本文件）开始进行文本分类。我们在 IMDB 情感分类数据集（未处理版本）上演示了工作流程。我们使用 TextVectorization 层进行分词和索引。
设置
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import keras
import tensorflow as tf
import numpy as np
from keras import layers
加载数据：IMDB 电影评论情感分类
让我们下载数据并检查其结构。
!curl -O https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -xf aclImdb_v1.tar.gz
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 80.2M 100 80.2M 0 0 87.7M 0 --:--:-- --:--:-- --:--:-- 87.7M
aclImdb 文件夹包含 train 和 test 子文件夹
!ls aclImdb
!ls aclImdb/test
!ls aclImdb/train
imdbEr.txt imdb.vocab README test train
labeledBow.feat neg pos urls_neg.txt urls_pos.txt
labeledBow.feat pos unsupBow.feat urls_pos.txt
neg unsup urls_neg.txt urls_unsup.txt
aclImdb/train/pos 和 aclImdb/train/neg 文件夹包含文本文件，每个文件代表一条评论（正面或负面）
!cat aclImdb/train/pos/6248_7.txt
Being an Austrian myself this has been a straight knock in my face. Fortunately I don't live nowhere near the place where this movie takes place but unfortunately it portrays everything that the rest of Austria hates about Viennese people (or people close to that region). And it is very easy to read that this is exactly the directors intention: to let your head sink into your hands and say "Oh my god, how can THAT be possible!". No, not with me, the (in my opinion) totally exaggerated uncensored swinger club scene is not necessary, I watch porn, sure, but in this context I was rather disgusted than put in the right context.<br /><br />This movie tells a story about how misled people who suffer from lack of education or bad company try to survive and live in a world of redundancy and boring horizons. A girl who is treated like a whore by her super-jealous boyfriend (and still keeps coming back), a female teacher who discovers her masochism by putting the life of her super-cruel "lover" on the line, an old couple who has an almost mathematical daily cycle (she is the "official replacement" of his ex wife), a couple that has just divorced and has the ex husband suffer under the acts of his former wife obviously having a relationship with her masseuse and finally a crazy hitchhiker who asks her drivers the most unusual questions and stretches their nerves by just being super-annoying.<br /><br />After having seen it you feel almost nothing. You're not even shocked, sad, depressed or feel like doing anything... Maybe that's why I gave it 7 points, it made me react in a way I never reacted before. If that's good or bad is up to you!
我们只对 pos 和 neg 子文件夹感兴趣，所以让我们删除其他包含文本文件的子文件夹
!rm -r aclImdb/train/unsup
您可以使用实用程序 keras.utils.text_dataset_from_directory 从磁盘上按类别文件夹组织的一组文本文件中生成带标签的 tf.data.Dataset 对象。
让我们用它来生成训练、验证和测试数据集。验证和训练数据集是从 train 目录的两个子集中生成的，其中 20% 的样本用于验证数据集，80% 用于训练数据集。
除了测试数据集之外，拥有验证数据集对于调整超参数（例如模型架构）非常有用，测试数据集不应用于此目的。
但是，在将模型投入实际应用之前，应该使用所有可用的训练数据（无需创建验证数据集）重新训练它，以便最大化其性能。
在使用 validation_split 和 subset 参数时，请确保指定随机种子或传递 shuffle=False，以便您获得的验证和训练拆分没有重叠。
batch_size = 32
raw_train_ds = keras.utils.text_dataset_from_directory(
"aclImdb/train",
batch_size=batch_size,
validation_split=0.2,
subset="training",
seed=1337,
)
raw_val_ds = keras.utils.text_dataset_from_directory(
"aclImdb/train",
batch_size=batch_size,
validation_split=0.2,
subset="validation",
seed=1337,
)
raw_test_ds = keras.utils.text_dataset_from_directory(
"aclImdb/test", batch_size=batch_size
)
print(f"Number of batches in raw_train_ds: {raw_train_ds.cardinality()}")
print(f"Number of batches in raw_val_ds: {raw_val_ds.cardinality()}")
print(f"Number of batches in raw_test_ds: {raw_test_ds.cardinality()}")
Found 25000 files belonging to 2 classes.
Using 20000 files for training.
Found 25000 files belonging to 2 classes.
Using 5000 files for validation.
Found 25000 files belonging to 2 classes.
Number of batches in raw_train_ds: 625
Number of batches in raw_val_ds: 157
Number of batches in raw_test_ds: 782
让我们预览一些样本
# It's important to take a look at your raw data to ensure your normalization
# and tokenization will work as expected. We can do that by taking a few
# examples from the training set and looking at them.
# This is one of the places where eager execution shines:
# we can just evaluate these tensors using .numpy()
# instead of needing to evaluate them in a Session/Graph context.
for text_batch, label_batch in raw_train_ds.take(1):
for i in range(5):
print(text_batch.numpy()[i])
print(label_batch.numpy()[i])
b'I\'ve seen tons of science fiction from the 70s; some horrendously bad, and others thought provoking and truly frightening. Soylent Green fits into the latter category. Yes, at times it\'s a little campy, and yes, the furniture is good for a giggle or two, but some of the film seems awfully prescient. Here we have a film, 9 years before Blade Runner, that dares to imagine the future as somthing dark, scary, and nihilistic. Both Charlton Heston and Edward G. Robinson fare far better in this than The Ten Commandments, and Robinson\'s assisted-suicide scene is creepily prescient of Kevorkian and his ilk. Some of the attitudes are dated (can you imagine a filmmaker getting away with the "women as furniture" concept in our oh-so-politically-correct-90s?), but it\'s rare to find a film from the Me Decade that actually can make you think. This is one I\'d love to see on the big screen, because even in a widescreen presentation, I don\'t think the overall scope of this film would receive its due. Check it out.'
1
b'First than anything, I\'m not going to praise I\xc3\xb1arritu\'s short film, even I\'m Mexican and proud of his success in mainstream Hollywood.<br /><br />In another hand, I see most of the reviews focuses on their favorite (and not so) short films; but we are forgetting that there is a subtle bottom line that circles the whole compilation, and maybe it will not be so pleasant for American people. (Even if that was not the main purpose of the producers) <br /><br />What i\'m talking about is that most of the short films does not show the suffering that WASP people went through because the terrorist attack on September 11th, but the suffering of the Other people.<br /><br />Do you need proofs about what i\'m saying? Look, in the Bosnia short film, the message is: "You cry because of the people who died in the Towers, but we (The Others = East Europeans) are crying long ago for the crimes committed against our women and nobody pay attention to us like the whole world has done to you".<br /><br />Even though the Burkina Fasso story is more in comedy, there is a the same thought: "You are angry because Osama Bin Laden punched you in an evil way, but we (The Others = Africans) should be more angry, because our people is dying of hunger, poverty and AIDS long time ago, and nobody pay attention to us like the whole world has done to you".<br /><br />Look now at the Sean Penn short: The fall of the Twin Towers makes happy to a lonely (and alienated) man. So the message is that the Power and the Greed (symbolized by the Towers) must fall for letting the people see the sun rise and the flowers blossom? It is remarkable that this terrible bottom line has been proposed by an American. There is so much irony in this short film that it is close to be subversive.<br /><br />Well, the Ken Loach (very know because his anti-capitalism ideology) is much more clearly and shameless in going straight to the point: "You are angry because your country has been attacked by evil forces, but we (The Others = Latin Americans) suffered at a similar date something worst, and nobody remembers our grief as the whole world has done to you".<br /><br />It is like if the creative of this project wanted to say to Americans: "You see now, America? You are not the only that have become victim of the world violence, you are not alone in your pain and by the way, we (the Others = the Non Americans) have been suffering a lot more than you from long time ago; so, we are in solidarity with you in your pain... and by the way, we are sorry because you have had some taste of your own medicine" Only the Mexican and the French short films showed some compassion and sympathy for American people; the others are like a slap on the face for the American State, that is not equal to American People.'
1
b'Blood Castle (aka Scream of the Demon Lover, Altar of Blood, Ivanna--the best, but least exploitation cinema-sounding title, and so on) is a very traditional Gothic Romance film. That means that it has big, creepy castles, a headstrong young woman, a mysterious older man, hints of horror and the supernatural, and romance elements in the contemporary sense of that genre term. It also means that it is very deliberately paced, and that the film will work best for horror mavens who are big fans of understatement. If you love films like Robert Wise\'s The Haunting (1963), but you also have a taste for late 1960s/early 1970s Spanish and Italian horror, you may love Blood Castle, as well.<br /><br />Baron Janos Dalmar (Carlos Quiney) lives in a large castle on the outskirts of a traditional, unspecified European village. The locals fear him because legend has it that whenever he beds a woman, she soon after ends up dead--the consensus is that he sets his ferocious dogs on them. This is quite a problem because the Baron has a very healthy appetite for women. At the beginning of the film, yet another woman has turned up dead and mutilated.<br /><br />Meanwhile, Dr. Ivanna Rakowsky (Erna Sch\xc3\xbcrer) has appeared in the center of the village, asking to be taken to Baron Dalmar\'s castle. She\'s an out-of-towner who has been hired by the Baron for her expertise in chemistry. Of course, no one wants to go near the castle. Finally, Ivanna finds a shady individual (who becomes even shadier) to take her. Once there, an odd woman who lives in the castle, Olga (Cristiana Galloni), rejects Ivanna and says that she shouldn\'t be there since she\'s a woman. Baron Dalmar vacillates over whether she should stay. She ends up staying, but somewhat reluctantly. The Baron has hired her to try to reverse the effects of severe burns, which the Baron\'s brother, Igor, is suffering from.<br /><br />Unfortunately, the Baron\'s brother appears to be just a lump of decomposing flesh in a vat of bizarre, blackish liquid. And furthermore, Ivanna is having bizarre, hallucinatory dreams. Just what is going on at the castle? Is the Baron responsible for the crimes? Is he insane? <br /><br />I wanted to like Blood Castle more than I did. As I mentioned, the film is very deliberate in its pacing, and most of it is very understated. I can go either way on material like that. I don\'t care for The Haunting (yes, I\'m in a very small minority there), but I\'m a big fan of 1960s and 1970s European horror. One of my favorite directors is Mario Bava. I also love Dario Argento\'s work from that period. But occasionally, Blood Castle moved a bit too slow for me at times. There are large chunks that amount to scenes of not very exciting talking alternated with scenes of Ivanna slowly walking the corridors of the castle.<br /><br />But the atmosphere of the film is decent. Director Jos\xc3\xa9 Luis Merino managed more than passable sets and locations, and they\'re shot fairly well by Emanuele Di Cola. However, Blood Castle feels relatively low budget, and this is a Roger Corman-produced film, after all (which usually means a low-budget, though often surprisingly high quality "quickie"). So while there is a hint of the lushness of Bava\'s colors and complex set decoration, everything is much more minimalist. Of course, it doesn\'t help that the Retromedia print I watched looks like a 30-year old photograph that\'s been left out in the sun too long. It appears "washed out", with compromised contrast.<br /><br />Still, Merino and Di Cola occasionally set up fantastic visuals. For example, a scene of Ivanna walking in a darkened hallway that\'s shot from an exaggerated angle, and where an important plot element is revealed through shadows on a wall only. There are also a couple Ingmar Bergmanesque shots, where actors are exquisitely blocked to imply complex relationships, besides just being visually attractive and pulling your eye deep into the frame.<br /><br />The performances are fairly good, and the women--especially Sch\xc3\xbcrer--are very attractive. Merino exploits this fact by incorporating a decent amount of nudity. Sch\xc3\xbcrer went on to do a number of films that were as much soft corn porn as they were other genres, with English titles such as Sex Life in a Woman\'s Prison (1974), Naked and Lustful (1974), Strip Nude for Your Killer (1975) and Erotic Exploits of a Sexy Seducer (1977). Blood Castle is much tamer, but in addition to the nudity, there are still mild scenes suggesting rape and bondage, and of course the scenes mixing sex and death.<br /><br />The primary attraction here, though, is probably the story, which is much a slow-burning romance as anything else. The horror elements, the mystery elements, and a somewhat unexpected twist near the end are bonuses, but in the end, Blood Castle is a love story, about a couple overcoming various difficulties and antagonisms (often with physical threats or harms) to be together.'
1
b"I was talked into watching this movie by a friend who blubbered on about what a cute story this was.<br /><br />Yuck.<br /><br />I want my two hours back, as I could have done SO many more productive things with my time...like, for instance, twiddling my thumbs. I see nothing redeeming about this film at all, save for the eye-candy aspect of it...<br /><br />3/10 (and that's being generous)"
0
b"Michelle Rodriguez is the defining actress who could be the charging force for other actresses to look out for. She has the audacity to place herself in a rarely seen tough-girl role very early in her career (and pull it off), which is a feat that should be recognized. Although her later films pigeonhole her to that same role, this film was made for her ruggedness.<br /><br />Her character is a romanticized student/fighter/lover, struggling to overcome her disenchanted existence in the projects, which is a little overdone in film...but not by a girl. That aspect of this film isn't very original, but the story goes in depth when the heated relationships that this girl has to deal with come to a boil and her primal rage takes over.<br /><br />I haven't seen an actress take such an aggressive stance in movie-making yet, and I'm glad that she's getting that original twist out there in Hollywood. This film got a 7 from me because of the average story of ghetto youth, but it has such a great actress portraying a rarely-seen role in a minimal budget movie. Great work."
1
准备数据
特别是，我们删除了 <br /> 标签。
import string
import re
# Having looked at our data above, we see that the raw text contains HTML break
# tags of the form '<br />'. These tags will not be removed by the default
# standardizer (which doesn't strip HTML). Because of this, we will need to
# create a custom standardization function.
def custom_standardization(input_data):
lowercase = tf.strings.lower(input_data)
stripped_html = tf.strings.regex_replace(lowercase, "<br />", " ")
return tf.strings.regex_replace(
stripped_html, f"[{re.escape(string.punctuation)}]", ""
)
# Model constants.
max_features = 20000
embedding_dim = 128
sequence_length = 500
# Now that we have our custom standardization, we can instantiate our text
# vectorization layer. We are using this layer to normalize, split, and map
# strings to integers, so we set our 'output_mode' to 'int'.
# Note that we're using the default split function,
# and the custom standardization defined above.
# We also set an explicit maximum sequence length, since the CNNs later in our
# model won't support ragged sequences.
vectorize_layer = keras.layers.TextVectorization(
standardize=custom_standardization,
max_tokens=max_features,
output_mode="int",
output_sequence_length=sequence_length,
)
# Now that the vectorize_layer has been created, call `adapt` on a text-only
# dataset to create the vocabulary. You don't have to batch, but for very large
# datasets this means you're not keeping spare copies of the dataset in memory.
# Let's make a text-only dataset (no labels):
text_ds = raw_train_ds.map(lambda x, y: x)
# Let's call `adapt`:
vectorize_layer.adapt(text_ds)
向量化数据的两种选择
我们可以用两种方法使用我们的文本向量化层
选项 1：将其作为模型的一部分，以便获得处理原始字符串的模型，如下所示
text_input = keras.Input(shape=(1,), dtype=tf.string, name='text')
x = vectorize_layer(text_input)
x = layers.Embedding(max_features + 1, embedding_dim)(x)
...
选项 2：将其应用于文本数据集以获得单词索引的数据集，然后将其馈送到期望整数序列作为输入的模型。
两者之间的一个重要区别是，选项 2 能够在 GPU 上训练时对数据进行异步 CPU 处理和缓冲。因此，如果您在 GPU 上训练模型，您可能希望选择此选项以获得最佳性能。这就是我们下面要做的。
如果我们要将模型导出到生产环境，我们将提供一个接受原始字符串作为输入的模型，就像上面选项 1 的代码片段中一样。这可以在训练后完成。我们在最后一节中执行此操作。
def vectorize_text(text, label):
text = tf.expand_dims(text, -1)
return vectorize_layer(text), label
# Vectorize the data.
train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)
test_ds = raw_test_ds.map(vectorize_text)
# Do async prefetching / buffering of the data for best performance on GPU.
train_ds = train_ds.cache().prefetch(buffer_size=10)
val_ds = val_ds.cache().prefetch(buffer_size=10)
test_ds = test_ds.cache().prefetch(buffer_size=10)
构建模型
我们选择一个简单的 1D 卷积神经网络，从 Embedding 层开始。
# A integer input for vocab indices.
inputs = keras.Input(shape=(None,), dtype="int64")
# Next, we add a layer to map those vocab indices into a space of dimensionality
# 'embedding_dim'.
x = layers.Embedding(max_features, embedding_dim)(inputs)
x = layers.Dropout(0.5)(x)
# Conv1D + global max pooling
x = layers.Conv1D(128, 7, padding="valid", activation="relu", strides=3)(x)
x = layers.Conv1D(128, 7, padding="valid", activation="relu", strides=3)(x)
x = layers.GlobalMaxPooling1D()(x)
# We add a vanilla hidden layer:
x = layers.Dense(128, activation="relu")(x)
x = layers.Dropout(0.5)(x)
# We project onto a single unit output layer, and squash it with a sigmoid:
predictions = layers.Dense(1, activation="sigmoid", name="predictions")(x)
model = keras.Model(inputs, predictions)
# Compile the model with binary crossentropy loss and an adam optimizer.
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
训练模型
epochs = 3
# Fit the model using the train and test datasets.
model.fit(train_ds, validation_data=val_ds, epochs=epochs)
Epoch 1/3
625/625 ━━━━━━━━━━━━━━━━━━━━ 5s 4ms/step - accuracy: 0.6082 - loss: 0.6121 - val_accuracy: 0.8589 - val_loss: 0.3313
Epoch 2/3
625/625 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - accuracy: 0.8855 - loss: 0.2748 - val_accuracy: 0.8662 - val_loss: 0.3499
Epoch 3/3
625/625 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - accuracy: 0.9463 - loss: 0.1432 - val_accuracy: 0.8758 - val_loss: 0.3789
<keras.src.callbacks.history.History at 0x7ff434de94b0>
评估模型在测试集上的性能
model.evaluate(test_ds)
782/782 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8634 - loss: 0.3848
[0.3857516348361969, 0.8642103672027588]
创建端到端模型
如果您想要获得能够处理原始字符串的模型，您可以简单地创建一个新模型（使用我们刚刚训练的权重）
# A string input
inputs = keras.Input(shape=(1,), dtype="string")
# Turn strings into vocab indices
indices = vectorize_layer(inputs)
# Turn vocab indices into predictions
outputs = model(indices)
# Our end to end model
end_to_end_model = keras.Model(inputs, outputs)
end_to_end_model.compile(
loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]
)
# Test it with `raw_test_ds`, which yields raw strings
end_to_end_model.evaluate(raw_test_ds)
782/782 ━━━━━━━━━━━━━━━━━━━━ 5s 5ms/step - accuracy: 0.8636 - loss: 0.3829
[0.38630548119544983, 0.8639705777168274]