► 代码示例 / 自然语言处理 / 使用 FNet 进行文本分类

使用 FNet 进行文本分类

作者： Abheesht Sharma
创建日期 2022/06/01
上次修改日期 2022/12/21
描述：使用 keras_hub.layers.FNetEncoder 层在 IMDB 数据集上进行文本分类。

ⓘ 此示例使用 Keras 3

在 Colab 中查看 • GitHub 源代码

简介

在本示例中，我们将演示 FNet 在文本分类任务上与普通 Transformer 模型取得可比结果的能力。我们将使用 IMDB 数据集，这是一个包含电影评论的集合，这些评论被标记为正面或负面（情感分析）。

为了构建分词器、模型等，我们将使用来自 KerasHub 的组件。KerasHub 使希望构建 NLP 管道的人们的生活变得更加轻松！ :)

模型

基于 Transformer 的语言模型 (LM)，例如 BERT、RoBERTa、XLNet 等，已经证明了自注意力机制在计算输入文本的丰富嵌入方面的有效性。但是，自注意力机制是一种昂贵的操作，其时间复杂度为 O(n^2)，其中 n 是输入中标记的数量。因此，人们一直努力降低自注意力机制的时间复杂度，并在不牺牲结果质量的情况下提高性能。

2020 年，一篇题为 FNet：使用傅里叶变换混合标记的论文用简单的傅里叶变换层替换了 BERT 中的自注意力层，用于“标记混合”。这使得准确率相当，并且训练速度加快。特别是，论文中的一些要点很突出

作者声称 FNet 在 GPU 上比 BERT 快 80%，在 TPU 上快 70%。这种加速有两个原因：a) 傅里叶变换层是无参数的，它没有任何参数，以及 b) 作者使用快速傅里叶变换 (FFT)；这将时间复杂度从 O(n^2)（在自注意力的情况下）降低到 O(n log n)。
FNet 设法在 GLUE 基准测试中实现了 BERT 92-97% 的准确率。

设置

在开始实现之前，让我们导入所有必要的包。

!pip install -q --upgrade keras-hub
!pip install -q --upgrade keras  # Upgrade to Keras 3.

import keras_hub
import keras
import tensorflow as tf
import os

keras.utils.set_random_seed(42)

让我们也定义我们的超参数。

BATCH_SIZE = 64
EPOCHS = 3
MAX_SEQUENCE_LENGTH = 512
VOCAB_SIZE = 15000

EMBED_DIM = 128
INTERMEDIATE_DIM = 512

加载数据集

首先，让我们下载 IMDB 数据集并解压它。

!wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -xzf aclImdb_v1.tar.gz

--2023-11-22 17:59:33--  http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Resolving ai.stanford.edu (ai.stanford.edu)... 171.64.68.10
Connecting to ai.stanford.edu (ai.stanford.edu)|171.64.68.10|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84125825 (80M) [application/x-gzip]
Saving to: ‘aclImdb_v1.tar.gz’

aclImdb_v1.tar.gz   100%[===================>]  80.23M  93.3MB/s    in 0.9s

2023-11-22 17:59:34 (93.3 MB/s) - ‘aclImdb_v1.tar.gz’ saved [84125825/84125825]

样本以文本文件的形式存在。让我们检查目录的结构。

print(os.listdir("./aclImdb"))
print(os.listdir("./aclImdb/train"))
print(os.listdir("./aclImdb/test"))

['README', 'imdb.vocab', 'imdbEr.txt', 'train', 'test']
['neg', 'unsup', 'pos', 'unsupBow.feat', 'urls_unsup.txt', 'urls_neg.txt', 'urls_pos.txt', 'labeledBow.feat']
['neg', 'pos', 'urls_neg.txt', 'urls_pos.txt', 'labeledBow.feat']

该目录包含两个子目录：train 和 test。每个子目录又包含两个文件夹：分别用于正面和负面评论的 pos 和 neg。在我们加载数据集之前，让我们删除 ./aclImdb/train/unsup 文件夹，因为它包含未标记的样本。

!rm -rf aclImdb/train/unsup

我们将使用 keras.utils.text_dataset_from_directory 实用程序从文本文件生成我们标记的 tf.data.Dataset 数据集。

train_ds = keras.utils.text_dataset_from_directory(
    "aclImdb/train",
    batch_size=BATCH_SIZE,
    validation_split=0.2,
    subset="training",
    seed=42,
)
val_ds = keras.utils.text_dataset_from_directory(
    "aclImdb/train",
    batch_size=BATCH_SIZE,
    validation_split=0.2,
    subset="validation",
    seed=42,
)
test_ds = keras.utils.text_dataset_from_directory("aclImdb/test", batch_size=BATCH_SIZE)

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.

我们现在将文本转换为小写。

train_ds = train_ds.map(lambda x, y: (tf.strings.lower(x), y))
val_ds = val_ds.map(lambda x, y: (tf.strings.lower(x), y))
test_ds = test_ds.map(lambda x, y: (tf.strings.lower(x), y))

让我们打印一些样本。

for text_batch, label_batch in train_ds.take(1):
    for i in range(3):
        print(text_batch.numpy()[i])
        print(label_batch.numpy()[i])

b'an illegal immigrant resists the social support system causing dire consequences for many. well filmed and acted even though the story is a bit forced, yet the slow pacing really sets off the conclusion. the feeling of being lost in the big city is effectively conveyed. the little person lost in the big society is something to which we can all relate, but i cannot endorse going out of your way to see this movie.'
0
b"to get in touch with the beauty of this film pay close attention to the sound track, not only the music, but the way all sounds help to weave the imagery. how beautifully the opening scene leading to the expulsion of gino establishes the theme of moral ambiguity! note the way music introduces the characters as we are led inside giovanna's marriage. don't expect to find much here of the political life of italy in 1943. that's not what this is about. on the other hand, if you are susceptible to the music of images and sounds, you will be led into a word that reaches beyond neo-realism. by the end of the film we there are moments antonioni-like landscape that has more to do with the inner life of the characters than with real places. this is one of my favorite visconti films."
1
b'"hollywood hotel" has relationships to many films like "ella cinders" and "merton of the movies" about someone winning a contest including a contract to make films in hollywood, only to find the road to stardom either paved with pitfalls or non-existent. in fact, as i was watching it tonight, on turner classic movies, i was considering whether or not the authors of the later musical classic "singing in the rain" may have taken some of their ideas from "hollywood hotel", most notably a temperamental leading lady star in a movie studio and a conclusion concerning one person singing a film score while another person got the credit by mouthing along on screen.<br /><br />"hollywood hotel" is a fascinating example of movie making in the 1930s. among the supporting players is louella parsons, playing herself (and, despite some negative comments i\'ve seen, she has a very ingratiating personality on screen and a natural command of her lines). she is not the only real person in the script. make-up specialist perc westmore briefly appears as himself to try to make one character resemble another.<br /><br />this film also was one of the first in the career of young mr. ronald reagan, playing a radio interviewer at a movie premiere. reagan actually does quite nicely in his brief scenes - particularly when he realizes that nobody dick powell is about to take over the microphone when it should be used with more important people.<br /><br />dick powell has won a hollywood contract in a contest, and is leaving his job as a saxophonist in benny goodman\'s band. the beginning of this film, by the way, is quite impressive, as the band drives in a parade of trucks to give a proper goodbye to powell. they end up singing "hooray for hollywood". the interesting thing about this wonderful number is that a lyric has been left out on purpose. throughout the johnny mercer lyrics are references to such hollywood as max factor the make-up king, rin tin tin, and even a hint of tarzan. but the original song lyric referred to looking like tyrone power. obviously jack warner and his brothers were not going to advertise the leading man of 20th century fox, and the name donald duck was substituted. in any event the number showed the singers and instrumentalists of goodman\'s orchestra at their best. so did a later five minute section of the film, where the band is rehearsing.<br /><br />powell leaves the band and his girl friend (frances langford) and goes to hollywood, only to find he is a contract player (most likely for musicals involving saxophonists). he is met by allen joslyn, the publicist of the studio (the owner is grant mitchell). joslyn is not a bad fellow, but he is busy and he tends to slough off people unless it is necessary to speak to them. he parks powell at a room at the hollywood hotel, which is also where the studio\'s temperamental star (lola lane) lives with her father (hugh herbert), her sister (mabel todd), and her sensible if cynical assistant (glenda farrell). lane is like jean hagen in "singing in the rain", except her speaking voice is good. her version of "dan lockwood" is one "alexander dupre" (alan mowbray, scene stealing with ease several times). the only difference is that mowbray is not a nice guy like gene kelly was, and lane (when not wrapped up in her ego) is fully aware of it. having a fit on being by-passed for an out-of-the ordinary role she wanted, she refuses to attend the premiere of her latest film. joslyn finds a double for her (lola\'s real life sister rosemary lane), and rosemary is made up to play the star at the premiere and the follow-up party. but she attends with powell (joslyn wanting someone who doesn\'t know the real lola). this leads to powell knocking down mowbray when the latter makes a pest of himself. but otherwise the evening is a success, and when the two are together they start finding each other attractive.<br /><br />the complications deal with lola coming back and slapping powell in the face, after mowbray complains he was attacked by powell ("and his gang of hoodlums"). powell\'s contract is bought out. working with photographer turned agent ted healey (actually not too bad in this film - he even tries to do a jolson imitation at one point), the two try to find work, ending up as employees at a hamburger stand run by bad tempered edgar kennedy (the number of broken dishes and singing customers in the restaurant give edgar plenty of time to do his slow burns with gusto). eventually powell gets a "break" by being hired to be dupre\'s singing voice in a rip-off of "gone with the wind". this leads to the final section of the film, when rosemary lane, herbert, and healey help give powell his chance to show it\'s his voice, not mowbrays.<br /><br />it\'s quite a cute and appealing film even now. the worst aspects are due to it\'s time. several jokes concerning african-americans are no longer tolerable (while trying to photograph powell as he arrives in hollywood, healey accidentally photographs a porter, and mentions to joslyn to watch out, powell photographs too darkly - get the point?). also a bit with curt bois as a fashion designer for lola lane, who is (shall we say) too high strung is not very tolerable either. herbert\'s "hoo-hoo"ing is a bit much (too much of the time) but it was really popular in 1937. and an incident where healey nearly gets into a brawl at the premiere (this was one of his last films) reminds people of the tragic, still mysterious end of the comedian in december 1937. but most of the film is quite good, and won\'t disappoint the viewer in 2008.'
1

对数据进行分词

我们将使用 keras_hub.tokenizers.WordPieceTokenizer 层对文本进行分词。 keras_hub.tokenizers.WordPieceTokenizer 获取 WordPiece 词汇表，并具有对文本进行分词和将标记序列去分词的功能。

在我们定义分词器之前，我们首先需要在我们拥有的数据集上对其进行训练。WordPiece 分词算法是一种子词分词算法；在语料库上对其进行训练会为我们提供一个子词词汇表。子词分词器是词分词器（词分词器需要非常大的词汇表才能很好地覆盖输入词）和字符分词器（字符不像词那样真正编码含义）之间的折衷方案。幸运的是，KerasHub 使用 keras_hub.tokenizers.compute_word_piece_vocabulary 实用程序使在语料库上训练 WordPiece 变得非常简单。

注意：FNet 的官方实现使用 SentencePiece 分词器。

def train_word_piece(ds, vocab_size, reserved_tokens):
    word_piece_ds = ds.unbatch().map(lambda x, y: x)
    vocab = keras_hub.tokenizers.compute_word_piece_vocabulary(
        word_piece_ds.batch(1000).prefetch(2),
        vocabulary_size=vocab_size,
        reserved_tokens=reserved_tokens,
    )
    return vocab

每个词汇表都有一些特殊的保留标记。我们有两个这样的标记

"[PAD]" - 填充标记。当输入序列长度小于最大序列长度时，将填充标记附加到输入序列长度。
"[UNK]" - 未知标记。

reserved_tokens = ["[PAD]", "[UNK]"]
train_sentences = [element[0] for element in train_ds]
vocab = train_word_piece(train_ds, VOCAB_SIZE, reserved_tokens)

让我们看看一些标记！

print("Tokens: ", vocab[100:110])

Tokens:  ['à', 'á', 'â', 'ã', 'ä', 'å', 'æ', 'ç', 'è', 'é']

现在，让我们定义分词器。我们将使用上面训练的词汇表配置分词器。我们将定义一个最大序列长度，以便将所有序列填充到相同的长度，如果序列的长度小于指定的序列长度。否则，序列将被截断。

tokenizer = keras_hub.tokenizers.WordPieceTokenizer(
    vocabulary=vocab,
    lowercase=False,
    sequence_length=MAX_SEQUENCE_LENGTH,
)

让我们尝试对我们数据集中的一个样本进行分词！为了验证文本是否已正确分词，我们还可以将标记列表去分词回原始文本。

input_sentence_ex = train_ds.take(1).get_single_element()[0][0]
input_tokens_ex = tokenizer(input_sentence_ex)

print("Sentence: ", input_sentence_ex)
print("Tokens: ", input_tokens_ex)
print("Recovered text after detokenizing: ", tokenizer.detokenize(input_tokens_ex))

Sentence:  tf.Tensor(b'this picture seemed way to slanted, it\'s almost as bad as the drum beating of the right wing kooks who say everything is rosy in iraq. it paints a picture so unredeemable that i can\'t help but wonder about it\'s legitimacy and bias. also it seemed to meander from being about the murderous carnage of our troops to the lack of health care in the states for ptsd. to me the subject matter seemed confused, it only cared about portraying the military in a bad light, as a) an organzation that uses mind control to turn ordinary peace loving civilians into baby killers and b) an organization that once having used and spent the bodies of it\'s soldiers then discards them to the despotic bureacracy of the v.a. this is a legitimate argument, but felt off topic for me, almost like a movie in and of itself. i felt that "the war tapes" and "blood of my brother" were much more fair and let the viewer draw some conclusions of their own rather than be beaten over the head with the film makers viewpoint. f-', shape=(), dtype=string)
Tokens:  [  145   576   608   228   140    58 13343    13   143     8    58   360
   148   209   148   137  9759  3681   139   137   344  3276    50 12092
   164   169   269   424   141    57  2093   292   144  5115    15   143
  7890    40   576   170  2970  2459  2412 10452   146    48   184     8
    59   478   152   733   177   143     8    58  4060  8069 13355   138
  8557    15   214   143   608   140   526  2121   171   247   177   137
  4726  7336   139   395  4985   140   137   711   139  3959   597   144
   137  1844   149    55  1175   288    15   140   203   137  1009   686
   608  1701    13   143   197  3979   177  2514   137  1442   144    40
   209   776    13   148    40    10   168 14198 13928   146  1260   470
  1300   140   604  2118  2836  1873  9991   217  1006  2318   138    41
    10   168  8469   146   422   400   480   138  1213   137  2541   139
   143     8    58  1487   227  4319 10720   229   140   137  6310  8532
   862    41  2215  6547 10768   139   137    61    15    40    15   145
   141    40  7738  4120    13   152   569   260  3297   149   203    13
   360   172    40   150   144   138   139   561    15    48   569   146
     3   137   466  6192     3   138     3   665   139   193   707     3
   204   207   185  1447   138   417   137   643  2731   182  8421   139
   199   342   385   206   161  3920   253   137   566   151   137   153
  1340  8845    15    45    14     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0     0     0     0     0
     0     0     0     0     0     0     0     0]
Recovered text after detokenizing:  tf.Tensor(b'this picture seemed way to slanted , it \' s almost as bad as the drum beating of the right wing kooks who say everything is rosy in iraq . it paints a picture so unredeemable that i can \' t help but wonder about it \' s legitimacy and bias . also it seemed to meander from being about the murderous carnage of our troops to the lack of health care in the states for ptsd . to me the subject matter seemed confused , it only cared about portraying the military in a bad light , as a ) an organzation that uses mind control to turn ordinary peace loving civilians into baby killers and b ) an organization that once having used and spent the bodies of it \' s soldiers then discards them to the despotic bureacracy of the v . a . this is a legitimate argument , but felt off topic for me , almost like a movie in and of itself . i felt that " the war tapes " and " blood of my brother " were much more fair and let the viewer draw some conclusions of their own rather than be beaten over the head with the film makers viewpoint . f - [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]', shape=(), dtype=string)

格式化数据集

接下来，我们将以将馈送到模型的形式格式化我们的数据集。我们需要对文本进行分词。

def format_dataset(sentence, label):
    sentence = tokenizer(sentence)
    return ({"input_ids": sentence}, label)


def make_dataset(dataset):
    dataset = dataset.map(format_dataset, num_parallel_calls=tf.data.AUTOTUNE)
    return dataset.shuffle(512).prefetch(16).cache()


train_ds = make_dataset(train_ds)
val_ds = make_dataset(val_ds)
test_ds = make_dataset(test_ds)

构建模型

现在，让我们继续激动人心的部分——定义我们的模型！我们首先需要一个嵌入层，即一个将输入序列中的每个标记映射到向量的层。此嵌入层可以随机初始化。我们还需要一个位置嵌入层，它对序列中的词序进行编码。惯例是将这两个嵌入相加，即求和。KerasHub 有一个 keras_hub.layers.TokenAndPositionEmbedding 层，它为我们完成了以上所有步骤。

我们的 FNet 分类模型由三个 keras_hub.layers.FNetEncoder 层和顶部的 keras.layers.Dense 层组成。

注意：对于 FNet，掩蔽填充标记对结果的影响很小。在官方实现中，填充标记未被掩蔽。

input_ids = keras.Input(shape=(None,), dtype="int64", name="input_ids")

x = keras_hub.layers.TokenAndPositionEmbedding(
    vocabulary_size=VOCAB_SIZE,
    sequence_length=MAX_SEQUENCE_LENGTH,
    embedding_dim=EMBED_DIM,
    mask_zero=True,
)(input_ids)

x = keras_hub.layers.FNetEncoder(intermediate_dim=INTERMEDIATE_DIM)(inputs=x)
x = keras_hub.layers.FNetEncoder(intermediate_dim=INTERMEDIATE_DIM)(inputs=x)
x = keras_hub.layers.FNetEncoder(intermediate_dim=INTERMEDIATE_DIM)(inputs=x)


x = keras.layers.GlobalAveragePooling1D()(x)
x = keras.layers.Dropout(0.1)(x)
outputs = keras.layers.Dense(1, activation="sigmoid")(x)

fnet_classifier = keras.Model(input_ids, outputs, name="fnet_classifier")

/home/matt/miniconda3/envs/keras-io/lib/python3.10/site-packages/keras/src/layers/layer.py:861: UserWarning: Layer 'f_net_encoder' (of type FNetEncoder) was passed an input with a mask attached to it. However, this layer does not support masking and will therefore destroy the mask information. Downstream layers will not see the mask.
  warnings.warn(

训练我们的模型

我们将使用准确率来监控验证数据上的训练进度。让我们训练我们的模型 3 个 epoch。

fnet_classifier.summary()
fnet_classifier.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss="binary_crossentropy",
    metrics=["accuracy"],
)
fnet_classifier.fit(train_ds, epochs=EPOCHS, validation_data=val_ds)

Model: "fnet_classifier"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape              ┃    Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ input_ids (InputLayer)          │ (None, None)              │          0 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ token_and_position_embedding    │ (None, None, 128)         │  1,985,536 │
│ (TokenAndPositionEmbedding)     │                           │            │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ f_net_encoder (FNetEncoder)     │ (None, None, 128)         │    132,224 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ f_net_encoder_1 (FNetEncoder)   │ (None, None, 128)         │    132,224 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ f_net_encoder_2 (FNetEncoder)   │ (None, None, 128)         │    132,224 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ global_average_pooling1d        │ (None, 128)               │          0 │
│ (GlobalAveragePooling1D)        │                           │            │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dropout (Dropout)               │ (None, 128)               │          0 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dense (Dense)                   │ (None, 1)                 │        129 │
└─────────────────────────────────┴───────────────────────────┴────────────┘

 Total params: 2,382,337 (9.09 MB)

 Trainable params: 2,382,337 (9.09 MB)

 Non-trainable params: 0 (0.00 B)

Epoch 1/3

/home/matt/miniconda3/envs/keras-io/lib/python3.10/site-packages/keras/src/backend/jax/core.py:64: UserWarning: Explicitly requested dtype int64 requested in array is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.
  return jnp.array(x, dtype=dtype)

 313/313 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.5916 - loss: 0.6542 - val_accuracy: 0.8479 - val_loss: 0.3536
Epoch 2/3
 313/313 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.8776 - loss: 0.2916 - val_accuracy: 0.8532 - val_loss: 0.3387
Epoch 3/3
 313/313 ━━━━━━━━━━━━━━━━━━━━ 4s 12ms/step - accuracy: 0.9442 - loss: 0.1543 - val_accuracy: 0.8534 - val_loss: 0.4018

<keras.src.callbacks.history.History at 0x7feb7169c0d0>

我们获得了大约 92% 的训练准确率和大约 85% 的验证准确率。此外，对于 3 个 epoch，训练模型大约需要 86 秒（在配备 16 GB Tesla T4 GPU 的 Colab 上）。

让我们计算测试准确率。

fnet_classifier.evaluate(test_ds, batch_size=BATCH_SIZE)

 391/391 ━━━━━━━━━━━━━━━━━━━━ 3s 5ms/step - accuracy: 0.8412 - loss: 0.4281

[0.4198716878890991, 0.8427909016609192]

与 Transformer 模型的比较

让我们将我们的 FNet 分类器模型与 Transformer 分类器模型进行比较。我们保持所有参数/超参数相同。例如，我们使用三个 TransformerEncoder 层。

我们将头数设置为 2。

NUM_HEADS = 2
input_ids = keras.Input(shape=(None,), dtype="int64", name="input_ids")


x = keras_hub.layers.TokenAndPositionEmbedding(
    vocabulary_size=VOCAB_SIZE,
    sequence_length=MAX_SEQUENCE_LENGTH,
    embedding_dim=EMBED_DIM,
    mask_zero=True,
)(input_ids)

x = keras_hub.layers.TransformerEncoder(
    intermediate_dim=INTERMEDIATE_DIM, num_heads=NUM_HEADS
)(inputs=x)
x = keras_hub.layers.TransformerEncoder(
    intermediate_dim=INTERMEDIATE_DIM, num_heads=NUM_HEADS
)(inputs=x)
x = keras_hub.layers.TransformerEncoder(
    intermediate_dim=INTERMEDIATE_DIM, num_heads=NUM_HEADS
)(inputs=x)


x = keras.layers.GlobalAveragePooling1D()(x)
x = keras.layers.Dropout(0.1)(x)
outputs = keras.layers.Dense(1, activation="sigmoid")(x)

transformer_classifier = keras.Model(input_ids, outputs, name="transformer_classifier")


transformer_classifier.summary()
transformer_classifier.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss="binary_crossentropy",
    metrics=["accuracy"],
)
transformer_classifier.fit(train_ds, epochs=EPOCHS, validation_data=val_ds)

Model: "transformer_classifier"

┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type)        ┃ Output Shape      ┃ Param # ┃ Connected to         ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ input_ids           │ (None, None)      │       0 │ -                    │
│ (InputLayer)        │                   │         │                      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ token_and_position… │ (None, None, 128) │ 1,985,… │ input_ids[0][0]      │
│ (TokenAndPositionE… │                   │         │                      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ transformer_encoder │ (None, None, 128) │ 198,272 │ token_and_position_… │
│ (TransformerEncode… │                   │         │                      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ transformer_encode… │ (None, None, 128) │ 198,272 │ transformer_encoder… │
│ (TransformerEncode… │                   │         │                      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ transformer_encode… │ (None, None, 128) │ 198,272 │ transformer_encoder… │
│ (TransformerEncode… │                   │         │                      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ not_equal_1         │ (None, None)      │       0 │ input_ids[0][0]      │
│ (NotEqual)          │                   │         │                      │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ global_average_poo… │ (None, 128)       │       0 │ transformer_encoder… │
│ (GlobalAveragePool… │                   │         │ not_equal_1[0][0]    │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ dropout_4 (Dropout) │ (None, 128)       │       0 │ global_average_pool… │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ dense_1 (Dense)     │ (None, 1)         │     129 │ dropout_4[0][0]      │
└─────────────────────┴───────────────────┴─────────┴──────────────────────┘

 Total params: 2,580,481 (9.84 MB)

 Trainable params: 2,580,481 (9.84 MB)

 Non-trainable params: 0 (0.00 B)

Epoch 1/3
 313/313 ━━━━━━━━━━━━━━━━━━━━ 14s 38ms/step - accuracy: 0.5895 - loss: 0.7401 - val_accuracy: 0.8912 - val_loss: 0.2694
Epoch 2/3
 313/313 ━━━━━━━━━━━━━━━━━━━━ 9s 29ms/step - accuracy: 0.9051 - loss: 0.2382 - val_accuracy: 0.8853 - val_loss: 0.2984
Epoch 3/3
 313/313 ━━━━━━━━━━━━━━━━━━━━ 9s 29ms/step - accuracy: 0.9496 - loss: 0.1366 - val_accuracy: 0.8730 - val_loss: 0.3607

<keras.src.callbacks.history.History at 0x7feaf9c56ad0>

我们获得了大约 94% 的训练准确率和大约 86.5% 的验证准确率。训练模型大约需要 146 秒（在配备 16 GB Tesla T4 GPU 的 Colab 上）。

让我们计算测试准确率。

transformer_classifier.evaluate(test_ds, batch_size=BATCH_SIZE)

 391/391 ━━━━━━━━━━━━━━━━━━━━ 4s 11ms/step - accuracy: 0.8399 - loss: 0.4579

[0.4496161639690399, 0.8423193097114563]

让我们做一个表格并比较这两个模型。我们可以看到，FNet 显着加快了我们的运行时间 (1.7 倍)，并且准确率仅略有下降 (下降 0.75%)。

	FNet 分类器	Transformer 分类器
训练时间	86 秒	146 秒
训练准确率	92.34%	93.85%
验证准确率	85.21%	86.42%
测试准确率	83.94%	84.69%
参数数量	2,321,921	2,520,065

使用 FNet 进行文本分类

◆ 简介

模型

◆ 设置

◆ 加载数据集

对数据进行分词

◆ 格式化数据集

◆ 构建模型

◆ 训练我们的模型

◆ 与 Transformer 模型的比较