البحث الدلالي مع أقرب الجيران التقريبيين ودمج النص

يوضح هذا البرنامج التعليمي كيفية إنشاء التضمينات من وحدة TensorFlow Hub (TF-Hub) مع توفير بيانات الإدخال، وإنشاء فهرس تقريبي لأقرب الجيران (ANN) باستخدام التضمينات المستخرجة. يمكن بعد ذلك استخدام الفهرس لمطابقة التشابه واسترجاعه في الوقت الفعلي.

عند التعامل مع مجموعة كبيرة من البيانات، ليس من الفعال إجراء مطابقة تامة عن طريق فحص المستودع بالكامل للعثور على العناصر الأكثر تشابهًا مع استعلام معين في الوقت الفعلي. وبالتالي، فإننا نستخدم خوارزمية مطابقة التشابه التقريبية التي تسمح لنا باستبدال القليل من الدقة في العثور على أقرب تطابقات مجاورة للحصول على زيادة كبيرة في السرعة.

في هذا البرنامج التعليمي، نعرض مثالاً للبحث النصي في الوقت الفعلي عبر مجموعة من عناوين الأخبار للعثور على العناوين الأكثر تشابهًا مع الاستعلام. وعلى عكس البحث عن الكلمات الرئيسية، فإن هذا يلتقط التشابه الدلالي المشفر في تضمين النص.

خطوات هذا البرنامج التعليمي هي:

1. تنزيل بيانات العينة.
2. إنشاء التضمينات للبيانات باستخدام وحدة TF-Hub
3. بناء فهرس ANN للتضمينات
4. استخدم الفهرس لمطابقة التشابه

نحن نستخدم Apache Beam مع TensorFlow Transform (TF-Transform) لإنشاء التضمينات من وحدة TF-Hub. نستخدم أيضًا مكتبة ANNOY الخاصة بـ Spotify لإنشاء فهرس تقريبي لأقرب الجيران. يمكنك العثور على معايير مرجعية لإطار عمل ANN في مستودع Github هذا.

يستخدم هذا البرنامج التعليمي TensorFlow 1.0 ويعمل فقط مع وحدات TF1 Hub من TF-Hub. راجع نسخة TF2 المحدثة من هذا البرنامج التعليمي .

يثبت

تثبيت المكتبات المطلوبة.

pip install -q apache_beam
pip install -q sklearn
pip install -q annoy

استيراد المكتبات المطلوبة

import os
import sys
import pathlib
import pickle
from collections import namedtuple
from datetime import datetime

import numpy as np
import apache_beam as beam
import annoy
from sklearn.random_projection import gaussian_random_matrix

import tensorflow.compat.v1 as tf
import tensorflow_hub as hub

# TFT needs to be installed afterwards
!pip install -q tensorflow_transform==0.24
import tensorflow_transform as tft
import tensorflow_transform.beam as tft_beam

print('TF version: {}'.format(tf.__version__))
print('TF-Hub version: {}'.format(hub.__version__))
print('TF-Transform version: {}'.format(tft.__version__))
print('Apache Beam version: {}'.format(beam.__version__))

TF version: 2.3.1
TF-Hub version: 0.10.0
TF-Transform version: 0.24.0
Apache Beam version: 2.25.0

1. قم بتنزيل بيانات العينة

تحتوي مجموعة بيانات Million News Headlines على عناوين الأخبار المنشورة على مدى 15 عامًا والتي تم الحصول عليها من هيئة الإذاعة الأسترالية (ABC) ذات السمعة الطيبة. تحتوي مجموعة البيانات الإخبارية هذه على سجل تاريخي موجز للأحداث الجديرة بالملاحظة في العالم من أوائل عام 2003 إلى نهاية عام 2017 مع تركيز أكثر تفصيلاً على أستراليا.

التنسيق : بيانات مكونة من عمودين مفصولة بعلامات جدولة: 1) تاريخ النشر و2) نص العنوان. نحن مهتمون فقط بالنص الرئيسي.

wget 'https://dataverse.harvard.edu/api/access/datafile/3450625?format=tab&gbrecs=true' -O raw.tsv
wc -l raw.tsv
head raw.tsv

--2020-12-03 12:12:21--  https://dataverse.harvard.edu/api/access/datafile/3450625?format=tab&gbrecs=true
Resolving dataverse.harvard.edu (dataverse.harvard.edu)... 206.191.184.198
Connecting to dataverse.harvard.edu (dataverse.harvard.edu)|206.191.184.198|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 57600231 (55M) [text/tab-separated-values]
Saving to: ‘raw.tsv’

raw.tsv             100%[===================>]  54.93M  15.1MB/s    in 4.3s    

2020-12-03 12:12:27 (12.7 MB/s) - ‘raw.tsv’ saved [57600231/57600231]

1103664 raw.tsv
publish_date    headline_text
20030219    "aba decides against community broadcasting licence"
20030219    "act fire witnesses must be aware of defamation"
20030219    "a g calls for infrastructure protection summit"
20030219    "air nz staff in aust strike for pay rise"
20030219    "air nz strike to affect australian travellers"
20030219    "ambitious olsson wins triple jump"
20030219    "antic delighted with record breaking barca"
20030219    "aussie qualifier stosur wastes four memphis match"
20030219    "aust addresses un security council over iraq"

للتبسيط، نحتفظ فقط بالنص الرئيسي ونزيل تاريخ النشر

!rm -r corpus
!mkdir corpus

with open('corpus/text.txt', 'w') as out_file:
  with open('raw.tsv', 'r') as in_file:
    for line in in_file:
      headline = line.split('\t')[1].strip().strip('"')
      out_file.write(headline+"\n")

rm: cannot remove 'corpus': No such file or directory

tail corpus/text.txt

severe storms forecast for nye in south east queensland
snake catcher pleads for people not to kill reptiles
south australia prepares for party to welcome new year
strikers cool off the heat with big win in adelaide
stunning images from the sydney to hobart yacht
the ashes smiths warners near miss liven up boxing day test
timelapse: brisbanes new year fireworks
what 2017 meant to the kids of australia
what the papodopoulos meeting may mean for ausus
who is george papadopoulos the former trump campaign aide

وظيفة مساعد لتحميل وحدة TF-Hub

def load_module(module_url):
  embed_module = hub.Module(module_url)
  placeholder = tf.placeholder(dtype=tf.string)
  embed = embed_module(placeholder)
  session = tf.Session()
  session.run([tf.global_variables_initializer(), tf.tables_initializer()])
  print('TF-Hub module is loaded.')

  def _embeddings_fn(sentences):
    computed_embeddings = session.run(
        embed, feed_dict={placeholder: sentences})
    return computed_embeddings

  return _embeddings_fn

2. إنشاء التضمينات للبيانات.

في هذا البرنامج التعليمي، نستخدم Universal Sentence Encoder لإنشاء مجموعات مضمنة لبيانات العنوان. يمكن بعد ذلك استخدام تضمينات الجملة بسهولة لحساب تشابه مستوى الجملة. نقوم بتشغيل عملية إنشاء التضمين باستخدام Apache Beam وTF-Transform.

طريقة استخراج التضمين

encoder = None

def embed_text(text, module_url, random_projection_matrix):
  # Beam will run this function in different processes that need to
  # import hub and load embed_fn (if not previously loaded)
  global encoder
  if not encoder:
    encoder = hub.Module(module_url)
  embedding = encoder(text)
  if random_projection_matrix is not None:
    # Perform random projection for the embedding
    embedding = tf.matmul(
        embedding, tf.cast(random_projection_matrix, embedding.dtype))
  return embedding

قم بعمل طريقة TFT preprocess_fn

def make_preprocess_fn(module_url, random_projection_matrix=None):
  '''Makes a tft preprocess_fn'''

  def _preprocess_fn(input_features):
    '''tft preprocess_fn'''
    text = input_features['text']
    # Generate the embedding for the input text
    embedding = embed_text(text, module_url, random_projection_matrix)

    output_features = {
        'text': text, 
        'embedding': embedding
        }

    return output_features

  return _preprocess_fn

إنشاء بيانات تعريف مجموعة البيانات

def create_metadata():
  '''Creates metadata for the raw data'''
  from tensorflow_transform.tf_metadata import dataset_metadata
  from tensorflow_transform.tf_metadata import schema_utils
  feature_spec = {'text': tf.FixedLenFeature([], dtype=tf.string)}
  schema = schema_utils.schema_from_feature_spec(feature_spec)
  metadata = dataset_metadata.DatasetMetadata(schema)
  return metadata

خط أنابيب شعاع

def run_hub2emb(args):
  '''Runs the embedding generation pipeline'''

  options = beam.options.pipeline_options.PipelineOptions(**args)
  args = namedtuple("options", args.keys())(*args.values())

  raw_metadata = create_metadata()
  converter = tft.coders.CsvCoder(
      column_names=['text'], schema=raw_metadata.schema)

  with beam.Pipeline(args.runner, options=options) as pipeline:
    with tft_beam.Context(args.temporary_dir):
      # Read the sentences from the input file
      sentences = ( 
          pipeline
          | 'Read sentences from files' >> beam.io.ReadFromText(
              file_pattern=args.data_dir)
          | 'Convert to dictionary' >> beam.Map(converter.decode)
      )

      sentences_dataset = (sentences, raw_metadata)
      preprocess_fn = make_preprocess_fn(args.module_url, args.random_projection_matrix)
      # Generate the embeddings for the sentence using the TF-Hub module
      embeddings_dataset, _ = (
          sentences_dataset
          | 'Extract embeddings' >> tft_beam.AnalyzeAndTransformDataset(preprocess_fn)
      )

      embeddings, transformed_metadata = embeddings_dataset
      # Write the embeddings to TFRecords files
      embeddings | 'Write embeddings to TFRecords' >> beam.io.tfrecordio.WriteToTFRecord(
          file_path_prefix='{}/emb'.format(args.output_dir),
          file_name_suffix='.tfrecords',
          coder=tft.coders.ExampleProtoCoder(transformed_metadata.schema))

توليد مصفوفة وزن الإسقاط العشوائي

الإسقاط العشوائي هو أسلوب بسيط ولكنه قوي يستخدم لتقليل أبعاد مجموعة من النقاط التي تقع في الفضاء الإقليدي. للحصول على خلفية نظرية، راجع جونسون-ليندنشتراوس ليما .

إن تقليل أبعاد التضمينات باستخدام الإسقاط العشوائي يعني وقتًا أقل مطلوبًا لإنشاء فهرس ANN والاستعلام عنه.

في هذا البرنامج التعليمي، نستخدم الإسقاط العشوائي Gaussian من مكتبة Scikit-learn .

def generate_random_projection_weights(original_dim, projected_dim):
  random_projection_matrix = None
  if projected_dim and original_dim > projected_dim:
    random_projection_matrix = gaussian_random_matrix(
        n_components=projected_dim, n_features=original_dim).T
    print("A Gaussian random weight matrix was creates with shape of {}".format(random_projection_matrix.shape))
    print('Storing random projection matrix to disk...')
    with open('random_projection_matrix', 'wb') as handle:
      pickle.dump(random_projection_matrix, 
                  handle, protocol=pickle.HIGHEST_PROTOCOL)

  return random_projection_matrix

تعيين المعلمات

إذا كنت تريد إنشاء فهرس باستخدام مساحة التضمين الأصلية دون إسقاط عشوائي، فاضبط المعلمة projected_dim على None . لاحظ أن هذا سيؤدي إلى إبطاء خطوة الفهرسة للتضمينات عالية الأبعاد.

تبديل الرمز

module_url = 'https://tfhub.dev/google/universal-sentence-encoder/2'
projected_dim = 64

تشغيل خط الأنابيب

import tempfile

output_dir = pathlib.Path(tempfile.mkdtemp())
temporary_dir = pathlib.Path(tempfile.mkdtemp())

g = tf.Graph()
with g.as_default():
  original_dim = load_module(module_url)(['']).shape[1]
  random_projection_matrix = None

  if projected_dim:
    random_projection_matrix = generate_random_projection_weights(
        original_dim, projected_dim)

args = {
    'job_name': 'hub2emb-{}'.format(datetime.utcnow().strftime('%y%m%d-%H%M%S')),
    'runner': 'DirectRunner',
    'batch_size': 1024,
    'data_dir': 'corpus/*.txt',
    'output_dir': output_dir,
    'temporary_dir': temporary_dir,
    'module_url': module_url,
    'random_projection_matrix': random_projection_matrix,
}

print("Pipeline args are set.")
args

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

TF-Hub module is loaded.
A Gaussian random weight matrix was creates with shape of (512, 64)
Storing random projection matrix to disk...
Pipeline args are set.

/home/kbuilder/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function gaussian_random_matrix is deprecated; gaussian_random_matrix is deprecated in 0.22 and will be removed in version 0.24.
  warnings.warn(msg, category=FutureWarning)

{'job_name': 'hub2emb-201203-121305',
 'runner': 'DirectRunner',
 'batch_size': 1024,
 'data_dir': 'corpus/*.txt',
 'output_dir': PosixPath('/tmp/tmp3_9agsp3'),
 'temporary_dir': PosixPath('/tmp/tmp75ty7xfk'),
 'module_url': 'https://tfhub.dev/google/universal-sentence-encoder/2',
 'random_projection_matrix': array([[ 0.21470759, -0.05258816, -0.0972597 , ...,  0.04385087,
         -0.14274348,  0.11220471],
        [ 0.03580492, -0.16426251, -0.14089037, ...,  0.0101535 ,
         -0.22515438, -0.21514454],
        [-0.15639698,  0.01808027, -0.13684782, ...,  0.11841098,
         -0.04303762,  0.00745478],
        ...,
        [-0.18584684,  0.14040793,  0.18339619, ...,  0.13763638,
         -0.13028201, -0.16183348],
        [ 0.20997704, -0.2241034 , -0.12709368, ..., -0.03352462,
          0.11281993, -0.16342795],
        [-0.23761595,  0.00275779, -0.1585855 , ..., -0.08995121,
          0.1475089 , -0.26595401]])}

!rm -r {output_dir}
!rm -r {temporary_dir}

print("Running pipeline...")
%time run_hub2emb(args)
print("Pipeline is done.")

WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.

Running pipeline...

Warning:tensorflow:Tensorflow version (2.3.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.3.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.3.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.3.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).

Warning:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:No assets to write.

INFO:tensorflow:No assets to write.

INFO:tensorflow:SavedModel written to: /tmp/tmp75ty7xfk/tftransform_tmp/0839c04b1a8d4dd0b3d2832fbe9f5904/saved_model.pb

INFO:tensorflow:SavedModel written to: /tmp/tmp75ty7xfk/tftransform_tmp/0839c04b1a8d4dd0b3d2832fbe9f5904/saved_model.pb

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:218: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:218: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.

Warning:tensorflow:Tensorflow version (2.3.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.3.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).

Warning:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.

CPU times: user 2min 50s, sys: 6.6 s, total: 2min 57s
Wall time: 2min 40s
Pipeline is done.

ls {output_dir}

emb-00000-of-00001.tfrecords

قراءة بعض التضمينات التي تم إنشاؤها ...

import itertools

embed_file = os.path.join(output_dir, 'emb-00000-of-00001.tfrecords')
sample = 5
record_iterator =  tf.io.tf_record_iterator(path=embed_file)
for string_record in itertools.islice(record_iterator, sample):
  example = tf.train.Example()
  example.ParseFromString(string_record)
  text = example.features.feature['text'].bytes_list.value
  embedding = np.array(example.features.feature['embedding'].float_list.value)
  print("Embedding dimensions: {}".format(embedding.shape[0]))
  print("{}: {}".format(text, embedding[:10]))

WARNING:tensorflow:From <ipython-input-1-3d6f4d54c65b>:5: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`

Warning:tensorflow:From <ipython-input-1-3d6f4d54c65b>:5: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`

Embedding dimensions: 64
[b'headline_text']: [-0.04724706  0.27573067 -0.02340046  0.12461437  0.04809146  0.00246292
  0.15367804 -0.17551982 -0.02778188 -0.185176  ]
Embedding dimensions: 64
[b'aba decides against community broadcasting licence']: [-0.0466345   0.00110549 -0.08875479  0.05938878  0.01933165 -0.05704207
  0.18913773 -0.12833942  0.1816328   0.06035798]
Embedding dimensions: 64
[b'act fire witnesses must be aware of defamation']: [-0.31556517 -0.07618773 -0.14239314 -0.14500496  0.04438541 -0.00983415
  0.01349827 -0.15908629 -0.12947078  0.31871504]
Embedding dimensions: 64
[b'a g calls for infrastructure protection summit']: [ 0.15422247 -0.09829048 -0.16913125 -0.17129296  0.01204466 -0.16008876
 -0.00540507 -0.20552996  0.11388192 -0.03878446]
Embedding dimensions: 64
[b'air nz staff in aust strike for pay rise']: [ 0.13039729 -0.06921542 -0.08830801 -0.09704516 -0.05936369 -0.13036506
 -0.16644046 -0.06228216  0.00742535 -0.13592219]

3. بناء مؤشر ANN للتضمينات

ANNOY (أقرب الجيران تقريبًا أوه نعم) هي مكتبة C++ تحتوي على روابط Python للبحث عن نقاط في الفضاء قريبة من نقطة استعلام معينة. كما يقوم أيضًا بإنشاء هياكل بيانات كبيرة قائمة على الملفات للقراءة فقط والتي يتم إدخالها في الذاكرة. تم بناؤه واستخدامه بواسطة Spotify لتوصيات الموسيقى.

def build_index(embedding_files_pattern, index_filename, vector_length, 
    metric='angular', num_trees=100):
  '''Builds an ANNOY index'''

  annoy_index = annoy.AnnoyIndex(vector_length, metric=metric)
  # Mapping between the item and its identifier in the index
  mapping = {}

  embed_files = tf.gfile.Glob(embedding_files_pattern)
  print('Found {} embedding file(s).'.format(len(embed_files)))

  item_counter = 0
  for f, embed_file in enumerate(embed_files):
    print('Loading embeddings in file {} of {}...'.format(
      f+1, len(embed_files)))
    record_iterator = tf.io.tf_record_iterator(
      path=embed_file)

    for string_record in record_iterator:
      example = tf.train.Example()
      example.ParseFromString(string_record)
      text = example.features.feature['text'].bytes_list.value[0].decode("utf-8")
      mapping[item_counter] = text
      embedding = np.array(
        example.features.feature['embedding'].float_list.value)
      annoy_index.add_item(item_counter, embedding)
      item_counter += 1
      if item_counter % 100000 == 0:
        print('{} items loaded to the index'.format(item_counter))

  print('A total of {} items added to the index'.format(item_counter))

  print('Building the index with {} trees...'.format(num_trees))
  annoy_index.build(n_trees=num_trees)
  print('Index is successfully built.')

  print('Saving index to disk...')
  annoy_index.save(index_filename)
  print('Index is saved to disk.')
  print("Index file size: {} GB".format(
    round(os.path.getsize(index_filename) / float(1024 ** 3), 2)))
  annoy_index.unload()

  print('Saving mapping to disk...')
  with open(index_filename + '.mapping', 'wb') as handle:
    pickle.dump(mapping, handle, protocol=pickle.HIGHEST_PROTOCOL)
  print('Mapping is saved to disk.')
  print("Mapping file size: {} MB".format(
    round(os.path.getsize(index_filename + '.mapping') / float(1024 ** 2), 2)))

embedding_files = "{}/emb-*.tfrecords".format(output_dir)
embedding_dimension = projected_dim
index_filename = "index"

!rm {index_filename}
!rm {index_filename}.mapping

%time build_index(embedding_files, index_filename, embedding_dimension)

rm: cannot remove 'index': No such file or directory
rm: cannot remove 'index.mapping': No such file or directory
Found 1 embedding file(s).
Loading embeddings in file 1 of 1...
100000 items loaded to the index
200000 items loaded to the index
300000 items loaded to the index
400000 items loaded to the index
500000 items loaded to the index
600000 items loaded to the index
700000 items loaded to the index
800000 items loaded to the index
900000 items loaded to the index
1000000 items loaded to the index
1100000 items loaded to the index
A total of 1103664 items added to the index
Building the index with 100 trees...
Index is successfully built.
Saving index to disk...
Index is saved to disk.
Index file size: 1.66 GB
Saving mapping to disk...
Mapping is saved to disk.
Mapping file size: 50.61 MB
CPU times: user 6min 10s, sys: 3.7 s, total: 6min 14s
Wall time: 1min 36s

ls

corpus  index.mapping         raw.tsv
index   random_projection_matrix  semantic_approximate_nearest_neighbors.ipynb

4. استخدم الفهرس لمطابقة التشابه

يمكننا الآن استخدام فهرس ANN للعثور على عناوين الأخبار القريبة لغويًا من استعلام الإدخال.

قم بتحميل الفهرس وملفات التعيين

index = annoy.AnnoyIndex(embedding_dimension)
index.load(index_filename, prefault=True)
print('Annoy index is loaded.')
with open(index_filename + '.mapping', 'rb') as handle:
  mapping = pickle.load(handle)
print('Mapping file is loaded.')

Annoy index is loaded.

/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: The default argument for metric will be removed in future version of Annoy. Please pass metric='angular' explicitly.
  """Entry point for launching an IPython kernel.

Mapping file is loaded.

طريقة مطابقة التشابه

def find_similar_items(embedding, num_matches=5):
  '''Finds similar items to a given embedding in the ANN index'''
  ids = index.get_nns_by_vector(
  embedding, num_matches, search_k=-1, include_distances=False)
  items = [mapping[i] for i in ids]
  return items

استخراج التضمين من استعلام معين

# Load the TF-Hub module
print("Loading the TF-Hub module...")
g = tf.Graph()
with g.as_default():
  embed_fn = load_module(module_url)
print("TF-Hub module is loaded.")

random_projection_matrix = None
if os.path.exists('random_projection_matrix'):
  print("Loading random projection matrix...")
  with open('random_projection_matrix', 'rb') as handle:
    random_projection_matrix = pickle.load(handle)
  print('random projection matrix is loaded.')

def extract_embeddings(query):
  '''Generates the embedding for the query'''
  query_embedding =  embed_fn([query])[0]
  if random_projection_matrix is not None:
    query_embedding = query_embedding.dot(random_projection_matrix)
  return query_embedding

Loading the TF-Hub module...
INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

TF-Hub module is loaded.
TF-Hub module is loaded.
Loading random projection matrix...
random projection matrix is loaded.

extract_embeddings("Hello Machine Learning!")[:10]

array([-0.06277051,  0.14012653, -0.15893948,  0.15775941, -0.1226441 ,
       -0.11202384,  0.07953477, -0.08003543,  0.03763271,  0.0302215 ])

أدخل استعلامًا للعثور على العناصر الأكثر تشابهًا

تبديل الرمز

query = "confronting global challenges"
print("Generating embedding for the query...")
%time query_embedding = extract_embeddings(query)

print("")
print("Finding relevant items in the index...")
%time items = find_similar_items(query_embedding, 10)

print("")
print("Results:")
print("=========")
for item in items:
  print(item)

Generating embedding for the query...
CPU times: user 32.9 ms, sys: 19.8 ms, total: 52.7 ms
Wall time: 6.96 ms

Finding relevant items in the index...
CPU times: user 7.19 ms, sys: 370 µs, total: 7.56 ms
Wall time: 953 µs

Results:
=========
confronting global challenges
downer challenges un to follow aust example
fairfax loses oshane challenge
jericho social media and the border farce
territory on search for raw comedy talent
interview gred jericho
interview: josh frydenberg; environment and energy
interview: josh frydenberg; environment and energy
world science festival music and climate change
interview with aussie bobsledder

هل تريد معرفة المزيد؟

يمكنك معرفة المزيد حول TensorFlow على Tensorflow.org والاطلاع على وثائق TF-Hub API على Tensorflow.org/hub . يمكنك العثور على وحدات TensorFlow Hub المتوفرة على tfhub.dev، بما في ذلك المزيد من وحدات تضمين النص ووحدات ناقلات ميزات الصور.

تحقق أيضًا من الدورة التدريبية المكثفة لتعلم الآلة والتي تعد مقدمة عملية وسريعة الوتيرة من Google للتعلم الآلي.