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Tutorial ini mengilustrasikan cara membuat penyematan dari modul TensorFlow Hub (TF-Hub) dengan data masukan, dan membuat indeks perkiraan tetangga terdekat (ANN) menggunakan penyematan yang diekstraksi. Indeks tersebut kemudian dapat digunakan untuk pencocokan dan pengambilan kesamaan secara real-time.
Saat menangani kumpulan data yang besar, melakukan pencocokan tepat dengan memindai seluruh repositori untuk menemukan item yang paling mirip dengan kueri tertentu secara real-time tidaklah efisien. Oleh karena itu, kami menggunakan algoritme pencocokan kemiripan perkiraan yang memungkinkan kami mengorbankan sedikit keakuratan dalam menemukan kecocokan tetangga terdekat yang tepat demi peningkatan kecepatan yang signifikan.
Dalam tutorial ini, kami menampilkan contoh penelusuran teks real-time pada kumpulan judul berita untuk menemukan judul yang paling mirip dengan kueri. Tidak seperti pencarian kata kunci, pencarian ini menangkap kesamaan semantik yang dikodekan dalam penyematan teks.
Langkah-langkah tutorial ini adalah:
- Unduh data sampel.
- Hasilkan penyematan untuk data menggunakan modul TF-Hub
- Buat indeks ANN untuk penyematan
- Gunakan indeks untuk pencocokan kesamaan
Kami menggunakan Apache Beam dengan TensorFlow Transform (TF-Transform) untuk menghasilkan embeddings dari modul TF-Hub. Kami juga menggunakan perpustakaan ANNOY Spotify untuk membuat perkiraan indeks tetangga terdekat. Anda dapat menemukan benchmarking kerangka ANN di repositori Github ini.
Tutorial ini menggunakan TensorFlow 1.0 dan hanya berfungsi dengan modul TF1 Hub dari TF-Hub. Lihat versi TF2 yang diperbarui dari tutorial ini .
Mempersiapkan
Instal perpustakaan yang diperlukan.
pip install -q apache_beampip install -q sklearnpip install -q annoy
Impor perpustakaan yang diperlukan
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. Unduh Contoh Data
Kumpulan data Sejuta Berita Utama berisi judul berita yang diterbitkan selama periode 15 tahun yang bersumber dari Australian Broadcasting Corp. (ABC) yang memiliki reputasi baik. Kumpulan data berita ini memiliki ringkasan catatan sejarah peristiwa-peristiwa penting di dunia dari awal tahun 2003 hingga akhir tahun 2017 dengan fokus yang lebih terperinci di Australia.
Format : Data dua kolom yang dipisahkan tab: 1) tanggal publikasi dan 2) teks judul. Kami hanya tertarik pada teks judul.
wget 'https://dataverse.harvard.edu/api/access/datafile/3450625?format=tab&gbrecs=true' -O raw.tsvwc -l raw.tsvhead 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"
Untuk mempermudah, kami hanya menyimpan teks judul dan menghapus tanggal publikasi
!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
Fungsi pembantu untuk memuat modul 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. Hasilkan Penyematan untuk Data.
Dalam tutorial ini, kami menggunakan Universal Sentence Encoder untuk menghasilkan emebeddings untuk data judul. Penyematan kalimat kemudian dapat dengan mudah digunakan untuk menghitung kesamaan makna tingkat kalimat. Kami menjalankan proses pembuatan penyematan menggunakan Apache Beam dan TF-Transform.
Menanamkan metode ekstraksi
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
Buat metode 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
Buat metadata kumpulan data
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
Pipa balok
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))
Menghasilkan Matriks Bobot Proyeksi Acak
Proyeksi acak adalah teknik sederhana namun ampuh yang digunakan untuk mereduksi dimensi sekumpulan titik yang terletak di ruang Euclidean. Untuk latar belakang teoretis, lihat lemma Johnson-Lindenstrauss .
Mengurangi dimensi penyematan dengan proyeksi acak berarti lebih sedikit waktu yang diperlukan untuk membuat dan mengkueri indeks ANN.
Dalam tutorial ini kami menggunakan Proyeksi Acak Gaussian dari perpustakaan 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
Tetapkan parameter
Jika Anda ingin membuat indeks menggunakan ruang penyematan asli tanpa proyeksi acak, atur parameter projected_dim ke None . Perhatikan bahwa ini akan memperlambat langkah pengindeksan untuk penyematan berdimensi tinggi.
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/2'
projected_dim = 64
Jalankan saluran pipa
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
Baca beberapa embeddings yang dihasilkan...
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. Buat Indeks ANN untuk Embeddings
ANNOY (Approximate Nearest Neighbors Oh Yeah) adalah pustaka C++ dengan binding Python untuk mencari titik dalam ruang yang dekat dengan titik kueri tertentu. Ini juga menciptakan struktur data berbasis file read-only yang besar yang dipetakan ke dalam memori. Itu dibuat dan digunakan oleh Spotify untuk rekomendasi musik.
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. Gunakan Indeks untuk Pencocokan Kesamaan
Sekarang kita dapat menggunakan indeks ANN untuk menemukan judul berita yang secara semantik dekat dengan kueri masukan.
Muat indeks dan file pemetaan
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.
Metode pencocokan kesamaan
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
Ekstrak penyematan dari kueri tertentu
# 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 ])
Masukkan kueri untuk menemukan item yang paling mirip
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
Ingin mempelajari lebih lanjut?
Anda dapat mempelajari TensorFlow lebih lanjut di tensorflow.org dan melihat dokumentasi TF-Hub API di tensorflow.org/hub . Temukan modul TensorFlow Hub yang tersedia di tfhub.dev termasuk lebih banyak modul penyematan teks dan modul vektor fitur gambar.
Lihat juga Kursus Singkat Machine Learning yang merupakan pengenalan praktis dan cepat dari Google tentang pembelajaran mesin.