 在 TensorFlow.org 上檢視
|
 在 Google Colab 中執行
|
 在 GitHub 上檢視
|
 下載筆記本
|
 查看 TF Hub 模型
|
本教學課程說明如何從給定的輸入資料，從 TensorFlow Hub (TF-Hub) 模組產生嵌入，並使用擷取的嵌入建構近似最近鄰 (ANN) 索引。然後，索引可用於即時相似度比對和擷取。
在處理大型資料語料庫時，掃描整個儲存庫以即時尋找與給定查詢最相似的項目，執行精確比對的效率不高。因此，我們使用近似相似度比對演算法，讓我們能夠犧牲一點尋找精確最近鄰比對的準確性，以大幅提升速度。
在本教學課程中，我們展示了一個在新聞標題語料庫上進行即時文字搜尋的範例，以尋找與查詢最相似的標題。與關鍵字搜尋不同，這捕捉了文字嵌入中編碼的語意相似度。
本教學課程的步驟如下
- 下載範例資料。
- 使用 TF-Hub 模組為資料產生嵌入
- 為嵌入建構 ANN 索引
- 使用索引進行相似度比對
我們使用 Apache Beam 和 TensorFlow Transform (TF-Transform) 從 TF-Hub 模組產生嵌入。我們也使用 Spotify 的 ANNOY 程式庫來建構近似最近鄰索引。您可以在這個 Github 儲存庫 中找到 ANN 框架的效能評測。
本教學課程使用 TensorFlow 1.0，且僅適用於 TF-Hub 的 TF1 Hub 模組。請參閱本教學課程的更新 TF2 版本。
設定
安裝必要的程式庫。
pip install -q apache_beampip install -q sklearnpip install -q annoy
匯入必要的程式庫
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. 下載範例資料
一百萬則新聞標題資料集包含澳洲廣播公司 (ABC) 這家知名媒體在 15 年期間發布的新聞標題。這個新聞資料集摘要記錄了從 2003 年初到 2017 年底全球值得注意的事件歷史記錄，並更精細地關注澳洲。
格式：以 Tab 字元分隔的兩欄資料：1) 發布日期和 2) 標題文字。我們只對標題文字感興趣。
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"
為了簡化，我們只保留標題文字並移除發布日期
!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
載入 TF-Hub 模組的輔助函式
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. 為資料產生嵌入。
在本教學課程中，我們使用 通用句子編碼器 為標題資料產生嵌入。然後，句子嵌入可以輕鬆用於計算句子層級的意義相似度。我們使用 Apache Beam 和 TF-Transform 執行嵌入產生程序。
嵌入擷取方法
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
建立 TFT preprocess_fn 方法
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
建立資料集metadata
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
Beam 管線
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))
產生隨機投影權重矩陣
隨機投影是一種簡單但功能強大的技術，用於降低歐幾里得空間中一組點的維度。如需理論背景，請參閱 Johnson-Lindenstrauss 引理。
使用隨機投影降低嵌入的維度意味著建構和查詢 ANN 索引所需的時間更少。
在本教學課程中，我們使用 高斯隨機投影，來自 Scikit-learn 程式庫。
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
設定參數
如果您想使用原始嵌入空間建構索引而不進行隨機投影，請將 projected_dim 參數設定為 None。請注意，這會減慢高維度嵌入的索引步驟。
執行管線
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
讀取一些產生的嵌入...
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. 為嵌入建構 ANN 索引
ANNOY (Approximate Nearest Neighbors Oh Yeah) 是一個 C++ 程式庫，具有 Python 繫結，可搜尋空間中靠近給定查詢點的點。它還建立大型唯讀檔案型資料結構，這些結構會對應到記憶體中。它由 Spotify 建構和使用，用於音樂推薦。
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. 使用索引進行相似度比對
現在我們可以使用 ANN 索引來尋找在語意上接近輸入查詢的新聞標題。
載入索引和對應檔案
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.
相似度比對方法
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
從給定查詢擷取嵌入
# 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 ])
輸入查詢以尋找最相似的項目
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
想瞭解更多資訊嗎？
您可以在 tensorflow.org 瞭解更多關於 TensorFlow 的資訊，並在 tensorflow.org/hub 查看 TF-Hub API 文件。在 tfhub.dev 尋找可用的 TensorFlow Hub 模組，包括更多文字嵌入模組和圖片特徵向量模組。
另請查看 機器學習速成課程，這是 Google 快速實用的機器學習入門課程。