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This tutorial illustrates how to generate embeddings from a TensorFlow Hub (TF-Hub) module given input data, and build an approximate nearest neighbours (ANN) index using the extracted embeddings. The index can then be used for real-time similarity matching and retrieval.
When dealing with a large corpus of data, it's not efficient to perform exact matching by scanning the whole repository to find the most similar items to a given query in real-time. Thus, we use an approximate similarity matching algorithm which allows us to trade off a little bit of accuracy in finding exact nearest neighbor matches for a significant boost in speed.
In this tutorial, we show an example of real-time text search over a corpus of news headlines to find the headlines that are most similar to a query. Unlike keyword search, this captures the semantic similarity encoded in the text embedding.
The steps of this tutorial are:
- Download sample data.
- Generate embeddings for the data using a TF-Hub module
- Build an ANN index for the embeddings
- Use the index for similarity matching
We use Apache Beam with TensorFlow Transform (TF-Transform) to generate the embeddings from the TF-Hub module. We also use Spotify's ANNOY library to build the approximate nearest neighbours index. You can find benchmarking of ANN framework in this Github repository.
This tutorial uses TensorFlow 1.0 and works only with TF1 Hub modules from TF-Hub. See the updated TF2 version of this tutorial.
Setup
Install the required libraries.
pip install -q apache_beampip install -q sklearnpip install -q annoy
Import the required libraries
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. Download Sample Data
A Million News Headlines dataset contains news headlines published over a period of 15 years sourced from the reputable Australian Broadcasting Corp. (ABC). This news dataset has a summarised historical record of noteworthy events in the globe from early-2003 to end-2017 with a more granular focus on Australia.
Format: Tab-separated two-column data: 1) publication date and 2) headline text. We are only interested in the headline text.
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"
For simplicity, we only keep the headline text and remove the publication date
!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.txtsevere 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
Helper function to load a TF-Hub module
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. Generate Embeddings for the Data.
In this tutorial, we use the Universal Sentence Encoder to generate emebeddings for the headline data. The sentence embeddings can then be easily used to compute sentence level meaning similarity. We run the embedding generation process using Apache Beam and TF-Transform.
Embedding extraction method
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
Make TFT preprocess_fn method
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
Create dataset 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 pipeline
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))
Generaring Random Projection Weight Matrix
Random projection is a simple, yet powerfull technique used to reduce the dimensionality of a set of points which lie in Euclidean space. For a theoretical background, see the Johnson-Lindenstrauss lemma.
Reducing the dimensionality of the embeddings with random projection means less time needed to build and query the ANN index.
In this tutorial we use Gaussian Random Projection from the Scikit-learn library.
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
Set parameters
If you want to build an index using the original embedding space without random projection, set the projected_dim parameter to None. Note that this will slow down the indexing step for high-dimensional embeddings.
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/2'
projected_dim = 64
Run pipeline
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
Read some of the generated embeddings...
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. Build the ANN Index for the Embeddings
ANNOY (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory. It is built and used by Spotify for music recommendations.
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
lscorpus index.mapping raw.tsv index random_projection_matrix semantic_approximate_nearest_neighbors.ipynb
4. Use the Index for Similarity Matching
Now we can use the ANN index to find news headlines that are semantically close to an input query.
Load the index and the mapping files
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.
Similarity matching method
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
Extract embedding from a given query
# 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 ])
Enter a query to find the most similar items
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
Want to learn more?
You can learn more about TensorFlow at tensorflow.org and see the TF-Hub API documentation at tensorflow.org/hub. Find available TensorFlow Hub modules at tfhub.dev including more text embedding modules and image feature vector modules.
Also check out the Machine Learning Crash Course which is Google's fast-paced, practical introduction to machine learning.