This tutorial describes graph regularization from the Neural Structured Learning framework and demonstrates an end-to-end workflow for sentiment classification in a TFX pipeline.
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This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary classification, an important and widely applicable kind of machine learning problem.
We will demonstrate the use of graph regularization in this notebook by building a graph from the given input. The general recipe for building a graph-regularized model using the Neural Structured Learning (NSL) framework when the input does not contain an explicit graph is as follows:
- Create embeddings for each text sample in the input. This can be done using pre-trained models such as word2vec, Swivel, BERT etc.
- Build a graph based on these embeddings by using a similarity metric such as the 'L2' distance, 'cosine' distance, etc. Nodes in the graph correspond to samples and edges in the graph correspond to similarity between pairs of samples.
- Generate training data from the above synthesized graph and sample features. The resulting training data will contain neighbor features in addition to the original node features.
- Create a neural network as a base model using Estimators.
- Wrap the base model with the
add_graph_regularizationwrapper function, which is provided by the NSL framework, to create a new graph Estimator model. This new model will include a graph regularization loss as the regularization term in its training objective. - Train and evaluate the graph Estimator model.
In this tutorial, we integrate the above workflow in a TFX pipeline using several custom TFX components as well as a custom graph-regularized trainer component.
Below is the schematic for our TFX pipeline. Orange boxes represent off-the-shelf TFX components and pink boxes represent custom TFX components.
Upgrade Pip
To avoid upgrading Pip in a system when running locally, check to make sure that we're running in Colab. Local systems can of course be upgraded separately.
try:
import colab
!pip install --upgrade pip
except:
pass
Install Required Packages
!pip install -q -U \
tfx==1.0.0 \
neural-structured-learning \
tensorflow-hub \
tensorflow-datasets
Did you restart the runtime?
If you are using Google Colab, the first time that you run the cell above, you must restart the runtime (Runtime > Restart runtime ...). This is because of the way that Colab loads packages.
Dependencies and imports
import apache_beam as beam
import gzip as gzip_lib
import numpy as np
import os
import pprint
import shutil
import tempfile
import urllib
import uuid
pp = pprint.PrettyPrinter()
import tensorflow as tf
import neural_structured_learning as nsl
import tfx
from tfx.components.evaluator.component import Evaluator
from tfx.components.example_gen.import_example_gen.component import ImportExampleGen
from tfx.components.example_validator.component import ExampleValidator
from tfx.components.model_validator.component import ModelValidator
from tfx.components.pusher.component import Pusher
from tfx.components.schema_gen.component import SchemaGen
from tfx.components.statistics_gen.component import StatisticsGen
from tfx.components.trainer import executor as trainer_executor
from tfx.components.trainer.component import Trainer
from tfx.components.transform.component import Transform
from tfx.dsl.components.base import executor_spec
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.proto import evaluator_pb2
from tfx.proto import example_gen_pb2
from tfx.proto import pusher_pb2
from tfx.proto import trainer_pb2
from tfx.types import artifact
from tfx.types import artifact_utils
from tfx.types import channel
from tfx.types import standard_artifacts
from tfx.types.standard_artifacts import Examples
from tfx.dsl.component.experimental.annotations import InputArtifact
from tfx.dsl.component.experimental.annotations import OutputArtifact
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.decorators import component
from tensorflow_metadata.proto.v0 import anomalies_pb2
from tensorflow_metadata.proto.v0 import schema_pb2
from tensorflow_metadata.proto.v0 import statistics_pb2
import tensorflow_data_validation as tfdv
import tensorflow_transform as tft
import tensorflow_model_analysis as tfma
import tensorflow_hub as hub
import tensorflow_datasets as tfds
print("TF Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print(
"GPU is",
"available" if tf.config.list_physical_devices("GPU") else "NOT AVAILABLE")
print("NSL Version: ", nsl.__version__)
print("TFX Version: ", tfx.__version__)
print("TFDV version: ", tfdv.__version__)
print("TFT version: ", tft.__version__)
print("TFMA version: ", tfma.__version__)
print("Hub version: ", hub.__version__)
print("Beam version: ", beam.__version__)
2021-07-27 09:11:37.702558: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0 TF Version: 2.5.0 Eager mode: True GPU is available NSL Version: 1.3.1 TFX Version: 1.0.0 TFDV version: 1.0.0 TFT version: 1.0.0 TFMA version: 0.31.0 Hub version: 0.12.0 Beam version: 2.31.0 2021-07-27 09:11:40.751607: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1 2021-07-27 09:11:41.797940: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:41.798952: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0 coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s 2021-07-27 09:11:41.798988: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0 2021-07-27 09:11:41.802699: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11 2021-07-27 09:11:41.802801: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11 2021-07-27 09:11:41.803991: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10 2021-07-27 09:11:41.804324: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10 2021-07-27 09:11:41.805464: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11 2021-07-27 09:11:41.806455: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11 2021-07-27 09:11:41.806646: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8 2021-07-27 09:11:41.806779: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:41.807827: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:41.808771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
IMDB dataset
The IMDB dataset contains the text of 50,000 movie reviews from the Internet Movie Database. These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are balanced, meaning they contain an equal number of positive and negative reviews. Moreover, there are 50,000 additional unlabeled movie reviews.
Download preprocessed IMDB dataset
The following code downloads the IMDB dataset (or uses a cached copy if it has already been downloaded) using TFDS. To speed up this notebook we will use only 10,000 labeled reviews and 10,000 unlabeled reviews for training, and 10,000 test reviews for evaluation.
train_set, eval_set = tfds.load(
"imdb_reviews:1.0.0",
split=["train[:10000]+unsupervised[:10000]", "test[:10000]"],
shuffle_files=False)
2021-07-27 09:11:46.214651: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-07-27 09:11:46.215481: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:46.216555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0 coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s 2021-07-27 09:11:46.216736: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:46.217742: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:46.218645: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2021-07-27 09:11:46.218771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0 2021-07-27 09:11:46.859431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2021-07-27 09:11:46.859472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2021-07-27 09:11:46.859481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N 2021-07-27 09:11:46.859773: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:46.860851: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:46.861877: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-07-27 09:11:46.862883: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
Let's look at a few reviews from the training set:
for tfrecord in train_set.take(4):
print("Review: {}".format(tfrecord["text"].numpy().decode("utf-8")[:300]))
print("Label: {}\n".format(tfrecord["label"].numpy()))
2021-07-27 09:11:46.943783: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2) 2021-07-27 09:11:46.944421: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2000185000 Hz Review: This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda pi Label: 0 Review: I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However on this occasion I fell asleep because the film was rubbish. The plot development was constant. Cons Label: 0 Review: Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Mountie telling the people of Dawson City, Yukon to elect themselves a marshal (yes a marshal!) and to e Label: 0 Review: This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas Cage (as always) gently row the plot along. There are no rapids to cr Label: 1
def _dict_to_example(instance):
"""Decoded CSV to tf example."""
feature = {}
for key, value in instance.items():
if value is None:
feature[key] = tf.train.Feature()
elif value.dtype == np.integer:
feature[key] = tf.train.Feature(
int64_list=tf.train.Int64List(value=value.tolist()))
elif value.dtype == np.float32:
feature[key] = tf.train.Feature(
float_list=tf.train.FloatList(value=value.tolist()))
else:
feature[key] = tf.train.Feature(
bytes_list=tf.train.BytesList(value=value.tolist()))
return tf.train.Example(features=tf.train.Features(feature=feature))
examples_path = tempfile.mkdtemp(prefix="tfx-data")
train_path = os.path.join(examples_path, "train.tfrecord")
eval_path = os.path.join(examples_path, "eval.tfrecord")
for path, dataset in [(train_path, train_set), (eval_path, eval_set)]:
with tf.io.TFRecordWriter(path) as writer:
for example in dataset:
writer.write(
_dict_to_example({
"label": np.array([example["label"].numpy()]),
"text": np.array([example["text"].numpy()]),
}).SerializeToString())
/home/kbuilder/.local/lib/python3.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`. import sys
Run TFX Components Interactively
In the cells that follow you will construct TFX components and run each one interactively within the InteractiveContext to obtain ExecutionResult objects. This mirrors the process of an orchestrator running components in a TFX DAG based on when the dependencies for each component are met.
context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw as root for pipeline outputs. WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/metadata.sqlite.
The ExampleGen Component
In any ML development process the first step when starting code development is to ingest the training and test datasets. The ExampleGen component brings data into the TFX pipeline.
Create an ExampleGen component and run it.
input_config = example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='train', pattern='train.tfrecord'),
example_gen_pb2.Input.Split(name='eval', pattern='eval.tfrecord')
])
example_gen = ImportExampleGen(input_base=examples_path, input_config=input_config)
context.run(example_gen, enable_cache=True)
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. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. 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.
for artifact in example_gen.outputs['examples'].get():
print(artifact)
print('\nexample_gen.outputs is a {}'.format(type(example_gen.outputs)))
print(example_gen.outputs)
print(example_gen.outputs['examples'].get()[0].split_names)
Artifact(artifact: id: 1
type_id: 5
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/ImportExampleGen/examples/1"
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:train,num_files:1,total_bytes:27706811,xor_checksum:1627377115,sum_checksum:1627377115\nsplit:eval,num_files:1,total_bytes:13374744,xor_checksum:1627377119,sum_checksum:1627377119"
}
}
custom_properties {
key: "payload_format"
value {
string_value: "FORMAT_TF_EXAMPLE"
}
}
custom_properties {
key: "span"
value {
int_value: 0
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
)
example_gen.outputs is a <class 'dict'>
{'examples': Channel(
type_name: Examples
artifacts: [Artifact(artifact: id: 1
type_id: 5
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/ImportExampleGen/examples/1"
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:train,num_files:1,total_bytes:27706811,xor_checksum:1627377115,sum_checksum:1627377115\nsplit:eval,num_files:1,total_bytes:13374744,xor_checksum:1627377119,sum_checksum:1627377119"
}
}
custom_properties {
key: "payload_format"
value {
string_value: "FORMAT_TF_EXAMPLE"
}
}
custom_properties {
key: "span"
value {
int_value: 0
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
)]
additional_properties: {}
additional_custom_properties: {}
)}
["train", "eval"]
The component's outputs include 2 artifacts:
- the training examples (10,000 labeled reviews + 10,000 unlabeled reviews)
- the eval examples (10,000 labeled reviews)
The IdentifyExamples Custom Component
To use NSL, we will need each instance to have a unique ID. We create a custom component that adds such a unique ID to all instances across all splits. We leverage Apache Beam to be able to easily scale to large datasets if needed.
def make_example_with_unique_id(example, id_feature_name):
"""Adds a unique ID to the given `tf.train.Example` proto.
This function uses Python's 'uuid' module to generate a universally unique
identifier for each example.
Args:
example: An instance of a `tf.train.Example` proto.
id_feature_name: The name of the feature in the resulting `tf.train.Example`
that will contain the unique identifier.
Returns:
A new `tf.train.Example` proto that includes a unique identifier as an
additional feature.
"""
result = tf.train.Example()
result.CopyFrom(example)
unique_id = uuid.uuid4()
result.features.feature.get_or_create(
id_feature_name).bytes_list.MergeFrom(
tf.train.BytesList(value=[str(unique_id).encode('utf-8')]))
return result
@component
def IdentifyExamples(orig_examples: InputArtifact[Examples],
identified_examples: OutputArtifact[Examples],
id_feature_name: Parameter[str],
component_name: Parameter[str]) -> None:
# Get a list of the splits in input_data
splits_list = artifact_utils.decode_split_names(
split_names=orig_examples.split_names)
# For completeness, encode the splits names and payload_format.
# We could also just use input_data.split_names.
identified_examples.split_names = artifact_utils.encode_split_names(
splits=splits_list)
# TODO(b/168616829): Remove populating payload_format after tfx 0.25.0.
identified_examples.set_string_custom_property(
"payload_format",
orig_examples.get_string_custom_property("payload_format"))
for split in splits_list:
input_dir = artifact_utils.get_split_uri([orig_examples], split)
output_dir = artifact_utils.get_split_uri([identified_examples], split)
os.mkdir(output_dir)
with beam.Pipeline() as pipeline:
(pipeline
| 'ReadExamples' >> beam.io.ReadFromTFRecord(
os.path.join(input_dir, '*'),
coder=beam.coders.coders.ProtoCoder(tf.train.Example))
| 'AddUniqueId' >> beam.Map(make_example_with_unique_id, id_feature_name)
| 'WriteIdentifiedExamples' >> beam.io.WriteToTFRecord(
file_path_prefix=os.path.join(output_dir, 'data_tfrecord'),
coder=beam.coders.coders.ProtoCoder(tf.train.Example),
file_name_suffix='.gz'))
return
identify_examples = IdentifyExamples(
orig_examples=example_gen.outputs['examples'],
component_name=u'IdentifyExamples',
id_feature_name=u'id')
context.run(identify_examples, enable_cache=False)
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
The StatisticsGen Component
The StatisticsGen component computes descriptive statistics for your dataset. The statistics that it generates can be visualized for review, and are used for example validation and to infer a schema.
Create a StatisticsGen component and run it.
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(
examples=identify_examples.outputs["identified_examples"])
context.run(statistics_gen, enable_cache=True)
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
The SchemaGen Component
The SchemaGen component generates a schema for your data based on the statistics from StatisticsGen. It tries to infer the data types of each of your features, and the ranges of legal values for categorical features.
Create a SchemaGen component and run it.
# Generates schema based on statistics files.
schema_gen = SchemaGen(
statistics=statistics_gen.outputs['statistics'], infer_feature_shape=False)
context.run(schema_gen, enable_cache=True)
2021-07-27 09:12:23.230961: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
The generated artifact is just a schema.pbtxt containing a text representation of a schema_pb2.Schema protobuf:
train_uri = schema_gen.outputs['schema'].get()[0].uri
schema_filename = os.path.join(train_uri, 'schema.pbtxt')
schema = tfx.utils.io_utils.parse_pbtxt_file(
file_name=schema_filename, message=schema_pb2.Schema())
It can be visualized using tfdv.display_schema() (we will look at this in more detail in a subsequent lab):
tfdv.display_schema(schema)
The ExampleValidator Component
The ExampleValidator performs anomaly detection, based on the statistics from StatisticsGen and the schema from SchemaGen. It looks for problems such as missing values, values of the wrong type, or categorical values outside of the domain of acceptable values.
Create an ExampleValidator component and run it.
# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
context.run(validate_stats, enable_cache=False)
The SynthesizeGraph Component
Graph construction involves creating embeddings for text samples and then using a similarity function to compare the embeddings.
We will use pretrained Swivel embeddings to create embeddings in the
tf.train.Example format for each sample in the input. We will store the
resulting embeddings in the TFRecord format along with the sample's ID.
This is important and will allow us match sample embeddings with corresponding
nodes in the graph later.
Once we have the sample embeddings, we will use them to build a similarity graph, i.e, nodes in this graph will correspond to samples and edges in this graph will correspond to similarity between pairs of nodes.
Neural Structured Learning provides a graph building library to build a graph based on sample embeddings. It uses cosine similarity as the similarity measure to compare embeddings and build edges between them. It also allows us to specify a similarity threshold, which can be used to discard dissimilar edges from the final graph. In the following example, using 0.99 as the similarity threshold, we end up with a graph that has 111,066 bi-directional edges.
swivel_url = 'https://hub.tensorflow.google.cn/google/tf2-preview/gnews-swivel-20dim/1'
hub_layer = hub.KerasLayer(swivel_url, input_shape=[], dtype=tf.string)
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def _float_feature(value):
"""Returns a float_list from a float / double."""
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def create_embedding_example(example):
"""Create tf.Example containing the sample's embedding and its ID."""
sentence_embedding = hub_layer(tf.sparse.to_dense(example['text']))
# Flatten the sentence embedding back to 1-D.
sentence_embedding = tf.reshape(sentence_embedding, shape=[-1])
feature_dict = {
'id': _bytes_feature(tf.sparse.to_dense(example['id']).numpy()),
'embedding': _float_feature(sentence_embedding.numpy().tolist())
}
return tf.train.Example(features=tf.train.Features(feature=feature_dict))
def create_dataset(uri):
tfrecord_filenames = [os.path.join(uri, name) for name in os.listdir(uri)]
return tf.data.TFRecordDataset(tfrecord_filenames, compression_type='GZIP')
def create_embeddings(train_path, output_path):
dataset = create_dataset(train_path)
embeddings_path = os.path.join(output_path, 'embeddings.tfr')
feature_map = {
'label': tf.io.FixedLenFeature([], tf.int64),
'id': tf.io.VarLenFeature(tf.string),
'text': tf.io.VarLenFeature(tf.string)
}
with tf.io.TFRecordWriter(embeddings_path) as writer:
for tfrecord in dataset:
tensor_dict = tf.io.parse_single_example(tfrecord, feature_map)
embedding_example = create_embedding_example(tensor_dict)
writer.write(embedding_example.SerializeToString())
def build_graph(output_path, similarity_threshold):
embeddings_path = os.path.join(output_path, 'embeddings.tfr')
graph_path = os.path.join(output_path, 'graph.tsv')
graph_builder_config = nsl.configs.GraphBuilderConfig(
similarity_threshold=similarity_threshold,
lsh_splits=32,
lsh_rounds=15,
random_seed=12345)
nsl.tools.build_graph_from_config([embeddings_path], graph_path,
graph_builder_config)
"""Custom Artifact type"""
class SynthesizedGraph(tfx.types.artifact.Artifact):
"""Output artifact of the SynthesizeGraph component"""
TYPE_NAME = 'SynthesizedGraphPath'
PROPERTIES = {
'span': standard_artifacts.SPAN_PROPERTY,
'split_names': standard_artifacts.SPLIT_NAMES_PROPERTY,
}
@component
def SynthesizeGraph(identified_examples: InputArtifact[Examples],
synthesized_graph: OutputArtifact[SynthesizedGraph],
similarity_threshold: Parameter[float],
component_name: Parameter[str]) -> None:
# Get a list of the splits in input_data
splits_list = artifact_utils.decode_split_names(
split_names=identified_examples.split_names)
# We build a graph only based on the 'Split-train' split which includes both
# labeled and unlabeled examples.
train_input_examples_uri = os.path.join(identified_examples.uri,
'Split-train')
output_graph_uri = os.path.join(synthesized_graph.uri, 'Split-train')
os.mkdir(output_graph_uri)
print('Creating embeddings...')
create_embeddings(train_input_examples_uri, output_graph_uri)
print('Synthesizing graph...')
build_graph(output_graph_uri, similarity_threshold)
synthesized_graph.split_names = artifact_utils.encode_split_names(
splits=['Split-train'])
return
synthesize_graph = SynthesizeGraph(
identified_examples=identify_examples.outputs['identified_examples'],
component_name=u'SynthesizeGraph',
similarity_threshold=0.99)
context.run(synthesize_graph, enable_cache=False)
Creating embeddings... Synthesizing graph...
train_uri = synthesize_graph.outputs["synthesized_graph"].get()[0].uri
os.listdir(train_uri)
['Split-train']
graph_path = os.path.join(train_uri, "Split-train", "graph.tsv")
print("node 1\t\t\t\t\tnode 2\t\t\t\t\tsimilarity")
!head {graph_path}
print("...")
!tail {graph_path}
node 1 node 2 similarity 748802aa-a483-4340-9a65-0590df997dd2 baef7e7f-c89f-4f19-8a85-0fa2aeb7f2b8 0.992471 baef7e7f-c89f-4f19-8a85-0fa2aeb7f2b8 748802aa-a483-4340-9a65-0590df997dd2 0.992471 98f1b9c8-9f2c-4777-a6a5-d0be466a93e5 4d9a030d-f894-4df1-8e5b-f0fd1611573b 0.992586 4d9a030d-f894-4df1-8e5b-f0fd1611573b 98f1b9c8-9f2c-4777-a6a5-d0be466a93e5 0.992586 98f1b9c8-9f2c-4777-a6a5-d0be466a93e5 32d8f5bf-6ba5-47d0-82ff-4cad66b02874 0.992823 32d8f5bf-6ba5-47d0-82ff-4cad66b02874 98f1b9c8-9f2c-4777-a6a5-d0be466a93e5 0.992823 98f1b9c8-9f2c-4777-a6a5-d0be466a93e5 fba6a0bf-4113-4979-aea4-159103f914bb 0.992505 fba6a0bf-4113-4979-aea4-159103f914bb 98f1b9c8-9f2c-4777-a6a5-d0be466a93e5 0.992505 1c1e8101-e62a-45c6-a60a-6a5f9ec83774 a37527b1-bb96-49c4-b582-c3f5b86ada9e 0.990020 a37527b1-bb96-49c4-b582-c3f5b86ada9e 1c1e8101-e62a-45c6-a60a-6a5f9ec83774 0.990020 ... 5043eaf4-aa1f-4475-864a-2d3c87e83f40 f0bc8fcd-9068-4c5c-adad-1e9b001cffaf 0.990002 f0bc8fcd-9068-4c5c-adad-1e9b001cffaf 5043eaf4-aa1f-4475-864a-2d3c87e83f40 0.990002 f388287c-14d1-4123-8c3d-379188d7ceee 8d6646d3-121f-4be6-8a0f-62a6377eabf5 0.991046 8d6646d3-121f-4be6-8a0f-62a6377eabf5 f388287c-14d1-4123-8c3d-379188d7ceee 0.991046 bf4b25ae-54bd-4b59-938d-a055c382668a dc710c10-0470-4fd0-8be1-0e51ddfc3105 0.991198 dc710c10-0470-4fd0-8be1-0e51ddfc3105 bf4b25ae-54bd-4b59-938d-a055c382668a 0.991198 f97a9b2e-73ad-4074-b068-a6ca270adf98 41be7cc4-e186-4f65-97c8-c7d3d6fe3603 0.990260 41be7cc4-e186-4f65-97c8-c7d3d6fe3603 f97a9b2e-73ad-4074-b068-a6ca270adf98 0.990260 451f159a-793f-4a7e-b8e4-8d86327ef824 1b367595-b6fa-4eb1-ae55-33094054e1dc 0.991317 1b367595-b6fa-4eb1-ae55-33094054e1dc 451f159a-793f-4a7e-b8e4-8d86327ef824 0.991317
wc -l {graph_path}
222132 /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/SynthesizeGraph/synthesized_graph/6/Split-train/graph.tsv
The Transform Component
The Transform component performs data transformations and feature engineering. The results include an input TensorFlow graph which is used during both training and serving to preprocess the data before training or inference. This graph becomes part of the SavedModel that is the result of model training. Since the same input graph is used for both training and serving, the preprocessing will always be the same, and only needs to be written once.
The Transform component requires more code than many other components because of the arbitrary complexity of the feature engineering that you may need for the data and/or model that you're working with. It requires code files to be available which define the processing needed.
Each sample will include the following three features:
- id: The node ID of the sample.
- text_xf: An int64 list containing word IDs.
- label_xf: A singleton int64 identifying the target class of the review: 0=negative, 1=positive.
Let's define a module containing the preprocessing_fn() function that we will pass to the Transform component:
_transform_module_file = 'imdb_transform.py'
%%writefile {_transform_module_file}
import tensorflow as tf
import tensorflow_transform as tft
SEQUENCE_LENGTH = 100
VOCAB_SIZE = 10000
OOV_SIZE = 100
def tokenize_reviews(reviews, sequence_length=SEQUENCE_LENGTH):
reviews = tf.strings.lower(reviews)
reviews = tf.strings.regex_replace(reviews, r" '| '|^'|'$", " ")
reviews = tf.strings.regex_replace(reviews, "[^a-z' ]", " ")
tokens = tf.strings.split(reviews)[:, :sequence_length]
start_tokens = tf.fill([tf.shape(reviews)[0], 1], "<START>")
end_tokens = tf.fill([tf.shape(reviews)[0], 1], "<END>")
tokens = tf.concat([start_tokens, tokens, end_tokens], axis=1)
tokens = tokens[:, :sequence_length]
tokens = tokens.to_tensor(default_value="<PAD>")
pad = sequence_length - tf.shape(tokens)[1]
tokens = tf.pad(tokens, [[0, 0], [0, pad]], constant_values="<PAD>")
return tf.reshape(tokens, [-1, sequence_length])
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
outputs["id"] = inputs["id"]
tokens = tokenize_reviews(_fill_in_missing(inputs["text"], ''))
outputs["text_xf"] = tft.compute_and_apply_vocabulary(
tokens,
top_k=VOCAB_SIZE,
num_oov_buckets=OOV_SIZE)
outputs["label_xf"] = _fill_in_missing(inputs["label"], -1)
return outputs
def _fill_in_missing(x, default_value):
"""Replace missing values in a SparseTensor.
Fills in missing values of `x` with the default_value.
Args:
x: A `SparseTensor` of rank 2. Its dense shape should have size at most 1
in the second dimension.
default_value: the value with which to replace the missing values.
Returns:
A rank 1 tensor where missing values of `x` have been filled in.
"""
if not isinstance(x, tf.sparse.SparseTensor):
return x
return tf.squeeze(
tf.sparse.to_dense(
tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
default_value),
axis=1)
Writing imdb_transform.py
Create and run the Transform component, referring to the files that were created above.
# Performs transformations and feature engineering in training and serving.
transform = Transform(
examples=identify_examples.outputs['identified_examples'],
schema=schema_gen.outputs['schema'],
module_file=_transform_module_file)
context.run(transform, enable_cache=True)
running bdist_wheel running build running build_py creating build creating build/lib copying imdb_transform.py -> build/lib installing to /tmp/tmp8qwk9f8j running install running install_lib copying build/lib/imdb_transform.py -> /tmp/tmp8qwk9f8j running install_egg_info running egg_info creating tfx_user_code_Transform.egg-info writing tfx_user_code_Transform.egg-info/PKG-INFO writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt' reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt' writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt' Copying tfx_user_code_Transform.egg-info to /tmp/tmp8qwk9f8j/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3.7.egg-info running install_scripts creating /tmp/tmp8qwk9f8j/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/WHEEL creating '/tmp/tmpau8udk0q/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3-none-any.whl' and adding '/tmp/tmp8qwk9f8j' to it adding 'imdb_transform.py' adding 'tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/METADATA' adding 'tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/WHEEL' adding 'tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/top_level.txt' adding 'tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/RECORD' removing /tmp/tmp8qwk9f8j 2021-07-27 09:14:08.178895: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type 2021-07-27 09:14:08.183573: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type Processing /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/_wheels/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3-none-any.whl Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50 WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available. You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command. Processing /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/_wheels/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3-none-any.whl Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50 WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available. You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: 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/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: 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:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. Processing /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/_wheels/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3-none-any.whl WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available. You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command. WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead. Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50 WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`. WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead. WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'> WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'> WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transform_graph/7/.temp_path/tftransform_tmp/0d8b617c4b39446d8dac05f6f2bf9a1d/assets 2021-07-27 09:14:18.985192: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them. INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transform_graph/7/.temp_path/tftransform_tmp/0d8b617c4b39446d8dac05f6f2bf9a1d/assets INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transform_graph/7/.temp_path/tftransform_tmp/d5b928df215f42d5bb12b72e3bfb0457/assets INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transform_graph/7/.temp_path/tftransform_tmp/d5b928df215f42d5bb12b72e3bfb0457/assets
The Transform component has 2 types of outputs:
transform_graphis the graph that can perform the preprocessing operations (this graph will be included in the serving and evaluation models).transformed_examplesrepresents the preprocessed training and evaluation data.
transform.outputs
{'transform_graph': Channel(
type_name: TransformGraph
artifacts: [Artifact(artifact: id: 7
type_id: 16
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transform_graph/7"
custom_properties {
key: "name"
value {
string_value: "transform_graph"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 16
name: "TransformGraph"
)]
additional_properties: {}
additional_custom_properties: {}
),
'transformed_examples': Channel(
type_name: Examples
artifacts: [Artifact(artifact: id: 8
type_id: 5
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transformed_examples/7"
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "name"
value {
string_value: "transformed_examples"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'updated_analyzer_cache': Channel(
type_name: TransformCache
artifacts: [Artifact(artifact: id: 9
type_id: 17
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/updated_analyzer_cache/7"
custom_properties {
key: "name"
value {
string_value: "updated_analyzer_cache"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 17
name: "TransformCache"
)]
additional_properties: {}
additional_custom_properties: {}
),
'pre_transform_schema': Channel(
type_name: Schema
artifacts: [Artifact(artifact: id: 10
type_id: 10
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/pre_transform_schema/7"
custom_properties {
key: "name"
value {
string_value: "pre_transform_schema"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 10
name: "Schema"
)]
additional_properties: {}
additional_custom_properties: {}
),
'pre_transform_stats': Channel(
type_name: ExampleStatistics
artifacts: [Artifact(artifact: id: 11
type_id: 8
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/pre_transform_stats/7"
custom_properties {
key: "name"
value {
string_value: "pre_transform_stats"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 8
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'post_transform_schema': Channel(
type_name: Schema
artifacts: [Artifact(artifact: id: 12
type_id: 10
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/post_transform_schema/7"
custom_properties {
key: "name"
value {
string_value: "post_transform_schema"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 10
name: "Schema"
)]
additional_properties: {}
additional_custom_properties: {}
),
'post_transform_stats': Channel(
type_name: ExampleStatistics
artifacts: [Artifact(artifact: id: 13
type_id: 8
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/post_transform_stats/7"
custom_properties {
key: "name"
value {
string_value: "post_transform_stats"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 8
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'post_transform_anomalies': Channel(
type_name: ExampleAnomalies
artifacts: [Artifact(artifact: id: 14
type_id: 12
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/post_transform_anomalies/7"
custom_properties {
key: "name"
value {
string_value: "post_transform_anomalies"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 12
name: "ExampleAnomalies"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
)}
Take a peek at the transform_graph artifact: it points to a directory containing 3 subdirectories:
train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']
The transform_fn subdirectory contains the actual preprocessing graph. The metadata subdirectory contains the schema of the original data. The transformed_metadata subdirectory contains the schema of the preprocessed data.
Take a look at some of the transformed examples and check that they are indeed processed as intended.
def pprint_examples(artifact, n_examples=3):
print("artifact:", artifact)
uri = os.path.join(artifact.uri, "Split-train")
print("uri:", uri)
tfrecord_filenames = [os.path.join(uri, name) for name in os.listdir(uri)]
print("tfrecord_filenames:", tfrecord_filenames)
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
for tfrecord in dataset.take(n_examples):
serialized_example = tfrecord.numpy()
example = tf.train.Example.FromString(serialized_example)
pp.pprint(example)
pprint_examples(transform.outputs['transformed_examples'].get()[0])
artifact: Artifact(artifact: id: 8
type_id: 5
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transformed_examples/7"
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "name"
value {
string_value: "transformed_examples"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
)
uri: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transformed_examples/7/Split-train
tfrecord_filenames: ['/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Transform/transformed_examples/7/Split-train/transformed_examples-00000-of-00001.gz']
features {
feature {
key: "id"
value {
bytes_list {
value: "5ea2f9ef-e6b5-4ba1-b278-1d08079ca6c3"
}
}
}
feature {
key: "label_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "text_xf"
value {
int64_list {
value: 13
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}
features {
feature {
key: "id"
value {
bytes_list {
value: "2532952c-2853-413e-8df6-e6d54a82da9d"
}
}
}
feature {
key: "label_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "text_xf"
value {
int64_list {
value: 13
value: 7
value: 23
value: 75
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value: 5
value: 748
value: 2155
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}
features {
feature {
key: "id"
value {
bytes_list {
value: "49970ec3-f99f-46a1-8bbe-8d153c1caa1a"
}
}
}
feature {
key: "label_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "text_xf"
value {
int64_list {
value: 13
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value: 0
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}
The GraphAugmentation Component
Since we have the sample features and the synthesized graph, we can generate the augmented training data for Neural Structured Learning. The NSL framework provides a library to combine the graph and the sample features to produce the final training data for graph regularization. The resulting training data will include original sample features as well as features of their corresponding neighbors.
In this tutorial, we consider undirected edges and use a maximum of 3 neighbors per sample to augment training data with graph neighbors.
def split_train_and_unsup(input_uri):
'Separate the labeled and unlabeled instances.'
tmp_dir = tempfile.mkdtemp(prefix='tfx-data')
tfrecord_filenames = [
os.path.join(input_uri, filename) for filename in os.listdir(input_uri)
]
train_path = os.path.join(tmp_dir, 'train.tfrecord')
unsup_path = os.path.join(tmp_dir, 'unsup.tfrecord')
with tf.io.TFRecordWriter(train_path) as train_writer, \
tf.io.TFRecordWriter(unsup_path) as unsup_writer:
for tfrecord in tf.data.TFRecordDataset(
tfrecord_filenames, compression_type='GZIP'):
example = tf.train.Example()
example.ParseFromString(tfrecord.numpy())
if ('label_xf' not in example.features.feature or
example.features.feature['label_xf'].int64_list.value[0] == -1):
writer = unsup_writer
else:
writer = train_writer
writer.write(tfrecord.numpy())
return train_path, unsup_path
def gzip(filepath):
with open(filepath, 'rb') as f_in:
with gzip_lib.open(filepath + '.gz', 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
os.remove(filepath)
def copy_tfrecords(input_uri, output_uri):
for filename in os.listdir(input_uri):
input_filename = os.path.join(input_uri, filename)
output_filename = os.path.join(output_uri, filename)
shutil.copyfile(input_filename, output_filename)
@component
def GraphAugmentation(identified_examples: InputArtifact[Examples],
synthesized_graph: InputArtifact[SynthesizedGraph],
augmented_examples: OutputArtifact[Examples],
num_neighbors: Parameter[int],
component_name: Parameter[str]) -> None:
# Get a list of the splits in input_data
splits_list = artifact_utils.decode_split_names(
split_names=identified_examples.split_names)
train_input_uri = os.path.join(identified_examples.uri, 'Split-train')
eval_input_uri = os.path.join(identified_examples.uri, 'Split-eval')
train_graph_uri = os.path.join(synthesized_graph.uri, 'Split-train')
train_output_uri = os.path.join(augmented_examples.uri, 'Split-train')
eval_output_uri = os.path.join(augmented_examples.uri, 'Split-eval')
os.mkdir(train_output_uri)
os.mkdir(eval_output_uri)
# Separate the labeled and unlabeled examples from the 'Split-train' split.
train_path, unsup_path = split_train_and_unsup(train_input_uri)
output_path = os.path.join(train_output_uri, 'nsl_train_data.tfr')
pack_nbrs_args = dict(
labeled_examples_path=train_path,
unlabeled_examples_path=unsup_path,
graph_path=os.path.join(train_graph_uri, 'graph.tsv'),
output_training_data_path=output_path,
add_undirected_edges=True,
max_nbrs=num_neighbors)
print('nsl.tools.pack_nbrs arguments:', pack_nbrs_args)
nsl.tools.pack_nbrs(**pack_nbrs_args)
# Downstream components expect gzip'ed TFRecords.
gzip(output_path)
# The test examples are left untouched and are simply copied over.
copy_tfrecords(eval_input_uri, eval_output_uri)
augmented_examples.split_names = identified_examples.split_names
return
# Augments training data with graph neighbors.
graph_augmentation = GraphAugmentation(
identified_examples=transform.outputs['transformed_examples'],
synthesized_graph=synthesize_graph.outputs['synthesized_graph'],
component_name=u'GraphAugmentation',
num_neighbors=3)
context.run(graph_augmentation, enable_cache=False)
nsl.tools.pack_nbrs arguments: {'labeled_examples_path': '/tmp/tfx-datao1kluw5r/train.tfrecord', 'unlabeled_examples_path': '/tmp/tfx-datao1kluw5r/unsup.tfrecord', 'graph_path': '/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/SynthesizeGraph/synthesized_graph/6/Split-train/graph.tsv', 'output_training_data_path': '/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/GraphAugmentation/augmented_examples/8/Split-train/nsl_train_data.tfr', 'add_undirected_edges': True, 'max_nbrs': 3}
pprint_examples(graph_augmentation.outputs['augmented_examples'].get()[0], 6)
artifact: Artifact(artifact: id: 15
type_id: 5
uri: "/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/GraphAugmentation/augmented_examples/8"
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "name"
value {
string_value: "augmented_examples"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "GraphAugmentation"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
)
uri: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/GraphAugmentation/augmented_examples/8/Split-train
tfrecord_filenames: ['/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/GraphAugmentation/augmented_examples/8/Split-train/nsl_train_data.tfr.gz']
features {
feature {
key: "NL_num_nbrs"
value {
int64_list {
value: 0
}
}
}
feature {
key: "id"
value {
bytes_list {
value: "5ea2f9ef-e6b5-4ba1-b278-1d08079ca6c3"
}
}
}
feature {
key: "label_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "text_xf"
value {
int64_list {
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}
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key: "NL_num_nbrs"
value {
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value: 0
}
}
}
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key: "id"
value {
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value: "2532952c-2853-413e-8df6-e6d54a82da9d"
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}
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key: "label_xf"
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key: "NL_num_nbrs"
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}
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key: "id"
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}
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key: "label_xf"
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}
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}
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key: "text_xf"
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value: 363
value: 12
value: 153
value: 3174
value: 9
value: 8
value: 18
value: 26
value: 667
value: 338
value: 1372
value: 0
value: 86
value: 46
value: 9200
value: 282
value: 0
value: 10091
value: 4
value: 0
value: 694
value: 10028
value: 52
value: 362
value: 26
value: 202
value: 39
value: 216
value: 5
value: 27
value: 5822
value: 19
value: 52
value: 58
value: 362
value: 26
value: 202
value: 39
value: 474
value: 0
value: 10029
value: 4
value: 2
value: 243
value: 143
value: 386
value: 3
value: 0
value: 386
value: 579
value: 2
value: 132
value: 57
value: 725
value: 88
value: 140
value: 30
value: 27
value: 33
value: 1359
value: 29
value: 8
value: 567
value: 35
value: 106
value: 230
value: 60
value: 0
value: 3041
value: 5
value: 7879
value: 28
value: 281
value: 110
value: 111
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
value: 1
}
}
}
}
features {
feature {
key: "NL_num_nbrs"
value {
int64_list {
value: 0
}
}
}
feature {
key: "id"
value {
bytes_list {
value: "cb42b672-39d3-4e7a-9521-a4610bf7cfde"
}
}
}
feature {
key: "label_xf"
value {
int64_list {
value: 1
}
}
}
feature {
key: "text_xf"
value {
int64_list {
value: 13
value: 8
value: 6
value: 2
value: 18
value: 69
value: 140
value: 27
value: 83
value: 31
value: 1877
value: 905
value: 9
value: 10057
value: 31
value: 43
value: 2115
value: 36
value: 32
value: 2057
value: 6133
value: 10
value: 6
value: 32
value: 2474
value: 1614
value: 3
value: 2707
value: 990
value: 4
value: 10067
value: 9
value: 2
value: 1532
value: 242
value: 90
value: 3757
value: 3
value: 90
value: 10026
value: 0
value: 242
value: 6
value: 260
value: 31
value: 24
value: 4
value: 0
value: 84
value: 497
value: 177
value: 1151
value: 777
value: 9
value: 397
value: 552
value: 7726
value: 10051
value: 34
value: 14
value: 379
value: 33
value: 1829
value: 9
value: 123
value: 0
value: 916
value: 10028
value: 7
value: 64
value: 571
value: 12
value: 8
value: 18
value: 27
value: 687
value: 9
value: 30
value: 5609
value: 16
value: 25
value: 99
value: 117
value: 66
value: 2
value: 130
value: 21
value: 8
value: 842
value: 7726
value: 10051
value: 6
value: 338
value: 1107
value: 3
value: 24
value: 10020
value: 29
value: 53
value: 1476
}
}
}
}
The Trainer Component
The Trainer component trains models using TensorFlow.
Create a Python module containing a trainer_fn function, which must return an estimator. If you prefer creating a Keras model, you can do so and then convert it to an estimator using keras.model_to_estimator().
# Setup paths.
_trainer_module_file = 'imdb_trainer.py'
%%writefile {_trainer_module_file}
import neural_structured_learning as nsl
import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
NBR_FEATURE_PREFIX = 'NL_nbr_'
NBR_WEIGHT_SUFFIX = '_weight'
LABEL_KEY = 'label'
ID_FEATURE_KEY = 'id'
def _transformed_name(key):
return key + '_xf'
def _transformed_names(keys):
return [_transformed_name(key) for key in keys]
# Hyperparameters:
#
# We will use an instance of `HParams` to inclue various hyperparameters and
# constants used for training and evaluation. We briefly describe each of them
# below:
#
# - max_seq_length: This is the maximum number of words considered from each
# movie review in this example.
# - vocab_size: This is the size of the vocabulary considered for this
# example.
# - oov_size: This is the out-of-vocabulary size considered for this example.
# - distance_type: This is the distance metric used to regularize the sample
# with its neighbors.
# - graph_regularization_multiplier: This controls the relative weight of the
# graph regularization term in the overall
# loss function.
# - num_neighbors: The number of neighbors used for graph regularization. This
# value has to be less than or equal to the `num_neighbors`
# argument used above in the GraphAugmentation component when
# invoking `nsl.tools.pack_nbrs`.
# - num_fc_units: The number of units in the fully connected layer of the
# neural network.
class HParams(object):
"""Hyperparameters used for training."""
def __init__(self):
### dataset parameters
# The following 3 values should match those defined in the Transform
# Component.
self.max_seq_length = 100
self.vocab_size = 10000
self.oov_size = 100
### Neural Graph Learning parameters
self.distance_type = nsl.configs.DistanceType.L2
self.graph_regularization_multiplier = 0.1
# The following value has to be at most the value of 'num_neighbors' used
# in the GraphAugmentation component.
self.num_neighbors = 1
### Model Architecture
self.num_embedding_dims = 16
self.num_fc_units = 64
HPARAMS = HParams()
def optimizer_fn():
"""Returns an instance of `tf.Optimizer`."""
return tf.compat.v1.train.RMSPropOptimizer(
learning_rate=0.0001, decay=1e-6)
def build_train_op(loss, global_step):
"""Builds a train op to optimize the given loss using gradient descent."""
with tf.name_scope('train'):
optimizer = optimizer_fn()
train_op = optimizer.minimize(loss=loss, global_step=global_step)
return train_op
# Building the model:
#
# A neural network is created by stacking layers—this requires two main
# architectural decisions:
# * How many layers to use in the model?
# * How many *hidden units* to use for each layer?
#
# In this example, the input data consists of an array of word-indices. The
# labels to predict are either 0 or 1. We will use a feed-forward neural network
# as our base model in this tutorial.
def feed_forward_model(features, is_training, reuse=tf.compat.v1.AUTO_REUSE):
"""Builds a simple 2 layer feed forward neural network.
The layers are effectively stacked sequentially to build the classifier. The
first layer is an Embedding layer, which takes the integer-encoded vocabulary
and looks up the embedding vector for each word-index. These vectors are
learned as the model trains. The vectors add a dimension to the output array.
The resulting dimensions are: (batch, sequence, embedding). Next is a global
average pooling 1D layer, which reduces the dimensionality of its inputs from
3D to 2D. This fixed-length output vector is piped through a fully-connected
(Dense) layer with 16 hidden units. The last layer is densely connected with a
single output node. Using the sigmoid activation function, this value is a
float between 0 and 1, representing a probability, or confidence level.
Args:
features: A dictionary containing batch features returned from the
`input_fn`, that include sample features, corresponding neighbor features,
and neighbor weights.
is_training: a Python Boolean value or a Boolean scalar Tensor, indicating
whether to apply dropout.
reuse: a Python Boolean value for reusing variable scope.
Returns:
logits: Tensor of shape [batch_size, 1].
representations: Tensor of shape [batch_size, _] for graph regularization.
This is the representation of each example at the graph regularization
layer.
"""
with tf.compat.v1.variable_scope('ff', reuse=reuse):
inputs = features[_transformed_name('text')]
embeddings = tf.compat.v1.get_variable(
'embeddings',
shape=[
HPARAMS.vocab_size + HPARAMS.oov_size, HPARAMS.num_embedding_dims
])
embedding_layer = tf.nn.embedding_lookup(embeddings, inputs)
pooling_layer = tf.compat.v1.layers.AveragePooling1D(
pool_size=HPARAMS.max_seq_length, strides=HPARAMS.max_seq_length)(
embedding_layer)
# Shape of pooling_layer is now [batch_size, 1, HPARAMS.num_embedding_dims]
pooling_layer = tf.reshape(pooling_layer, [-1, HPARAMS.num_embedding_dims])
dense_layer = tf.compat.v1.layers.Dense(
16, activation='relu')(
pooling_layer)
output_layer = tf.compat.v1.layers.Dense(
1, activation='sigmoid')(
dense_layer)
# Graph regularization will be done on the penultimate (dense) layer
# because the output layer is a single floating point number.
return output_layer, dense_layer
# A note on hidden units:
#
# The above model has two intermediate or "hidden" layers, between the input and
# output, and excluding the Embedding layer. The number of outputs (units,
# nodes, or neurons) is the dimension of the representational space for the
# layer. In other words, the amount of freedom the network is allowed when
# learning an internal representation. If a model has more hidden units
# (a higher-dimensional representation space), and/or more layers, then the
# network can learn more complex representations. However, it makes the network
# more computationally expensive and may lead to learning unwanted
# patterns—patterns that improve performance on training data but not on the
# test data. This is called overfitting.
# This function will be used to generate the embeddings for samples and their
# corresponding neighbors, which will then be used for graph regularization.
def embedding_fn(features, mode):
"""Returns the embedding corresponding to the given features.
Args:
features: A dictionary containing batch features returned from the
`input_fn`, that include sample features, corresponding neighbor features,
and neighbor weights.
mode: Specifies if this is training, evaluation, or prediction. See
tf.estimator.ModeKeys.
Returns:
The embedding that will be used for graph regularization.
"""
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
_, embedding = feed_forward_model(features, is_training)
return embedding
def feed_forward_model_fn(features, labels, mode, params, config):
"""Implementation of the model_fn for the base feed-forward model.
Args:
features: This is the first item returned from the `input_fn` passed to
`train`, `evaluate`, and `predict`. This should be a single `Tensor` or
`dict` of same.
labels: This is the second item returned from the `input_fn` passed to
`train`, `evaluate`, and `predict`. This should be a single `Tensor` or
`dict` of same (for multi-head models). If mode is `ModeKeys.PREDICT`,
`labels=None` will be passed. If the `model_fn`'s signature does not
accept `mode`, the `model_fn` must still be able to handle `labels=None`.
mode: Optional. Specifies if this training, evaluation or prediction. See
`ModeKeys`.
params: An HParams instance as returned by get_hyper_parameters().
config: Optional configuration object. Will receive what is passed to
Estimator in `config` parameter, or the default `config`. Allows updating
things in your model_fn based on configuration such as `num_ps_replicas`,
or `model_dir`. Unused currently.
Returns:
A `tf.estimator.EstimatorSpec` for the base feed-forward model. This does
not include graph-based regularization.
"""
is_training = mode == tf.estimator.ModeKeys.TRAIN
# Build the computation graph.
probabilities, _ = feed_forward_model(features, is_training)
predictions = tf.round(probabilities)
if mode == tf.estimator.ModeKeys.PREDICT:
# labels will be None, and no loss to compute.
cross_entropy_loss = None
eval_metric_ops = None
else:
# Loss is required in train and eval modes.
# Flatten 'probabilities' to 1-D.
probabilities = tf.reshape(probabilities, shape=[-1])
cross_entropy_loss = tf.compat.v1.keras.losses.binary_crossentropy(
labels, probabilities)
eval_metric_ops = {
'accuracy': tf.compat.v1.metrics.accuracy(labels, predictions)
}
if is_training:
global_step = tf.compat.v1.train.get_or_create_global_step()
train_op = build_train_op(cross_entropy_loss, global_step)
else:
train_op = None
return tf.estimator.EstimatorSpec(
mode=mode,
predictions={
'probabilities': probabilities,
'predictions': predictions
},
loss=cross_entropy_loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
# Tf.Transform considers these features as "raw"
def _get_raw_feature_spec(schema):
return schema_utils.schema_as_feature_spec(schema).feature_spec
def _gzip_reader_fn(filenames):
"""Small utility returning a record reader that can read gzip'ed files."""
return tf.data.TFRecordDataset(
filenames,
compression_type='GZIP')
def _example_serving_receiver_fn(tf_transform_output, schema):
"""Build the serving in inputs.
Args:
tf_transform_output: A TFTransformOutput.
schema: the schema of the input data.
Returns:
Tensorflow graph which parses examples, applying tf-transform to them.
"""
raw_feature_spec = _get_raw_feature_spec(schema)
raw_feature_spec.pop(LABEL_KEY)
# We don't need the ID feature for serving.
raw_feature_spec.pop(ID_FEATURE_KEY)
raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
raw_feature_spec, default_batch_size=None)
serving_input_receiver = raw_input_fn()
transformed_features = tf_transform_output.transform_raw_features(
serving_input_receiver.features)
# Even though, LABEL_KEY was removed from 'raw_feature_spec', the transform
# operation would have injected the transformed LABEL_KEY feature with a
# default value.
transformed_features.pop(_transformed_name(LABEL_KEY))
return tf.estimator.export.ServingInputReceiver(
transformed_features, serving_input_receiver.receiver_tensors)
def _eval_input_receiver_fn(tf_transform_output, schema):
"""Build everything needed for the tf-model-analysis to run the model.
Args:
tf_transform_output: A TFTransformOutput.
schema: the schema of the input data.
Returns:
EvalInputReceiver function, which contains:
- Tensorflow graph which parses raw untransformed features, applies the
tf-transform preprocessing operators.
- Set of raw, untransformed features.
- Label against which predictions will be compared.
"""
# Notice that the inputs are raw features, not transformed features here.
raw_feature_spec = _get_raw_feature_spec(schema)
# We don't need the ID feature for TFMA.
raw_feature_spec.pop(ID_FEATURE_KEY)
raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
raw_feature_spec, default_batch_size=None)
serving_input_receiver = raw_input_fn()
transformed_features = tf_transform_output.transform_raw_features(
serving_input_receiver.features)
labels = transformed_features.pop(_transformed_name(LABEL_KEY))
return tfma.export.EvalInputReceiver(
features=transformed_features,
receiver_tensors=serving_input_receiver.receiver_tensors,
labels=labels)
def _augment_feature_spec(feature_spec, num_neighbors):
"""Augments `feature_spec` to include neighbor features.
Args:
feature_spec: Dictionary of feature keys mapping to TF feature types.
num_neighbors: Number of neighbors to use for feature key augmentation.
Returns:
An augmented `feature_spec` that includes neighbor feature keys.
"""
for i in range(num_neighbors):
feature_spec['{}{}_{}'.format(NBR_FEATURE_PREFIX, i, 'id')] = \
tf.io.VarLenFeature(dtype=tf.string)
# We don't care about the neighbor features corresponding to
# _transformed_name(LABEL_KEY) because the LABEL_KEY feature will be
# removed from the feature spec during training/evaluation.
feature_spec['{}{}_{}'.format(NBR_FEATURE_PREFIX, i, 'text_xf')] = \
tf.io.FixedLenFeature(shape=[HPARAMS.max_seq_length], dtype=tf.int64,
default_value=tf.constant(0, dtype=tf.int64,
shape=[HPARAMS.max_seq_length]))
# The 'NL_num_nbrs' features is currently not used.
# Set the neighbor weight feature keys.
for i in range(num_neighbors):
feature_spec['{}{}{}'.format(NBR_FEATURE_PREFIX, i, NBR_WEIGHT_SUFFIX)] = \
tf.io.FixedLenFeature(shape=[1], dtype=tf.float32, default_value=[0.0])
return feature_spec
def _input_fn(filenames, tf_transform_output, is_training, batch_size=200):
"""Generates features and labels for training or evaluation.
Args:
filenames: [str] list of CSV files to read data from.
tf_transform_output: A TFTransformOutput.
is_training: Boolean indicating if we are in training mode.
batch_size: int First dimension size of the Tensors returned by input_fn
Returns:
A (features, indices) tuple where features is a dictionary of
Tensors, and indices is a single Tensor of label indices.
"""
transformed_feature_spec = (
tf_transform_output.transformed_feature_spec().copy())
# During training, NSL uses augmented training data (which includes features
# from graph neighbors). So, update the feature spec accordingly. This needs
# to be done because we are using different schemas for NSL training and eval,
# but the Trainer Component only accepts a single schema.
if is_training:
transformed_feature_spec =_augment_feature_spec(transformed_feature_spec,
HPARAMS.num_neighbors)
dataset = tf.data.experimental.make_batched_features_dataset(
filenames, batch_size, transformed_feature_spec, reader=_gzip_reader_fn)
transformed_features = tf.compat.v1.data.make_one_shot_iterator(
dataset).get_next()
# We pop the label because we do not want to use it as a feature while we're
# training.
return transformed_features, transformed_features.pop(
_transformed_name(LABEL_KEY))
# TFX will call this function
def trainer_fn(hparams, schema):
"""Build the estimator using the high level API.
Args:
hparams: Holds hyperparameters used to train the model as name/value pairs.
schema: Holds the schema of the training examples.
Returns:
A dict of the following:
- estimator: The estimator that will be used for training and eval.
- train_spec: Spec for training.
- eval_spec: Spec for eval.
- eval_input_receiver_fn: Input function for eval.
"""
train_batch_size = 40
eval_batch_size = 40
tf_transform_output = tft.TFTransformOutput(hparams.transform_output)
train_input_fn = lambda: _input_fn(
hparams.train_files,
tf_transform_output,
is_training=True,
batch_size=train_batch_size)
eval_input_fn = lambda: _input_fn(
hparams.eval_files,
tf_transform_output,
is_training=False,
batch_size=eval_batch_size)
train_spec = tf.estimator.TrainSpec(
train_input_fn,
max_steps=hparams.train_steps)
serving_receiver_fn = lambda: _example_serving_receiver_fn(
tf_transform_output, schema)
exporter = tf.estimator.FinalExporter('imdb', serving_receiver_fn)
eval_spec = tf.estimator.EvalSpec(
eval_input_fn,
steps=hparams.eval_steps,
exporters=[exporter],
name='imdb-eval')
run_config = tf.estimator.RunConfig(
save_checkpoints_steps=999, keep_checkpoint_max=1)
run_config = run_config.replace(model_dir=hparams.serving_model_dir)
estimator = tf.estimator.Estimator(
model_fn=feed_forward_model_fn, config=run_config, params=HPARAMS)
# Create a graph regularization config.
graph_reg_config = nsl.configs.make_graph_reg_config(
max_neighbors=HPARAMS.num_neighbors,
multiplier=HPARAMS.graph_regularization_multiplier,
distance_type=HPARAMS.distance_type,
sum_over_axis=-1)
# Invoke the Graph Regularization Estimator wrapper to incorporate
# graph-based regularization for training.
graph_nsl_estimator = nsl.estimator.add_graph_regularization(
estimator,
embedding_fn,
optimizer_fn=optimizer_fn,
graph_reg_config=graph_reg_config)
# Create an input receiver for TFMA processing
receiver_fn = lambda: _eval_input_receiver_fn(
tf_transform_output, schema)
return {
'estimator': graph_nsl_estimator,
'train_spec': train_spec,
'eval_spec': eval_spec,
'eval_input_receiver_fn': receiver_fn
}
Writing imdb_trainer.py
Create and run the Trainer component, passing it the file that we created above.
# Uses user-provided Python function that implements a model using TensorFlow's
# Estimators API.
trainer = Trainer(
module_file=_trainer_module_file,
custom_executor_spec=executor_spec.ExecutorClassSpec(
trainer_executor.Executor),
transformed_examples=graph_augmentation.outputs['augmented_examples'],
schema=schema_gen.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
context.run(trainer)
WARNING:absl:`custom_executor_spec` is deprecated. Please customize component directly.
WARNING:absl:`transformed_examples` is deprecated. Please use `examples` instead.
2021-07-27 09:14:50.215390: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
2021-07-27 09:14:50.219967: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying imdb_transform.py -> build/lib
copying imdb_trainer.py -> build/lib
installing to /tmp/tmpi_e26_65
running install
running install_lib
copying build/lib/imdb_trainer.py -> /tmp/tmpi_e26_65
copying build/lib/imdb_transform.py -> /tmp/tmpi_e26_65
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmpi_e26_65/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d-py3.7.egg-info
running install_scripts
creating /tmp/tmpi_e26_65/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/WHEEL
creating '/tmp/tmpeptp4r8g/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d-py3-none-any.whl' and adding '/tmp/tmpi_e26_65' to it
adding 'imdb_trainer.py'
adding 'imdb_transform.py'
adding 'tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/RECORD'
removing /tmp/tmpi_e26_65
Processing /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/_wheels/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d-py3-none-any.whl
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 999, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 999, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 999 or save_checkpoints_secs None.
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 999 or save_checkpoints_secs None.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/rmsprop.py:123: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/rmsprop.py:123: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.
Instructions for updating:
The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.
Instructions for updating:
The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
2021-07-27 09:14:52.531561: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:14:52.531986: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:14:52.532161: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:14:52.532492: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:14:52.532785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:14:52.532840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-27 09:14:52.532849: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2021-07-27 09:14:52.532856: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2021-07-27 09:14:52.532977: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:14:52.533323: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:14:52.533620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
2021-07-27 09:14:53.258544: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2021-07-27 09:14:55.355026: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8100
2021-07-27 09:14:55.359182: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2021-07-27 09:14:55.748941: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
INFO:tensorflow:loss = 0.69322926, step = 0
INFO:tensorflow:loss = 0.69322926, step = 0
INFO:tensorflow:global_step/sec: 211.694
INFO:tensorflow:global_step/sec: 211.694
INFO:tensorflow:loss = 0.69236696, step = 100 (0.474 sec)
INFO:tensorflow:loss = 0.69236696, step = 100 (0.474 sec)
INFO:tensorflow:global_step/sec: 275.739
INFO:tensorflow:global_step/sec: 275.739
INFO:tensorflow:loss = 0.6926228, step = 200 (0.363 sec)
INFO:tensorflow:loss = 0.6926228, step = 200 (0.363 sec)
INFO:tensorflow:global_step/sec: 274.571
INFO:tensorflow:global_step/sec: 274.571
INFO:tensorflow:loss = 0.691752, step = 300 (0.365 sec)
INFO:tensorflow:loss = 0.691752, step = 300 (0.365 sec)
INFO:tensorflow:global_step/sec: 274.726
INFO:tensorflow:global_step/sec: 274.726
INFO:tensorflow:loss = 0.69001484, step = 400 (0.364 sec)
INFO:tensorflow:loss = 0.69001484, step = 400 (0.364 sec)
INFO:tensorflow:global_step/sec: 270.769
INFO:tensorflow:global_step/sec: 270.769
INFO:tensorflow:loss = 0.6899564, step = 500 (0.369 sec)
INFO:tensorflow:loss = 0.6899564, step = 500 (0.369 sec)
INFO:tensorflow:global_step/sec: 277.988
INFO:tensorflow:global_step/sec: 277.988
INFO:tensorflow:loss = 0.6896905, step = 600 (0.359 sec)
INFO:tensorflow:loss = 0.6896905, step = 600 (0.359 sec)
INFO:tensorflow:global_step/sec: 280.015
INFO:tensorflow:global_step/sec: 280.015
INFO:tensorflow:loss = 0.6895627, step = 700 (0.357 sec)
INFO:tensorflow:loss = 0.6895627, step = 700 (0.357 sec)
INFO:tensorflow:global_step/sec: 278.32
INFO:tensorflow:global_step/sec: 278.32
INFO:tensorflow:loss = 0.68509215, step = 800 (0.359 sec)
INFO:tensorflow:loss = 0.68509215, step = 800 (0.359 sec)
INFO:tensorflow:global_step/sec: 285.247
INFO:tensorflow:global_step/sec: 285.247
INFO:tensorflow:loss = 0.688862, step = 900 (0.351 sec)
INFO:tensorflow:loss = 0.688862, step = 900 (0.351 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/saver.py:971: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/saver.py:971: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-07-27T09:15:00
INFO:tensorflow:Starting evaluation at 2021-07-27T09:15:00
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-999
2021-07-27 09:15:00.071872: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:00.072285: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:15:00.072492: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:00.072873: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:00.073192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:15:00.073239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-27 09:15:00.073247: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2021-07-27 09:15:00.073256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2021-07-27 09:15:00.073432: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:00.073877: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:00.074208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 5.49521s
INFO:tensorflow:Inference Time : 5.49521s
INFO:tensorflow:Finished evaluation at 2021-07-27-09:15:05
INFO:tensorflow:Finished evaluation at 2021-07-27-09:15:05
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.6677, global_step = 999, loss = 0.68569756
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.6677, global_step = 999, loss = 0.68569756
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:global_step/sec: 16.4135
INFO:tensorflow:global_step/sec: 16.4135
INFO:tensorflow:loss = 0.68576753, step = 1000 (6.092 sec)
INFO:tensorflow:loss = 0.68576753, step = 1000 (6.092 sec)
INFO:tensorflow:global_step/sec: 276.683
INFO:tensorflow:global_step/sec: 276.683
INFO:tensorflow:loss = 0.6788543, step = 1100 (0.362 sec)
INFO:tensorflow:loss = 0.6788543, step = 1100 (0.362 sec)
INFO:tensorflow:global_step/sec: 274.503
INFO:tensorflow:global_step/sec: 274.503
INFO:tensorflow:loss = 0.6834281, step = 1200 (0.365 sec)
INFO:tensorflow:loss = 0.6834281, step = 1200 (0.365 sec)
INFO:tensorflow:global_step/sec: 274.221
INFO:tensorflow:global_step/sec: 274.221
INFO:tensorflow:loss = 0.68370265, step = 1300 (0.365 sec)
INFO:tensorflow:loss = 0.68370265, step = 1300 (0.365 sec)
INFO:tensorflow:global_step/sec: 272.284
INFO:tensorflow:global_step/sec: 272.284
INFO:tensorflow:loss = 0.6844999, step = 1400 (0.367 sec)
INFO:tensorflow:loss = 0.6844999, step = 1400 (0.367 sec)
INFO:tensorflow:global_step/sec: 272.698
INFO:tensorflow:global_step/sec: 272.698
INFO:tensorflow:loss = 0.68151146, step = 1500 (0.367 sec)
INFO:tensorflow:loss = 0.68151146, step = 1500 (0.367 sec)
INFO:tensorflow:global_step/sec: 277.091
INFO:tensorflow:global_step/sec: 277.091
INFO:tensorflow:loss = 0.6727173, step = 1600 (0.361 sec)
INFO:tensorflow:loss = 0.6727173, step = 1600 (0.361 sec)
INFO:tensorflow:global_step/sec: 274.492
INFO:tensorflow:global_step/sec: 274.492
INFO:tensorflow:loss = 0.67641914, step = 1700 (0.364 sec)
INFO:tensorflow:loss = 0.67641914, step = 1700 (0.364 sec)
INFO:tensorflow:global_step/sec: 277.889
INFO:tensorflow:global_step/sec: 277.889
INFO:tensorflow:loss = 0.66065145, step = 1800 (0.360 sec)
INFO:tensorflow:loss = 0.66065145, step = 1800 (0.360 sec)
INFO:tensorflow:global_step/sec: 278.998
INFO:tensorflow:global_step/sec: 278.998
INFO:tensorflow:loss = 0.6694759, step = 1900 (0.359 sec)
INFO:tensorflow:loss = 0.6694759, step = 1900 (0.359 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 229.839
INFO:tensorflow:global_step/sec: 229.839
INFO:tensorflow:loss = 0.6578656, step = 2000 (0.434 sec)
INFO:tensorflow:loss = 0.6578656, step = 2000 (0.434 sec)
INFO:tensorflow:global_step/sec: 272.315
INFO:tensorflow:global_step/sec: 272.315
INFO:tensorflow:loss = 0.67634356, step = 2100 (0.367 sec)
INFO:tensorflow:loss = 0.67634356, step = 2100 (0.367 sec)
INFO:tensorflow:global_step/sec: 282.632
INFO:tensorflow:global_step/sec: 282.632
INFO:tensorflow:loss = 0.64694864, step = 2200 (0.354 sec)
INFO:tensorflow:loss = 0.64694864, step = 2200 (0.354 sec)
INFO:tensorflow:global_step/sec: 276.824
INFO:tensorflow:global_step/sec: 276.824
INFO:tensorflow:loss = 0.66624814, step = 2300 (0.361 sec)
INFO:tensorflow:loss = 0.66624814, step = 2300 (0.361 sec)
INFO:tensorflow:global_step/sec: 269.425
INFO:tensorflow:global_step/sec: 269.425
INFO:tensorflow:loss = 0.6768476, step = 2400 (0.371 sec)
INFO:tensorflow:loss = 0.6768476, step = 2400 (0.371 sec)
INFO:tensorflow:global_step/sec: 265.784
INFO:tensorflow:global_step/sec: 265.784
INFO:tensorflow:loss = 0.6642151, step = 2500 (0.376 sec)
INFO:tensorflow:loss = 0.6642151, step = 2500 (0.376 sec)
INFO:tensorflow:global_step/sec: 277.728
INFO:tensorflow:global_step/sec: 277.728
INFO:tensorflow:loss = 0.6666919, step = 2600 (0.361 sec)
INFO:tensorflow:loss = 0.6666919, step = 2600 (0.361 sec)
INFO:tensorflow:global_step/sec: 276.673
INFO:tensorflow:global_step/sec: 276.673
INFO:tensorflow:loss = 0.6315029, step = 2700 (0.361 sec)
INFO:tensorflow:loss = 0.6315029, step = 2700 (0.361 sec)
INFO:tensorflow:global_step/sec: 266.139
INFO:tensorflow:global_step/sec: 266.139
INFO:tensorflow:loss = 0.61445504, step = 2800 (0.376 sec)
INFO:tensorflow:loss = 0.61445504, step = 2800 (0.376 sec)
INFO:tensorflow:global_step/sec: 268.77
INFO:tensorflow:global_step/sec: 268.77
INFO:tensorflow:loss = 0.6582615, step = 2900 (0.372 sec)
INFO:tensorflow:loss = 0.6582615, step = 2900 (0.372 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 227.589
INFO:tensorflow:global_step/sec: 227.589
INFO:tensorflow:loss = 0.6330004, step = 3000 (0.440 sec)
INFO:tensorflow:loss = 0.6330004, step = 3000 (0.440 sec)
INFO:tensorflow:global_step/sec: 257.036
INFO:tensorflow:global_step/sec: 257.036
INFO:tensorflow:loss = 0.6225431, step = 3100 (0.389 sec)
INFO:tensorflow:loss = 0.6225431, step = 3100 (0.389 sec)
INFO:tensorflow:global_step/sec: 267.359
INFO:tensorflow:global_step/sec: 267.359
INFO:tensorflow:loss = 0.59520555, step = 3200 (0.374 sec)
INFO:tensorflow:loss = 0.59520555, step = 3200 (0.374 sec)
INFO:tensorflow:global_step/sec: 246.66
INFO:tensorflow:global_step/sec: 246.66
INFO:tensorflow:loss = 0.6436679, step = 3300 (0.406 sec)
INFO:tensorflow:loss = 0.6436679, step = 3300 (0.406 sec)
INFO:tensorflow:global_step/sec: 235.989
INFO:tensorflow:global_step/sec: 235.989
INFO:tensorflow:loss = 0.6019606, step = 3400 (0.424 sec)
INFO:tensorflow:loss = 0.6019606, step = 3400 (0.424 sec)
INFO:tensorflow:global_step/sec: 256.192
INFO:tensorflow:global_step/sec: 256.192
INFO:tensorflow:loss = 0.6313018, step = 3500 (0.390 sec)
INFO:tensorflow:loss = 0.6313018, step = 3500 (0.390 sec)
INFO:tensorflow:global_step/sec: 252.023
INFO:tensorflow:global_step/sec: 252.023
INFO:tensorflow:loss = 0.63757026, step = 3600 (0.397 sec)
INFO:tensorflow:loss = 0.63757026, step = 3600 (0.397 sec)
INFO:tensorflow:global_step/sec: 243.633
INFO:tensorflow:global_step/sec: 243.633
INFO:tensorflow:loss = 0.5859189, step = 3700 (0.410 sec)
INFO:tensorflow:loss = 0.5859189, step = 3700 (0.410 sec)
INFO:tensorflow:global_step/sec: 242.902
INFO:tensorflow:global_step/sec: 242.902
INFO:tensorflow:loss = 0.54519206, step = 3800 (0.412 sec)
INFO:tensorflow:loss = 0.54519206, step = 3800 (0.412 sec)
INFO:tensorflow:global_step/sec: 258.566
INFO:tensorflow:global_step/sec: 258.566
INFO:tensorflow:loss = 0.5704173, step = 3900 (0.387 sec)
INFO:tensorflow:loss = 0.5704173, step = 3900 (0.387 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 215.072
INFO:tensorflow:global_step/sec: 215.072
INFO:tensorflow:loss = 0.59502316, step = 4000 (0.465 sec)
INFO:tensorflow:loss = 0.59502316, step = 4000 (0.465 sec)
INFO:tensorflow:global_step/sec: 252.848
INFO:tensorflow:global_step/sec: 252.848
INFO:tensorflow:loss = 0.5610915, step = 4100 (0.396 sec)
INFO:tensorflow:loss = 0.5610915, step = 4100 (0.396 sec)
INFO:tensorflow:global_step/sec: 253.373
INFO:tensorflow:global_step/sec: 253.373
INFO:tensorflow:loss = 0.5591887, step = 4200 (0.394 sec)
INFO:tensorflow:loss = 0.5591887, step = 4200 (0.394 sec)
INFO:tensorflow:global_step/sec: 251.582
INFO:tensorflow:global_step/sec: 251.582
INFO:tensorflow:loss = 0.49893284, step = 4300 (0.398 sec)
INFO:tensorflow:loss = 0.49893284, step = 4300 (0.398 sec)
INFO:tensorflow:global_step/sec: 248.965
INFO:tensorflow:global_step/sec: 248.965
INFO:tensorflow:loss = 0.5927149, step = 4400 (0.402 sec)
INFO:tensorflow:loss = 0.5927149, step = 4400 (0.402 sec)
INFO:tensorflow:global_step/sec: 257.041
INFO:tensorflow:global_step/sec: 257.041
INFO:tensorflow:loss = 0.5431159, step = 4500 (0.389 sec)
INFO:tensorflow:loss = 0.5431159, step = 4500 (0.389 sec)
INFO:tensorflow:global_step/sec: 252.152
INFO:tensorflow:global_step/sec: 252.152
INFO:tensorflow:loss = 0.5833171, step = 4600 (0.397 sec)
INFO:tensorflow:loss = 0.5833171, step = 4600 (0.397 sec)
INFO:tensorflow:global_step/sec: 250.862
INFO:tensorflow:global_step/sec: 250.862
INFO:tensorflow:loss = 0.5355993, step = 4700 (0.399 sec)
INFO:tensorflow:loss = 0.5355993, step = 4700 (0.399 sec)
INFO:tensorflow:global_step/sec: 253.628
INFO:tensorflow:global_step/sec: 253.628
INFO:tensorflow:loss = 0.619254, step = 4800 (0.394 sec)
INFO:tensorflow:loss = 0.619254, step = 4800 (0.394 sec)
INFO:tensorflow:global_step/sec: 250.356
INFO:tensorflow:global_step/sec: 250.356
INFO:tensorflow:loss = 0.54462916, step = 4900 (0.399 sec)
INFO:tensorflow:loss = 0.54462916, step = 4900 (0.399 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 215.532
INFO:tensorflow:global_step/sec: 215.532
INFO:tensorflow:loss = 0.5465646, step = 5000 (0.464 sec)
INFO:tensorflow:loss = 0.5465646, step = 5000 (0.464 sec)
INFO:tensorflow:global_step/sec: 246.76
INFO:tensorflow:global_step/sec: 246.76
INFO:tensorflow:loss = 0.51135373, step = 5100 (0.405 sec)
INFO:tensorflow:loss = 0.51135373, step = 5100 (0.405 sec)
INFO:tensorflow:global_step/sec: 250.25
INFO:tensorflow:global_step/sec: 250.25
INFO:tensorflow:loss = 0.591072, step = 5200 (0.400 sec)
INFO:tensorflow:loss = 0.591072, step = 5200 (0.400 sec)
INFO:tensorflow:global_step/sec: 253.138
INFO:tensorflow:global_step/sec: 253.138
INFO:tensorflow:loss = 0.48519018, step = 5300 (0.395 sec)
INFO:tensorflow:loss = 0.48519018, step = 5300 (0.395 sec)
INFO:tensorflow:global_step/sec: 250.641
INFO:tensorflow:global_step/sec: 250.641
INFO:tensorflow:loss = 0.47934756, step = 5400 (0.399 sec)
INFO:tensorflow:loss = 0.47934756, step = 5400 (0.399 sec)
INFO:tensorflow:global_step/sec: 251.855
INFO:tensorflow:global_step/sec: 251.855
INFO:tensorflow:loss = 0.44151902, step = 5500 (0.397 sec)
INFO:tensorflow:loss = 0.44151902, step = 5500 (0.397 sec)
INFO:tensorflow:global_step/sec: 250.974
INFO:tensorflow:global_step/sec: 250.974
INFO:tensorflow:loss = 0.4974475, step = 5600 (0.398 sec)
INFO:tensorflow:loss = 0.4974475, step = 5600 (0.398 sec)
INFO:tensorflow:global_step/sec: 252.506
INFO:tensorflow:global_step/sec: 252.506
INFO:tensorflow:loss = 0.57592094, step = 5700 (0.396 sec)
INFO:tensorflow:loss = 0.57592094, step = 5700 (0.396 sec)
INFO:tensorflow:global_step/sec: 250.984
INFO:tensorflow:global_step/sec: 250.984
INFO:tensorflow:loss = 0.47122863, step = 5800 (0.398 sec)
INFO:tensorflow:loss = 0.47122863, step = 5800 (0.398 sec)
INFO:tensorflow:global_step/sec: 260.372
INFO:tensorflow:global_step/sec: 260.372
INFO:tensorflow:loss = 0.43418798, step = 5900 (0.384 sec)
INFO:tensorflow:loss = 0.43418798, step = 5900 (0.384 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 221.857
INFO:tensorflow:global_step/sec: 221.857
INFO:tensorflow:loss = 0.5193545, step = 6000 (0.451 sec)
INFO:tensorflow:loss = 0.5193545, step = 6000 (0.451 sec)
INFO:tensorflow:global_step/sec: 258.13
INFO:tensorflow:global_step/sec: 258.13
INFO:tensorflow:loss = 0.5118042, step = 6100 (0.387 sec)
INFO:tensorflow:loss = 0.5118042, step = 6100 (0.387 sec)
INFO:tensorflow:global_step/sec: 256.472
INFO:tensorflow:global_step/sec: 256.472
INFO:tensorflow:loss = 0.520128, step = 6200 (0.390 sec)
INFO:tensorflow:loss = 0.520128, step = 6200 (0.390 sec)
INFO:tensorflow:global_step/sec: 271.654
INFO:tensorflow:global_step/sec: 271.654
INFO:tensorflow:loss = 0.49134195, step = 6300 (0.368 sec)
INFO:tensorflow:loss = 0.49134195, step = 6300 (0.368 sec)
INFO:tensorflow:global_step/sec: 264.465
INFO:tensorflow:global_step/sec: 264.465
INFO:tensorflow:loss = 0.48210445, step = 6400 (0.378 sec)
INFO:tensorflow:loss = 0.48210445, step = 6400 (0.378 sec)
INFO:tensorflow:global_step/sec: 256.224
INFO:tensorflow:global_step/sec: 256.224
INFO:tensorflow:loss = 0.47818863, step = 6500 (0.390 sec)
INFO:tensorflow:loss = 0.47818863, step = 6500 (0.390 sec)
INFO:tensorflow:global_step/sec: 255.261
INFO:tensorflow:global_step/sec: 255.261
INFO:tensorflow:loss = 0.5382578, step = 6600 (0.392 sec)
INFO:tensorflow:loss = 0.5382578, step = 6600 (0.392 sec)
INFO:tensorflow:global_step/sec: 272.234
INFO:tensorflow:global_step/sec: 272.234
INFO:tensorflow:loss = 0.47908917, step = 6700 (0.367 sec)
INFO:tensorflow:loss = 0.47908917, step = 6700 (0.367 sec)
INFO:tensorflow:global_step/sec: 271.08
INFO:tensorflow:global_step/sec: 271.08
INFO:tensorflow:loss = 0.47381815, step = 6800 (0.369 sec)
INFO:tensorflow:loss = 0.47381815, step = 6800 (0.369 sec)
INFO:tensorflow:global_step/sec: 259.457
INFO:tensorflow:global_step/sec: 259.457
INFO:tensorflow:loss = 0.39517635, step = 6900 (0.386 sec)
INFO:tensorflow:loss = 0.39517635, step = 6900 (0.386 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 223.964
INFO:tensorflow:global_step/sec: 223.964
INFO:tensorflow:loss = 0.42984772, step = 7000 (0.446 sec)
INFO:tensorflow:loss = 0.42984772, step = 7000 (0.446 sec)
INFO:tensorflow:global_step/sec: 278.579
INFO:tensorflow:global_step/sec: 278.579
INFO:tensorflow:loss = 0.40576378, step = 7100 (0.359 sec)
INFO:tensorflow:loss = 0.40576378, step = 7100 (0.359 sec)
INFO:tensorflow:global_step/sec: 262.381
INFO:tensorflow:global_step/sec: 262.381
INFO:tensorflow:loss = 0.502881, step = 7200 (0.381 sec)
INFO:tensorflow:loss = 0.502881, step = 7200 (0.381 sec)
INFO:tensorflow:global_step/sec: 254.754
INFO:tensorflow:global_step/sec: 254.754
INFO:tensorflow:loss = 0.4110231, step = 7300 (0.393 sec)
INFO:tensorflow:loss = 0.4110231, step = 7300 (0.393 sec)
INFO:tensorflow:global_step/sec: 249.425
INFO:tensorflow:global_step/sec: 249.425
INFO:tensorflow:loss = 0.37873524, step = 7400 (0.401 sec)
INFO:tensorflow:loss = 0.37873524, step = 7400 (0.401 sec)
INFO:tensorflow:global_step/sec: 250.202
INFO:tensorflow:global_step/sec: 250.202
INFO:tensorflow:loss = 0.51536375, step = 7500 (0.400 sec)
INFO:tensorflow:loss = 0.51536375, step = 7500 (0.400 sec)
INFO:tensorflow:global_step/sec: 253.281
INFO:tensorflow:global_step/sec: 253.281
INFO:tensorflow:loss = 0.60245466, step = 7600 (0.394 sec)
INFO:tensorflow:loss = 0.60245466, step = 7600 (0.394 sec)
INFO:tensorflow:global_step/sec: 253.165
INFO:tensorflow:global_step/sec: 253.165
INFO:tensorflow:loss = 0.44345674, step = 7700 (0.395 sec)
INFO:tensorflow:loss = 0.44345674, step = 7700 (0.395 sec)
INFO:tensorflow:global_step/sec: 255.968
INFO:tensorflow:global_step/sec: 255.968
INFO:tensorflow:loss = 0.49975634, step = 7800 (0.391 sec)
INFO:tensorflow:loss = 0.49975634, step = 7800 (0.391 sec)
INFO:tensorflow:global_step/sec: 253.47
INFO:tensorflow:global_step/sec: 253.47
INFO:tensorflow:loss = 0.5979585, step = 7900 (0.394 sec)
INFO:tensorflow:loss = 0.5979585, step = 7900 (0.394 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 217.451
INFO:tensorflow:global_step/sec: 217.451
INFO:tensorflow:loss = 0.4377802, step = 8000 (0.460 sec)
INFO:tensorflow:loss = 0.4377802, step = 8000 (0.460 sec)
INFO:tensorflow:global_step/sec: 256.419
INFO:tensorflow:global_step/sec: 256.419
INFO:tensorflow:loss = 0.505089, step = 8100 (0.390 sec)
INFO:tensorflow:loss = 0.505089, step = 8100 (0.390 sec)
INFO:tensorflow:global_step/sec: 252.919
INFO:tensorflow:global_step/sec: 252.919
INFO:tensorflow:loss = 0.40602866, step = 8200 (0.395 sec)
INFO:tensorflow:loss = 0.40602866, step = 8200 (0.395 sec)
INFO:tensorflow:global_step/sec: 256.346
INFO:tensorflow:global_step/sec: 256.346
INFO:tensorflow:loss = 0.43653098, step = 8300 (0.390 sec)
INFO:tensorflow:loss = 0.43653098, step = 8300 (0.390 sec)
INFO:tensorflow:global_step/sec: 260.355
INFO:tensorflow:global_step/sec: 260.355
INFO:tensorflow:loss = 0.3623276, step = 8400 (0.384 sec)
INFO:tensorflow:loss = 0.3623276, step = 8400 (0.384 sec)
INFO:tensorflow:global_step/sec: 260.462
INFO:tensorflow:global_step/sec: 260.462
INFO:tensorflow:loss = 0.39351046, step = 8500 (0.384 sec)
INFO:tensorflow:loss = 0.39351046, step = 8500 (0.384 sec)
INFO:tensorflow:global_step/sec: 252.832
INFO:tensorflow:global_step/sec: 252.832
INFO:tensorflow:loss = 0.36336032, step = 8600 (0.395 sec)
INFO:tensorflow:loss = 0.36336032, step = 8600 (0.395 sec)
INFO:tensorflow:global_step/sec: 257.437
INFO:tensorflow:global_step/sec: 257.437
INFO:tensorflow:loss = 0.45432475, step = 8700 (0.388 sec)
INFO:tensorflow:loss = 0.45432475, step = 8700 (0.388 sec)
INFO:tensorflow:global_step/sec: 255.461
INFO:tensorflow:global_step/sec: 255.461
INFO:tensorflow:loss = 0.5483323, step = 8800 (0.392 sec)
INFO:tensorflow:loss = 0.5483323, step = 8800 (0.392 sec)
INFO:tensorflow:global_step/sec: 258.549
INFO:tensorflow:global_step/sec: 258.549
INFO:tensorflow:loss = 0.39868888, step = 8900 (0.387 sec)
INFO:tensorflow:loss = 0.39868888, step = 8900 (0.387 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 219.514
INFO:tensorflow:global_step/sec: 219.514
INFO:tensorflow:loss = 0.38975185, step = 9000 (0.455 sec)
INFO:tensorflow:loss = 0.38975185, step = 9000 (0.455 sec)
INFO:tensorflow:global_step/sec: 251.244
INFO:tensorflow:global_step/sec: 251.244
INFO:tensorflow:loss = 0.4212112, step = 9100 (0.398 sec)
INFO:tensorflow:loss = 0.4212112, step = 9100 (0.398 sec)
INFO:tensorflow:global_step/sec: 272.072
INFO:tensorflow:global_step/sec: 272.072
INFO:tensorflow:loss = 0.4536277, step = 9200 (0.368 sec)
INFO:tensorflow:loss = 0.4536277, step = 9200 (0.368 sec)
INFO:tensorflow:global_step/sec: 270.537
INFO:tensorflow:global_step/sec: 270.537
INFO:tensorflow:loss = 0.39709157, step = 9300 (0.370 sec)
INFO:tensorflow:loss = 0.39709157, step = 9300 (0.370 sec)
INFO:tensorflow:global_step/sec: 255.055
INFO:tensorflow:global_step/sec: 255.055
INFO:tensorflow:loss = 0.4788674, step = 9400 (0.392 sec)
INFO:tensorflow:loss = 0.4788674, step = 9400 (0.392 sec)
INFO:tensorflow:global_step/sec: 259.597
INFO:tensorflow:global_step/sec: 259.597
INFO:tensorflow:loss = 0.3967464, step = 9500 (0.385 sec)
INFO:tensorflow:loss = 0.3967464, step = 9500 (0.385 sec)
INFO:tensorflow:global_step/sec: 258.95
INFO:tensorflow:global_step/sec: 258.95
INFO:tensorflow:loss = 0.3898618, step = 9600 (0.386 sec)
INFO:tensorflow:loss = 0.3898618, step = 9600 (0.386 sec)
INFO:tensorflow:global_step/sec: 258.698
INFO:tensorflow:global_step/sec: 258.698
INFO:tensorflow:loss = 0.3142761, step = 9700 (0.387 sec)
INFO:tensorflow:loss = 0.3142761, step = 9700 (0.387 sec)
INFO:tensorflow:global_step/sec: 265.135
INFO:tensorflow:global_step/sec: 265.135
INFO:tensorflow:loss = 0.328832, step = 9800 (0.377 sec)
INFO:tensorflow:loss = 0.328832, step = 9800 (0.377 sec)
INFO:tensorflow:global_step/sec: 263.242
INFO:tensorflow:global_step/sec: 263.242
INFO:tensorflow:loss = 0.4077425, step = 9900 (0.380 sec)
INFO:tensorflow:loss = 0.4077425, step = 9900 (0.380 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-07-27T09:15:41
INFO:tensorflow:Starting evaluation at 2021-07-27T09:15:41
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-10000
2021-07-27 09:15:41.058084: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:41.058476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:15:41.058629: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:41.058970: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:41.059237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:15:41.059282: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-27 09:15:41.059289: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2021-07-27 09:15:41.059296: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2021-07-27 09:15:41.059413: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:41.059723: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:41.059991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 5.51757s
INFO:tensorflow:Inference Time : 5.51757s
INFO:tensorflow:Finished evaluation at 2021-07-27-09:15:46
INFO:tensorflow:Finished evaluation at 2021-07-27-09:15:46
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.8013, global_step = 10000, loss = 0.44217905
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.8013, global_step = 10000, loss = 0.44217905
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Performing the final export in the end of training.
INFO:tensorflow:Performing the final export in the end of training.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/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/python3.7/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:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-10000
2021-07-27 09:15:48.407821: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.408315: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:15:48.408563: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.409077: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.409448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:15:48.409503: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-27 09:15:48.409517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2021-07-27 09:15:48.409527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2021-07-27 09:15:48.409698: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.410244: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.410662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/export/imdb/temp-1627377346/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/export/imdb/temp-1627377346/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/export/imdb/temp-1627377346/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/export/imdb/temp-1627377346/saved_model.pb
INFO:tensorflow:Loss for final step: 0.47436312.
INFO:tensorflow:Loss for final step: 0.47436312.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-10000
2021-07-27 09:15:48.855551: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.856008: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:15:48.856222: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.856639: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.856952: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:15:48.857013: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-27 09:15:48.857021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2021-07-27 09:15:48.857027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2021-07-27 09:15:48.857170: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.857568: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:15:48.857913: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-TFMA/temp-1627377348/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-TFMA/temp-1627377348/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-TFMA/temp-1627377348/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-27T09_11_59.954153-vciyoyjw/Trainer/model_run/9/Format-TFMA/temp-1627377348/saved_model.pb
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/serving_model_dir/saved_model.pb"
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/eval_model_dir/saved_model.pb"
Take a peek at the trained model which was exported from Trainer.
train_uri = trainer.outputs['model'].get()[0].uri
serving_model_path = os.path.join(train_uri, 'Format-Serving')
exported_model = tf.saved_model.load(serving_model_path)
exported_model.graph.get_operations()[:10] + ["..."]
[<tf.Operation 'global_step/Initializer/zeros' type=Const>, <tf.Operation 'global_step' type=VarHandleOp>, <tf.Operation 'global_step/IsInitialized/VarIsInitializedOp' type=VarIsInitializedOp>, <tf.Operation 'global_step/Assign' type=AssignVariableOp>, <tf.Operation 'global_step/Read/ReadVariableOp' type=ReadVariableOp>, <tf.Operation 'input_example_tensor' type=Placeholder>, <tf.Operation 'ParseExample/ParseExampleV2/names' type=Const>, <tf.Operation 'ParseExample/ParseExampleV2/sparse_keys' type=Const>, <tf.Operation 'ParseExample/ParseExampleV2/dense_keys' type=Const>, <tf.Operation 'ParseExample/ParseExampleV2/ragged_keys' type=Const>, '...']
Let's visualize the model's metrics using Tensorboard.
# Get the URI of the output artifact representing the training logs,
# which is a directory
model_run_dir = trainer.outputs['model_run'].get()[0].uri
%load_ext tensorboard
%tensorboard --logdir {model_run_dir}
Model Serving
Graph regularization only affects the training workflow by adding a regularization term to the loss function. As a result, the model evaluation and serving workflows remain unchanged. It is for the same reason that we've also omitted downstream TFX components that typically come after the Trainer component like the Evaluator, Pusher, etc.
Conclusion
We have demonstrated the use of graph regularization using the Neural Structured Learning (NSL) framework in a TFX pipeline even when the input does not contain an explicit graph. We considered the task of sentiment classification of IMDB movie reviews for which we synthesized a similarity graph based on review embeddings. We encourage users to experiment further by using different embeddings for graph construction, varying hyperparameters, changing the amount of supervision, and by defining different model architectures.