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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Overview
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.
import sys
if 'google.colab' in sys.modules:
!pip install --upgrade pip
Install Required Packages
!pip install -q \
tfx \
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__)
2024-04-30 09:33:21.388168: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-04-30 09:33:21.388207: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-04-30 09:33:21.389692: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered TF Version: 2.15.1 Eager mode: True GPU is available NSL Version: 1.4.0 TFX Version: 1.15.0 TFDV version: 1.15.1 TFT version: 1.15.0 TFMA version: 0.46.0 Hub version: 0.15.0 Beam version: 2.55.1
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)
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()))
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())
/tmpfs/tmp/ipykernel_125590/2578414870.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`. elif value.dtype == np.integer:
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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz as root for pipeline outputs. WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: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: 14
uri: "/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/ImportExampleGen/examples/1"
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "file_format"
value {
string_value: "tfrecords_gzip"
}
}
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:train,num_files:1,total_bytes:27706811,xor_checksum:1714469615,sum_checksum:1714469615\nsplit:eval,num_files:1,total_bytes:13374744,xor_checksum:1714469618,sum_checksum:1714469618"
}
}
custom_properties {
key: "payload_format"
value {
string_value: "FORMAT_TF_EXAMPLE"
}
}
custom_properties {
key: "span"
value {
int_value: 0
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
, artifact_type: id: 14
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)
example_gen.outputs is a <class 'dict'>
{'examples': OutputChannel(artifact_type=Examples, producer_component_id=ImportExampleGen, output_key=examples, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)}
["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)
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)
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)
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://tfhub.dev/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 715c143c-576f-4826-83a8-1fe5b0f295f5 58b59bbb-4ed8-4e67-b37a-063d99451bda 0.992586 58b59bbb-4ed8-4e67-b37a-063d99451bda 715c143c-576f-4826-83a8-1fe5b0f295f5 0.992586 715c143c-576f-4826-83a8-1fe5b0f295f5 e886f08d-ef46-42ae-8179-5bccf40279e3 0.992823 e886f08d-ef46-42ae-8179-5bccf40279e3 715c143c-576f-4826-83a8-1fe5b0f295f5 0.992823 715c143c-576f-4826-83a8-1fe5b0f295f5 f639209d-5d2a-41fb-bd5f-5d2db8831c66 0.992505 f639209d-5d2a-41fb-bd5f-5d2db8831c66 715c143c-576f-4826-83a8-1fe5b0f295f5 0.992505 3ca8cfe2-cf8d-4cee-905d-c2dbb70b002f d757e2ae-e150-4493-8f24-7a2a9566ecfa 0.990184 d757e2ae-e150-4493-8f24-7a2a9566ecfa 3ca8cfe2-cf8d-4cee-905d-c2dbb70b002f 0.990184 58b59bbb-4ed8-4e67-b37a-063d99451bda 110a1231-0c9b-4dd1-96aa-f9dc32b64e32 0.990616 110a1231-0c9b-4dd1-96aa-f9dc32b64e32 58b59bbb-4ed8-4e67-b37a-063d99451bda 0.990616 ... 7ef7b17d-f933-4e58-823f-c51f2e8d6cb6 838290de-4b69-4807-800d-baa2f9146797 0.990002 838290de-4b69-4807-800d-baa2f9146797 7ef7b17d-f933-4e58-823f-c51f2e8d6cb6 0.990002 d8f92902-9819-4556-bdb8-5f4a802091d5 ead46669-e4df-4325-bd01-4a140b352d95 0.991046 ead46669-e4df-4325-bd01-4a140b352d95 d8f92902-9819-4556-bdb8-5f4a802091d5 0.991046 f013c057-1997-477f-b9a9-ddd94ec2cf12 cbca5a1c-93d5-4476-b05e-844229e82f33 0.991198 cbca5a1c-93d5-4476-b05e-844229e82f33 f013c057-1997-477f-b9a9-ddd94ec2cf12 0.991198 00e1b65b-78c1-4871-bdef-b22a27a2115c ca8be049-ce7e-4e73-8f22-d07dee098620 0.990260 ca8be049-ce7e-4e73-8f22-d07dee098620 00e1b65b-78c1-4871-bdef-b22a27a2115c 0.990260 81c3aa06-f52a-4d86-92f3-184a1382dc1a 87c88c61-d7e1-40f9-bd00-d2ec31b9209e 0.991317 87c88c61-d7e1-40f9-bd00-d2ec31b9209e 81c3aa06-f52a-4d86-92f3-184a1382dc1a 0.991317
wc -l {graph_path}
222132 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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 /tmpfs/tmp/tmpib83kspk
running install
running install_lib
copying build/lib/imdb_transform.py -> /tmpfs/tmp/tmpib83kspk
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 /tmpfs/tmp/tmpib83kspk/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3.9.egg-info
running install_scripts
creating /tmpfs/tmp/tmpib83kspk/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/WHEEL
creating '/tmpfs/tmp/tmpi4q5xaer/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3-none-any.whl' and adding '/tmpfs/tmp/tmpib83kspk' 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 /tmpfs/tmp/tmpib83kspk
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!
********************************************************************************
Please avoid running ``setup.py`` directly.
Instead, use pypa/build, pypa/installer or other
standards-based tools.
See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
********************************************************************************
!!
self.initialize_options()
Processing /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/_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
Processing /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/_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
Processing /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/_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:absl: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`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl: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`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Transform/transform_graph/7/.temp_path/tftransform_tmp/d116966d633f4fa69b1c057273fb5da8/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Transform/transform_graph/7/.temp_path/tftransform_tmp/d116966d633f4fa69b1c057273fb5da8/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Transform/transform_graph/7/.temp_path/tftransform_tmp/93bb59f6a708485bac797e8811d099fd/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Transform/transform_graph/7/.temp_path/tftransform_tmp/93bb59f6a708485bac797e8811d099fd/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
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': OutputChannel(artifact_type=TransformGraph, producer_component_id=Transform, output_key=transform_graph, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
'transformed_examples': OutputChannel(artifact_type=Examples, producer_component_id=Transform, output_key=transformed_examples, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
'updated_analyzer_cache': OutputChannel(artifact_type=TransformCache, producer_component_id=Transform, output_key=updated_analyzer_cache, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
'pre_transform_schema': OutputChannel(artifact_type=Schema, producer_component_id=Transform, output_key=pre_transform_schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
'pre_transform_stats': OutputChannel(artifact_type=ExampleStatistics, producer_component_id=Transform, output_key=pre_transform_stats, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
'post_transform_schema': OutputChannel(artifact_type=Schema, producer_component_id=Transform, output_key=post_transform_schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
'post_transform_stats': OutputChannel(artifact_type=ExampleStatistics, producer_component_id=Transform, output_key=post_transform_stats, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
'post_transform_anomalies': OutputChannel(artifact_type=ExampleAnomalies, producer_component_id=Transform, output_key=post_transform_anomalies, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)}
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)
['metadata', 'transform_fn', 'transformed_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: 14
uri: "/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Transform/transformed_examples/7"
properties {
key: "split_names"
value {
string_value: "[\"eval\", \"train\"]"
}
}
custom_properties {
key: "name"
value {
string_value: "transformed_examples:2024-04-30T09:35:38.684977"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
name: "transformed_examples:2024-04-30T09:35:38.684977"
, artifact_type: id: 14
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)
uri: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Transform/transformed_examples/7/Split-train
tfrecord_filenames: ['/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Transform/transformed_examples/7/Split-train/transformed_examples-00000-of-00001.gz']
features {
feature {
key: "id"
value {
bytes_list {
value: "362ec447-5e84-41ef-98bc-440d122f8aa3"
}
}
}
feature {
key: "label_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "text_xf"
value {
int64_list {
value: 13
value: 8
value: 14
value: 32
value: 338
value: 310
value: 15
value: 95
value: 27
value: 10001
value: 9
value: 31
value: 1173
value: 3153
value: 43
value: 495
value: 10060
value: 214
value: 26
value: 71
value: 142
value: 19
value: 8
value: 204
value: 339
value: 27
value: 74
value: 181
value: 238
value: 9
value: 440
value: 67
value: 74
value: 71
value: 94
value: 100
value: 22
value: 5442
value: 8
value: 1573
value: 607
value: 530
value: 8
value: 15
value: 6
value: 32
value: 378
value: 6292
value: 207
value: 2276
value: 388
value: 0
value: 84
value: 1023
value: 154
value: 65
value: 155
value: 52
value: 0
value: 10080
value: 7871
value: 65
value: 250
value: 74
value: 3202
value: 20
value: 10000
value: 3720
value: 10020
value: 10008
value: 1282
value: 3862
value: 3
value: 53
value: 3952
value: 110
value: 1879
value: 17
value: 3153
value: 14
value: 166
value: 19
value: 2
value: 1023
value: 1007
value: 9405
value: 9
value: 2
value: 15
value: 12
value: 14
value: 4504
value: 4
value: 109
value: 158
value: 1202
value: 7
value: 174
value: 505
value: 12
}
}
}
}
features {
feature {
key: "id"
value {
bytes_list {
value: "049fd6b0-72cc-4a17-92e7-e075f4224ee1"
}
}
}
feature {
key: "label_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "text_xf"
value {
int64_list {
value: 13
value: 7
value: 23
value: 75
value: 494
value: 5
value: 748
value: 2155
value: 307
value: 91
value: 19
value: 8
value: 6
value: 499
value: 763
value: 5
value: 2
value: 1690
value: 4
value: 200
value: 593
value: 57
value: 1244
value: 120
value: 2364
value: 3
value: 4407
value: 21
value: 0
value: 10081
value: 3
value: 263
value: 42
value: 6947
value: 2
value: 169
value: 185
value: 21
value: 8
value: 5143
value: 7
value: 1339
value: 2155
value: 81
value: 0
value: 18
value: 14
value: 1468
value: 0
value: 86
value: 986
value: 14
value: 2259
value: 1790
value: 562
value: 3
value: 284
value: 200
value: 401
value: 5
value: 668
value: 19
value: 17
value: 58
value: 1934
value: 4
value: 45
value: 14
value: 4212
value: 113
value: 43
value: 135
value: 7
value: 753
value: 7
value: 224
value: 23
value: 1155
value: 179
value: 4
value: 0
value: 18
value: 19
value: 7
value: 191
value: 0
value: 2047
value: 4
value: 10
value: 3
value: 283
value: 42
value: 401
value: 5
value: 668
value: 4
value: 90
value: 234
value: 10023
value: 227
}
}
}
}
features {
feature {
key: "id"
value {
bytes_list {
value: "b923937c-3303-46af-855c-263b68a703ad"
}
}
}
feature {
key: "label_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "text_xf"
value {
int64_list {
value: 13
value: 4577
value: 7158
value: 0
value: 10047
value: 3778
value: 3346
value: 9
value: 2
value: 758
value: 1915
value: 3
value: 2280
value: 1511
value: 3
value: 2003
value: 10020
value: 225
value: 786
value: 382
value: 16
value: 39
value: 203
value: 361
value: 5
value: 93
value: 11
value: 11
value: 19
value: 220
value: 21
value: 341
value: 2
value: 10000
value: 966
value: 0
value: 77
value: 4
value: 6677
value: 464
value: 10071
value: 5
value: 10042
value: 630
value: 2
value: 10044
value: 404
value: 2
value: 10044
value: 3
value: 5
value: 10008
value: 0
value: 1259
value: 630
value: 106
value: 10042
value: 6721
value: 10
value: 49
value: 21
value: 0
value: 2071
value: 20
value: 1292
value: 4
value: 0
value: 431
value: 11
value: 11
value: 166
value: 67
value: 2342
value: 5815
value: 12
value: 575
value: 21
value: 0
value: 1691
value: 537
value: 4
value: 0
value: 3605
value: 307
value: 0
value: 10054
value: 1563
value: 3115
value: 467
value: 4577
value: 3
value: 1069
value: 1158
value: 5
value: 23
value: 4279
value: 6677
value: 464
value: 20
value: 10004
}
}
}
}
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': '/tmpfs/tmp/tfx-datah7b5a9e6/train.tfrecord', 'unlabeled_examples_path': '/tmpfs/tmp/tfx-datah7b5a9e6/unsup.tfrecord', 'graph_path': '/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/SynthesizeGraph/synthesized_graph/6/Split-train/graph.tsv', 'output_training_data_path': '/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 14
uri: "/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/GraphAugmentation/augmented_examples/8"
properties {
key: "split_names"
value {
string_value: "[\"eval\", \"train\"]"
}
}
custom_properties {
key: "name"
value {
string_value: "augmented_examples:2024-04-30T09:36:14.477125"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "GraphAugmentation"
}
}
name: "augmented_examples:2024-04-30T09:36:14.477125"
, artifact_type: id: 14
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)
uri: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/GraphAugmentation/augmented_examples/8/Split-train
tfrecord_filenames: ['/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/GraphAugmentation/augmented_examples/8/Split-train/nsl_train_data.tfr.gz']
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feature {
key: "id"
value {
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}
}
feature {
key: "label_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "text_xf"
value {
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feature {
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}
feature {
key: "label_xf"
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feature {
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feature {
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feature {
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}
feature {
key: "text_xf"
value {
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feature {
key: "id"
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}
feature {
key: "label_xf"
value {
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value: 1
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}
feature {
key: "text_xf"
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features {
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key: "NL_num_nbrs"
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value: 0
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}
}
feature {
key: "id"
value {
bytes_list {
value: "e52d7c42-0b8a-44d6-b2fe-99cc367eb575"
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}
}
feature {
key: "label_xf"
value {
int64_list {
value: 1
}
}
}
feature {
key: "text_xf"
value {
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}
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, **params):
"""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.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!
********************************************************************************
Please avoid running ``setup.py`` directly.
Instead, use pypa/build, pypa/installer or other
standards-based tools.
See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
********************************************************************************
!!
self.initialize_options()
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 /tmpfs/tmp/tmpevwlq_es
running install
running install_lib
copying build/lib/imdb_transform.py -> /tmpfs/tmp/tmpevwlq_es
copying build/lib/imdb_trainer.py -> /tmpfs/tmp/tmpevwlq_es
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 /tmpfs/tmp/tmpevwlq_es/tfx_user_code_Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9-py3.9.egg-info
running install_scripts
creating /tmpfs/tmp/tmpevwlq_es/tfx_user_code_Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9.dist-info/WHEEL
creating '/tmpfs/tmp/tmp1vxi9kt0/tfx_user_code_Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9-py3-none-any.whl' and adding '/tmpfs/tmp/tmpevwlq_es' to it
adding 'imdb_trainer.py'
adding 'imdb_transform.py'
adding 'tfx_user_code_Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9.dist-info/RECORD'
removing /tmpfs/tmp/tmpevwlq_es
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
Processing /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/_wheels/tfx_user_code_Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9-py3-none-any.whl
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+02c7b97b194ad02bcc21de6184519fc73d0572a63c6c5af25fcac6f33b1320f9
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:415: TrainSpec.__new__ (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:415: TrainSpec.__new__ (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:422: FinalExporter.__init__ (from tensorflow_estimator.python.estimator.exporter) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:422: FinalExporter.__init__ (from tensorflow_estimator.python.estimator.exporter) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:423: EvalSpec.__new__ (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:423: EvalSpec.__new__ (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:429: RunConfig.__init__ (from tensorflow_estimator.python.estimator.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:429: RunConfig.__init__ (from tensorflow_estimator.python.estimator.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:434: Estimator.__init__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:434: Estimator.__init__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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:Using config: {'_model_dir': '/tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tfx/components/trainer/executor.py:270: train_and_evaluate (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tfx/components/trainer/executor.py:270: train_and_evaluate (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
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/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:385: StopAtStepHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:385: StopAtStepHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/data/experimental/ops/readers.py:1086: parse_example_dataset (from tensorflow.python.data.experimental.ops.parsing_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map(tf.io.parse_example(...))` instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/data/experimental/ops/readers.py:1086: parse_example_dataset (from tensorflow.python.data.experimental.ops.parsing_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map(tf.io.parse_example(...))` instead.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/rmsprop.py:188: 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/python3.9/site-packages/tensorflow/python/training/rmsprop.py:188: 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/temp/docs/tutorials/tfx/imdb_trainer.py:234: EstimatorSpec.__new__ (from tensorflow_estimator.python.estimator.model_fn) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:234: EstimatorSpec.__new__ (from tensorflow_estimator.python.estimator.model_fn) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1416: NanTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1416: NanTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1419: LoggingTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1419: LoggingTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/basic_session_run_hooks.py:232: SecondOrStepTimer.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/basic_session_run_hooks.py:232: SecondOrStepTimer.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1456: CheckpointSaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1456: CheckpointSaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:579: StepCounterHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:579: StepCounterHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:586: SummarySaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:586: SummarySaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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...
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1455: SessionRunArgs.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1455: SessionRunArgs.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1454: SessionRunContext.__init__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1454: SessionRunContext.__init__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1474: SessionRunValues.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1474: SessionRunValues.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:loss = 0.6931381, step = 0
INFO:tensorflow:loss = 0.6931381, step = 0
INFO:tensorflow:global_step/sec: 244.345
INFO:tensorflow:global_step/sec: 244.345
INFO:tensorflow:loss = 0.693187, step = 100 (0.411 sec)
INFO:tensorflow:loss = 0.693187, step = 100 (0.411 sec)
INFO:tensorflow:global_step/sec: 327.903
INFO:tensorflow:global_step/sec: 327.903
INFO:tensorflow:loss = 0.6921295, step = 200 (0.305 sec)
INFO:tensorflow:loss = 0.6921295, step = 200 (0.305 sec)
INFO:tensorflow:global_step/sec: 331.804
INFO:tensorflow:global_step/sec: 331.804
INFO:tensorflow:loss = 0.69160867, step = 300 (0.301 sec)
INFO:tensorflow:loss = 0.69160867, step = 300 (0.301 sec)
INFO:tensorflow:global_step/sec: 332.983
INFO:tensorflow:global_step/sec: 332.983
INFO:tensorflow:loss = 0.69053686, step = 400 (0.300 sec)
INFO:tensorflow:loss = 0.69053686, step = 400 (0.300 sec)
INFO:tensorflow:global_step/sec: 332.833
INFO:tensorflow:global_step/sec: 332.833
INFO:tensorflow:loss = 0.69058716, step = 500 (0.301 sec)
INFO:tensorflow:loss = 0.69058716, step = 500 (0.301 sec)
INFO:tensorflow:global_step/sec: 333.073
INFO:tensorflow:global_step/sec: 333.073
INFO:tensorflow:loss = 0.68858355, step = 600 (0.300 sec)
INFO:tensorflow:loss = 0.68858355, step = 600 (0.300 sec)
INFO:tensorflow:global_step/sec: 331.142
INFO:tensorflow:global_step/sec: 331.142
INFO:tensorflow:loss = 0.69051415, step = 700 (0.302 sec)
INFO:tensorflow:loss = 0.69051415, step = 700 (0.302 sec)
INFO:tensorflow:global_step/sec: 332.395
INFO:tensorflow:global_step/sec: 332.395
INFO:tensorflow:loss = 0.686792, step = 800 (0.301 sec)
INFO:tensorflow:loss = 0.686792, step = 800 (0.301 sec)
INFO:tensorflow:global_step/sec: 335.741
INFO:tensorflow:global_step/sec: 335.741
INFO:tensorflow:loss = 0.68623376, step = 900 (0.298 sec)
INFO:tensorflow:loss = 0.68623376, step = 900 (0.298 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 999 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/saver.py:1067: remove_checkpoint (from tensorflow.python.checkpoint.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/python3.9/site-packages/tensorflow/python/training/saver.py:1067: remove_checkpoint (from tensorflow.python.checkpoint.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 2024-04-30T09:36:32
INFO:tensorflow:Starting evaluation at 2024-04-30T09:36:32
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/evaluation.py:260: FinalOpsHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/evaluation.py:260: FinalOpsHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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 : 5.88371s
INFO:tensorflow:Inference Time : 5.88371s
INFO:tensorflow:Finished evaluation at 2024-04-30-09:36:38
INFO:tensorflow:Finished evaluation at 2024-04-30-09:36:38
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.7081, global_step = 999, loss = 0.6856311
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.7081, global_step = 999, loss = 0.6856311
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:global_step/sec: 15.5388
INFO:tensorflow:global_step/sec: 15.5388
INFO:tensorflow:loss = 0.68520856, step = 1000 (6.435 sec)
INFO:tensorflow:loss = 0.68520856, step = 1000 (6.435 sec)
INFO:tensorflow:global_step/sec: 320.147
INFO:tensorflow:global_step/sec: 320.147
INFO:tensorflow:loss = 0.68823534, step = 1100 (0.313 sec)
INFO:tensorflow:loss = 0.68823534, step = 1100 (0.313 sec)
INFO:tensorflow:global_step/sec: 319.98
INFO:tensorflow:global_step/sec: 319.98
INFO:tensorflow:loss = 0.68485975, step = 1200 (0.312 sec)
INFO:tensorflow:loss = 0.68485975, step = 1200 (0.312 sec)
INFO:tensorflow:global_step/sec: 322.054
INFO:tensorflow:global_step/sec: 322.054
INFO:tensorflow:loss = 0.68126017, step = 1300 (0.311 sec)
INFO:tensorflow:loss = 0.68126017, step = 1300 (0.311 sec)
INFO:tensorflow:global_step/sec: 318.083
INFO:tensorflow:global_step/sec: 318.083
INFO:tensorflow:loss = 0.6741244, step = 1400 (0.314 sec)
INFO:tensorflow:loss = 0.6741244, step = 1400 (0.314 sec)
INFO:tensorflow:global_step/sec: 320.684
INFO:tensorflow:global_step/sec: 320.684
INFO:tensorflow:loss = 0.67759305, step = 1500 (0.312 sec)
INFO:tensorflow:loss = 0.67759305, step = 1500 (0.312 sec)
INFO:tensorflow:global_step/sec: 318.939
INFO:tensorflow:global_step/sec: 318.939
INFO:tensorflow:loss = 0.67118335, step = 1600 (0.313 sec)
INFO:tensorflow:loss = 0.67118335, step = 1600 (0.313 sec)
INFO:tensorflow:global_step/sec: 320.46
INFO:tensorflow:global_step/sec: 320.46
INFO:tensorflow:loss = 0.66135645, step = 1700 (0.312 sec)
INFO:tensorflow:loss = 0.66135645, step = 1700 (0.312 sec)
INFO:tensorflow:global_step/sec: 318.748
INFO:tensorflow:global_step/sec: 318.748
INFO:tensorflow:loss = 0.66992307, step = 1800 (0.314 sec)
INFO:tensorflow:loss = 0.66992307, step = 1800 (0.314 sec)
INFO:tensorflow:global_step/sec: 321.666
INFO:tensorflow:global_step/sec: 321.666
INFO:tensorflow:loss = 0.66313255, step = 1900 (0.311 sec)
INFO:tensorflow:loss = 0.66313255, step = 1900 (0.311 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1998 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 264.174
INFO:tensorflow:global_step/sec: 264.174
INFO:tensorflow:loss = 0.6561048, step = 2000 (0.378 sec)
INFO:tensorflow:loss = 0.6561048, step = 2000 (0.378 sec)
INFO:tensorflow:global_step/sec: 323.274
INFO:tensorflow:global_step/sec: 323.274
INFO:tensorflow:loss = 0.6543522, step = 2100 (0.310 sec)
INFO:tensorflow:loss = 0.6543522, step = 2100 (0.310 sec)
INFO:tensorflow:global_step/sec: 319.215
INFO:tensorflow:global_step/sec: 319.215
INFO:tensorflow:loss = 0.6738936, step = 2200 (0.313 sec)
INFO:tensorflow:loss = 0.6738936, step = 2200 (0.313 sec)
INFO:tensorflow:global_step/sec: 318.284
INFO:tensorflow:global_step/sec: 318.284
INFO:tensorflow:loss = 0.6466409, step = 2300 (0.314 sec)
INFO:tensorflow:loss = 0.6466409, step = 2300 (0.314 sec)
INFO:tensorflow:global_step/sec: 320.028
INFO:tensorflow:global_step/sec: 320.028
INFO:tensorflow:loss = 0.6395003, step = 2400 (0.313 sec)
INFO:tensorflow:loss = 0.6395003, step = 2400 (0.313 sec)
INFO:tensorflow:global_step/sec: 323.853
INFO:tensorflow:global_step/sec: 323.853
INFO:tensorflow:loss = 0.62881154, step = 2500 (0.308 sec)
INFO:tensorflow:loss = 0.62881154, step = 2500 (0.308 sec)
INFO:tensorflow:global_step/sec: 321.15
INFO:tensorflow:global_step/sec: 321.15
INFO:tensorflow:loss = 0.65067893, step = 2600 (0.311 sec)
INFO:tensorflow:loss = 0.65067893, step = 2600 (0.311 sec)
INFO:tensorflow:global_step/sec: 319.634
INFO:tensorflow:global_step/sec: 319.634
INFO:tensorflow:loss = 0.64512616, step = 2700 (0.313 sec)
INFO:tensorflow:loss = 0.64512616, step = 2700 (0.313 sec)
INFO:tensorflow:global_step/sec: 321.034
INFO:tensorflow:global_step/sec: 321.034
INFO:tensorflow:loss = 0.64009774, step = 2800 (0.311 sec)
INFO:tensorflow:loss = 0.64009774, step = 2800 (0.311 sec)
INFO:tensorflow:global_step/sec: 319.308
INFO:tensorflow:global_step/sec: 319.308
INFO:tensorflow:loss = 0.6344252, step = 2900 (0.313 sec)
INFO:tensorflow:loss = 0.6344252, step = 2900 (0.313 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 2997 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 264.343
INFO:tensorflow:global_step/sec: 264.343
INFO:tensorflow:loss = 0.6392811, step = 3000 (0.378 sec)
INFO:tensorflow:loss = 0.6392811, step = 3000 (0.378 sec)
INFO:tensorflow:global_step/sec: 321.343
INFO:tensorflow:global_step/sec: 321.343
INFO:tensorflow:loss = 0.6504424, step = 3100 (0.312 sec)
INFO:tensorflow:loss = 0.6504424, step = 3100 (0.312 sec)
INFO:tensorflow:global_step/sec: 321.145
INFO:tensorflow:global_step/sec: 321.145
INFO:tensorflow:loss = 0.61601394, step = 3200 (0.311 sec)
INFO:tensorflow:loss = 0.61601394, step = 3200 (0.311 sec)
INFO:tensorflow:global_step/sec: 318.938
INFO:tensorflow:global_step/sec: 318.938
INFO:tensorflow:loss = 0.62401587, step = 3300 (0.314 sec)
INFO:tensorflow:loss = 0.62401587, step = 3300 (0.314 sec)
INFO:tensorflow:global_step/sec: 323.993
INFO:tensorflow:global_step/sec: 323.993
INFO:tensorflow:loss = 0.62293965, step = 3400 (0.309 sec)
INFO:tensorflow:loss = 0.62293965, step = 3400 (0.309 sec)
INFO:tensorflow:global_step/sec: 320.072
INFO:tensorflow:global_step/sec: 320.072
INFO:tensorflow:loss = 0.61230767, step = 3500 (0.313 sec)
INFO:tensorflow:loss = 0.61230767, step = 3500 (0.313 sec)
INFO:tensorflow:global_step/sec: 323.584
INFO:tensorflow:global_step/sec: 323.584
INFO:tensorflow:loss = 0.59779567, step = 3600 (0.309 sec)
INFO:tensorflow:loss = 0.59779567, step = 3600 (0.309 sec)
INFO:tensorflow:global_step/sec: 319.415
INFO:tensorflow:global_step/sec: 319.415
INFO:tensorflow:loss = 0.59942234, step = 3700 (0.313 sec)
INFO:tensorflow:loss = 0.59942234, step = 3700 (0.313 sec)
INFO:tensorflow:global_step/sec: 325.266
INFO:tensorflow:global_step/sec: 325.266
INFO:tensorflow:loss = 0.56394255, step = 3800 (0.308 sec)
INFO:tensorflow:loss = 0.56394255, step = 3800 (0.308 sec)
INFO:tensorflow:global_step/sec: 320.173
INFO:tensorflow:global_step/sec: 320.173
INFO:tensorflow:loss = 0.6095542, step = 3900 (0.312 sec)
INFO:tensorflow:loss = 0.6095542, step = 3900 (0.312 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 3996 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 264.814
INFO:tensorflow:global_step/sec: 264.814
INFO:tensorflow:loss = 0.5936497, step = 4000 (0.377 sec)
INFO:tensorflow:loss = 0.5936497, step = 4000 (0.377 sec)
INFO:tensorflow:global_step/sec: 323.824
INFO:tensorflow:global_step/sec: 323.824
INFO:tensorflow:loss = 0.57761985, step = 4100 (0.309 sec)
INFO:tensorflow:loss = 0.57761985, step = 4100 (0.309 sec)
INFO:tensorflow:global_step/sec: 320.959
INFO:tensorflow:global_step/sec: 320.959
INFO:tensorflow:loss = 0.57935584, step = 4200 (0.312 sec)
INFO:tensorflow:loss = 0.57935584, step = 4200 (0.312 sec)
INFO:tensorflow:global_step/sec: 320.745
INFO:tensorflow:global_step/sec: 320.745
INFO:tensorflow:loss = 0.600055, step = 4300 (0.312 sec)
INFO:tensorflow:loss = 0.600055, step = 4300 (0.312 sec)
INFO:tensorflow:global_step/sec: 324.973
INFO:tensorflow:global_step/sec: 324.973
INFO:tensorflow:loss = 0.5549063, step = 4400 (0.308 sec)
INFO:tensorflow:loss = 0.5549063, step = 4400 (0.308 sec)
INFO:tensorflow:global_step/sec: 322.21
INFO:tensorflow:global_step/sec: 322.21
INFO:tensorflow:loss = 0.5302505, step = 4500 (0.310 sec)
INFO:tensorflow:loss = 0.5302505, step = 4500 (0.310 sec)
INFO:tensorflow:global_step/sec: 322.552
INFO:tensorflow:global_step/sec: 322.552
INFO:tensorflow:loss = 0.6239404, step = 4600 (0.310 sec)
INFO:tensorflow:loss = 0.6239404, step = 4600 (0.310 sec)
INFO:tensorflow:global_step/sec: 322.232
INFO:tensorflow:global_step/sec: 322.232
INFO:tensorflow:loss = 0.53617376, step = 4700 (0.310 sec)
INFO:tensorflow:loss = 0.53617376, step = 4700 (0.310 sec)
INFO:tensorflow:global_step/sec: 323.505
INFO:tensorflow:global_step/sec: 323.505
INFO:tensorflow:loss = 0.57872057, step = 4800 (0.309 sec)
INFO:tensorflow:loss = 0.57872057, step = 4800 (0.309 sec)
INFO:tensorflow:global_step/sec: 321.861
INFO:tensorflow:global_step/sec: 321.861
INFO:tensorflow:loss = 0.63397115, step = 4900 (0.311 sec)
INFO:tensorflow:loss = 0.63397115, step = 4900 (0.311 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4995 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 266.763
INFO:tensorflow:global_step/sec: 266.763
INFO:tensorflow:loss = 0.5715669, step = 5000 (0.375 sec)
INFO:tensorflow:loss = 0.5715669, step = 5000 (0.375 sec)
INFO:tensorflow:global_step/sec: 323.011
INFO:tensorflow:global_step/sec: 323.011
INFO:tensorflow:loss = 0.5819284, step = 5100 (0.310 sec)
INFO:tensorflow:loss = 0.5819284, step = 5100 (0.310 sec)
INFO:tensorflow:global_step/sec: 321.378
INFO:tensorflow:global_step/sec: 321.378
INFO:tensorflow:loss = 0.5076318, step = 5200 (0.311 sec)
INFO:tensorflow:loss = 0.5076318, step = 5200 (0.311 sec)
INFO:tensorflow:global_step/sec: 317.401
INFO:tensorflow:global_step/sec: 317.401
INFO:tensorflow:loss = 0.49976814, step = 5300 (0.315 sec)
INFO:tensorflow:loss = 0.49976814, step = 5300 (0.315 sec)
INFO:tensorflow:global_step/sec: 318.317
INFO:tensorflow:global_step/sec: 318.317
INFO:tensorflow:loss = 0.56264436, step = 5400 (0.314 sec)
INFO:tensorflow:loss = 0.56264436, step = 5400 (0.314 sec)
INFO:tensorflow:global_step/sec: 322.907
INFO:tensorflow:global_step/sec: 322.907
INFO:tensorflow:loss = 0.5834718, step = 5500 (0.310 sec)
INFO:tensorflow:loss = 0.5834718, step = 5500 (0.310 sec)
INFO:tensorflow:global_step/sec: 325.879
INFO:tensorflow:global_step/sec: 325.879
INFO:tensorflow:loss = 0.46263543, step = 5600 (0.307 sec)
INFO:tensorflow:loss = 0.46263543, step = 5600 (0.307 sec)
INFO:tensorflow:global_step/sec: 324.335
INFO:tensorflow:global_step/sec: 324.335
INFO:tensorflow:loss = 0.48665735, step = 5700 (0.308 sec)
INFO:tensorflow:loss = 0.48665735, step = 5700 (0.308 sec)
INFO:tensorflow:global_step/sec: 322.695
INFO:tensorflow:global_step/sec: 322.695
INFO:tensorflow:loss = 0.5484906, step = 5800 (0.310 sec)
INFO:tensorflow:loss = 0.5484906, step = 5800 (0.310 sec)
INFO:tensorflow:global_step/sec: 322.74
INFO:tensorflow:global_step/sec: 322.74
INFO:tensorflow:loss = 0.46942788, step = 5900 (0.310 sec)
INFO:tensorflow:loss = 0.46942788, step = 5900 (0.310 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 5994 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 265.689
INFO:tensorflow:global_step/sec: 265.689
INFO:tensorflow:loss = 0.48362094, step = 6000 (0.376 sec)
INFO:tensorflow:loss = 0.48362094, step = 6000 (0.376 sec)
INFO:tensorflow:global_step/sec: 321.546
INFO:tensorflow:global_step/sec: 321.546
INFO:tensorflow:loss = 0.5540118, step = 6100 (0.311 sec)
INFO:tensorflow:loss = 0.5540118, step = 6100 (0.311 sec)
INFO:tensorflow:global_step/sec: 318.636
INFO:tensorflow:global_step/sec: 318.636
INFO:tensorflow:loss = 0.4771434, step = 6200 (0.314 sec)
INFO:tensorflow:loss = 0.4771434, step = 6200 (0.314 sec)
INFO:tensorflow:global_step/sec: 322.459
INFO:tensorflow:global_step/sec: 322.459
INFO:tensorflow:loss = 0.49102104, step = 6300 (0.310 sec)
INFO:tensorflow:loss = 0.49102104, step = 6300 (0.310 sec)
INFO:tensorflow:global_step/sec: 323.15
INFO:tensorflow:global_step/sec: 323.15
INFO:tensorflow:loss = 0.49969676, step = 6400 (0.309 sec)
INFO:tensorflow:loss = 0.49969676, step = 6400 (0.309 sec)
INFO:tensorflow:global_step/sec: 320.555
INFO:tensorflow:global_step/sec: 320.555
INFO:tensorflow:loss = 0.47957024, step = 6500 (0.312 sec)
INFO:tensorflow:loss = 0.47957024, step = 6500 (0.312 sec)
INFO:tensorflow:global_step/sec: 322.618
INFO:tensorflow:global_step/sec: 322.618
INFO:tensorflow:loss = 0.4307827, step = 6600 (0.310 sec)
INFO:tensorflow:loss = 0.4307827, step = 6600 (0.310 sec)
INFO:tensorflow:global_step/sec: 320.278
INFO:tensorflow:global_step/sec: 320.278
INFO:tensorflow:loss = 0.5226569, step = 6700 (0.312 sec)
INFO:tensorflow:loss = 0.5226569, step = 6700 (0.312 sec)
INFO:tensorflow:global_step/sec: 323.627
INFO:tensorflow:global_step/sec: 323.627
INFO:tensorflow:loss = 0.49262106, step = 6800 (0.309 sec)
INFO:tensorflow:loss = 0.49262106, step = 6800 (0.309 sec)
INFO:tensorflow:global_step/sec: 319.715
INFO:tensorflow:global_step/sec: 319.715
INFO:tensorflow:loss = 0.585998, step = 6900 (0.312 sec)
INFO:tensorflow:loss = 0.585998, step = 6900 (0.312 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 6993 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 264.949
INFO:tensorflow:global_step/sec: 264.949
INFO:tensorflow:loss = 0.5352516, step = 7000 (0.377 sec)
INFO:tensorflow:loss = 0.5352516, step = 7000 (0.377 sec)
INFO:tensorflow:global_step/sec: 323.11
INFO:tensorflow:global_step/sec: 323.11
INFO:tensorflow:loss = 0.46150157, step = 7100 (0.310 sec)
INFO:tensorflow:loss = 0.46150157, step = 7100 (0.310 sec)
INFO:tensorflow:global_step/sec: 314.098
INFO:tensorflow:global_step/sec: 314.098
INFO:tensorflow:loss = 0.5018957, step = 7200 (0.319 sec)
INFO:tensorflow:loss = 0.5018957, step = 7200 (0.319 sec)
INFO:tensorflow:global_step/sec: 317.462
INFO:tensorflow:global_step/sec: 317.462
INFO:tensorflow:loss = 0.45075873, step = 7300 (0.315 sec)
INFO:tensorflow:loss = 0.45075873, step = 7300 (0.315 sec)
INFO:tensorflow:global_step/sec: 318.216
INFO:tensorflow:global_step/sec: 318.216
INFO:tensorflow:loss = 0.4124067, step = 7400 (0.314 sec)
INFO:tensorflow:loss = 0.4124067, step = 7400 (0.314 sec)
INFO:tensorflow:global_step/sec: 322.463
INFO:tensorflow:global_step/sec: 322.463
INFO:tensorflow:loss = 0.4619487, step = 7500 (0.310 sec)
INFO:tensorflow:loss = 0.4619487, step = 7500 (0.310 sec)
INFO:tensorflow:global_step/sec: 325.769
INFO:tensorflow:global_step/sec: 325.769
INFO:tensorflow:loss = 0.41939846, step = 7600 (0.307 sec)
INFO:tensorflow:loss = 0.41939846, step = 7600 (0.307 sec)
INFO:tensorflow:global_step/sec: 324.047
INFO:tensorflow:global_step/sec: 324.047
INFO:tensorflow:loss = 0.44739968, step = 7700 (0.309 sec)
INFO:tensorflow:loss = 0.44739968, step = 7700 (0.309 sec)
INFO:tensorflow:global_step/sec: 320.539
INFO:tensorflow:global_step/sec: 320.539
INFO:tensorflow:loss = 0.42963296, step = 7800 (0.312 sec)
INFO:tensorflow:loss = 0.42963296, step = 7800 (0.312 sec)
INFO:tensorflow:global_step/sec: 319.263
INFO:tensorflow:global_step/sec: 319.263
INFO:tensorflow:loss = 0.35999703, step = 7900 (0.313 sec)
INFO:tensorflow:loss = 0.35999703, step = 7900 (0.313 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 7992 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 262.307
INFO:tensorflow:global_step/sec: 262.307
INFO:tensorflow:loss = 0.35040936, step = 8000 (0.381 sec)
INFO:tensorflow:loss = 0.35040936, step = 8000 (0.381 sec)
INFO:tensorflow:global_step/sec: 321.91
INFO:tensorflow:global_step/sec: 321.91
INFO:tensorflow:loss = 0.5294919, step = 8100 (0.311 sec)
INFO:tensorflow:loss = 0.5294919, step = 8100 (0.311 sec)
INFO:tensorflow:global_step/sec: 318.149
INFO:tensorflow:global_step/sec: 318.149
INFO:tensorflow:loss = 0.40685037, step = 8200 (0.314 sec)
INFO:tensorflow:loss = 0.40685037, step = 8200 (0.314 sec)
INFO:tensorflow:global_step/sec: 321.016
INFO:tensorflow:global_step/sec: 321.016
INFO:tensorflow:loss = 0.4743625, step = 8300 (0.312 sec)
INFO:tensorflow:loss = 0.4743625, step = 8300 (0.312 sec)
INFO:tensorflow:global_step/sec: 321.166
INFO:tensorflow:global_step/sec: 321.166
INFO:tensorflow:loss = 0.39370725, step = 8400 (0.311 sec)
INFO:tensorflow:loss = 0.39370725, step = 8400 (0.311 sec)
INFO:tensorflow:global_step/sec: 321.772
INFO:tensorflow:global_step/sec: 321.772
INFO:tensorflow:loss = 0.40521982, step = 8500 (0.311 sec)
INFO:tensorflow:loss = 0.40521982, step = 8500 (0.311 sec)
INFO:tensorflow:global_step/sec: 317.198
INFO:tensorflow:global_step/sec: 317.198
INFO:tensorflow:loss = 0.4662877, step = 8600 (0.315 sec)
INFO:tensorflow:loss = 0.4662877, step = 8600 (0.315 sec)
INFO:tensorflow:global_step/sec: 320.027
INFO:tensorflow:global_step/sec: 320.027
INFO:tensorflow:loss = 0.4724004, step = 8700 (0.313 sec)
INFO:tensorflow:loss = 0.4724004, step = 8700 (0.313 sec)
INFO:tensorflow:global_step/sec: 325.404
INFO:tensorflow:global_step/sec: 325.404
INFO:tensorflow:loss = 0.4096289, step = 8800 (0.307 sec)
INFO:tensorflow:loss = 0.4096289, step = 8800 (0.307 sec)
INFO:tensorflow:global_step/sec: 322.542
INFO:tensorflow:global_step/sec: 322.542
INFO:tensorflow:loss = 0.38237604, step = 8900 (0.310 sec)
INFO:tensorflow:loss = 0.38237604, step = 8900 (0.310 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 8991 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: 268.988
INFO:tensorflow:global_step/sec: 268.988
INFO:tensorflow:loss = 0.3808998, step = 9000 (0.371 sec)
INFO:tensorflow:loss = 0.3808998, step = 9000 (0.371 sec)
INFO:tensorflow:global_step/sec: 319.96
INFO:tensorflow:global_step/sec: 319.96
INFO:tensorflow:loss = 0.40259895, step = 9100 (0.313 sec)
INFO:tensorflow:loss = 0.40259895, step = 9100 (0.313 sec)
INFO:tensorflow:global_step/sec: 325.163
INFO:tensorflow:global_step/sec: 325.163
INFO:tensorflow:loss = 0.31507605, step = 9200 (0.308 sec)
INFO:tensorflow:loss = 0.31507605, step = 9200 (0.308 sec)
INFO:tensorflow:global_step/sec: 324.406
INFO:tensorflow:global_step/sec: 324.406
INFO:tensorflow:loss = 0.4514978, step = 9300 (0.308 sec)
INFO:tensorflow:loss = 0.4514978, step = 9300 (0.308 sec)
INFO:tensorflow:global_step/sec: 322.79
INFO:tensorflow:global_step/sec: 322.79
INFO:tensorflow:loss = 0.4187292, step = 9400 (0.310 sec)
INFO:tensorflow:loss = 0.4187292, step = 9400 (0.310 sec)
INFO:tensorflow:global_step/sec: 324.626
INFO:tensorflow:global_step/sec: 324.626
INFO:tensorflow:loss = 0.3633909, step = 9500 (0.308 sec)
INFO:tensorflow:loss = 0.3633909, step = 9500 (0.308 sec)
INFO:tensorflow:global_step/sec: 323.726
INFO:tensorflow:global_step/sec: 323.726
INFO:tensorflow:loss = 0.37573534, step = 9600 (0.309 sec)
INFO:tensorflow:loss = 0.37573534, step = 9600 (0.309 sec)
INFO:tensorflow:global_step/sec: 319.612
INFO:tensorflow:global_step/sec: 319.612
INFO:tensorflow:loss = 0.3091408, step = 9700 (0.313 sec)
INFO:tensorflow:loss = 0.3091408, step = 9700 (0.313 sec)
INFO:tensorflow:global_step/sec: 322.054
INFO:tensorflow:global_step/sec: 322.054
INFO:tensorflow:loss = 0.41117656, step = 9800 (0.311 sec)
INFO:tensorflow:loss = 0.41117656, step = 9800 (0.311 sec)
INFO:tensorflow:global_step/sec: 324.924
INFO:tensorflow:global_step/sec: 324.924
INFO:tensorflow:loss = 0.25739446, step = 9900 (0.308 sec)
INFO:tensorflow:loss = 0.25739446, step = 9900 (0.308 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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 9990 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 10000 into /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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 2024-04-30T09:37:07
INFO:tensorflow:Starting evaluation at 2024-04-30T09:37:07
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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 : 5.89491s
INFO:tensorflow:Inference Time : 5.89491s
INFO:tensorflow:Finished evaluation at 2024-04-30-09:37:12
INFO:tensorflow:Finished evaluation at 2024-04-30-09:37:12
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.8014, global_step = 10000, loss = 0.44089586
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.8014, global_step = 10000, loss = 0.44089586
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:273: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/imdb_trainer.py:273: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
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.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:365: PredictOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:365: PredictOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:225: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:225: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/export/imdb/temp-1714469832/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/export/imdb/temp-1714469832/assets
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/export/imdb/temp-1714469832/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/export/imdb/temp-1714469832/saved_model.pb
INFO:tensorflow:Loss for final step: 0.3274519.
INFO:tensorflow:Loss for final step: 0.3274519.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
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/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:345: _SupervisedOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:345: _SupervisedOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
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 /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/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: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-TFMA/temp-1714469834/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-TFMA/temp-1714469834/assets
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-TFMA/temp-1714469834/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfx-interactive-2024-04-30T09_33_38.693502-idxcm6tz/Trainer/model_run/9/Format-TFMA/temp-1714469834/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.
#docs_infra: no_execute
# 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.