सहायता Kaggle पर TensorFlow साथ ग्रेट बैरियर रीफ की रक्षा चैलेंज में शामिल हों

टीएफएक्स में ग्राफ-आधारित तंत्रिका संरचित सीखना Learning

इस ट्यूटोरियल से ग्राफ नियमितीकरण का वर्णन करता है तंत्रिका संरचित सीखने ढांचे और एक TFX पाइप लाइन में भावना वर्गीकरण के लिए एक अंत से अंत कार्यप्रवाह को दर्शाता है।

अवलोकन

सकारात्मक या नकारात्मक रूप में इस नोटबुक का वर्गीकरण फिल्म समीक्षा समीक्षा के पाठ का उपयोग कर। यह द्विआधारी वर्गीकरण, मशीन सीखने समस्या का एक महत्वपूर्ण और व्यापक रूप से लागू तरह का एक उदाहरण है।

हम दिए गए इनपुट से ग्राफ बनाकर इस नोटबुक में ग्राफ नियमितीकरण के उपयोग को प्रदर्शित करेंगे। न्यूरल स्ट्रक्चर्ड लर्निंग (NSL) फ्रेमवर्क का उपयोग करके ग्राफ़-नियमित मॉडल के निर्माण के लिए सामान्य नुस्खा जब इनपुट में एक स्पष्ट ग्राफ़ नहीं होता है:

  1. इनपुट में प्रत्येक टेक्स्ट नमूने के लिए एम्बेडिंग बनाएं। इस तरह के रूप में पूर्व प्रशिक्षित मॉडल का उपयोग किया जा सकता है word2vec , फिरकी , बर्ट आदि
  2. समानता मीट्रिक जैसे 'L2' दूरी, 'कोसाइन' दूरी, आदि का उपयोग करके इन एम्बेडिंग के आधार पर एक ग्राफ़ बनाएं। ग्राफ़ में नोड्स नमूनों के अनुरूप होते हैं और ग्राफ़ में किनारों नमूनों के जोड़े के बीच समानता के अनुरूप होते हैं।
  3. उपरोक्त संश्लेषित ग्राफ और नमूना सुविधाओं से प्रशिक्षण डेटा उत्पन्न करें। परिणामी प्रशिक्षण डेटा में मूल नोड सुविधाओं के अलावा पड़ोसी विशेषताएं होंगी।
  4. एस्टिमेटर्स का उपयोग करके बेस मॉडल के रूप में एक न्यूरल नेटवर्क बनाएं।
  5. साथ बेस मॉडल लपेटें add_graph_regularization आवरण समारोह है, जो NSL ढांचे द्वारा प्रदान की जाती है, एक नया ग्राफ अनुमानक मॉडल बनाने के लिए। इस नए मॉडल में अपने प्रशिक्षण उद्देश्य में नियमितीकरण अवधि के रूप में एक ग्राफ नियमितीकरण हानि शामिल होगी।
  6. ग्राफ अनुमानक मॉडल को प्रशिक्षित और मूल्यांकन करें।

इस ट्यूटोरियल में, हम कई कस्टम TFX घटकों के साथ-साथ एक कस्टम ग्राफ़-नियमित ट्रेनर घटक का उपयोग करके TFX पाइपलाइन में उपरोक्त वर्कफ़्लो को एकीकृत करते हैं।

नीचे हमारी टीएफएक्स पाइपलाइन के लिए योजनाबद्ध है। ऑरेंज बॉक्स ऑफ-द-शेल्फ TFX घटकों का प्रतिनिधित्व करते हैं और गुलाबी बॉक्स कस्टम TFX घटकों का प्रतिनिधित्व करते हैं।

टीएफएक्स पाइपलाइन

पिप अपग्रेड करें

स्थानीय रूप से चलते समय सिस्टम में पिप को अपग्रेड करने से बचने के लिए, यह सुनिश्चित करने के लिए जांचें कि हम कोलाब में चल रहे हैं। स्थानीय प्रणालियों को निश्चित रूप से अलग से अपग्रेड किया जा सकता है।

try:
  import colab
  !pip install --upgrade pip
except:
  pass

आवश्यक पैकेज स्थापित करें

!pip install -q -U \
  tfx==1.2.0 \
  neural-structured-learning \
  tensorflow-hub \
  tensorflow-datasets

क्या आपने रनटाइम को पुनरारंभ किया?

यदि आप Google Colab का उपयोग कर रहे हैं, जब आप पहली बार ऊपर सेल चलाते हैं, तो आपको रनटाइम को पुनरारंभ करना होगा (रनटाइम> रनटाइम पुनरारंभ करें ...)। ऐसा इसलिए है क्योंकि Colab संकुल लोड करता है।

निर्भरता और आयात

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__)
TF Version:  2.5.1
Eager mode:  True
GPU is available
NSL Version:  1.3.1
TFX Version:  1.2.0
TFDV version:  1.2.0
TFT version:  1.2.0
TFMA version:  0.33.0
Hub version:  0.12.0
Beam version:  2.33.0

आईएमडीबी डेटासेट

IMDB डाटासेट से 50,000 फिल्म समीक्षा की पाठ होता है इंटरनेट मूवी डाटाबेस । इन्हें प्रशिक्षण के लिए 25,000 समीक्षाओं और परीक्षण के लिए 25,000 समीक्षाओं में विभाजित किया गया है। प्रशिक्षण और परीक्षण सेट संतुलित जाता है, वे सकारात्मक और नकारात्मक समीक्षा की संख्या बराबर होते हैं। इसके अलावा, 50,000 अतिरिक्त बिना लेबल वाली मूवी समीक्षाएं हैं।

प्रीप्रोसेस्ड IMDB डेटासेट डाउनलोड करें

निम्नलिखित कोड TFDS का उपयोग करके IMDB डेटासेट डाउनलोड करता है (या कैश्ड कॉपी का उपयोग करता है यदि इसे पहले ही डाउनलोड किया जा चुका है)। इस नोटबुक को गति देने के लिए हम प्रशिक्षण के लिए केवल 10,000 लेबल वाली समीक्षाओं और 10,000 बिना लेबल वाली समीक्षाओं का और मूल्यांकन के लिए 10,000 परीक्षण समीक्षाओं का उपयोग करेंगे।

train_set, eval_set = tfds.load(
    "imdb_reviews:1.0.0",
    split=["train[:10000]+unsupervised[:10000]", "test[:10000]"],
    shuffle_files=False)

आइए प्रशिक्षण सेट से कुछ समीक्षाएँ देखें:

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/src/tf_docs_env/lib/python3.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.
  import sys

TFX घटकों को अंतःक्रियात्मक रूप से चलाएं

कोशिकाओं का पालन करें कि आप TFX घटकों का निर्माण और प्राप्त करने के लिए InteractiveContext भीतर सहभागी हर एक चलेगा में ExecutionResult वस्तुओं। यह एक TFX DAG में ऑर्केस्ट्रेटर चलाने वाले घटकों की प्रक्रिया को प्रतिबिंबित करता है, जब प्रत्येक घटक के लिए निर्भरता पूरी होती है।

context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92 as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/metadata.sqlite.

उदाहरणजेन घटक

किसी भी एमएल विकास प्रक्रिया में कोड विकास शुरू करते समय पहला कदम प्रशिक्षण और परीक्षण डेटासेट को निगलना होता है। ExampleGen घटक TFX पाइपलाइन में डेटा लाता है।

एक exampleGen घटक बनाएँ और उसे चलाएँ।

input_config = example_gen_pb2.Input(splits=[
    example_gen_pb2.Input.Split(name='train', pattern='train.tfrecord'),
    example_gen_pb2.Input.Split(name='eval', pattern='eval.tfrecord')
])

example_gen = ImportExampleGen(input_base=examples_path, input_config=input_config)

context.run(example_gen, enable_cache=True)
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
for artifact in example_gen.outputs['examples'].get():
  print(artifact)

print('\nexample_gen.outputs is a {}'.format(type(example_gen.outputs)))
print(example_gen.outputs)

print(example_gen.outputs['examples'].get()[0].split_names)
Artifact(artifact: id: 1
type_id: 14
uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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:1635389104,sum_checksum:1635389104\nsplit:eval,num_files:1,total_bytes:13374744,xor_checksum:1635389108,sum_checksum:1635389108"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.2.0"
  }
}
state: LIVE
, artifact_type: id: 14
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)

example_gen.outputs is a <class 'dict'>
{'examples': Channel(
    type_name: Examples
    artifacts: [Artifact(artifact: id: 1
type_id: 14
uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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:1635389104,sum_checksum:1635389104\nsplit:eval,num_files:1,total_bytes:13374744,xor_checksum:1635389108,sum_checksum:1635389108"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.2.0"
  }
}
state: LIVE
, artifact_type: id: 14
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]
    additional_properties: {}
    additional_custom_properties: {}
)}
["train", "eval"]

घटक के आउटपुट में 2 कलाकृतियां शामिल हैं:

  • प्रशिक्षण के उदाहरण (10,000 लेबल वाली समीक्षाएं + 10,000 बिना लेबल वाली समीक्षाएं)
  • eval उदाहरण (10,000 लेबल वाली समीक्षाएं)

पहचान उदाहरण कस्टम घटक

एनएसएल का उपयोग करने के लिए, हमें प्रत्येक उदाहरण के लिए एक विशिष्ट आईडी की आवश्यकता होगी। हम एक कस्टम घटक बनाते हैं जो सभी विभाजनों के सभी उदाहरणों में ऐसी अनूठी आईडी जोड़ता है। हम लाभ उठाने अपाचे बीम आसानी से बड़े डेटा सेट करने के लिए पैमाने पर करने के यदि आवश्यक हो तो सक्षम होने के लिए।

def make_example_with_unique_id(example, id_feature_name):
  """Adds a unique ID to the given `tf.train.Example` proto.

  This function uses Python's 'uuid' module to generate a universally unique
  identifier for each example.

  Args:
    example: An instance of a `tf.train.Example` proto.
    id_feature_name: The name of the feature in the resulting `tf.train.Example`
      that will contain the unique identifier.

  Returns:
    A new `tf.train.Example` proto that includes a unique identifier as an
    additional feature.
  """
  result = tf.train.Example()
  result.CopyFrom(example)
  unique_id = uuid.uuid4()
  result.features.feature.get_or_create(
      id_feature_name).bytes_list.MergeFrom(
          tf.train.BytesList(value=[str(unique_id).encode('utf-8')]))
  return result


@component
def IdentifyExamples(orig_examples: InputArtifact[Examples],
                     identified_examples: OutputArtifact[Examples],
                     id_feature_name: Parameter[str],
                     component_name: Parameter[str]) -> None:

  # Get a list of the splits in input_data
  splits_list = artifact_utils.decode_split_names(
      split_names=orig_examples.split_names)
  # For completeness, encode the splits names and payload_format.
  # We could also just use input_data.split_names.
  identified_examples.split_names = artifact_utils.encode_split_names(
      splits=splits_list)
  # TODO(b/168616829): Remove populating payload_format after tfx 0.25.0.
  identified_examples.set_string_custom_property(
      "payload_format",
      orig_examples.get_string_custom_property("payload_format"))


  for split in splits_list:
    input_dir = artifact_utils.get_split_uri([orig_examples], split)
    output_dir = artifact_utils.get_split_uri([identified_examples], split)
    os.mkdir(output_dir)
    with beam.Pipeline() as pipeline:
      (pipeline
       | 'ReadExamples' >> beam.io.ReadFromTFRecord(
           os.path.join(input_dir, '*'),
           coder=beam.coders.coders.ProtoCoder(tf.train.Example))
       | 'AddUniqueId' >> beam.Map(make_example_with_unique_id, id_feature_name)
       | 'WriteIdentifiedExamples' >> beam.io.WriteToTFRecord(
           file_path_prefix=os.path.join(output_dir, 'data_tfrecord'),
           coder=beam.coders.coders.ProtoCoder(tf.train.Example),
           file_name_suffix='.gz'))

  return
identify_examples = IdentifyExamples(
    orig_examples=example_gen.outputs['examples'],
    component_name=u'IdentifyExamples',
    id_feature_name=u'id')
context.run(identify_examples, enable_cache=False)
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.

सांख्यिकीजेन घटक

StatisticsGen घटक अपने डेटासेट के लिए वर्णनात्मक सांख्यिकी गणना करता है। इसके द्वारा उत्पन्न आँकड़ों को समीक्षा के लिए देखा जा सकता है, और उदाहरण के लिए सत्यापन और एक स्कीमा का अनुमान लगाने के लिए उपयोग किया जाता है।

एक स्टैटिस्टिक्सजेन घटक बनाएं और इसे चलाएं।

# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(
    examples=identify_examples.outputs["identified_examples"])
context.run(statistics_gen, enable_cache=True)
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.

स्कीमाजेन घटक

SchemaGen घटक StatisticsGen से आंकड़ों के आधार पर अपने डेटा के लिए स्कीमा उत्पन्न करता है। यह आपकी प्रत्येक विशेषता के डेटा प्रकारों और श्रेणीबद्ध सुविधाओं के लिए कानूनी मूल्यों की श्रेणी का अनुमान लगाने का प्रयास करता है।

एक स्कीमाजेन घटक बनाएं और इसे चलाएं।

# 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)
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1028 02:45:28.295687 25039 rdbms_metadata_access_object.cc:686] No property is defined for the Type

उत्पन्न विरूपण साक्ष्य सिर्फ एक है schema.pbtxt एक का एक पाठ प्रतिनिधित्व युक्त 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())

यह का उपयोग कर देखे जा सकते हैं tfdv.display_schema() (हम बाद में एक प्रयोगशाला में और अधिक विस्तार में इस पर नजर डालेंगे):

tfdv.display_schema(schema)

उदाहरण सत्यापनकर्ता घटक

ExampleValidator StatisticsGen से आंकड़े और SchemaGen से स्कीमा के आधार पर, विसंगति का पता लगाने प्रदर्शन करती है। यह अनुपलब्ध मान, गलत प्रकार के मान, या स्वीकार्य मानों के डोमेन के बाहर श्रेणीबद्ध मान जैसी समस्याओं की तलाश करता है।

एक उदाहरण वैलिडेटर घटक बनाएं और इसे चलाएं।

# 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)

सिंथेसाइजग्राफ घटक

ग्राफ़ निर्माण में टेक्स्ट नमूनों के लिए एम्बेडिंग बनाना और फिर एम्बेडिंग की तुलना करने के लिए समानता फ़ंक्शन का उपयोग करना शामिल है।

हम pretrained फिरकी embeddings प्रयोग embeddings बनाने के लिए होगा tf.train.Example इनपुट में प्रत्येक नमूने के लिए प्रारूप। हम में जिसके परिणामस्वरूप embeddings स्टोर करेगा TFRecord नमूना के आईडी के साथ प्रारूप। यह महत्वपूर्ण है और हमें बाद में ग्राफ़ में संबंधित नोड्स के साथ नमूना एम्बेडिंग का मिलान करने की अनुमति देगा।

एक बार हमारे पास नमूना एम्बेडिंग हो जाने के बाद, हम उनका उपयोग एक समानता ग्राफ़ बनाने के लिए करेंगे, अर्थात, इस ग्राफ़ में नोड्स नमूनों के अनुरूप होंगे और इस ग्राफ़ में किनारे नोड्स के जोड़े के बीच समानता के अनुरूप होंगे।

न्यूरल स्ट्रक्चर्ड लर्निंग नमूना एम्बेडिंग के आधार पर एक ग्राफ बनाने के लिए एक ग्राफ बिल्डिंग लाइब्रेरी प्रदान करता है। यह समानता उपाय के रूप में कोज्या समानता का उपयोग करता है उन दोनों के बीच embeddings और निर्माण किनारों तुलना करने के लिए। यह हमें एक समानता सीमा निर्दिष्ट करने की भी अनुमति देता है, जिसका उपयोग अंतिम ग्राफ से भिन्न किनारों को त्यागने के लिए किया जा सकता है। निम्नलिखित उदाहरण में, समानता सीमा के रूप में 0.99 का उपयोग करते हुए, हम एक ग्राफ के साथ समाप्त होते हैं जिसमें 111,066 द्वि-दिशात्मक किनारे होते हैं।

swivel_url = 'https://hub.tensorflow.google.cn/google/tf2-preview/gnews-swivel-20dim/1'
hub_layer = hub.KerasLayer(swivel_url, input_shape=[], dtype=tf.string)


def _bytes_feature(value):
  """Returns a bytes_list from a string / byte."""
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))


def _float_feature(value):
  """Returns a float_list from a float / double."""
  return tf.train.Feature(float_list=tf.train.FloatList(value=value))


def create_embedding_example(example):
  """Create tf.Example containing the sample's embedding and its ID."""
  sentence_embedding = hub_layer(tf.sparse.to_dense(example['text']))

  # Flatten the sentence embedding back to 1-D.
  sentence_embedding = tf.reshape(sentence_embedding, shape=[-1])

  feature_dict = {
      'id': _bytes_feature(tf.sparse.to_dense(example['id']).numpy()),
      'embedding': _float_feature(sentence_embedding.numpy().tolist())
  }

  return tf.train.Example(features=tf.train.Features(feature=feature_dict))


def create_dataset(uri):
  tfrecord_filenames = [os.path.join(uri, name) for name in os.listdir(uri)]
  return tf.data.TFRecordDataset(tfrecord_filenames, compression_type='GZIP')


def create_embeddings(train_path, output_path):
  dataset = create_dataset(train_path)
  embeddings_path = os.path.join(output_path, 'embeddings.tfr')

  feature_map = {
      'label': tf.io.FixedLenFeature([], tf.int64),
      'id': tf.io.VarLenFeature(tf.string),
      'text': tf.io.VarLenFeature(tf.string)
  }

  with tf.io.TFRecordWriter(embeddings_path) as writer:
    for tfrecord in dataset:
      tensor_dict = tf.io.parse_single_example(tfrecord, feature_map)
      embedding_example = create_embedding_example(tensor_dict)
      writer.write(embedding_example.SerializeToString())


def build_graph(output_path, similarity_threshold):
  embeddings_path = os.path.join(output_path, 'embeddings.tfr')
  graph_path = os.path.join(output_path, 'graph.tsv')
  graph_builder_config = nsl.configs.GraphBuilderConfig(
      similarity_threshold=similarity_threshold,
      lsh_splits=32,
      lsh_rounds=15,
      random_seed=12345)
  nsl.tools.build_graph_from_config([embeddings_path], graph_path,
                                    graph_builder_config)
"""Custom Artifact type"""


class SynthesizedGraph(tfx.types.artifact.Artifact):
  """Output artifact of the SynthesizeGraph component"""
  TYPE_NAME = 'SynthesizedGraphPath'
  PROPERTIES = {
      'span': standard_artifacts.SPAN_PROPERTY,
      'split_names': standard_artifacts.SPLIT_NAMES_PROPERTY,
  }


@component
def SynthesizeGraph(identified_examples: InputArtifact[Examples],
                    synthesized_graph: OutputArtifact[SynthesizedGraph],
                    similarity_threshold: Parameter[float],
                    component_name: Parameter[str]) -> None:

  # Get a list of the splits in input_data
  splits_list = artifact_utils.decode_split_names(
      split_names=identified_examples.split_names)

  # We build a graph only based on the 'Split-train' split which includes both
  # labeled and unlabeled examples.
  train_input_examples_uri = os.path.join(identified_examples.uri,
                                          'Split-train')
  output_graph_uri = os.path.join(synthesized_graph.uri, 'Split-train')
  os.mkdir(output_graph_uri)

  print('Creating embeddings...')
  create_embeddings(train_input_examples_uri, output_graph_uri)

  print('Synthesizing graph...')
  build_graph(output_graph_uri, similarity_threshold)

  synthesized_graph.split_names = artifact_utils.encode_split_names(
      splits=['Split-train'])

  return
synthesize_graph = SynthesizeGraph(
    identified_examples=identify_examples.outputs['identified_examples'],
    component_name=u'SynthesizeGraph',
    similarity_threshold=0.99)
context.run(synthesize_graph, enable_cache=False)
Creating embeddings...
Synthesizing graph...
train_uri = synthesize_graph.outputs["synthesized_graph"].get()[0].uri
os.listdir(train_uri)
['Split-train']
graph_path = os.path.join(train_uri, "Split-train", "graph.tsv")
print("node 1\t\t\t\t\tnode 2\t\t\t\t\tsimilarity")
!head {graph_path}
print("...")
!tail {graph_path}
node 1                  node 2                  similarity
f23eaa12-b6dd-4b18-a9d1-ca1fe8fab862    e424f95f-617d-4735-9b44-dcd443c0f1ba    0.992505
e424f95f-617d-4735-9b44-dcd443c0f1ba    f23eaa12-b6dd-4b18-a9d1-ca1fe8fab862    0.992505
04d87a8f-de82-4df8-804e-9d51e226b92f    deab1bc8-a9ce-4d3f-ac4c-8377f650e00e    0.990020
deab1bc8-a9ce-4d3f-ac4c-8377f650e00e    04d87a8f-de82-4df8-804e-9d51e226b92f    0.990020
942a1d61-9ae3-42f8-a9b0-e153d2e256dd    4641cd3d-4d42-4977-9d17-3d861c521c46    0.990838
4641cd3d-4d42-4977-9d17-3d861c521c46    942a1d61-9ae3-42f8-a9b0-e153d2e256dd    0.990838
942a1d61-9ae3-42f8-a9b0-e153d2e256dd    644c2813-f351-4725-a7c3-0d14f87db8fb    0.991234
644c2813-f351-4725-a7c3-0d14f87db8fb    942a1d61-9ae3-42f8-a9b0-e153d2e256dd    0.991234
574bc503-8aac-46e9-8176-a68b71972057    3b8bb6ea-b119-4649-9aa9-324f95719fac    0.990616
3b8bb6ea-b119-4649-9aa9-324f95719fac    574bc503-8aac-46e9-8176-a68b71972057    0.990616
...
cf3dc0b4-c50e-4876-b3a3-80849538aebb    e67f7a9b-2085-4212-9f79-9b26ef29ab39    0.990002
e67f7a9b-2085-4212-9f79-9b26ef29ab39    cf3dc0b4-c50e-4876-b3a3-80849538aebb    0.990002
83c044bd-0252-4eb9-8981-0ad2e682d220    5c1e3e54-5584-4296-b8ec-14ee63c4d864    0.991046
5c1e3e54-5584-4296-b8ec-14ee63c4d864    83c044bd-0252-4eb9-8981-0ad2e682d220    0.991046
0afd7664-eb99-4a34-aabb-497d6bd4c077    49057286-f388-40f8-8228-e1ac912dfb03    0.991198
49057286-f388-40f8-8228-e1ac912dfb03    0afd7664-eb99-4a34-aabb-497d6bd4c077    0.991198
7f396e05-140a-430f-89d1-c45c792fdf05    46e051c1-f712-4da0-8f06-f558332e0f16    0.990260
46e051c1-f712-4da0-8f06-f558332e0f16    7f396e05-140a-430f-89d1-c45c792fdf05    0.990260
14540fa5-598a-4caf-ad2c-510f958964e1    d6419190-3857-42ff-a871-f6385313f005    0.991317
d6419190-3857-42ff-a871-f6385313f005    14540fa5-598a-4caf-ad2c-510f958964e1    0.991317
wc -l {graph_path}
222132 /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/SynthesizeGraph/synthesized_graph/6/Split-train/graph.tsv

परिवर्तन घटक

Transform घटक प्रदर्शन डेटा परिवर्तनों और सुविधा इंजीनियरिंग। परिणामों में एक इनपुट TensorFlow ग्राफ़ शामिल होता है जिसका उपयोग प्रशिक्षण और सेवा दोनों के दौरान प्रशिक्षण या अनुमान से पहले डेटा को प्रीप्रोसेस करने के लिए किया जाता है। यह ग्राफ सेव्डमॉडल का हिस्सा बन जाता है जो मॉडल प्रशिक्षण का परिणाम है। चूंकि प्रशिक्षण और सेवा दोनों के लिए एक ही इनपुट ग्राफ़ का उपयोग किया जाता है, इसलिए प्रीप्रोसेसिंग हमेशा समान होगी, और केवल एक बार लिखा जाना चाहिए।

फीचर इंजीनियरिंग की मनमानी जटिलता के कारण ट्रांसफॉर्म घटक को कई अन्य घटकों की तुलना में अधिक कोड की आवश्यकता होती है, जिसकी आपको उस डेटा और/या मॉडल के लिए आवश्यकता हो सकती है जिसके साथ आप काम कर रहे हैं। इसके लिए कोड फाइलें उपलब्ध होने की आवश्यकता होती है जो आवश्यक प्रसंस्करण को परिभाषित करती हैं।

प्रत्येक नमूने में निम्नलिखित तीन विशेषताएं शामिल होंगी:

  1. आईडी: नमूने के नोड आईडी।
  2. text_xf: एक int64 शब्द आईडी युक्त सूची।
  3. label_xf: समीक्षा का लक्ष्य वर्ग की पहचान करने int64 एक सिंगलटन: 0 = नकारात्मक, 1 = सकारात्मक।

चलो एक मॉड्यूल युक्त परिभाषित preprocessing_fn() समारोह है कि हम करने के लिए पारित करेंगे Transform घटक:

_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

बनाएँ और चलाने के Transform घटक, फ़ाइलें ऊपर बनाए गए का जिक्र है।

# Performs transformations and feature engineering in training and serving.
transform = Transform(
    examples=identify_examples.outputs['identified_examples'],
    schema=schema_gen.outputs['schema'],
    module_file=_transform_module_file)
context.run(transform, enable_cache=True)
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying imdb_transform.py -> build/lib
installing to /tmp/tmpsj74ngli
running install
running install_lib
copying build/lib/imdb_transform.py -> /tmp/tmpsj74ngli
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing tfx_user_code_Transform.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
Copying tfx_user_code_Transform.egg-info to /tmp/tmpsj74ngli/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3.7.egg-info
running install_scripts
creating /tmp/tmpsj74ngli/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/WHEEL
creating '/tmp/tmp8qugurgf/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3-none-any.whl' and adding '/tmp/tmpsj74ngli' to it
adding 'imdb_transform.py'
adding 'tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/RECORD'
removing /tmp/tmpsj74ngli
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
I1028 02:46:57.045398 25039 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1028 02:46:57.049113 25039 rdbms_metadata_access_object.cc:686] No property is defined for the Type
Processing /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/_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 /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/_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:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
Processing /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/_wheels/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3-none-any.whl
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50
WARNING: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
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transform_graph/7/.temp_path/tftransform_tmp/b54fad3f9c254ad090d5e73302e30ef0/assets
2021-10-28 02:47:06.449906: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transform_graph/7/.temp_path/tftransform_tmp/b54fad3f9c254ad090d5e73302e30ef0/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transform_graph/7/.temp_path/tftransform_tmp/38e9ba29df364e4a99093667c2f00638/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transform_graph/7/.temp_path/tftransform_tmp/38e9ba29df364e4a99093667c2f00638/assets

Transform घटक आउटपुट के 2 प्रकार हैं:

  • transform_graph ग्राफ कि preprocessing कार्रवाई कर सकते हैं (यह ग्राफ सेवारत मूल्यांकन मॉडल में शामिल किया जाएगा) है।
  • transformed_examples preprocessed प्रशिक्षण और मूल्यांकन डेटा प्रतिनिधित्व करता है।
transform.outputs
{'transform_graph': Channel(
     type_name: TransformGraph
     artifacts: [Artifact(artifact: id: 7
 type_id: 25
 uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transform_graph/7"
 custom_properties {
   key: "name"
   value {
     string_value: "transform_graph"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 25
 name: "TransformGraph"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'transformed_examples': Channel(
     type_name: Examples
     artifacts: [Artifact(artifact: id: 8
 type_id: 14
 uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transformed_examples/7"
 properties {
   key: "split_names"
   value {
     string_value: "[\"train\", \"eval\"]"
   }
 }
 custom_properties {
   key: "name"
   value {
     string_value: "transformed_examples"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 14
 name: "Examples"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 properties {
   key: "version"
   value: INT
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'updated_analyzer_cache': Channel(
     type_name: TransformCache
     artifacts: [Artifact(artifact: id: 9
 type_id: 26
 uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/updated_analyzer_cache/7"
 custom_properties {
   key: "name"
   value {
     string_value: "updated_analyzer_cache"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 26
 name: "TransformCache"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 10
 type_id: 19
 uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/pre_transform_schema/7"
 custom_properties {
   key: "name"
   value {
     string_value: "pre_transform_schema"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 19
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 11
 type_id: 17
 uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/pre_transform_stats/7"
 custom_properties {
   key: "name"
   value {
     string_value: "pre_transform_stats"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 17
 name: "ExampleStatistics"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 12
 type_id: 19
 uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/post_transform_schema/7"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_schema"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 19
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 13
 type_id: 17
 uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/post_transform_stats/7"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_stats"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 17
 name: "ExampleStatistics"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_anomalies': Channel(
     type_name: ExampleAnomalies
     artifacts: [Artifact(artifact: id: 14
 type_id: 21
 uri: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/post_transform_anomalies/7"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_anomalies"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 21
 name: "ExampleAnomalies"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

पर पर नज़र transform_graph विरूपण साक्ष्य: यह 3 उपनिर्देशिका युक्त एक निर्देशिका के लिए अंक:

train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']

transform_fn उपनिर्देशिका वास्तविक preprocessing ग्राफ में शामिल है। metadata उपनिर्देशिका मूल डेटा के स्कीमा में शामिल है। transformed_metadata उपनिर्देशिका preprocessed डेटा की स्कीमा में शामिल है।

कुछ रूपांतरित उदाहरणों पर एक नज़र डालें और जांचें कि वे वास्तव में इच्छित तरीके से संसाधित किए गए हैं।

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: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transformed_examples/7"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "transformed_examples"
  }
}
custom_properties {
  key: "producer_component"
  value {
    string_value: "Transform"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.2.0"
  }
}
state: LIVE
, artifact_type: id: 14
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)
uri: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transformed_examples/7/Split-train
tfrecord_filenames: ['/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Transform/transformed_examples/7/Split-train/transformed_examples-00000-of-00001.gz']
features {
  feature {
    key: "id"
    value {
      bytes_list {
        value: "6b1af1b0-991a-4f6e-a0b5-9861e0c0b170"
      }
    }
  }
  feature {
    key: "label_xf"
    value {
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  feature {
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      bytes_list {
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  feature {
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  feature {
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features {
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}

ग्राफ़ऑगमेंटेशन घटक

चूंकि हमारे पास नमूना विशेषताएं और संश्लेषित ग्राफ हैं, इसलिए हम तंत्रिका संरचित सीखने के लिए संवर्धित प्रशिक्षण डेटा उत्पन्न कर सकते हैं। एनएसएल ढांचा ग्राफ नियमितीकरण के लिए अंतिम प्रशिक्षण डेटा तैयार करने के लिए ग्राफ और नमूना सुविधाओं को संयोजित करने के लिए एक पुस्तकालय प्रदान करता है। परिणामी प्रशिक्षण डेटा में मूल नमूना सुविधाओं के साथ-साथ उनके संबंधित पड़ोसियों की विशेषताएं भी शामिल होंगी।

इस ट्यूटोरियल में, हम अप्रत्यक्ष किनारों पर विचार करते हैं और ग्राफ़ पड़ोसियों के साथ प्रशिक्षण डेटा बढ़ाने के लिए प्रति नमूना अधिकतम 3 पड़ोसियों का उपयोग करते हैं।

def split_train_and_unsup(input_uri):
  'Separate the labeled and unlabeled instances.'

  tmp_dir = tempfile.mkdtemp(prefix='tfx-data')
  tfrecord_filenames = [
      os.path.join(input_uri, filename) for filename in os.listdir(input_uri)
  ]
  train_path = os.path.join(tmp_dir, 'train.tfrecord')
  unsup_path = os.path.join(tmp_dir, 'unsup.tfrecord')
  with tf.io.TFRecordWriter(train_path) as train_writer, \
       tf.io.TFRecordWriter(unsup_path) as unsup_writer:
    for tfrecord in tf.data.TFRecordDataset(
        tfrecord_filenames, compression_type='GZIP'):
      example = tf.train.Example()
      example.ParseFromString(tfrecord.numpy())
      if ('label_xf' not in example.features.feature or
          example.features.feature['label_xf'].int64_list.value[0] == -1):
        writer = unsup_writer
      else:
        writer = train_writer
      writer.write(tfrecord.numpy())
  return train_path, unsup_path


def gzip(filepath):
  with open(filepath, 'rb') as f_in:
    with gzip_lib.open(filepath + '.gz', 'wb') as f_out:
      shutil.copyfileobj(f_in, f_out)
  os.remove(filepath)


def copy_tfrecords(input_uri, output_uri):
  for filename in os.listdir(input_uri):
    input_filename = os.path.join(input_uri, filename)
    output_filename = os.path.join(output_uri, filename)
    shutil.copyfile(input_filename, output_filename)


@component
def GraphAugmentation(identified_examples: InputArtifact[Examples],
                      synthesized_graph: InputArtifact[SynthesizedGraph],
                      augmented_examples: OutputArtifact[Examples],
                      num_neighbors: Parameter[int],
                      component_name: Parameter[str]) -> None:

  # Get a list of the splits in input_data
  splits_list = artifact_utils.decode_split_names(
      split_names=identified_examples.split_names)

  train_input_uri = os.path.join(identified_examples.uri, 'Split-train')
  eval_input_uri = os.path.join(identified_examples.uri, 'Split-eval')
  train_graph_uri = os.path.join(synthesized_graph.uri, 'Split-train')
  train_output_uri = os.path.join(augmented_examples.uri, 'Split-train')
  eval_output_uri = os.path.join(augmented_examples.uri, 'Split-eval')

  os.mkdir(train_output_uri)
  os.mkdir(eval_output_uri)

  # Separate the labeled and unlabeled examples from the 'Split-train' split.
  train_path, unsup_path = split_train_and_unsup(train_input_uri)

  output_path = os.path.join(train_output_uri, 'nsl_train_data.tfr')
  pack_nbrs_args = dict(
      labeled_examples_path=train_path,
      unlabeled_examples_path=unsup_path,
      graph_path=os.path.join(train_graph_uri, 'graph.tsv'),
      output_training_data_path=output_path,
      add_undirected_edges=True,
      max_nbrs=num_neighbors)
  print('nsl.tools.pack_nbrs arguments:', pack_nbrs_args)
  nsl.tools.pack_nbrs(**pack_nbrs_args)

  # Downstream components expect gzip'ed TFRecords.
  gzip(output_path)

  # The test examples are left untouched and are simply copied over.
  copy_tfrecords(eval_input_uri, eval_output_uri)

  augmented_examples.split_names = identified_examples.split_names

  return
# Augments training data with graph neighbors.
graph_augmentation = GraphAugmentation(
    identified_examples=transform.outputs['transformed_examples'],
    synthesized_graph=synthesize_graph.outputs['synthesized_graph'],
    component_name=u'GraphAugmentation',
    num_neighbors=3)
context.run(graph_augmentation, enable_cache=False)
nsl.tools.pack_nbrs arguments: {'labeled_examples_path': '/tmp/tfx-dataravxnxo7/train.tfrecord', 'unlabeled_examples_path': '/tmp/tfx-dataravxnxo7/unsup.tfrecord', 'graph_path': '/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/SynthesizeGraph/synthesized_graph/6/Split-train/graph.tsv', 'output_training_data_path': '/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: "/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/GraphAugmentation/augmented_examples/8"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "augmented_examples"
  }
}
custom_properties {
  key: "producer_component"
  value {
    string_value: "GraphAugmentation"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.2.0"
  }
}
state: LIVE
, artifact_type: id: 14
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)
uri: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/GraphAugmentation/augmented_examples/8/Split-train
tfrecord_filenames: ['/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/GraphAugmentation/augmented_examples/8/Split-train/nsl_train_data.tfr.gz']
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ट्रेनर घटक

Trainer घटक गाड़ियों मॉडल TensorFlow का उपयोग कर।

एक से युक्त एक पायथन मॉड्यूल बनाएं trainer_fn समारोह है, जो एक आकलनकर्ता लौट जाना चाहिए। यदि आप एक Keras मॉडल बनाने चाहें, तो आप ऐसा करते हैं और फिर इसे एक आकलनकर्ता में बदलने का प्रयोग कर सकते keras.model_to_estimator()

# Setup paths.
_trainer_module_file = 'imdb_trainer.py'
%%writefile {_trainer_module_file}

import neural_structured_learning as nsl

import tensorflow as tf

import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils


NBR_FEATURE_PREFIX = 'NL_nbr_'
NBR_WEIGHT_SUFFIX = '_weight'
LABEL_KEY = 'label'
ID_FEATURE_KEY = 'id'

def _transformed_name(key):
  return key + '_xf'


def _transformed_names(keys):
  return [_transformed_name(key) for key in keys]


# Hyperparameters:
#
# We will use an instance of `HParams` to inclue various hyperparameters and
# constants used for training and evaluation. We briefly describe each of them
# below:
#
# -   max_seq_length: This is the maximum number of words considered from each
#                     movie review in this example.
# -   vocab_size: This is the size of the vocabulary considered for this
#                 example.
# -   oov_size: This is the out-of-vocabulary size considered for this example.
# -   distance_type: This is the distance metric used to regularize the sample
#                    with its neighbors.
# -   graph_regularization_multiplier: This controls the relative weight of the
#                                      graph regularization term in the overall
#                                      loss function.
# -   num_neighbors: The number of neighbors used for graph regularization. This
#                    value has to be less than or equal to the `num_neighbors`
#                    argument used above in the GraphAugmentation component when
#                    invoking `nsl.tools.pack_nbrs`.
# -   num_fc_units: The number of units in the fully connected layer of the
#                   neural network.
class HParams(object):
  """Hyperparameters used for training."""
  def __init__(self):
    ### dataset parameters
    # The following 3 values should match those defined in the Transform
    # Component.
    self.max_seq_length = 100
    self.vocab_size = 10000
    self.oov_size = 100
    ### Neural Graph Learning parameters
    self.distance_type = nsl.configs.DistanceType.L2
    self.graph_regularization_multiplier = 0.1
    # The following value has to be at most the value of 'num_neighbors' used
    # in the GraphAugmentation component.
    self.num_neighbors = 1
    ### Model Architecture
    self.num_embedding_dims = 16
    self.num_fc_units = 64

HPARAMS = HParams()


def optimizer_fn():
  """Returns an instance of `tf.Optimizer`."""
  return tf.compat.v1.train.RMSPropOptimizer(
    learning_rate=0.0001, decay=1e-6)


def build_train_op(loss, global_step):
  """Builds a train op to optimize the given loss using gradient descent."""
  with tf.name_scope('train'):
    optimizer = optimizer_fn()
    train_op = optimizer.minimize(loss=loss, global_step=global_step)
  return train_op


# Building the model:
#
# A neural network is created by stacking layers—this requires two main
# architectural decisions:
# * How many layers to use in the model?
# * How many *hidden units* to use for each layer?
#
# In this example, the input data consists of an array of word-indices. The
# labels to predict are either 0 or 1. We will use a feed-forward neural network
# as our base model in this tutorial.
def feed_forward_model(features, is_training, reuse=tf.compat.v1.AUTO_REUSE):
  """Builds a simple 2 layer feed forward neural network.

  The layers are effectively stacked sequentially to build the classifier. The
  first layer is an Embedding layer, which takes the integer-encoded vocabulary
  and looks up the embedding vector for each word-index. These vectors are
  learned as the model trains. The vectors add a dimension to the output array.
  The resulting dimensions are: (batch, sequence, embedding). Next is a global
  average pooling 1D layer, which reduces the dimensionality of its inputs from
  3D to 2D. This fixed-length output vector is piped through a fully-connected
  (Dense) layer with 16 hidden units. The last layer is densely connected with a
  single output node. Using the sigmoid activation function, this value is a
  float between 0 and 1, representing a probability, or confidence level.

  Args:
    features: A dictionary containing batch features returned from the
      `input_fn`, that include sample features, corresponding neighbor features,
      and neighbor weights.
    is_training: a Python Boolean value or a Boolean scalar Tensor, indicating
      whether to apply dropout.
    reuse: a Python Boolean value for reusing variable scope.

  Returns:
    logits: Tensor of shape [batch_size, 1].
    representations: Tensor of shape [batch_size, _] for graph regularization.
      This is the representation of each example at the graph regularization
      layer.
  """

  with tf.compat.v1.variable_scope('ff', reuse=reuse):
    inputs = features[_transformed_name('text')]
    embeddings = tf.compat.v1.get_variable(
        'embeddings',
        shape=[
            HPARAMS.vocab_size + HPARAMS.oov_size, HPARAMS.num_embedding_dims
        ])
    embedding_layer = tf.nn.embedding_lookup(embeddings, inputs)

    pooling_layer = tf.compat.v1.layers.AveragePooling1D(
        pool_size=HPARAMS.max_seq_length, strides=HPARAMS.max_seq_length)(
            embedding_layer)
    # Shape of pooling_layer is now [batch_size, 1, HPARAMS.num_embedding_dims]
    pooling_layer = tf.reshape(pooling_layer, [-1, HPARAMS.num_embedding_dims])

    dense_layer = tf.compat.v1.layers.Dense(
        16, activation='relu')(
            pooling_layer)

    output_layer = tf.compat.v1.layers.Dense(
        1, activation='sigmoid')(
            dense_layer)

    # Graph regularization will be done on the penultimate (dense) layer
    # because the output layer is a single floating point number.
    return output_layer, dense_layer


# A note on hidden units:
#
# The above model has two intermediate or "hidden" layers, between the input and
# output, and excluding the Embedding layer. The number of outputs (units,
# nodes, or neurons) is the dimension of the representational space for the
# layer. In other words, the amount of freedom the network is allowed when
# learning an internal representation. If a model has more hidden units
# (a higher-dimensional representation space), and/or more layers, then the
# network can learn more complex representations. However, it makes the network
# more computationally expensive and may lead to learning unwanted
# patterns—patterns that improve performance on training data but not on the
# test data. This is called overfitting.


# This function will be used to generate the embeddings for samples and their
# corresponding neighbors, which will then be used for graph regularization.
def embedding_fn(features, mode):
  """Returns the embedding corresponding to the given features.

  Args:
    features: A dictionary containing batch features returned from the
      `input_fn`, that include sample features, corresponding neighbor features,
      and neighbor weights.
    mode: Specifies if this is training, evaluation, or prediction. See
      tf.estimator.ModeKeys.

  Returns:
    The embedding that will be used for graph regularization.
  """
  is_training = (mode == tf.estimator.ModeKeys.TRAIN)
  _, embedding = feed_forward_model(features, is_training)
  return embedding


def feed_forward_model_fn(features, labels, mode, params, config):
  """Implementation of the model_fn for the base feed-forward model.

  Args:
    features: This is the first item returned from the `input_fn` passed to
      `train`, `evaluate`, and `predict`. This should be a single `Tensor` or
      `dict` of same.
    labels: This is the second item returned from the `input_fn` passed to
      `train`, `evaluate`, and `predict`. This should be a single `Tensor` or
      `dict` of same (for multi-head models). If mode is `ModeKeys.PREDICT`,
      `labels=None` will be passed. If the `model_fn`'s signature does not
      accept `mode`, the `model_fn` must still be able to handle `labels=None`.
    mode: Optional. Specifies if this training, evaluation or prediction. See
      `ModeKeys`.
    params: An HParams instance as returned by get_hyper_parameters().
    config: Optional configuration object. Will receive what is passed to
      Estimator in `config` parameter, or the default `config`. Allows updating
      things in your model_fn based on configuration such as `num_ps_replicas`,
      or `model_dir`. Unused currently.

  Returns:
     A `tf.estimator.EstimatorSpec` for the base feed-forward model. This does
     not include graph-based regularization.
  """

  is_training = mode == tf.estimator.ModeKeys.TRAIN

  # Build the computation graph.
  probabilities, _ = feed_forward_model(features, is_training)
  predictions = tf.round(probabilities)

  if mode == tf.estimator.ModeKeys.PREDICT:
    # labels will be None, and no loss to compute.
    cross_entropy_loss = None
    eval_metric_ops = None
  else:
    # Loss is required in train and eval modes.
    # Flatten 'probabilities' to 1-D.
    probabilities = tf.reshape(probabilities, shape=[-1])
    cross_entropy_loss = tf.compat.v1.keras.losses.binary_crossentropy(
        labels, probabilities)
    eval_metric_ops = {
        'accuracy': tf.compat.v1.metrics.accuracy(labels, predictions)
    }

  if is_training:
    global_step = tf.compat.v1.train.get_or_create_global_step()
    train_op = build_train_op(cross_entropy_loss, global_step)
  else:
    train_op = None

  return tf.estimator.EstimatorSpec(
      mode=mode,
      predictions={
          'probabilities': probabilities,
          'predictions': predictions
      },
      loss=cross_entropy_loss,
      train_op=train_op,
      eval_metric_ops=eval_metric_ops)


# Tf.Transform considers these features as "raw"
def _get_raw_feature_spec(schema):
  return schema_utils.schema_as_feature_spec(schema).feature_spec


def _gzip_reader_fn(filenames):
  """Small utility returning a record reader that can read gzip'ed files."""
  return tf.data.TFRecordDataset(
      filenames,
      compression_type='GZIP')


def _example_serving_receiver_fn(tf_transform_output, schema):
  """Build the serving in inputs.

  Args:
    tf_transform_output: A TFTransformOutput.
    schema: the schema of the input data.

  Returns:
    Tensorflow graph which parses examples, applying tf-transform to them.
  """
  raw_feature_spec = _get_raw_feature_spec(schema)
  raw_feature_spec.pop(LABEL_KEY)

  # We don't need the ID feature for serving.
  raw_feature_spec.pop(ID_FEATURE_KEY)

  raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
      raw_feature_spec, default_batch_size=None)
  serving_input_receiver = raw_input_fn()

  transformed_features = tf_transform_output.transform_raw_features(
      serving_input_receiver.features)

  # Even though, LABEL_KEY was removed from 'raw_feature_spec', the transform
  # operation would have injected the transformed LABEL_KEY feature with a
  # default value.
  transformed_features.pop(_transformed_name(LABEL_KEY))
  return tf.estimator.export.ServingInputReceiver(
      transformed_features, serving_input_receiver.receiver_tensors)


def _eval_input_receiver_fn(tf_transform_output, schema):
  """Build everything needed for the tf-model-analysis to run the model.

  Args:
    tf_transform_output: A TFTransformOutput.
    schema: the schema of the input data.

  Returns:
    EvalInputReceiver function, which contains:
      - Tensorflow graph which parses raw untransformed features, applies the
        tf-transform preprocessing operators.
      - Set of raw, untransformed features.
      - Label against which predictions will be compared.
  """
  # Notice that the inputs are raw features, not transformed features here.
  raw_feature_spec = _get_raw_feature_spec(schema)

  # We don't need the ID feature for TFMA.
  raw_feature_spec.pop(ID_FEATURE_KEY)

  raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
      raw_feature_spec, default_batch_size=None)
  serving_input_receiver = raw_input_fn()

  transformed_features = tf_transform_output.transform_raw_features(
      serving_input_receiver.features)

  labels = transformed_features.pop(_transformed_name(LABEL_KEY))
  return tfma.export.EvalInputReceiver(
      features=transformed_features,
      receiver_tensors=serving_input_receiver.receiver_tensors,
      labels=labels)


def _augment_feature_spec(feature_spec, num_neighbors):
  """Augments `feature_spec` to include neighbor features.
    Args:
      feature_spec: Dictionary of feature keys mapping to TF feature types.
      num_neighbors: Number of neighbors to use for feature key augmentation.
    Returns:
      An augmented `feature_spec` that includes neighbor feature keys.
  """
  for i in range(num_neighbors):
    feature_spec['{}{}_{}'.format(NBR_FEATURE_PREFIX, i, 'id')] = \
        tf.io.VarLenFeature(dtype=tf.string)
    # We don't care about the neighbor features corresponding to
    # _transformed_name(LABEL_KEY) because the LABEL_KEY feature will be
    # removed from the feature spec during training/evaluation.
    feature_spec['{}{}_{}'.format(NBR_FEATURE_PREFIX, i, 'text_xf')] = \
        tf.io.FixedLenFeature(shape=[HPARAMS.max_seq_length], dtype=tf.int64,
                              default_value=tf.constant(0, dtype=tf.int64,
                                                        shape=[HPARAMS.max_seq_length]))
    # The 'NL_num_nbrs' features is currently not used.

  # Set the neighbor weight feature keys.
  for i in range(num_neighbors):
    feature_spec['{}{}{}'.format(NBR_FEATURE_PREFIX, i, NBR_WEIGHT_SUFFIX)] = \
        tf.io.FixedLenFeature(shape=[1], dtype=tf.float32, default_value=[0.0])

  return feature_spec


def _input_fn(filenames, tf_transform_output, is_training, batch_size=200):
  """Generates features and labels for training or evaluation.

  Args:
    filenames: [str] list of CSV files to read data from.
    tf_transform_output: A TFTransformOutput.
    is_training: Boolean indicating if we are in training mode.
    batch_size: int First dimension size of the Tensors returned by input_fn

  Returns:
    A (features, indices) tuple where features is a dictionary of
      Tensors, and indices is a single Tensor of label indices.
  """
  transformed_feature_spec = (
      tf_transform_output.transformed_feature_spec().copy())

  # During training, NSL uses augmented training data (which includes features
  # from graph neighbors). So, update the feature spec accordingly. This needs
  # to be done because we are using different schemas for NSL training and eval,
  # but the Trainer Component only accepts a single schema.
  if is_training:
    transformed_feature_spec =_augment_feature_spec(transformed_feature_spec,
                                                    HPARAMS.num_neighbors)

  dataset = tf.data.experimental.make_batched_features_dataset(
      filenames, batch_size, transformed_feature_spec, reader=_gzip_reader_fn)

  transformed_features = tf.compat.v1.data.make_one_shot_iterator(
      dataset).get_next()
  # We pop the label because we do not want to use it as a feature while we're
  # training.
  return transformed_features, transformed_features.pop(
      _transformed_name(LABEL_KEY))


# TFX will call this function
def trainer_fn(hparams, schema):
  """Build the estimator using the high level API.
  Args:
    hparams: Holds hyperparameters used to train the model as name/value pairs.
    schema: Holds the schema of the training examples.
  Returns:
    A dict of the following:
      - estimator: The estimator that will be used for training and eval.
      - train_spec: Spec for training.
      - eval_spec: Spec for eval.
      - eval_input_receiver_fn: Input function for eval.
  """
  train_batch_size = 40
  eval_batch_size = 40

  tf_transform_output = tft.TFTransformOutput(hparams.transform_output)

  train_input_fn = lambda: _input_fn(
      hparams.train_files,
      tf_transform_output,
      is_training=True,
      batch_size=train_batch_size)

  eval_input_fn = lambda: _input_fn(
      hparams.eval_files,
      tf_transform_output,
      is_training=False,
      batch_size=eval_batch_size)

  train_spec = tf.estimator.TrainSpec(
      train_input_fn,
      max_steps=hparams.train_steps)

  serving_receiver_fn = lambda: _example_serving_receiver_fn(
      tf_transform_output, schema)

  exporter = tf.estimator.FinalExporter('imdb', serving_receiver_fn)
  eval_spec = tf.estimator.EvalSpec(
      eval_input_fn,
      steps=hparams.eval_steps,
      exporters=[exporter],
      name='imdb-eval')

  run_config = tf.estimator.RunConfig(
      save_checkpoints_steps=999, keep_checkpoint_max=1)

  run_config = run_config.replace(model_dir=hparams.serving_model_dir)

  estimator = tf.estimator.Estimator(
      model_fn=feed_forward_model_fn, config=run_config, params=HPARAMS)

  # Create a graph regularization config.
  graph_reg_config = nsl.configs.make_graph_reg_config(
      max_neighbors=HPARAMS.num_neighbors,
      multiplier=HPARAMS.graph_regularization_multiplier,
      distance_type=HPARAMS.distance_type,
      sum_over_axis=-1)

  # Invoke the Graph Regularization Estimator wrapper to incorporate
  # graph-based regularization for training.
  graph_nsl_estimator = nsl.estimator.add_graph_regularization(
      estimator,
      embedding_fn,
      optimizer_fn=optimizer_fn,
      graph_reg_config=graph_reg_config)

  # Create an input receiver for TFMA processing
  receiver_fn = lambda: _eval_input_receiver_fn(
      tf_transform_output, schema)

  return {
      'estimator': graph_nsl_estimator,
      'train_spec': train_spec,
      'eval_spec': eval_spec,
      'eval_input_receiver_fn': receiver_fn
  }
Writing imdb_trainer.py

बनाएँ और चलाने Trainer घटक है, यह फ़ाइल कि हम ऊपर बनाया गुजर।

# 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/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
I1028 02:47:33.654170 25039 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1028 02:47:33.657624 25039 rdbms_metadata_access_object.cc:686] No property is defined for the Type
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying imdb_trainer.py -> build/lib
copying imdb_transform.py -> build/lib
installing to /tmp/tmpvfj1ovfc
running install
running install_lib
copying build/lib/imdb_trainer.py -> /tmp/tmpvfj1ovfc
copying build/lib/imdb_transform.py -> /tmp/tmpvfj1ovfc
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmpvfj1ovfc/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d-py3.7.egg-info
running install_scripts
creating /tmp/tmpvfj1ovfc/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/WHEEL
creating '/tmp/tmpkvidvnl3/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d-py3-none-any.whl' and adding '/tmp/tmpvfj1ovfc' to it
adding 'imdb_trainer.py'
adding 'imdb_transform.py'
adding 'tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/RECORD'
removing /tmp/tmpvfj1ovfc
Processing /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/_wheels/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d-py3-none-any.whl
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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': '/tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 999, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 999 or save_checkpoints_secs None.
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 999 or save_checkpoints_secs None.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/rmsprop.py:123: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/rmsprop.py:123: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.
Instructions for updating:
The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.
Instructions for updating:
The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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...
INFO:tensorflow:loss = 0.69298816, step = 0
INFO:tensorflow:loss = 0.69298816, step = 0
INFO:tensorflow:global_step/sec: 243.588
INFO:tensorflow:global_step/sec: 243.588
INFO:tensorflow:loss = 0.69301873, step = 100 (0.412 sec)
INFO:tensorflow:loss = 0.69301873, step = 100 (0.412 sec)
INFO:tensorflow:global_step/sec: 321.473
INFO:tensorflow:global_step/sec: 321.473
INFO:tensorflow:loss = 0.6922546, step = 200 (0.311 sec)
INFO:tensorflow:loss = 0.6922546, step = 200 (0.311 sec)
INFO:tensorflow:global_step/sec: 324.517
INFO:tensorflow:global_step/sec: 324.517
INFO:tensorflow:loss = 0.69180906, step = 300 (0.309 sec)
INFO:tensorflow:loss = 0.69180906, step = 300 (0.309 sec)
INFO:tensorflow:global_step/sec: 319.969
INFO:tensorflow:global_step/sec: 319.969
INFO:tensorflow:loss = 0.6910258, step = 400 (0.312 sec)
INFO:tensorflow:loss = 0.6910258, step = 400 (0.312 sec)
INFO:tensorflow:global_step/sec: 310.005
INFO:tensorflow:global_step/sec: 310.005
INFO:tensorflow:loss = 0.69065493, step = 500 (0.323 sec)
INFO:tensorflow:loss = 0.69065493, step = 500 (0.323 sec)
INFO:tensorflow:global_step/sec: 324.222
INFO:tensorflow:global_step/sec: 324.222
INFO:tensorflow:loss = 0.6917675, step = 600 (0.309 sec)
INFO:tensorflow:loss = 0.6917675, step = 600 (0.309 sec)
INFO:tensorflow:global_step/sec: 324.223
INFO:tensorflow:global_step/sec: 324.223
INFO:tensorflow:loss = 0.68843853, step = 700 (0.308 sec)
INFO:tensorflow:loss = 0.68843853, step = 700 (0.308 sec)
INFO:tensorflow:global_step/sec: 320.468
INFO:tensorflow:global_step/sec: 320.468
INFO:tensorflow:loss = 0.6887336, step = 800 (0.312 sec)
INFO:tensorflow:loss = 0.6887336, step = 800 (0.312 sec)
INFO:tensorflow:global_step/sec: 318.165
INFO:tensorflow:global_step/sec: 318.165
INFO:tensorflow:loss = 0.68774813, step = 900 (0.314 sec)
INFO:tensorflow:loss = 0.68774813, step = 900 (0.314 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/saver.py:971: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/saver.py:971: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-10-28T02:47:40
INFO:tensorflow:Starting evaluation at 2021-10-28T02:47:40
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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 : 4.96037s
INFO:tensorflow:Inference Time : 4.96037s
INFO:tensorflow:Finished evaluation at 2021-10-28-02:47:45
INFO:tensorflow:Finished evaluation at 2021-10-28-02:47:45
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.6885, global_step = 999, loss = 0.6875502
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.6885, global_step = 999, loss = 0.6875502
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:global_step/sec: 18.2139
INFO:tensorflow:global_step/sec: 18.2139
INFO:tensorflow:loss = 0.6852747, step = 1000 (5.490 sec)
INFO:tensorflow:loss = 0.6852747, step = 1000 (5.490 sec)
INFO:tensorflow:global_step/sec: 306.553
INFO:tensorflow:global_step/sec: 306.553
INFO:tensorflow:loss = 0.6868101, step = 1100 (0.327 sec)
INFO:tensorflow:loss = 0.6868101, step = 1100 (0.327 sec)
INFO:tensorflow:global_step/sec: 316.326
INFO:tensorflow:global_step/sec: 316.326
INFO:tensorflow:loss = 0.68222797, step = 1200 (0.316 sec)
INFO:tensorflow:loss = 0.68222797, step = 1200 (0.316 sec)
INFO:tensorflow:global_step/sec: 319.366
INFO:tensorflow:global_step/sec: 319.366
INFO:tensorflow:loss = 0.6802751, step = 1300 (0.313 sec)
INFO:tensorflow:loss = 0.6802751, step = 1300 (0.313 sec)
INFO:tensorflow:global_step/sec: 315.694
INFO:tensorflow:global_step/sec: 315.694
INFO:tensorflow:loss = 0.6784301, step = 1400 (0.317 sec)
INFO:tensorflow:loss = 0.6784301, step = 1400 (0.317 sec)
INFO:tensorflow:global_step/sec: 320.591
INFO:tensorflow:global_step/sec: 320.591
INFO:tensorflow:loss = 0.66767603, step = 1500 (0.312 sec)
INFO:tensorflow:loss = 0.66767603, step = 1500 (0.312 sec)
INFO:tensorflow:global_step/sec: 319.248
INFO:tensorflow:global_step/sec: 319.248
INFO:tensorflow:loss = 0.67331743, step = 1600 (0.313 sec)
INFO:tensorflow:loss = 0.67331743, step = 1600 (0.313 sec)
INFO:tensorflow:global_step/sec: 307.423
INFO:tensorflow:global_step/sec: 307.423
INFO:tensorflow:loss = 0.6701387, step = 1700 (0.326 sec)
INFO:tensorflow:loss = 0.6701387, step = 1700 (0.326 sec)
INFO:tensorflow:global_step/sec: 318.334
INFO:tensorflow:global_step/sec: 318.334
INFO:tensorflow:loss = 0.67697465, step = 1800 (0.314 sec)
INFO:tensorflow:loss = 0.67697465, step = 1800 (0.314 sec)
INFO:tensorflow:global_step/sec: 318.521
INFO:tensorflow:global_step/sec: 318.521
INFO:tensorflow:loss = 0.67850894, step = 1900 (0.314 sec)
INFO:tensorflow:loss = 0.67850894, step = 1900 (0.314 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: 271.246
INFO:tensorflow:global_step/sec: 271.246
INFO:tensorflow:loss = 0.6711386, step = 2000 (0.368 sec)
INFO:tensorflow:loss = 0.6711386, step = 2000 (0.368 sec)
INFO:tensorflow:global_step/sec: 319.677
INFO:tensorflow:global_step/sec: 319.677
INFO:tensorflow:loss = 0.6755986, step = 2100 (0.313 sec)
INFO:tensorflow:loss = 0.6755986, step = 2100 (0.313 sec)
INFO:tensorflow:global_step/sec: 318.589
INFO:tensorflow:global_step/sec: 318.589
INFO:tensorflow:loss = 0.65216315, step = 2200 (0.314 sec)
INFO:tensorflow:loss = 0.65216315, step = 2200 (0.314 sec)
INFO:tensorflow:global_step/sec: 320.648
INFO:tensorflow:global_step/sec: 320.648
INFO:tensorflow:loss = 0.667237, step = 2300 (0.312 sec)
INFO:tensorflow:loss = 0.667237, step = 2300 (0.312 sec)
INFO:tensorflow:global_step/sec: 322.202
INFO:tensorflow:global_step/sec: 322.202
INFO:tensorflow:loss = 0.6621568, step = 2400 (0.311 sec)
INFO:tensorflow:loss = 0.6621568, step = 2400 (0.311 sec)
INFO:tensorflow:global_step/sec: 318.64
INFO:tensorflow:global_step/sec: 318.64
INFO:tensorflow:loss = 0.6612643, step = 2500 (0.314 sec)
INFO:tensorflow:loss = 0.6612643, step = 2500 (0.314 sec)
INFO:tensorflow:global_step/sec: 321.318
INFO:tensorflow:global_step/sec: 321.318
INFO:tensorflow:loss = 0.64929426, step = 2600 (0.311 sec)
INFO:tensorflow:loss = 0.64929426, step = 2600 (0.311 sec)
INFO:tensorflow:global_step/sec: 315.913
INFO:tensorflow:global_step/sec: 315.913
INFO:tensorflow:loss = 0.6500967, step = 2700 (0.317 sec)
INFO:tensorflow:loss = 0.6500967, step = 2700 (0.317 sec)
INFO:tensorflow:global_step/sec: 322.475
INFO:tensorflow:global_step/sec: 322.475
INFO:tensorflow:loss = 0.6355903, step = 2800 (0.310 sec)
INFO:tensorflow:loss = 0.6355903, step = 2800 (0.310 sec)
INFO:tensorflow:global_step/sec: 323.875
INFO:tensorflow:global_step/sec: 323.875
INFO:tensorflow:loss = 0.66079515, step = 2900 (0.309 sec)
INFO:tensorflow:loss = 0.66079515, step = 2900 (0.309 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: 267.211
INFO:tensorflow:global_step/sec: 267.211
INFO:tensorflow:loss = 0.644119, step = 3000 (0.374 sec)
INFO:tensorflow:loss = 0.644119, step = 3000 (0.374 sec)
INFO:tensorflow:global_step/sec: 310.211
INFO:tensorflow:global_step/sec: 310.211
INFO:tensorflow:loss = 0.6239173, step = 3100 (0.323 sec)
INFO:tensorflow:loss = 0.6239173, step = 3100 (0.323 sec)
INFO:tensorflow:global_step/sec: 317.532
INFO:tensorflow:global_step/sec: 317.532
INFO:tensorflow:loss = 0.62653995, step = 3200 (0.315 sec)
INFO:tensorflow:loss = 0.62653995, step = 3200 (0.315 sec)
INFO:tensorflow:global_step/sec: 315.576
INFO:tensorflow:global_step/sec: 315.576
INFO:tensorflow:loss = 0.6394695, step = 3300 (0.317 sec)
INFO:tensorflow:loss = 0.6394695, step = 3300 (0.317 sec)
INFO:tensorflow:global_step/sec: 315.671
INFO:tensorflow:global_step/sec: 315.671
INFO:tensorflow:loss = 0.59796745, step = 3400 (0.316 sec)
INFO:tensorflow:loss = 0.59796745, step = 3400 (0.316 sec)
INFO:tensorflow:global_step/sec: 318.206
INFO:tensorflow:global_step/sec: 318.206
INFO:tensorflow:loss = 0.6123538, step = 3500 (0.315 sec)
INFO:tensorflow:loss = 0.6123538, step = 3500 (0.315 sec)
INFO:tensorflow:global_step/sec: 297.048
INFO:tensorflow:global_step/sec: 297.048
INFO:tensorflow:loss = 0.58024055, step = 3600 (0.336 sec)
INFO:tensorflow:loss = 0.58024055, step = 3600 (0.336 sec)
INFO:tensorflow:global_step/sec: 318.749
INFO:tensorflow:global_step/sec: 318.749
INFO:tensorflow:loss = 0.6457875, step = 3700 (0.314 sec)
INFO:tensorflow:loss = 0.6457875, step = 3700 (0.314 sec)
INFO:tensorflow:global_step/sec: 320.89
INFO:tensorflow:global_step/sec: 320.89
INFO:tensorflow:loss = 0.6119324, step = 3800 (0.311 sec)
INFO:tensorflow:loss = 0.6119324, step = 3800 (0.311 sec)
INFO:tensorflow:global_step/sec: 317.382
INFO:tensorflow:global_step/sec: 317.382
INFO:tensorflow:loss = 0.5877368, step = 3900 (0.316 sec)
INFO:tensorflow:loss = 0.5877368, step = 3900 (0.316 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: 269.801
INFO:tensorflow:global_step/sec: 269.801
INFO:tensorflow:loss = 0.57802856, step = 4000 (0.370 sec)
INFO:tensorflow:loss = 0.57802856, step = 4000 (0.370 sec)
INFO:tensorflow:global_step/sec: 317.578
INFO:tensorflow:global_step/sec: 317.578
INFO:tensorflow:loss = 0.63977724, step = 4100 (0.315 sec)
INFO:tensorflow:loss = 0.63977724, step = 4100 (0.315 sec)
INFO:tensorflow:global_step/sec: 315.982
INFO:tensorflow:global_step/sec: 315.982
INFO:tensorflow:loss = 0.61006814, step = 4200 (0.317 sec)
INFO:tensorflow:loss = 0.61006814, step = 4200 (0.317 sec)
INFO:tensorflow:global_step/sec: 312.6
INFO:tensorflow:global_step/sec: 312.6
INFO:tensorflow:loss = 0.5987768, step = 4300 (0.320 sec)
INFO:tensorflow:loss = 0.5987768, step = 4300 (0.320 sec)
INFO:tensorflow:global_step/sec: 316.047
INFO:tensorflow:global_step/sec: 316.047
INFO:tensorflow:loss = 0.56720686, step = 4400 (0.316 sec)
INFO:tensorflow:loss = 0.56720686, step = 4400 (0.316 sec)
INFO:tensorflow:global_step/sec: 323.508
INFO:tensorflow:global_step/sec: 323.508
INFO:tensorflow:loss = 0.61733603, step = 4500 (0.309 sec)
INFO:tensorflow:loss = 0.61733603, step = 4500 (0.309 sec)
INFO:tensorflow:global_step/sec: 317.505
INFO:tensorflow:global_step/sec: 317.505
INFO:tensorflow:loss = 0.568853, step = 4600 (0.315 sec)
INFO:tensorflow:loss = 0.568853, step = 4600 (0.315 sec)
INFO:tensorflow:global_step/sec: 321.277
INFO:tensorflow:global_step/sec: 321.277
INFO:tensorflow:loss = 0.591113, step = 4700 (0.311 sec)
INFO:tensorflow:loss = 0.591113, step = 4700 (0.311 sec)
INFO:tensorflow:global_step/sec: 313.548
INFO:tensorflow:global_step/sec: 313.548
INFO:tensorflow:loss = 0.5735426, step = 4800 (0.319 sec)
INFO:tensorflow:loss = 0.5735426, step = 4800 (0.319 sec)
INFO:tensorflow:global_step/sec: 319.56
INFO:tensorflow:global_step/sec: 319.56
INFO:tensorflow:loss = 0.5779941, step = 4900 (0.313 sec)
INFO:tensorflow:loss = 0.5779941, step = 4900 (0.313 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: 269.818
INFO:tensorflow:global_step/sec: 269.818
INFO:tensorflow:loss = 0.54833865, step = 5000 (0.370 sec)
INFO:tensorflow:loss = 0.54833865, step = 5000 (0.370 sec)
INFO:tensorflow:global_step/sec: 306.899
INFO:tensorflow:global_step/sec: 306.899
INFO:tensorflow:loss = 0.5404458, step = 5100 (0.326 sec)
INFO:tensorflow:loss = 0.5404458, step = 5100 (0.326 sec)
INFO:tensorflow:global_step/sec: 321.348
INFO:tensorflow:global_step/sec: 321.348
INFO:tensorflow:loss = 0.5042118, step = 5200 (0.311 sec)
INFO:tensorflow:loss = 0.5042118, step = 5200 (0.311 sec)
INFO:tensorflow:global_step/sec: 315.826
INFO:tensorflow:global_step/sec: 315.826
INFO:tensorflow:loss = 0.5446397, step = 5300 (0.316 sec)
INFO:tensorflow:loss = 0.5446397, step = 5300 (0.316 sec)
INFO:tensorflow:global_step/sec: 316.402
INFO:tensorflow:global_step/sec: 316.402
INFO:tensorflow:loss = 0.6106853, step = 5400 (0.316 sec)
INFO:tensorflow:loss = 0.6106853, step = 5400 (0.316 sec)
INFO:tensorflow:global_step/sec: 310.282
INFO:tensorflow:global_step/sec: 310.282
INFO:tensorflow:loss = 0.587035, step = 5500 (0.322 sec)
INFO:tensorflow:loss = 0.587035, step = 5500 (0.322 sec)
INFO:tensorflow:global_step/sec: 319.818
INFO:tensorflow:global_step/sec: 319.818
INFO:tensorflow:loss = 0.5458641, step = 5600 (0.313 sec)
INFO:tensorflow:loss = 0.5458641, step = 5600 (0.313 sec)
INFO:tensorflow:global_step/sec: 319.431
INFO:tensorflow:global_step/sec: 319.431
INFO:tensorflow:loss = 0.43642598, step = 5700 (0.313 sec)
INFO:tensorflow:loss = 0.43642598, step = 5700 (0.313 sec)
INFO:tensorflow:global_step/sec: 317.408
INFO:tensorflow:global_step/sec: 317.408
INFO:tensorflow:loss = 0.49558413, step = 5800 (0.315 sec)
INFO:tensorflow:loss = 0.49558413, step = 5800 (0.315 sec)
INFO:tensorflow:global_step/sec: 309.985
INFO:tensorflow:global_step/sec: 309.985
INFO:tensorflow:loss = 0.46257922, step = 5900 (0.322 sec)
INFO:tensorflow:loss = 0.46257922, step = 5900 (0.322 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: 271.273
INFO:tensorflow:global_step/sec: 271.273
INFO:tensorflow:loss = 0.6500386, step = 6000 (0.369 sec)
INFO:tensorflow:loss = 0.6500386, step = 6000 (0.369 sec)
INFO:tensorflow:global_step/sec: 315.42
INFO:tensorflow:global_step/sec: 315.42
INFO:tensorflow:loss = 0.5061226, step = 6100 (0.318 sec)
INFO:tensorflow:loss = 0.5061226, step = 6100 (0.318 sec)
INFO:tensorflow:global_step/sec: 316.96
INFO:tensorflow:global_step/sec: 316.96
INFO:tensorflow:loss = 0.4198026, step = 6200 (0.315 sec)
INFO:tensorflow:loss = 0.4198026, step = 6200 (0.315 sec)
INFO:tensorflow:global_step/sec: 308.406
INFO:tensorflow:global_step/sec: 308.406
INFO:tensorflow:loss = 0.4695281, step = 6300 (0.324 sec)
INFO:tensorflow:loss = 0.4695281, step = 6300 (0.324 sec)
INFO:tensorflow:global_step/sec: 317.948
INFO:tensorflow:global_step/sec: 317.948
INFO:tensorflow:loss = 0.5069947, step = 6400 (0.315 sec)
INFO:tensorflow:loss = 0.5069947, step = 6400 (0.315 sec)
INFO:tensorflow:global_step/sec: 323.767
INFO:tensorflow:global_step/sec: 323.767
INFO:tensorflow:loss = 0.47904342, step = 6500 (0.309 sec)
INFO:tensorflow:loss = 0.47904342, step = 6500 (0.309 sec)
INFO:tensorflow:global_step/sec: 318.574
INFO:tensorflow:global_step/sec: 318.574
INFO:tensorflow:loss = 0.4616745, step = 6600 (0.314 sec)
INFO:tensorflow:loss = 0.4616745, step = 6600 (0.314 sec)
INFO:tensorflow:global_step/sec: 319.307
INFO:tensorflow:global_step/sec: 319.307
INFO:tensorflow:loss = 0.46637583, step = 6700 (0.313 sec)
INFO:tensorflow:loss = 0.46637583, step = 6700 (0.313 sec)
INFO:tensorflow:global_step/sec: 312.805
INFO:tensorflow:global_step/sec: 312.805
INFO:tensorflow:loss = 0.48392227, step = 6800 (0.320 sec)
INFO:tensorflow:loss = 0.48392227, step = 6800 (0.320 sec)
INFO:tensorflow:global_step/sec: 309.576
INFO:tensorflow:global_step/sec: 309.576
INFO:tensorflow:loss = 0.41203576, step = 6900 (0.323 sec)
INFO:tensorflow:loss = 0.41203576, step = 6900 (0.323 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: 268.599
INFO:tensorflow:global_step/sec: 268.599
INFO:tensorflow:loss = 0.41761953, step = 7000 (0.372 sec)
INFO:tensorflow:loss = 0.41761953, step = 7000 (0.372 sec)
INFO:tensorflow:global_step/sec: 313.762
INFO:tensorflow:global_step/sec: 313.762
INFO:tensorflow:loss = 0.49612182, step = 7100 (0.319 sec)
INFO:tensorflow:loss = 0.49612182, step = 7100 (0.319 sec)
INFO:tensorflow:global_step/sec: 321.066
INFO:tensorflow:global_step/sec: 321.066
INFO:tensorflow:loss = 0.47146606, step = 7200 (0.311 sec)
INFO:tensorflow:loss = 0.47146606, step = 7200 (0.311 sec)
INFO:tensorflow:global_step/sec: 317.665
INFO:tensorflow:global_step/sec: 317.665
INFO:tensorflow:loss = 0.5139851, step = 7300 (0.315 sec)
INFO:tensorflow:loss = 0.5139851, step = 7300 (0.315 sec)
INFO:tensorflow:global_step/sec: 318.187
INFO:tensorflow:global_step/sec: 318.187
INFO:tensorflow:loss = 0.3835188, step = 7400 (0.314 sec)
INFO:tensorflow:loss = 0.3835188, step = 7400 (0.314 sec)
INFO:tensorflow:global_step/sec: 313.408
INFO:tensorflow:global_step/sec: 313.408
INFO:tensorflow:loss = 0.38438293, step = 7500 (0.319 sec)
INFO:tensorflow:loss = 0.38438293, step = 7500 (0.319 sec)
INFO:tensorflow:global_step/sec: 315.206
INFO:tensorflow:global_step/sec: 315.206
INFO:tensorflow:loss = 0.51628244, step = 7600 (0.317 sec)
INFO:tensorflow:loss = 0.51628244, step = 7600 (0.317 sec)
INFO:tensorflow:global_step/sec: 312.699
INFO:tensorflow:global_step/sec: 312.699
INFO:tensorflow:loss = 0.50062245, step = 7700 (0.320 sec)
INFO:tensorflow:loss = 0.50062245, step = 7700 (0.320 sec)
INFO:tensorflow:global_step/sec: 326.366
INFO:tensorflow:global_step/sec: 326.366
INFO:tensorflow:loss = 0.4062972, step = 7800 (0.306 sec)
INFO:tensorflow:loss = 0.4062972, step = 7800 (0.306 sec)
INFO:tensorflow:global_step/sec: 309.612
INFO:tensorflow:global_step/sec: 309.612
INFO:tensorflow:loss = 0.42835328, step = 7900 (0.323 sec)
INFO:tensorflow:loss = 0.42835328, step = 7900 (0.323 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: 265.605
INFO:tensorflow:global_step/sec: 265.605
INFO:tensorflow:loss = 0.3621876, step = 8000 (0.376 sec)
INFO:tensorflow:loss = 0.3621876, step = 8000 (0.376 sec)
INFO:tensorflow:global_step/sec: 306.326
INFO:tensorflow:global_step/sec: 306.326
INFO:tensorflow:loss = 0.46075007, step = 8100 (0.326 sec)
INFO:tensorflow:loss = 0.46075007, step = 8100 (0.326 sec)
INFO:tensorflow:global_step/sec: 314.602
INFO:tensorflow:global_step/sec: 314.602
INFO:tensorflow:loss = 0.44456515, step = 8200 (0.318 sec)
INFO:tensorflow:loss = 0.44456515, step = 8200 (0.318 sec)
INFO:tensorflow:global_step/sec: 317.593
INFO:tensorflow:global_step/sec: 317.593
INFO:tensorflow:loss = 0.46498927, step = 8300 (0.315 sec)
INFO:tensorflow:loss = 0.46498927, step = 8300 (0.315 sec)
INFO:tensorflow:global_step/sec: 309.464
INFO:tensorflow:global_step/sec: 309.464
INFO:tensorflow:loss = 0.3697742, step = 8400 (0.323 sec)
INFO:tensorflow:loss = 0.3697742, step = 8400 (0.323 sec)
INFO:tensorflow:global_step/sec: 315.682
INFO:tensorflow:global_step/sec: 315.682
INFO:tensorflow:loss = 0.43523034, step = 8500 (0.317 sec)
INFO:tensorflow:loss = 0.43523034, step = 8500 (0.317 sec)
INFO:tensorflow:global_step/sec: 317.504
INFO:tensorflow:global_step/sec: 317.504
INFO:tensorflow:loss = 0.41306037, step = 8600 (0.315 sec)
INFO:tensorflow:loss = 0.41306037, step = 8600 (0.315 sec)
INFO:tensorflow:global_step/sec: 320.336
INFO:tensorflow:global_step/sec: 320.336
INFO:tensorflow:loss = 0.43034118, step = 8700 (0.312 sec)
INFO:tensorflow:loss = 0.43034118, step = 8700 (0.312 sec)
INFO:tensorflow:global_step/sec: 320.703
INFO:tensorflow:global_step/sec: 320.703
INFO:tensorflow:loss = 0.46746066, step = 8800 (0.311 sec)
INFO:tensorflow:loss = 0.46746066, step = 8800 (0.311 sec)
INFO:tensorflow:global_step/sec: 322.076
INFO:tensorflow:global_step/sec: 322.076
INFO:tensorflow:loss = 0.46441948, step = 8900 (0.311 sec)
INFO:tensorflow:loss = 0.46441948, step = 8900 (0.311 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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: 278.942
INFO:tensorflow:global_step/sec: 278.942
INFO:tensorflow:loss = 0.4126982, step = 9000 (0.358 sec)
INFO:tensorflow:loss = 0.4126982, step = 9000 (0.358 sec)
INFO:tensorflow:global_step/sec: 318.234
INFO:tensorflow:global_step/sec: 318.234
INFO:tensorflow:loss = 0.482938, step = 9100 (0.314 sec)
INFO:tensorflow:loss = 0.482938, step = 9100 (0.314 sec)
INFO:tensorflow:global_step/sec: 315.142
INFO:tensorflow:global_step/sec: 315.142
INFO:tensorflow:loss = 0.37345544, step = 9200 (0.317 sec)
INFO:tensorflow:loss = 0.37345544, step = 9200 (0.317 sec)
INFO:tensorflow:global_step/sec: 315.877
INFO:tensorflow:global_step/sec: 315.877
INFO:tensorflow:loss = 0.48009828, step = 9300 (0.317 sec)
INFO:tensorflow:loss = 0.48009828, step = 9300 (0.317 sec)
INFO:tensorflow:global_step/sec: 319.972
INFO:tensorflow:global_step/sec: 319.972
INFO:tensorflow:loss = 0.3803263, step = 9400 (0.312 sec)
INFO:tensorflow:loss = 0.3803263, step = 9400 (0.312 sec)
INFO:tensorflow:global_step/sec: 320.043
INFO:tensorflow:global_step/sec: 320.043
INFO:tensorflow:loss = 0.52932256, step = 9500 (0.312 sec)
INFO:tensorflow:loss = 0.52932256, step = 9500 (0.312 sec)
INFO:tensorflow:global_step/sec: 323.459
INFO:tensorflow:global_step/sec: 323.459
INFO:tensorflow:loss = 0.34036633, step = 9600 (0.310 sec)
INFO:tensorflow:loss = 0.34036633, step = 9600 (0.310 sec)
INFO:tensorflow:global_step/sec: 323.28
INFO:tensorflow:global_step/sec: 323.28
INFO:tensorflow:loss = 0.46806738, step = 9700 (0.309 sec)
INFO:tensorflow:loss = 0.46806738, step = 9700 (0.309 sec)
INFO:tensorflow:global_step/sec: 316.938
INFO:tensorflow:global_step/sec: 316.938
INFO:tensorflow:loss = 0.37460634, step = 9800 (0.316 sec)
INFO:tensorflow:loss = 0.37460634, step = 9800 (0.316 sec)
INFO:tensorflow:global_step/sec: 318.631
INFO:tensorflow:global_step/sec: 318.631
INFO:tensorflow:loss = 0.37890875, step = 9900 (0.314 sec)
INFO:tensorflow:loss = 0.37890875, step = 9900 (0.314 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-10-28T02:48:14
INFO:tensorflow:Starting evaluation at 2021-10-28T02:48:14
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/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 : 5.00659s
INFO:tensorflow:Inference Time : 5.00659s
INFO:tensorflow:Finished evaluation at 2021-10-28-02:48:19
INFO:tensorflow:Finished evaluation at 2021-10-28-02:48:19
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.7977, global_step = 10000, loss = 0.44678783
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.7977, global_step = 10000, loss = 0.44678783
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Performing the final export in the end of training.
INFO:tensorflow:Performing the final export in the end of training.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/export/imdb/temp-1635389299/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/export/imdb/temp-1635389299/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/export/imdb/temp-1635389299/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/export/imdb/temp-1635389299/saved_model.pb
INFO:tensorflow:Loss for final step: 0.31756505.
INFO:tensorflow:Loss for final step: 0.31756505.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-TFMA/temp-1635389301/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-TFMA/temp-1635389301/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-TFMA/temp-1635389301/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-10-28T02_45_08.232114-53797g92/Trainer/model_run/9/Format-TFMA/temp-1635389301/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"

प्रशिक्षित मॉडल है जहाँ से निर्यात किया गया था पर पर नज़र 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>,
 '...']

आइए Tensorboard का उपयोग करके मॉडल के मेट्रिक्स की कल्पना करें।


# 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}

मॉडल सर्विंग

ग्राफ़ नियमितीकरण केवल हानि फ़ंक्शन में नियमितीकरण शब्द जोड़कर प्रशिक्षण कार्यप्रवाह को प्रभावित करता है। परिणामस्वरूप, मॉडल मूल्यांकन और प्रस्तुतिकरण कार्यप्रवाह अपरिवर्तित रहते हैं। ऐसा नहीं है कि हम भी नीचे की ओर TFX घटक है कि आम तौर पर मूल्यांकनकर्ता, पुशर, आदि जैसे ट्रेनर घटक के बाद आते हैं हटा दिया है इसी कारण से है

निष्कर्ष

हमने टीएफएक्स पाइपलाइन में न्यूरल स्ट्रक्चर्ड लर्निंग (एनएसएल) ढांचे का उपयोग करके ग्राफ नियमितीकरण के उपयोग का प्रदर्शन किया है, भले ही इनपुट में स्पष्ट ग्राफ न हो। हमने IMDB मूवी समीक्षाओं के भावना वर्गीकरण के कार्य पर विचार किया, जिसके लिए हमने समीक्षा एम्बेडिंग के आधार पर एक समानता ग्राफ़ को संश्लेषित किया। हम उपयोगकर्ताओं को ग्राफ़ निर्माण के लिए अलग-अलग एम्बेडिंग का उपयोग करके, अलग-अलग हाइपरपैरामीटर, पर्यवेक्षण की मात्रा को बदलकर, और विभिन्न मॉडल आर्किटेक्चर को परिभाषित करके आगे प्रयोग करने के लिए प्रोत्साहित करते हैं।