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Aprendizaje estructurado neuronal basado en gráficos en TFX

Este tutorial describe regularización gráfico de la Learning Neural estructurado marco y demuestra un flujo de trabajo de extremo a extremo para la clasificación sentimiento en una tubería TFX.

Visión general

Este clasifica portátiles de películas críticas como positivas o negativas utilizando el texto de la revisión. Este es un ejemplo de clasificación binaria, un importante y ampliamente aplicable tipo de problema de aprendizaje automático.

Demostraremos el uso de la regularización de gráficos en este cuaderno construyendo un gráfico a partir de la entrada dada. La receta general para construir un modelo de gráfico regularizado utilizando el marco de aprendizaje estructurado neuronal (NSL) cuando la entrada no contiene un gráfico explícito es la siguiente:

  1. Cree incrustaciones para cada muestra de texto en la entrada. Esto se puede hacer utilizando modelos pre-formados como word2vec , giratorio , BERT etc.
  2. Construya un gráfico basado en estas incrustaciones usando una métrica de similitud como la distancia 'L2', la distancia 'coseno', etc. Los nodos en el gráfico corresponden a muestras y los bordes en el gráfico corresponden a similitudes entre pares de muestras.
  3. Genere datos de entrenamiento a partir del gráfico sintetizado y las funciones de muestra anteriores. Los datos de entrenamiento resultantes contendrán características vecinas además de las características originales del nodo.
  4. Cree una red neuronal como modelo base utilizando Estimadores.
  5. Envolver el modelo de base con el add_graph_regularization función de contenedor, que es proporcionado por el marco NSL, para crear un nuevo gráfico modelo Estimador. Este nuevo modelo incluirá un gráfico de pérdida de regularización como término de regularización en su objetivo de entrenamiento.
  6. Entrenar y evaluar el modelo de estimador de grafos.

En este tutorial, integramos el flujo de trabajo anterior en una canalización TFX utilizando varios componentes TFX personalizados, así como un componente de entrenador personalizado con gráficos regularizados.

A continuación se muestra el esquema de nuestra canalización TFX. Las cajas naranjas representan componentes TFX disponibles en el mercado y las cajas rosas representan componentes TFX personalizados.

Oleoducto TFX

Actualizar Pip

Para evitar actualizar Pip en un sistema cuando se ejecuta localmente, verifique que estemos ejecutando en Colab. Por supuesto, los sistemas locales se pueden actualizar por separado.

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

Instalar paquetes requeridos

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

¿Reinició el tiempo de ejecución?

Si está utilizando Google Colab, la primera vez que ejecuta la celda anterior, debe reiniciar el tiempo de ejecución (Tiempo de ejecución> Reiniciar tiempo de ejecución ...). Esto se debe a la forma en que Colab carga los paquetes.

Dependencias e importaciones

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:  2.5.2
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:  2.34.0

Conjunto de datos de IMDB

El conjunto de datos IMDB contiene el texto de 50.000 reseñas de películas de la Internet Movie Database . Estos se dividen en 25,000 revisiones para capacitación y 25,000 revisiones para pruebas. Las prácticas y pruebas conjuntos están equilibrados, lo que significa que contienen el mismo número de críticas positivas y negativas. Además, hay 50.000 reseñas de películas adicionales sin etiquetar.

Descargar el conjunto de datos de IMDB preprocesado

El siguiente código descarga el conjunto de datos de IMDB (o usa una copia en caché si ya se descargó) usando TFDS. Para acelerar este portátil, utilizaremos solo 10,000 revisiones etiquetadas y 10,000 revisiones no etiquetadas para capacitación y 10,000 revisiones de prueba para evaluación.

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

Veamos algunas reseñas del conjunto de entrenamiento:

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

Ejecute componentes TFX de forma interactiva

En las células que siguen se pueden construir componentes TFX y ejecutar cada uno de forma interactiva dentro de la InteractiveContext obtener ExecutionResult objetos. Esto refleja el proceso de un orquestador que ejecuta componentes en un TFX DAG en función de cuándo se cumplen las dependencias de cada componente.

context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9 as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/metadata.sqlite.

El componente ExampleGen

En cualquier proceso de desarrollo de ML, el primer paso al iniciar el desarrollo de código es ingerir los conjuntos de datos de entrenamiento y prueba. El ExampleGen componente aporta datos en la tubería TFX.

Cree un componente ExampleGen y ejecútelo.

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-2021-12-04T11_41_51.482724-py59cet9/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:1638618106,sum_checksum:1638618106\nsplit:eval,num_files:1,total_bytes:13374744,xor_checksum:1638618111,sum_checksum:1638618111"
  }
}
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-2021-12-04T11_41_51.482724-py59cet9/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:1638618106,sum_checksum:1638618106\nsplit:eval,num_files:1,total_bytes:13374744,xor_checksum:1638618111,sum_checksum:1638618111"
  }
}
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"]

Las salidas del componente incluyen 2 artefactos:

  • los ejemplos de capacitación (10,000 reseñas etiquetadas + 10,000 reseñas sin etiqueta)
  • los ejemplos de evaluación (10,000 reseñas etiquetadas)

El componente personalizado IdentifyExamples

Para usar NSL, necesitaremos que cada instancia tenga una ID única. Creamos un componente personalizado que agrega una ID única a todas las instancias en todas las divisiones. Nos aprovechamos de Apache Beam para poder escalar fácilmente a grandes conjuntos de datos si es necesario.

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.

El componente StatisticsGen

El StatisticsGen componente calcula la estadística descriptiva para el conjunto de datos. Las estadísticas que genera se pueden visualizar para su revisión y se utilizan, por ejemplo, para la validación y para inferir un esquema.

Cree un componente StatisticsGen y ejecútelo.

# 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.

El componente SchemaGen

El SchemaGen componente genera un esquema para los datos basados en las estadísticas de StatisticsGen. Intenta inferir los tipos de datos de cada una de sus características y los rangos de valores legales para las características categóricas.

Cree un componente SchemaGen y ejecútelo.

# 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
I1204 11:42:13.777263  6839 rdbms_metadata_access_object.cc:686] No property is defined for the Type

El artefacto generado es sólo un schema.pbtxt que contiene una representación de texto de un 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())

Puede ser visualizado utilizando tfdv.display_schema() (veremos esto con más detalle en un laboratorio posterior):

tfdv.display_schema(schema)

El componente ExampleValidator

El ExampleValidator realiza la detección de anomalías, en base a las estadísticas de StatisticsGen y el esquema de SchemaGen. Busca problemas como valores perdidos, valores del tipo incorrecto o valores categóricos fuera del dominio de valores aceptables.

Cree un componente ExampleValidator y ejecútelo.

# 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)

El componente SynthesizeGraph

La construcción de gráficos implica crear incrustaciones para muestras de texto y luego usar una función de similitud para comparar las incrustaciones.

Vamos a utilizar incrustaciones giratorios pretrained para crear inclusiones en el tf.train.Example formato para cada muestra en la entrada. Vamos a almacenar las inclusiones resultantes en el TFRecord formato junto con la identificación de la muestra. Esto es importante y nos permitirá hacer coincidir las incrustaciones de muestra con los nodos correspondientes en el gráfico más adelante.

Una vez que tengamos las incrustaciones de muestra, las usaremos para construir un gráfico de similitud, es decir, los nodos en este gráfico corresponderán a las muestras y los bordes en este gráfico corresponderán a la similitud entre pares de nodos.

El aprendizaje estructurado neuronal proporciona una biblioteca de creación de gráficos para crear un gráfico basado en incrustaciones de muestra. Utiliza similitud del coseno como la medida de similitud para comparar las incrustaciones y bordes de construcción entre ellos. También nos permite especificar un umbral de similitud, que se puede utilizar para descartar bordes diferentes del gráfico final. En el siguiente ejemplo, usando 0,99 como umbral de similitud, terminamos con un gráfico que tiene 111,066 bordes bidireccionales.

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


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


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


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

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

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

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


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


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

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

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


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


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


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

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

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

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

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

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

  return
synthesize_graph = SynthesizeGraph(
    identified_examples=identify_examples.outputs['identified_examples'],
    component_name=u'SynthesizeGraph',
    similarity_threshold=0.99)
context.run(synthesize_graph, enable_cache=False)
Creating embeddings...
Synthesizing graph...
train_uri = synthesize_graph.outputs["synthesized_graph"].get()[0].uri
os.listdir(train_uri)
['Split-train']
graph_path = os.path.join(train_uri, "Split-train", "graph.tsv")
print("node 1\t\t\t\t\tnode 2\t\t\t\t\tsimilarity")
!head {graph_path}
print("...")
!tail {graph_path}
node 1                  node 2                  similarity
8c4f4c09-3dfa-4b8f-b3eb-e1596f7509ed    638cfa94-ebb5-4182-bb18-a8f4cc332131    0.990838
638cfa94-ebb5-4182-bb18-a8f4cc332131    8c4f4c09-3dfa-4b8f-b3eb-e1596f7509ed    0.990838
8c4f4c09-3dfa-4b8f-b3eb-e1596f7509ed    1f9023b1-d312-4fc5-b87f-52636c7b0ea8    0.990184
1f9023b1-d312-4fc5-b87f-52636c7b0ea8    8c4f4c09-3dfa-4b8f-b3eb-e1596f7509ed    0.990184
292e3cc8-7c6b-4463-98d8-5dbfa88a75f9    1ec31309-2b4a-4a4c-9f72-083f201d54a7    0.992471
1ec31309-2b4a-4a4c-9f72-083f201d54a7    292e3cc8-7c6b-4463-98d8-5dbfa88a75f9    0.992471
d5560e01-40d9-4cc0-9cd0-23355c7378f2    b78d8ee6-e404-44bf-a5bc-977b883d1913    0.992505
b78d8ee6-e404-44bf-a5bc-977b883d1913    d5560e01-40d9-4cc0-9cd0-23355c7378f2    0.992505
e138ef2e-4fe4-44b0-a4dc-8b01266b7ae6    b78d8ee6-e404-44bf-a5bc-977b883d1913    0.992823
b78d8ee6-e404-44bf-a5bc-977b883d1913    e138ef2e-4fe4-44b0-a4dc-8b01266b7ae6    0.992823
...
11f44e7c-8393-4d17-8810-ca1f5e60e692    029e39a4-cd35-4e33-bdb1-8547f56a1ca7    0.991879
029e39a4-cd35-4e33-bdb1-8547f56a1ca7    11f44e7c-8393-4d17-8810-ca1f5e60e692    0.991879
4bdebeac-2f54-47a2-889c-3c2cf190e2dd    5eb7cfca-1f3d-4a32-9746-ebcca805b1d0    0.991046
5eb7cfca-1f3d-4a32-9746-ebcca805b1d0    4bdebeac-2f54-47a2-889c-3c2cf190e2dd    0.991046
e75e90af-8093-484a-883f-9f545a126208    3b2258ce-d8d7-40d5-ba1f-d771e7ddc56f    0.991198
3b2258ce-d8d7-40d5-ba1f-d771e7ddc56f    e75e90af-8093-484a-883f-9f545a126208    0.991198
ce73d577-0f4d-4919-aaee-bbf8aadb12ec    ba933752-a08b-4615-9b90-0731c8bfc23d    0.990260
ba933752-a08b-4615-9b90-0731c8bfc23d    ce73d577-0f4d-4919-aaee-bbf8aadb12ec    0.990260
d20d75c6-eb13-41f5-865c-e6e54725fe13    648ff28d-6860-4e8f-a411-d1577a1d78ca    0.991317
648ff28d-6860-4e8f-a411-d1577a1d78ca    d20d75c6-eb13-41f5-865c-e6e54725fe13    0.991317
wc -l {graph_path}
222132 /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/SynthesizeGraph/synthesized_graph/6/Split-train/graph.tsv

El componente de transformación

El Transform realice el componente transformaciones de datos e ingeniería característica. Los resultados incluyen un gráfico de TensorFlow de entrada que se usa durante el entrenamiento y sirve para preprocesar los datos antes del entrenamiento o la inferencia. Este gráfico se convierte en parte del modelo guardado que es el resultado del entrenamiento del modelo. Dado que se utiliza el mismo gráfico de entrada tanto para el entrenamiento como para el servicio, el preprocesamiento siempre será el mismo y solo debe escribirse una vez.

El componente Transform requiere más código que muchos otros componentes debido a la complejidad arbitraria de la ingeniería de características que puede necesitar para los datos y / o el modelo con el que está trabajando. Requiere que estén disponibles archivos de código que definan el procesamiento necesario.

Cada muestra incluirá las siguientes tres características:

  1. id: El ID de nodo de la muestra.
  2. text_xf: Una lista de Int64 que contiene los ID de palabras.
  3. label_xf: A singleton Int64 la identificación de la clase objetivo de la revisión: 0 = negativo, 1 = positivo.

Vamos a definir un módulo que contiene el preprocessing_fn() la función que vamos a pasar a la Transform componente:

_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

Crear y ejecutar la Transform componente, en referencia a los archivos creados anteriormente.

# 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/tmpiem_52h3
running install
running install_lib
copying build/lib/imdb_transform.py -> /tmp/tmpiem_52h3
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/tmpiem_52h3/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3.7.egg-info
running install_scripts
creating /tmp/tmpiem_52h3/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50.dist-info/WHEEL
creating '/tmp/tmps6mh09_9/tfx_user_code_Transform-0.0+074f608d1f54105225e2fee77ebe4b6159a009eca01b5a0791099840a2185d50-py3-none-any.whl' and adding '/tmp/tmpiem_52h3' 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/tmpiem_52h3
/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,
listing git files failed - pretending there aren't any
I1204 11:43:54.715353  6839 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1204 11:43:54.719055  6839 rdbms_metadata_access_object.cc:686] No property is defined for the Type
Processing /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/_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-2021-12-04T11_41_51.482724-py59cet9/_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-2021-12-04T11_41_51.482724-py59cet9/_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-2021-12-04T11_41_51.482724-py59cet9/Transform/transform_graph/7/.temp_path/tftransform_tmp/41946dd1d2594124b929c5ec8c7f82cd/assets
2021-12-04 11:44:05.216878: 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-2021-12-04T11_41_51.482724-py59cet9/Transform/transform_graph/7/.temp_path/tftransform_tmp/41946dd1d2594124b929c5ec8c7f82cd/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Transform/transform_graph/7/.temp_path/tftransform_tmp/50d5168031d643728b9fd8d8ede0362b/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Transform/transform_graph/7/.temp_path/tftransform_tmp/50d5168031d643728b9fd8d8ede0362b/assets

El Transform componente tiene 2 tipos de salidas:

  • transform_graph es el gráfico que pueden realizar las operaciones de preprocesamiento (este gráfico se incluirá en los modelos de la porción y de evaluación).
  • transformed_examples representa los datos de entrenamiento y evaluación preprocesados.
transform.outputs
{'transform_graph': Channel(
     type_name: TransformGraph
     artifacts: [Artifact(artifact: id: 7
 type_id: 25
 uri: "/tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/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: {}
 )}

Echar un vistazo a la transform_graph artefacto: apunta a un directorio que contiene subdirectorios 3:

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

El transform_fn subdirectorio contiene el gráfico de preprocesamiento real. El metadata subdirectorio contiene el esquema de los datos originales. El transformed_metadata subdirectorio contiene el esquema de los datos que se procesan.

Eche un vistazo a algunos de los ejemplos transformados y compruebe que se hayan procesado según lo previsto.

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
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El componente GraphAugmentation

Dado que tenemos las características de muestra y el gráfico sintetizado, podemos generar los datos de entrenamiento aumentados para el aprendizaje estructurado neuronal. El marco NSL proporciona una biblioteca para combinar el gráfico y las características de muestra para producir los datos de entrenamiento finales para la regularización del gráfico. Los datos de entrenamiento resultantes incluirán características de muestra originales, así como características de sus vecinos correspondientes.

En este tutorial, consideramos los bordes no dirigidos y usamos un máximo de 3 vecinos por muestra para aumentar los datos de entrenamiento con los vecinos del gráfico.

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-datajju3fxrq/train.tfrecord', 'unlabeled_examples_path': '/tmp/tfx-datajju3fxrq/unsup.tfrecord', 'graph_path': '/tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/SynthesizeGraph/synthesized_graph/6/Split-train/graph.tsv', 'output_training_data_path': '/tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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)
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        value: 12
        value: 1252
        value: 0
        value: 2142
        value: 10
        value: 1832
        value: 111
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
      }
    }
  }
}

features {
  feature {
    key: "NL_num_nbrs"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "id"
    value {
      bytes_list {
        value: "072dc782-850b-4286-8f4f-2f6f527db6cf"
      }
    }
  }
  feature {
    key: "label_xf"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "text_xf"
    value {
      int64_list {
        value: 13
        value: 16
        value: 423
        value: 23
        value: 1367
        value: 30
        value: 0
        value: 363
        value: 12
        value: 153
        value: 3174
        value: 9
        value: 8
        value: 18
        value: 26
        value: 667
        value: 338
        value: 1372
        value: 0
        value: 86
        value: 46
        value: 9200
        value: 282
        value: 0
        value: 10091
        value: 4
        value: 0
        value: 694
        value: 10028
        value: 52
        value: 362
        value: 26
        value: 202
        value: 39
        value: 216
        value: 5
        value: 27
        value: 5822
        value: 19
        value: 52
        value: 58
        value: 362
        value: 26
        value: 202
        value: 39
        value: 474
        value: 0
        value: 10029
        value: 4
        value: 2
        value: 243
        value: 143
        value: 386
        value: 3
        value: 0
        value: 386
        value: 579
        value: 2
        value: 132
        value: 57
        value: 725
        value: 88
        value: 140
        value: 30
        value: 27
        value: 33
        value: 1359
        value: 29
        value: 8
        value: 567
        value: 35
        value: 106
        value: 230
        value: 60
        value: 0
        value: 3041
        value: 5
        value: 7879
        value: 28
        value: 281
        value: 110
        value: 111
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
        value: 1
      }
    }
  }
}

features {
  feature {
    key: "NL_num_nbrs"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "id"
    value {
      bytes_list {
        value: "27da61c0-3dff-46e9-8588-d12176b3798f"
      }
    }
  }
  feature {
    key: "label_xf"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "text_xf"
    value {
      int64_list {
        value: 13
        value: 8
        value: 6
        value: 2
        value: 18
        value: 69
        value: 140
        value: 27
        value: 83
        value: 31
        value: 1877
        value: 905
        value: 9
        value: 10057
        value: 31
        value: 43
        value: 2115
        value: 36
        value: 32
        value: 2057
        value: 6133
        value: 10
        value: 6
        value: 32
        value: 2474
        value: 1614
        value: 3
        value: 2707
        value: 990
        value: 4
        value: 10067
        value: 9
        value: 2
        value: 1532
        value: 242
        value: 90
        value: 3757
        value: 3
        value: 90
        value: 10026
        value: 0
        value: 242
        value: 6
        value: 260
        value: 31
        value: 24
        value: 4
        value: 0
        value: 84
        value: 497
        value: 177
        value: 1151
        value: 777
        value: 9
        value: 397
        value: 552
        value: 7726
        value: 10051
        value: 34
        value: 14
        value: 379
        value: 33
        value: 1829
        value: 9
        value: 123
        value: 0
        value: 916
        value: 10028
        value: 7
        value: 64
        value: 571
        value: 12
        value: 8
        value: 18
        value: 27
        value: 687
        value: 9
        value: 30
        value: 5609
        value: 16
        value: 25
        value: 99
        value: 117
        value: 66
        value: 2
        value: 130
        value: 21
        value: 8
        value: 842
        value: 7726
        value: 10051
        value: 6
        value: 338
        value: 1107
        value: 3
        value: 24
        value: 10020
        value: 29
        value: 53
        value: 1476
      }
    }
  }
}

El componente de entrenador

Los Trainer modelos de trenes de componentes utilizando TensorFlow.

Crear un módulo de Python que contiene una trainer_fn función, que debe devolver un estimador. Si prefiere crear un modelo Keras, puede hacerlo y después convertirlo a un estimador usando 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

Crear y ejecutar el Trainer componente, pasándole el archivo que hemos creado anteriormente.

# 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,
listing git files failed - pretending there aren't any
I1204 11:44:36.000404  6839 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1204 11:44:36.003713  6839 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/tmpyr89v7kz
running install
running install_lib
copying build/lib/imdb_trainer.py -> /tmp/tmpyr89v7kz
copying build/lib/imdb_transform.py -> /tmp/tmpyr89v7kz
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/tmpyr89v7kz/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d-py3.7.egg-info
running install_scripts
creating /tmp/tmpyr89v7kz/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d.dist-info/WHEEL
creating '/tmp/tmpl71r0gnq/tfx_user_code_Trainer-0.0+b990a2c6a4f23081880867efa3bd3c38db9d7bd0a87a0c9b277ae63714defc8d-py3-none-any.whl' and adding '/tmp/tmpyr89v7kz' 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/tmpyr89v7kz
Processing /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/_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-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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 = 0.6933001, step = 0
INFO:tensorflow:loss = 0.6933001, step = 0
INFO:tensorflow:global_step/sec: 220.68
INFO:tensorflow:global_step/sec: 220.68
INFO:tensorflow:loss = 0.69297814, step = 100 (0.454 sec)
INFO:tensorflow:loss = 0.69297814, step = 100 (0.454 sec)
INFO:tensorflow:global_step/sec: 288.754
INFO:tensorflow:global_step/sec: 288.754
INFO:tensorflow:loss = 0.6923192, step = 200 (0.347 sec)
INFO:tensorflow:loss = 0.6923192, step = 200 (0.347 sec)
INFO:tensorflow:global_step/sec: 286.927
INFO:tensorflow:global_step/sec: 286.927
INFO:tensorflow:loss = 0.6908457, step = 300 (0.348 sec)
INFO:tensorflow:loss = 0.6908457, step = 300 (0.348 sec)
INFO:tensorflow:global_step/sec: 286.211
INFO:tensorflow:global_step/sec: 286.211
INFO:tensorflow:loss = 0.6921471, step = 400 (0.350 sec)
INFO:tensorflow:loss = 0.6921471, step = 400 (0.350 sec)
INFO:tensorflow:global_step/sec: 282.252
INFO:tensorflow:global_step/sec: 282.252
INFO:tensorflow:loss = 0.69014025, step = 500 (0.354 sec)
INFO:tensorflow:loss = 0.69014025, step = 500 (0.354 sec)
INFO:tensorflow:global_step/sec: 288.814
INFO:tensorflow:global_step/sec: 288.814
INFO:tensorflow:loss = 0.6904064, step = 600 (0.346 sec)
INFO:tensorflow:loss = 0.6904064, step = 600 (0.346 sec)
INFO:tensorflow:global_step/sec: 275.969
INFO:tensorflow:global_step/sec: 275.969
INFO:tensorflow:loss = 0.6891232, step = 700 (0.363 sec)
INFO:tensorflow:loss = 0.6891232, step = 700 (0.363 sec)
INFO:tensorflow:global_step/sec: 280.819
INFO:tensorflow:global_step/sec: 280.819
INFO:tensorflow:loss = 0.69049495, step = 800 (0.356 sec)
INFO:tensorflow:loss = 0.69049495, step = 800 (0.356 sec)
INFO:tensorflow:global_step/sec: 278.558
INFO:tensorflow:global_step/sec: 278.558
INFO:tensorflow:loss = 0.68652004, step = 900 (0.359 sec)
INFO:tensorflow:loss = 0.68652004, step = 900 (0.359 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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 2021-12-04T11:44:45
INFO:tensorflow:Starting evaluation at 2021-12-04T11:44:45
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 5.56428s
INFO:tensorflow:Inference Time : 5.56428s
INFO:tensorflow:Finished evaluation at 2021-12-04-11:44:51
INFO:tensorflow:Finished evaluation at 2021-12-04-11:44:51
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.7047, global_step = 999, loss = 0.68605316
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.7047, global_step = 999, loss = 0.68605316
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt-999
INFO:tensorflow:global_step/sec: 16.1827
INFO:tensorflow:global_step/sec: 16.1827
INFO:tensorflow:loss = 0.68512, step = 1000 (6.179 sec)
INFO:tensorflow:loss = 0.68512, step = 1000 (6.179 sec)
INFO:tensorflow:global_step/sec: 278.496
INFO:tensorflow:global_step/sec: 278.496
INFO:tensorflow:loss = 0.6872438, step = 1100 (0.360 sec)
INFO:tensorflow:loss = 0.6872438, step = 1100 (0.360 sec)
INFO:tensorflow:global_step/sec: 276.552
INFO:tensorflow:global_step/sec: 276.552
INFO:tensorflow:loss = 0.6817854, step = 1200 (0.361 sec)
INFO:tensorflow:loss = 0.6817854, step = 1200 (0.361 sec)
INFO:tensorflow:global_step/sec: 271.064
INFO:tensorflow:global_step/sec: 271.064
INFO:tensorflow:loss = 0.6696973, step = 1300 (0.369 sec)
INFO:tensorflow:loss = 0.6696973, step = 1300 (0.369 sec)
INFO:tensorflow:global_step/sec: 275.856
INFO:tensorflow:global_step/sec: 275.856
INFO:tensorflow:loss = 0.6826827, step = 1400 (0.362 sec)
INFO:tensorflow:loss = 0.6826827, step = 1400 (0.362 sec)
INFO:tensorflow:global_step/sec: 270.879
INFO:tensorflow:global_step/sec: 270.879
INFO:tensorflow:loss = 0.6712682, step = 1500 (0.369 sec)
INFO:tensorflow:loss = 0.6712682, step = 1500 (0.369 sec)
INFO:tensorflow:global_step/sec: 277.073
INFO:tensorflow:global_step/sec: 277.073
INFO:tensorflow:loss = 0.67981917, step = 1600 (0.361 sec)
INFO:tensorflow:loss = 0.67981917, step = 1600 (0.361 sec)
INFO:tensorflow:global_step/sec: 270.234
INFO:tensorflow:global_step/sec: 270.234
INFO:tensorflow:loss = 0.67373323, step = 1700 (0.370 sec)
INFO:tensorflow:loss = 0.67373323, step = 1700 (0.370 sec)
INFO:tensorflow:global_step/sec: 279.658
INFO:tensorflow:global_step/sec: 279.658
INFO:tensorflow:loss = 0.66337496, step = 1800 (0.358 sec)
INFO:tensorflow:loss = 0.66337496, step = 1800 (0.358 sec)
INFO:tensorflow:global_step/sec: 279.271
INFO:tensorflow:global_step/sec: 279.271
INFO:tensorflow:loss = 0.6738259, step = 1900 (0.358 sec)
INFO:tensorflow:loss = 0.6738259, step = 1900 (0.358 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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: 237.544
INFO:tensorflow:global_step/sec: 237.544
INFO:tensorflow:loss = 0.66583055, step = 2000 (0.421 sec)
INFO:tensorflow:loss = 0.66583055, step = 2000 (0.421 sec)
INFO:tensorflow:global_step/sec: 277.133
INFO:tensorflow:global_step/sec: 277.133
INFO:tensorflow:loss = 0.6637004, step = 2100 (0.361 sec)
INFO:tensorflow:loss = 0.6637004, step = 2100 (0.361 sec)
INFO:tensorflow:global_step/sec: 272.248
INFO:tensorflow:global_step/sec: 272.248
INFO:tensorflow:loss = 0.6696273, step = 2200 (0.367 sec)
INFO:tensorflow:loss = 0.6696273, step = 2200 (0.367 sec)
INFO:tensorflow:global_step/sec: 277.247
INFO:tensorflow:global_step/sec: 277.247
INFO:tensorflow:loss = 0.6513475, step = 2300 (0.361 sec)
INFO:tensorflow:loss = 0.6513475, step = 2300 (0.361 sec)
INFO:tensorflow:global_step/sec: 276.598
INFO:tensorflow:global_step/sec: 276.598
INFO:tensorflow:loss = 0.6662655, step = 2400 (0.362 sec)
INFO:tensorflow:loss = 0.6662655, step = 2400 (0.362 sec)
INFO:tensorflow:global_step/sec: 272.004
INFO:tensorflow:global_step/sec: 272.004
INFO:tensorflow:loss = 0.6493275, step = 2500 (0.368 sec)
INFO:tensorflow:loss = 0.6493275, step = 2500 (0.368 sec)
INFO:tensorflow:global_step/sec: 279.613
INFO:tensorflow:global_step/sec: 279.613
INFO:tensorflow:loss = 0.64058864, step = 2600 (0.358 sec)
INFO:tensorflow:loss = 0.64058864, step = 2600 (0.358 sec)
INFO:tensorflow:global_step/sec: 279.725
INFO:tensorflow:global_step/sec: 279.725
INFO:tensorflow:loss = 0.6401115, step = 2700 (0.357 sec)
INFO:tensorflow:loss = 0.6401115, step = 2700 (0.357 sec)
INFO:tensorflow:global_step/sec: 275.868
INFO:tensorflow:global_step/sec: 275.868
INFO:tensorflow:loss = 0.66073626, step = 2800 (0.363 sec)
INFO:tensorflow:loss = 0.66073626, step = 2800 (0.363 sec)
INFO:tensorflow:global_step/sec: 279.9
INFO:tensorflow:global_step/sec: 279.9
INFO:tensorflow:loss = 0.61275744, step = 2900 (0.357 sec)
INFO:tensorflow:loss = 0.61275744, step = 2900 (0.357 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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: 239.223
INFO:tensorflow:global_step/sec: 239.223
INFO:tensorflow:loss = 0.6508343, step = 3000 (0.418 sec)
INFO:tensorflow:loss = 0.6508343, step = 3000 (0.418 sec)
INFO:tensorflow:global_step/sec: 278.547
INFO:tensorflow:global_step/sec: 278.547
INFO:tensorflow:loss = 0.65112776, step = 3100 (0.359 sec)
INFO:tensorflow:loss = 0.65112776, step = 3100 (0.359 sec)
INFO:tensorflow:global_step/sec: 279.487
INFO:tensorflow:global_step/sec: 279.487
INFO:tensorflow:loss = 0.63657844, step = 3200 (0.358 sec)
INFO:tensorflow:loss = 0.63657844, step = 3200 (0.358 sec)
INFO:tensorflow:global_step/sec: 277.617
INFO:tensorflow:global_step/sec: 277.617
INFO:tensorflow:loss = 0.6216135, step = 3300 (0.360 sec)
INFO:tensorflow:loss = 0.6216135, step = 3300 (0.360 sec)
INFO:tensorflow:global_step/sec: 279.256
INFO:tensorflow:global_step/sec: 279.256
INFO:tensorflow:loss = 0.64972967, step = 3400 (0.358 sec)
INFO:tensorflow:loss = 0.64972967, step = 3400 (0.358 sec)
INFO:tensorflow:global_step/sec: 281.028
INFO:tensorflow:global_step/sec: 281.028
INFO:tensorflow:loss = 0.6309604, step = 3500 (0.356 sec)
INFO:tensorflow:loss = 0.6309604, step = 3500 (0.356 sec)
INFO:tensorflow:global_step/sec: 282.144
INFO:tensorflow:global_step/sec: 282.144
INFO:tensorflow:loss = 0.59252113, step = 3600 (0.355 sec)
INFO:tensorflow:loss = 0.59252113, step = 3600 (0.355 sec)
INFO:tensorflow:global_step/sec: 275.802
INFO:tensorflow:global_step/sec: 275.802
INFO:tensorflow:loss = 0.5944205, step = 3700 (0.363 sec)
INFO:tensorflow:loss = 0.5944205, step = 3700 (0.363 sec)
INFO:tensorflow:global_step/sec: 273.658
INFO:tensorflow:global_step/sec: 273.658
INFO:tensorflow:loss = 0.63925326, step = 3800 (0.365 sec)
INFO:tensorflow:loss = 0.63925326, step = 3800 (0.365 sec)
INFO:tensorflow:global_step/sec: 274.902
INFO:tensorflow:global_step/sec: 274.902
INFO:tensorflow:loss = 0.6255677, step = 3900 (0.365 sec)
INFO:tensorflow:loss = 0.6255677, step = 3900 (0.365 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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: 235.472
INFO:tensorflow:global_step/sec: 235.472
INFO:tensorflow:loss = 0.5732498, step = 4000 (0.424 sec)
INFO:tensorflow:loss = 0.5732498, step = 4000 (0.424 sec)
INFO:tensorflow:global_step/sec: 277.885
INFO:tensorflow:global_step/sec: 277.885
INFO:tensorflow:loss = 0.59263897, step = 4100 (0.360 sec)
INFO:tensorflow:loss = 0.59263897, step = 4100 (0.360 sec)
INFO:tensorflow:global_step/sec: 272.498
INFO:tensorflow:global_step/sec: 272.498
INFO:tensorflow:loss = 0.6244205, step = 4200 (0.367 sec)
INFO:tensorflow:loss = 0.6244205, step = 4200 (0.367 sec)
INFO:tensorflow:global_step/sec: 273.911
INFO:tensorflow:global_step/sec: 273.911
INFO:tensorflow:loss = 0.5709779, step = 4300 (0.365 sec)
INFO:tensorflow:loss = 0.5709779, step = 4300 (0.365 sec)
INFO:tensorflow:global_step/sec: 272.385
INFO:tensorflow:global_step/sec: 272.385
INFO:tensorflow:loss = 0.57497543, step = 4400 (0.367 sec)
INFO:tensorflow:loss = 0.57497543, step = 4400 (0.367 sec)
INFO:tensorflow:global_step/sec: 277.073
INFO:tensorflow:global_step/sec: 277.073
INFO:tensorflow:loss = 0.62753403, step = 4500 (0.361 sec)
INFO:tensorflow:loss = 0.62753403, step = 4500 (0.361 sec)
INFO:tensorflow:global_step/sec: 279.972
INFO:tensorflow:global_step/sec: 279.972
INFO:tensorflow:loss = 0.5253285, step = 4600 (0.357 sec)
INFO:tensorflow:loss = 0.5253285, step = 4600 (0.357 sec)
INFO:tensorflow:global_step/sec: 283.916
INFO:tensorflow:global_step/sec: 283.916
INFO:tensorflow:loss = 0.5570012, step = 4700 (0.353 sec)
INFO:tensorflow:loss = 0.5570012, step = 4700 (0.353 sec)
INFO:tensorflow:global_step/sec: 286.699
INFO:tensorflow:global_step/sec: 286.699
INFO:tensorflow:loss = 0.54549825, step = 4800 (0.348 sec)
INFO:tensorflow:loss = 0.54549825, step = 4800 (0.348 sec)
INFO:tensorflow:global_step/sec: 287.171
INFO:tensorflow:global_step/sec: 287.171
INFO:tensorflow:loss = 0.58005756, step = 4900 (0.348 sec)
INFO:tensorflow:loss = 0.58005756, step = 4900 (0.348 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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: 246.035
INFO:tensorflow:global_step/sec: 246.035
INFO:tensorflow:loss = 0.55126476, step = 5000 (0.406 sec)
INFO:tensorflow:loss = 0.55126476, step = 5000 (0.406 sec)
INFO:tensorflow:global_step/sec: 286.048
INFO:tensorflow:global_step/sec: 286.048
INFO:tensorflow:loss = 0.5440348, step = 5100 (0.350 sec)
INFO:tensorflow:loss = 0.5440348, step = 5100 (0.350 sec)
INFO:tensorflow:global_step/sec: 288.158
INFO:tensorflow:global_step/sec: 288.158
INFO:tensorflow:loss = 0.530152, step = 5200 (0.347 sec)
INFO:tensorflow:loss = 0.530152, step = 5200 (0.347 sec)
INFO:tensorflow:global_step/sec: 282.667
INFO:tensorflow:global_step/sec: 282.667
INFO:tensorflow:loss = 0.61745214, step = 5300 (0.354 sec)
INFO:tensorflow:loss = 0.61745214, step = 5300 (0.354 sec)
INFO:tensorflow:global_step/sec: 283.025
INFO:tensorflow:global_step/sec: 283.025
INFO:tensorflow:loss = 0.5531441, step = 5400 (0.354 sec)
INFO:tensorflow:loss = 0.5531441, step = 5400 (0.354 sec)
INFO:tensorflow:global_step/sec: 284.596
INFO:tensorflow:global_step/sec: 284.596
INFO:tensorflow:loss = 0.55586976, step = 5500 (0.351 sec)
INFO:tensorflow:loss = 0.55586976, step = 5500 (0.351 sec)
INFO:tensorflow:global_step/sec: 283.212
INFO:tensorflow:global_step/sec: 283.212
INFO:tensorflow:loss = 0.5627943, step = 5600 (0.353 sec)
INFO:tensorflow:loss = 0.5627943, step = 5600 (0.353 sec)
INFO:tensorflow:global_step/sec: 281.121
INFO:tensorflow:global_step/sec: 281.121
INFO:tensorflow:loss = 0.45171082, step = 5700 (0.356 sec)
INFO:tensorflow:loss = 0.45171082, step = 5700 (0.356 sec)
INFO:tensorflow:global_step/sec: 281.568
INFO:tensorflow:global_step/sec: 281.568
INFO:tensorflow:loss = 0.51796657, step = 5800 (0.355 sec)
INFO:tensorflow:loss = 0.51796657, step = 5800 (0.355 sec)
INFO:tensorflow:global_step/sec: 272.14
INFO:tensorflow:global_step/sec: 272.14
INFO:tensorflow:loss = 0.570162, step = 5900 (0.368 sec)
INFO:tensorflow:loss = 0.570162, step = 5900 (0.368 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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: 234.957
INFO:tensorflow:global_step/sec: 234.957
INFO:tensorflow:loss = 0.5400977, step = 6000 (0.425 sec)
INFO:tensorflow:loss = 0.5400977, step = 6000 (0.425 sec)
INFO:tensorflow:global_step/sec: 259.23
INFO:tensorflow:global_step/sec: 259.23
INFO:tensorflow:loss = 0.4981569, step = 6100 (0.386 sec)
INFO:tensorflow:loss = 0.4981569, step = 6100 (0.386 sec)
INFO:tensorflow:global_step/sec: 267.697
INFO:tensorflow:global_step/sec: 267.697
INFO:tensorflow:loss = 0.5613683, step = 6200 (0.373 sec)
INFO:tensorflow:loss = 0.5613683, step = 6200 (0.373 sec)
INFO:tensorflow:global_step/sec: 266.623
INFO:tensorflow:global_step/sec: 266.623
INFO:tensorflow:loss = 0.48216385, step = 6300 (0.375 sec)
INFO:tensorflow:loss = 0.48216385, step = 6300 (0.375 sec)
INFO:tensorflow:global_step/sec: 266.123
INFO:tensorflow:global_step/sec: 266.123
INFO:tensorflow:loss = 0.4599746, step = 6400 (0.376 sec)
INFO:tensorflow:loss = 0.4599746, step = 6400 (0.376 sec)
INFO:tensorflow:global_step/sec: 269.688
INFO:tensorflow:global_step/sec: 269.688
INFO:tensorflow:loss = 0.4796008, step = 6500 (0.371 sec)
INFO:tensorflow:loss = 0.4796008, step = 6500 (0.371 sec)
INFO:tensorflow:global_step/sec: 258.906
INFO:tensorflow:global_step/sec: 258.906
INFO:tensorflow:loss = 0.5626136, step = 6600 (0.386 sec)
INFO:tensorflow:loss = 0.5626136, step = 6600 (0.386 sec)
INFO:tensorflow:global_step/sec: 261.596
INFO:tensorflow:global_step/sec: 261.596
INFO:tensorflow:loss = 0.5001174, step = 6700 (0.382 sec)
INFO:tensorflow:loss = 0.5001174, step = 6700 (0.382 sec)
INFO:tensorflow:global_step/sec: 266.467
INFO:tensorflow:global_step/sec: 266.467
INFO:tensorflow:loss = 0.44604325, step = 6800 (0.376 sec)
INFO:tensorflow:loss = 0.44604325, step = 6800 (0.376 sec)
INFO:tensorflow:global_step/sec: 267.785
INFO:tensorflow:global_step/sec: 267.785
INFO:tensorflow:loss = 0.4936733, step = 6900 (0.373 sec)
INFO:tensorflow:loss = 0.4936733, step = 6900 (0.373 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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: 231.159
INFO:tensorflow:global_step/sec: 231.159
INFO:tensorflow:loss = 0.44407076, step = 7000 (0.433 sec)
INFO:tensorflow:loss = 0.44407076, step = 7000 (0.433 sec)
INFO:tensorflow:global_step/sec: 259.935
INFO:tensorflow:global_step/sec: 259.935
INFO:tensorflow:loss = 0.4649738, step = 7100 (0.385 sec)
INFO:tensorflow:loss = 0.4649738, step = 7100 (0.385 sec)
INFO:tensorflow:global_step/sec: 261.497
INFO:tensorflow:global_step/sec: 261.497
INFO:tensorflow:loss = 0.48575532, step = 7200 (0.382 sec)
INFO:tensorflow:loss = 0.48575532, step = 7200 (0.382 sec)
INFO:tensorflow:global_step/sec: 264.401
INFO:tensorflow:global_step/sec: 264.401
INFO:tensorflow:loss = 0.5566124, step = 7300 (0.378 sec)
INFO:tensorflow:loss = 0.5566124, step = 7300 (0.378 sec)
INFO:tensorflow:global_step/sec: 263.189
INFO:tensorflow:global_step/sec: 263.189
INFO:tensorflow:loss = 0.485472, step = 7400 (0.380 sec)
INFO:tensorflow:loss = 0.485472, step = 7400 (0.380 sec)
INFO:tensorflow:global_step/sec: 262.158
INFO:tensorflow:global_step/sec: 262.158
INFO:tensorflow:loss = 0.39120063, step = 7500 (0.381 sec)
INFO:tensorflow:loss = 0.39120063, step = 7500 (0.381 sec)
INFO:tensorflow:global_step/sec: 266.983
INFO:tensorflow:global_step/sec: 266.983
INFO:tensorflow:loss = 0.35777277, step = 7600 (0.374 sec)
INFO:tensorflow:loss = 0.35777277, step = 7600 (0.374 sec)
INFO:tensorflow:global_step/sec: 267.642
INFO:tensorflow:global_step/sec: 267.642
INFO:tensorflow:loss = 0.5350034, step = 7700 (0.374 sec)
INFO:tensorflow:loss = 0.5350034, step = 7700 (0.374 sec)
INFO:tensorflow:global_step/sec: 269.459
INFO:tensorflow:global_step/sec: 269.459
INFO:tensorflow:loss = 0.42015103, step = 7800 (0.371 sec)
INFO:tensorflow:loss = 0.42015103, step = 7800 (0.371 sec)
INFO:tensorflow:global_step/sec: 267.026
INFO:tensorflow:global_step/sec: 267.026
INFO:tensorflow:loss = 0.54285204, step = 7900 (0.375 sec)
INFO:tensorflow:loss = 0.54285204, step = 7900 (0.375 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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: 226.498
INFO:tensorflow:global_step/sec: 226.498
INFO:tensorflow:loss = 0.36296645, step = 8000 (0.441 sec)
INFO:tensorflow:loss = 0.36296645, step = 8000 (0.441 sec)
INFO:tensorflow:global_step/sec: 262.545
INFO:tensorflow:global_step/sec: 262.545
INFO:tensorflow:loss = 0.5328135, step = 8100 (0.381 sec)
INFO:tensorflow:loss = 0.5328135, step = 8100 (0.381 sec)
INFO:tensorflow:global_step/sec: 264.823
INFO:tensorflow:global_step/sec: 264.823
INFO:tensorflow:loss = 0.42400876, step = 8200 (0.377 sec)
INFO:tensorflow:loss = 0.42400876, step = 8200 (0.377 sec)
INFO:tensorflow:global_step/sec: 270.946
INFO:tensorflow:global_step/sec: 270.946
INFO:tensorflow:loss = 0.4334933, step = 8300 (0.369 sec)
INFO:tensorflow:loss = 0.4334933, step = 8300 (0.369 sec)
INFO:tensorflow:global_step/sec: 271.252
INFO:tensorflow:global_step/sec: 271.252
INFO:tensorflow:loss = 0.44592458, step = 8400 (0.369 sec)
INFO:tensorflow:loss = 0.44592458, step = 8400 (0.369 sec)
INFO:tensorflow:global_step/sec: 272.492
INFO:tensorflow:global_step/sec: 272.492
INFO:tensorflow:loss = 0.44213057, step = 8500 (0.367 sec)
INFO:tensorflow:loss = 0.44213057, step = 8500 (0.367 sec)
INFO:tensorflow:global_step/sec: 273.226
INFO:tensorflow:global_step/sec: 273.226
INFO:tensorflow:loss = 0.46779203, step = 8600 (0.366 sec)
INFO:tensorflow:loss = 0.46779203, step = 8600 (0.366 sec)
INFO:tensorflow:global_step/sec: 261.518
INFO:tensorflow:global_step/sec: 261.518
INFO:tensorflow:loss = 0.5460389, step = 8700 (0.382 sec)
INFO:tensorflow:loss = 0.5460389, step = 8700 (0.382 sec)
INFO:tensorflow:global_step/sec: 277.202
INFO:tensorflow:global_step/sec: 277.202
INFO:tensorflow:loss = 0.5019726, step = 8800 (0.361 sec)
INFO:tensorflow:loss = 0.5019726, step = 8800 (0.361 sec)
INFO:tensorflow:global_step/sec: 276.724
INFO:tensorflow:global_step/sec: 276.724
INFO:tensorflow:loss = 0.45209432, step = 8900 (0.361 sec)
INFO:tensorflow:loss = 0.45209432, step = 8900 (0.361 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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: 226.305
INFO:tensorflow:global_step/sec: 226.305
INFO:tensorflow:loss = 0.34912163, step = 9000 (0.442 sec)
INFO:tensorflow:loss = 0.34912163, step = 9000 (0.442 sec)
INFO:tensorflow:global_step/sec: 271.186
INFO:tensorflow:global_step/sec: 271.186
INFO:tensorflow:loss = 0.5445255, step = 9100 (0.369 sec)
INFO:tensorflow:loss = 0.5445255, step = 9100 (0.369 sec)
INFO:tensorflow:global_step/sec: 267.761
INFO:tensorflow:global_step/sec: 267.761
INFO:tensorflow:loss = 0.35654712, step = 9200 (0.373 sec)
INFO:tensorflow:loss = 0.35654712, step = 9200 (0.373 sec)
INFO:tensorflow:global_step/sec: 262.439
INFO:tensorflow:global_step/sec: 262.439
INFO:tensorflow:loss = 0.42294815, step = 9300 (0.381 sec)
INFO:tensorflow:loss = 0.42294815, step = 9300 (0.381 sec)
INFO:tensorflow:global_step/sec: 262.881
INFO:tensorflow:global_step/sec: 262.881
INFO:tensorflow:loss = 0.45307142, step = 9400 (0.380 sec)
INFO:tensorflow:loss = 0.45307142, step = 9400 (0.380 sec)
INFO:tensorflow:global_step/sec: 264.643
INFO:tensorflow:global_step/sec: 264.643
INFO:tensorflow:loss = 0.43050554, step = 9500 (0.378 sec)
INFO:tensorflow:loss = 0.43050554, step = 9500 (0.378 sec)
INFO:tensorflow:global_step/sec: 270.757
INFO:tensorflow:global_step/sec: 270.757
INFO:tensorflow:loss = 0.40443382, step = 9600 (0.369 sec)
INFO:tensorflow:loss = 0.40443382, step = 9600 (0.369 sec)
INFO:tensorflow:global_step/sec: 268.755
INFO:tensorflow:global_step/sec: 268.755
INFO:tensorflow:loss = 0.37255523, step = 9700 (0.372 sec)
INFO:tensorflow:loss = 0.37255523, step = 9700 (0.372 sec)
INFO:tensorflow:global_step/sec: 264.603
INFO:tensorflow:global_step/sec: 264.603
INFO:tensorflow:loss = 0.4721123, step = 9800 (0.378 sec)
INFO:tensorflow:loss = 0.4721123, step = 9800 (0.378 sec)
INFO:tensorflow:global_step/sec: 273.682
INFO:tensorflow:global_step/sec: 273.682
INFO:tensorflow:loss = 0.52799636, step = 9900 (0.365 sec)
INFO:tensorflow:loss = 0.52799636, step = 9900 (0.365 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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt.
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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 2021-12-04T11:45:25
INFO:tensorflow:Starting evaluation at 2021-12-04T11:45:25
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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 : 5.60779s
INFO:tensorflow:Inference Time : 5.60779s
INFO:tensorflow:Finished evaluation at 2021-12-04-11:45:30
INFO:tensorflow:Finished evaluation at 2021-12-04-11:45:30
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.8008, global_step = 10000, loss = 0.4497029
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.8008, global_step = 10000, loss = 0.4497029
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/export/imdb/temp-1638618330/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/export/imdb/temp-1638618330/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/export/imdb/temp-1638618330/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/export/imdb/temp-1638618330/saved_model.pb
INFO:tensorflow:Loss for final step: 0.43356365.
INFO:tensorflow:Loss for final step: 0.43356365.
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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-Serving/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/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-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-TFMA/temp-1638618332/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-TFMA/temp-1638618332/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-TFMA/temp-1638618332/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-12-04T11_41_51.482724-py59cet9/Trainer/model_run/9/Format-TFMA/temp-1638618332/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"

Echar un vistazo en el modelo entrenado, que se exportó desde 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>,
 '...']

Visualicemos las métricas del modelo usando Tensorboard.


# Get the URI of the output artifact representing the training logs,
# which is a directory
model_run_dir = trainer.outputs['model_run'].get()[0].uri

%load_ext tensorboard
%tensorboard --logdir {model_run_dir}

Modelo de servicio

La regularización de gráficos solo afecta el flujo de trabajo de entrenamiento al agregar un término de regularización a la función de pérdida. Como resultado, la evaluación del modelo y los flujos de trabajo de servicio permanecen sin cambios. Es por la misma razón que también hemos omitido los componentes TFX aguas abajo que normalmente vienen después del componente entrenador como el evaluador, empujador, etc.

Conclusión

Hemos demostrado el uso de la regularización de gráficos utilizando el marco de aprendizaje estructurado neuronal (NSL) en una canalización TFX incluso cuando la entrada no contiene un gráfico explícito. Consideramos la tarea de clasificación de sentimientos de las reseñas de películas de IMDB para lo cual sintetizamos un gráfico de similitud basado en incrustaciones de reseñas. Alentamos a los usuarios a experimentar más mediante el uso de diferentes incorporaciones para la construcción de gráficos, la variación de los hiperparámetros, el cambio de la cantidad de supervisión y la definición de diferentes arquitecturas de modelos.