기술 사업 및 스타트업 테크노에 대한 모든 것

텐서 플로트 추정기는 감가 상각됩니다

텐서 플로트 추정기는 감가 상각됩니다

콘텐츠 개요

  • 설정
  • 장점
  • 추정기 기능
  • 미리 제작 된 추정기 사용
  1. 입력 함수를 작성하십시오
  2. 기능 열을 정의합니다.
  3. 관련 사전 제작자를 인스턴스화하십시오.
  4. 교육, 평가 또는 추론 방법을 호출하십시오.
  • 사전 제작자의 이점
  • 맞춤형 추정기
  • Keras 모델에서 추정기를 만듭니다
  • 추정기로 객체 기반 체크 포인트를 저장합니다
  • 추정기의 저장 모델
  • Tf.distribute.Strategy 사용 사용 (제한된 지원)
  • 지금 지원되는 것은 무엇입니까?
  • 예와 튜토리얼

\

:::경고
경고:Tensorflow 2.15는 The의 최종 릴리스를 포함했습니다tf-estimator패키지. 추정기는 텐서 플로 2.16 또는 그 이후로 제공되지 않습니다. 참조마이그레이션 가이드추정기를 변환하는 방법에 대한 자세한 내용은

:::

이 문서가 소개됩니다tf.estimator-높은 수준의 텐서 플로우 API. 추정기는 다음 조치를 캡슐화합니다.

  • 훈련
  • 평가
  • 예측
  • 서빙 수출

텐서 플로우는 여러 미리 제작 된 추정기를 구현합니다. 커스텀 추정기는 여전히 서명하지만 주로 거꾸로 호환성 측정 값으로 변합니다.새 코드에는 사용자 정의 추정기를 사용해서는 안됩니다. 제작 또는 맞춤형 추정기는 모든 추정기를 기준으로 클래스입니다tf.estimator.Estimator수업.

간단한 예를 들어, 추정 자 튜토리얼을 사용해보십시오. API 설계에 대한 개요는 백서를 확인하십시오.

설정

pip install -U tensorflow_datasets

\

import tempfile
import os

import tensorflow as tf
import tensorflow_datasets as tfds

\

2024-01-24 02:20:44.732508: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-24 02:20:44.732557: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-24 02:20:44.734001: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

장점

a와 비슷합니다tf.keras.Modelanestimator모델 수준의 추상화입니다. 그만큼tf.estimator현재 아직 개발중인 일부 기능을 제공합니다tf.keras. 이들은 다음과 같습니다.

  • 매개 변수 서버 기반 교육
  • 전체 TFX 통합

추정기 기능

추정기는 다음과 같은 이점을 제공합니다.

  • 모델을 변경하지 않고 로컬 호스트 또는 분산 멀티 서버 환경에서 추정기 기반 모델을 실행할 수 있습니다. 또한 모델을 다시 코딩하지 않고도 CPU, GPU 또는 TPU에서 추정기 기반 모델을 실행할 수 있습니다.
  • 추정기는 방법과시기를 제어하는 ​​안전한 분산 교육 루프를 제공합니다.
  • 로드 데이터
  • 예외를 처리하십시오
  • 체크 포인트 파일을 생성하고 실패에서 복구하십시오
  • Tensorboard의 요약을 저장하십시오

추정기가있는 응용 프로그램을 작성할 때는 모델에서 데이터 입력 파이프 라인을 분리해야합니다. 이 분리는 다른 데이터 세트로 실험을 단순화합니다.

미리 제작 된 추정기 사용

사전 제작 된 추정기를 사용하면 기본 텐서 플로우 API보다 훨씬 높은 개념적 수준에서 작업 할 수 있습니다. 추정기가 모든 “배관”을 처리하기 때문에 더 이상 계산 그래프 또는 세션을 작성하는 것에 대해 걱정할 필요가 없습니다. 또한 사전 제작 된 추정기를 사용하면 최소한의 코드를 변경하여 다른 모델 아키텍처를 실험 할 수 있습니다.tf.estimator.DNNClassifier예를 들어, 조밀 한 피드 포워드 신경망을 기반으로 분류 모델을 훈련시키는 사전 제작 된 추정기 클래스입니다.

미리 제작 된 추정기에 의존하는 Tensorflow 프로그램은 일반적으로 다음 4 단계로 구성됩니다.

1. 입력 함수를 작성하십시오

예를 들어, 훈련 세트를 가져 오는 하나의 함수와 테스트 세트를 가져 오는 다른 기능을 만들 수 있습니다. 추정기는 입력이 한 쌍의 객체로 서식 될 것으로 예상합니다.

  • 키가 피처 이름이고 값이 해당 기능 데이터를 포함하는 텐서 (또는 Sparsetensors) 인 사전
  • 하나 이상의 라벨을 포함하는 텐서

그만큼input_fn반환해야합니다tf.data.Dataset그것은 그 형식의 쌍을 생성합니다.

예를 들어, 다음 코드는 atf.data.Dataset타이타닉 데이터 세트에서train.csv파일:

\

def train_input_fn():
  titanic_file = tf.keras.utils.get_file("train.csv", "
  titanic = tf.data.experimental.make_csv_dataset(
      titanic_file, batch_size=32,
      label_name="survived")
  titanic_batches = (
      titanic.cache().repeat().shuffle(500)
      .prefetch(tf.data.AUTOTUNE))
  return titanic_batches

그만큼input_fnatf.Graph또한 직접 반환 할 수 있습니다(features_dics, labels)그래프 텐서를 포함하는 쌍은 반환 상수와 같은 간단한 경우 외에는 오류가 발생합니다.

2. 기능 열을 정의합니다.

각tf.feature_column기능 이름, 유형 및 입력 사전 프로세싱을 식별합니다.

예를 들어, 다음 스 니펫은 세 가지 기능 열을 만듭니다.

  • 첫 번째는age플로팅 포인트 입력으로 직접 기능합니다.
  • 두 번째는class범주 형 입력으로 기능합니다.
  • 세 번째는embark_town범주 형 입력으로 사용하지만 사용합니다hashing trick옵션을 열거하고 옵션 수를 설정할 필요가 없습니다.

자세한 내용은 기능 열 튜토리얼을 확인하십시오.

\

age = tf.feature_column.numeric_column('age')
cls = tf.feature_column.categorical_column_with_vocabulary_list('class', ['First', 'Second', 'Third']) 
embark = tf.feature_column.categorical_column_with_hash_bucket('embark_town', 32)

\

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4100412726.py:1: numeric_column (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4100412726.py:2: categorical_column_with_vocabulary_list (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4100412726.py:3: categorical_column_with_hash_bucket (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.

3. 관련 사전 제작자를 인스턴스화하십시오.

예를 들어, 다음은 사전 제작 된 추정기의 샘플 인스턴스화입니다.LinearClassifier:

\

model_dir = tempfile.mkdtemp()
model = tf.estimator.LinearClassifier(
    model_dir=model_dir,
    feature_columns=[embark, cls, age],
    n_classes=2
)

\

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/1904628810.py:2: LinearClassifierV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/head_utils.py:54: BinaryClassHead.__init__ (from tensorflow_estimator.python.estimator.head.binary_class_head) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:944: Estimator.__init__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1844: RunConfig.__init__ (from tensorflow_estimator.python.estimator.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpbt9n791j', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_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}

자세한 내용은 선형 분류기 자습서로 이동할 수 있습니다.

4. 교육, 평가 또는 추론 방법에 전화하십시오.

모든 추정기가 제공합니다train,,,evaluate그리고predict행동 양식.

\

model = model.train(input_fn=train_input_fn, steps=100)

\

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:385: StopAtStepHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
Downloading data from 
30874/30874 [==============================] - 0s 0us/step
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/optimizers/legacy/ftrl.py:173: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/model_fn.py:250: EstimatorSpec.__new__ (from tensorflow_estimator.python.estimator.model_fn) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1416: NanTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1419: LoggingTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/basic_session_run_hooks.py:232: SecondOrStepTimer.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1456: CheckpointSaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Create CheckpointSaverHook.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:579: StepCounterHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:586: SummarySaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpbt9n791j/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1455: SessionRunArgs.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1454: SessionRunContext.__init__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1474: SessionRunValues.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:loss = 0.6931472, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 100...
INFO:tensorflow:Saving checkpoints for 100 into /tmpfs/tmp/tmpbt9n791j/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 100...
INFO:tensorflow:Loss for final step: 0.59910536.
2024-01-24 02:20:52.016569: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

\

result = model.evaluate(train_input_fn, steps=10)

for key, value in result.items():
  print(key, ":", value)

\

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2024-01-24T02:20:52
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/evaluation.py:260: FinalOpsHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpbt9n791j/model.ckpt-100
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 3.02373s
INFO:tensorflow:Finished evaluation at 2024-01-24-02:20:55
INFO:tensorflow:Saving dict for global step 100: accuracy = 0.715625, accuracy_baseline = 0.615625, auc = 0.7548389, auc_precision_recall = 0.66771877, average_loss = 0.56671846, global_step = 100, label/mean = 0.384375, loss = 0.56671846, precision = 0.7, prediction/mean = 0.37144834, recall = 0.45528457
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100: /tmpfs/tmp/tmpbt9n791j/model.ckpt-100
accuracy : 0.715625
accuracy_baseline : 0.615625
auc : 0.7548389
auc_precision_recall : 0.66771877
average_loss : 0.56671846
label/mean : 0.384375
loss : 0.56671846
precision : 0.7
prediction/mean : 0.37144834
recall : 0.45528457
global_step : 100
2024-01-24 02:20:55.868009: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

\

for pred in model.predict(train_input_fn):
  for key, value in pred.items():
    print(key, ":", value)
  break

\

INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/base_head.py:786: ClassificationOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py:561: RegressionOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py:563: PredictOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpbt9n791j/model.ckpt-100
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
logits : [-0.6829183]
logistic : [0.33561027]
probabilities : [0.6643897  0.33561027]
class_ids : [0]
classes : [b'0']
all_class_ids : [0 1]
all_classes : [b'0' b'1']
2024-01-24 02:20:56.898963: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

사전 제작자의 이점

사전 제작 된 견적서는 모범 사례를 인코딩하여 다음과 같은 이점을 제공합니다.

  • 계산 그래프의 다른 부분이 실행되어야하는 위치를 결정하기위한 모범 사례, 단일 시스템 또는 클러스터에서 전략을 구현합니다.
  • 이벤트 (요약) 쓰기 및 보편적으로 유용한 요약을위한 모범 사례.

미리 제작 된 추정기를 사용하지 않으면 앞의 기능을 직접 구현해야합니다.

맞춤형 추정기

사전 제작 또는 관습이든 모든 추정기의 핵심은 그 것입니다모델 기능,,,model_fn이는 교육, 평가 및 예측을위한 그래프를 작성하는 방법입니다. 사전 제작 된 추정기를 사용하는 경우 다른 사람이 이미 모델 기능을 구현했습니다. 사용자 정의 추정기에 의존 할 때는 모델 기능을 직접 작성해야합니다.

\

:::팁
메모:관습model_fn여전히 1.x 스타일 그래프 모드에서 실행됩니다. 이는 열망하는 실행이없고 자동 제어 종속성이 없음을 의미합니다. 당신은 멀리 마이그레이션 할 계획입니다tf.estimator관습으로model_fn. 대체 API는입니다tf.keras그리고tf.distribute. 아직 필요한 경우Estimator훈련의 일부를 위해tf.keras.estimator.model_to_estimator변환기를 만들려면Estimatorakeras.Model.

:::

Keras 모델에서 추정기를 만듭니다

기존 케라 모델을 견적으로 변환 할 수 있습니다tf.keras.estimator.model_to_estimator. 모델 코드를 현대화하려는 경우 도움이되지만 교육 파이프 라인에는 여전히 추정기가 필요합니다.

Keras Mobilenet V2 모델을 인스턴스화하고 최적화, 손실 및 메트릭으로 모델을 컴파일 할 수 있습니다.

\

keras_mobilenet_v2 = tf.keras.applications.MobileNetV2(
    input_shape=(160, 160, 3), include_top=False)
keras_mobilenet_v2.trainable = False

estimator_model = tf.keras.Sequential([
    keras_mobilenet_v2,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(1)
])

# Compile the model
estimator_model.compile(
    optimizer="adam",
    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
    metrics=['accuracy'])

\

Downloading data from 
9406464/9406464 [==============================] - 0s 0us/step

생성Estimator컴파일 된 Keras 모델에서. Keras 모델의 초기 모델 상태는 생성 된 부분에 보존됩니다.Estimator:

\

est_mobilenet_v2 = tf.keras.estimator.model_to_estimator(keras_model=estimator_model)

\

INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjcucleiz
INFO:tensorflow:Using the Keras model provided.
WARNING:absl:You are using `tf.keras.optimizers.experimental.Optimizer` in TF estimator, which only supports `tf.keras.optimizers.legacy.Optimizer`. Automatically converting your optimizer to `tf.keras.optimizers.legacy.Optimizer`.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/backend.py:452: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn(
2024-01-24 02:21:01.520653: W tensorflow/c/c_api.cc:305] Operation '{name:'block_16_project_BN/gamma/Assign' id:2164 op device:{requested: '', assigned: ''} def:{ { {node block_16_project_BN/gamma/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](block_16_project_BN/gamma, block_16_project_BN/gamma/Initializer/ones)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjcucleiz', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjcucleiz', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:2404: WarmStartSettings.__new__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:2404: WarmStartSettings.__new__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.

파생을 취급하십시오Estimator당신과 마찬가지로 다른 것과 마찬가지로Estimator.

\

IMG_SIZE = 160  # All images will be resized to 160x160

def preprocess(image, label):
  image = tf.cast(image, tf.float32)
  image = (image/127.5) - 1
  image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
  return image, label

\

def train_input_fn(batch_size):
  data = tfds.load('cats_vs_dogs', as_supervised=True)
  train_data = data['train']
  train_data = train_data.map(preprocess).shuffle(500).batch(batch_size)
  return train_data

훈련하려면 추정기의 열차 기능에 전화하십시오.

\

est_mobilenet_v2.train(input_fn=lambda: train_input_fn(32), steps=50)

\

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmpfs/tmp/tmpjcucleiz/keras/keras_model.ckpt', vars_to_warm_start=".*", var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmpfs/tmp/tmpjcucleiz/keras/keras_model.ckpt', vars_to_warm_start=".*", var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting from: /tmpfs/tmp/tmpjcucleiz/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting from: /tmpfs/tmp/tmpjcucleiz/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-started 158 variables.
INFO:tensorflow:Warm-started 158 variables.
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 /tmpfs/tmp/tmpjcucleiz/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjcucleiz/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 0.6796611, step = 0
INFO:tensorflow:loss = 0.6796611, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50...
INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmpjcucleiz/model.ckpt.
INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmpjcucleiz/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50...
INFO:tensorflow:Loss for final step: 0.69532585.
INFO:tensorflow:Loss for final step: 0.69532585.
<tensorflow_estimator.python.estimator.estimator.EstimatorV2 at 0x7f29400d3670>

마찬가지로 평가를 위해 추정기의 평가 기능에 전화하십시오.

\

est_mobilenet_v2.evaluate(input_fn=lambda: train_input_fn(32), steps=10)

\

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/engine/training_v1.py:2335: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
  updates = self.state_updates
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:19
INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:19
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjcucleiz/model.ckpt-50
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjcucleiz/model.ckpt-50
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 [1/10]
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 2.81526s
INFO:tensorflow:Inference Time : 2.81526s
INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:22
INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:22
INFO:tensorflow:Saving dict for global step 50: accuracy = 0.54375, global_step = 50, loss = 0.6558426
INFO:tensorflow:Saving dict for global step 50: accuracy = 0.54375, global_step = 50, loss = 0.6558426
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50: /tmpfs/tmp/tmpjcucleiz/model.ckpt-50
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50: /tmpfs/tmp/tmpjcucleiz/model.ckpt-50
{'accuracy': 0.54375, 'loss': 0.6558426, 'global_step': 50}

자세한 내용은 문서를 참조하십시오tf.keras.estimator.model_to_estimator.

추정기로 객체 기반 체크 포인트를 저장합니다

기본적으로 추정기 체크 포인트 가이드에 설명 된 개체 그래프 대신 가변 이름을 가진 검문소를 저장합니다.tf.train.Checkpoint이름 기반 체크 포인트를 읽지 만 추정기 외부의 모델의 일부를 이동할 때 변동 이름이 변경 될 수 있습니다.model_fn. 전달을 위해 호환성을 위해 객체 기반 체크 포인트를 저장하면 추정기 내부의 모델을보다 쉽게 ​​훈련시킨 다음 하나 밖에서 사용할 수 있습니다.

\

import tensorflow.compat.v1 as tf_compat

\

def toy_dataset():
  inputs = tf.range(10.)[:, None]
  labels = inputs * 5. + tf.range(5.)[None, :]
  return tf.data.Dataset.from_tensor_slices(
    dict(x=inputs, y=labels)).repeat().batch(2)

\

class Net(tf.keras.Model):
  """A simple linear model."""

  def __init__(self):
    super(Net, self).__init__()
    self.l1 = tf.keras.layers.Dense(5)

  def call(self, x):
    return self.l1(x)

\

def model_fn(features, labels, mode):
  net = Net()
  opt = tf.keras.optimizers.Adam(0.1)
  ckpt = tf.train.Checkpoint(step=tf_compat.train.get_global_step(),
                             optimizer=opt, net=net)
  with tf.GradientTape() as tape:
    output = net(features['x'])
    loss = tf.reduce_mean(tf.abs(output - features['y']))
  variables = net.trainable_variables
  gradients = tape.gradient(loss, variables)
  return tf.estimator.EstimatorSpec(
    mode,
    loss=loss,
    train_op=tf.group(opt.apply_gradients(zip(gradients, variables)),
                      ckpt.step.assign_add(1)),
    # Tell the Estimator to save "ckpt" in an object-based format.
    scaffold=tf_compat.train.Scaffold(saver=ckpt))

tf.keras.backend.clear_session()
est = tf.estimator.Estimator(model_fn, './tf_estimator_example/')
est.train(toy_dataset, steps=10)

\

INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': './tf_estimator_example/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_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': './tf_estimator_example/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_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:Calling model_fn.
INFO:tensorflow:Calling model_fn.
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 ./tf_estimator_example/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into ./tf_estimator_example/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 4.4765882, step = 1
INFO:tensorflow:loss = 4.4765882, step = 1
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 4 vs previous value: 4. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 4 vs previous value: 4. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10...
INFO:tensorflow:Saving checkpoints for 10 into ./tf_estimator_example/model.ckpt.
INFO:tensorflow:Saving checkpoints for 10 into ./tf_estimator_example/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10...
INFO:tensorflow:Loss for final step: 44.10201.
INFO:tensorflow:Loss for final step: 44.10201.
<tensorflow_estimator.python.estimator.estimator.EstimatorV2 at 0x7f29407836a0>

tf.train.Checkpoint그런 다음 추정기의 체크 포인트를 ITS에서로드 할 수 있습니다model_dir.

\

opt = tf.keras.optimizers.Adam(0.1)
net = Net()
ckpt = tf.train.Checkpoint(
  step=tf.Variable(1, dtype=tf.int64), optimizer=opt, net=net)
ckpt.restore(tf.train.latest_checkpoint('./tf_estimator_example/'))
ckpt.step.numpy()  # From est.train(..., steps=10)

\

10

추정기의 저장 모델

추정기는 저장 모델을 수출합니다tf.Estimator.export_saved_model.

\

input_column = tf.feature_column.numeric_column("x")

estimator = tf.estimator.LinearClassifier(feature_columns=[input_column])

def input_fn():
  return tf.data.Dataset.from_tensor_slices(
    ({"x": [1., 2., 3., 4.]}, [1, 1, 0, 0])).repeat(200).shuffle(64).batch(16)
estimator.train(input_fn)

\

INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmps_vspwiv
WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmps_vspwiv
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmps_vspwiv', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmps_vspwiv', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_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:Calling model_fn.
INFO:tensorflow:Calling model_fn.
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 /tmpfs/tmp/tmps_vspwiv/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmps_vspwiv/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 0.6931472, step = 0
INFO:tensorflow:loss = 0.6931472, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50...
INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmps_vspwiv/model.ckpt.
INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmps_vspwiv/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50...
INFO:tensorflow:Loss for final step: 0.42848104.
INFO:tensorflow:Loss for final step: 0.42848104.
<tensorflow_estimator.python.estimator.canned.linear.LinearClassifierV2 at 0x7f2940787640>

저장하려면Estimator당신은 만들어야합니다serving_input_receiver. 이 함수는 a의 일부를 구축합니다tf.GraphSavedModel이받은 원시 데이터를 구문 분석합니다.

그만큼tf.estimator.export모듈에는 이들을 구축하는 데 도움이되는 기능이 포함되어 있습니다receivers.

다음 코드는 다음을 기반으로 수신기를 빌드합니다feature_columns직렬화 된 것을 허용합니다tf.Example종종 TF-Serving과 함께 사용되는 프로토콜 버퍼.

\

tmpdir = tempfile.mkdtemp()

serving_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
  tf.feature_column.make_parse_example_spec([input_column]))

estimator_base_path = os.path.join(tmpdir, 'from_estimator')
estimator_path = estimator.export_saved_model(estimator_base_path, serving_input_fn)

\

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:4: make_parse_example_spec_v2 (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:4: make_parse_example_spec_v2 (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:3: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:3: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:168: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See  for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:168: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See  for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See  for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See  for instructions on how to migrate your code to TensorFlow v2.
INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']
INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']
INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']
INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmps_vspwiv/model.ckpt-50
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmps_vspwiv/model.ckpt-50
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tmpvsrxtilj/from_estimator/temp-1706062885/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tmpvsrxtilj/from_estimator/temp-1706062885/saved_model.pb

Python에서 해당 모델을로드하고 실행할 수도 있습니다.

\

imported = tf.saved_model.load(estimator_path)

def predict(x):
  example = tf.train.Example()
  example.features.feature["x"].float_list.value.extend([x])
  return imported.signatures["predict"](
    examples=tf.constant([example.SerializeToString()]))

\

print(predict(1.5))
print(predict(3.5))

\

{'logistic': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[0.576523]], dtype=float32)>, 'probabilities': <tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[0.42347696, 0.576523  ]], dtype=float32)>, 'class_ids': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[1]])>, 'logits': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[0.30851614]], dtype=float32)>, 'classes': <tf.Tensor: shape=(1, 1), dtype=string, numpy=array([[b'1']], dtype=object)>, 'all_class_ids': <tf.Tensor: shape=(1, 2), dtype=int32, numpy=array([[0, 1]], dtype=int32)>, 'all_classes': <tf.Tensor: shape=(1, 2), dtype=string, numpy=array([[b'0', b'1']], dtype=object)>}
{'logistic': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[0.2338471]], dtype=float32)>, 'probabilities': <tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[0.7661529 , 0.23384713]], dtype=float32)>, 'class_ids': <tf.Tensor: shape=(1, 1), dtype=int64, numpy=array([[0]])>, 'logits': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[-1.1867142]], dtype=float32)>, 'classes': <tf.Tensor: shape=(1, 1), dtype=string, numpy=array([[b'0']], dtype=object)>, 'all_class_ids': <tf.Tensor: shape=(1, 2), dtype=int32, numpy=array([[0, 1]], dtype=int32)>, 'all_classes': <tf.Tensor: shape=(1, 2), dtype=string, numpy=array([[b'0', b'1']], dtype=object)>}

tf.estimator.export.build_raw_serving_input_receiver_fn당신은 당신이 생성 된 텐서를 취하는 입력 함수를 만들 수 있습니다.tf.train.Example에스.

사용tf.distribute.Strategy추정기 (제한된 지원)

tf.estimator원래 비동기 매개 변수 서버 접근법을 지원하는 분산 교육 인 클로 플로우 API입니다.tf.estimator이제 지원합니다tf.distribute.Strategy. 사용하는 경우tf.estimator코드를 거의 변경하지 않고 분산 교육으로 변경할 수 있습니다. 이를 통해 추정기 사용자는 이제 여러 GPU 및 여러 작업자에 대한 동기 분산 교육을 수행하고 TPU를 사용할 수 있습니다. 그러나 추정기에서 의이 지원은 제한적입니다. 자세한 내용은 아래에 지원되는 내용 섹션을 확인하십시오.

사용tf.distribute.Strategy추정기의 경우 Keras 케이스와 약간 다릅니다. 사용하는 대신strategy.scope이제 당신은 전략 객체를RunConfig추정기를 위해.

자세한 내용은 분산 교육 안내서를 참조하십시오.

다음은 미리 미드 추정기로 이것을 보여주는 코드 스 니펫입니다.LinearRegressor그리고MirroredStrategy:

\

mirrored_strategy = tf.distribute.MirroredStrategy()
config = tf.estimator.RunConfig(
    train_distribute=mirrored_strategy, eval_distribute=mirrored_strategy)
regressor = tf.estimator.LinearRegressor(
    feature_columns=[tf.feature_column.numeric_column('feats')],
    optimizer="SGD",
    config=config)

\

INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3')
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3')
INFO:tensorflow:Initializing RunConfig with distribution strategies.
INFO:tensorflow:Initializing RunConfig with distribution strategies.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Not using Distribute Coordinator.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4277059788.py:4: LinearRegressorV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4277059788.py:4: LinearRegressorV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:1344: RegressionHead.__init__ (from tensorflow_estimator.python.estimator.head.regression_head) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:1344: RegressionHead.__init__ (from tensorflow_estimator.python.estimator.head.regression_head) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjagdxixr
WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjagdxixr
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjagdxixr', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': <tensorflow.python.distribute.mirrored_strategy.MirroredStrategy object at 0x7f28cc70eaf0>, '_device_fn': None, '_protocol': None, '_eval_distribute': <tensorflow.python.distribute.mirrored_strategy.MirroredStrategy object at 0x7f28cc70eaf0>, '_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, '_distribute_coordinator_mode': None}
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjagdxixr', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': <tensorflow.python.distribute.mirrored_strategy.MirroredStrategy object at 0x7f28cc70eaf0>, '_device_fn': None, '_protocol': None, '_eval_distribute': <tensorflow.python.distribute.mirrored_strategy.MirroredStrategy object at 0x7f28cc70eaf0>, '_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, '_distribute_coordinator_mode': None}

여기서는 미리 메이드 추정기를 사용하지만 동일한 코드는 사용자 정의 추정기와도 작동합니다.train_distribute훈련이 어떻게 배포되는지 결정합니다eval_distribute평가가 어떻게 분배되는지 결정합니다. 이것은 훈련과 평가에 동일한 전략을 사용하는 Keras와의 또 다른 차이점입니다.

이제 입력 기능 으로이 추정기를 교육하고 평가할 수 있습니다.

\

def input_fn():
  dataset = tf.data.Dataset.from_tensors(({"feats":[1.]}, [1.]))
  return dataset.repeat(1000).batch(10)
regressor.train(input_fn=input_fn, steps=10)
regressor.evaluate(input_fn=input_fn, steps=10)

\

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1246: StrategyBase.configure (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
use `update_config_proto` instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1246: StrategyBase.configure (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
use `update_config_proto` instead.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/data/ops/dataset_ops.py:462: UserWarning: To make it possible to preserve tf.data options across serialization boundaries, their implementation has moved to be part of the TensorFlow graph. As a consequence, the options value is in general no longer known at graph construction time. Invoking this method in graph mode retains the legacy behavior of the original implementation, but note that the returned value might not reflect the actual value of the options.
  warnings.warn("To make it possible to preserve tf.data options across "
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Collective all_reduce tensors: 2 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 2 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/util.py:95: DistributedIteratorV1.initialize (from tensorflow.python.distribute.v1.input_lib) is deprecated and will be removed in a future version.
Instructions for updating:
Use the iterator's `initializer` property instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/util.py:95: DistributedIteratorV1.initialize (from tensorflow.python.distribute.v1.input_lib) is deprecated and will be removed in a future version.
Instructions for updating:
Use the iterator's `initializer` property instead.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjagdxixr/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjagdxixr/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
2024-01-24 02:21:31.119820: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} }
    .  Registered:  device="CPU"

2024-01-24 02:21:31.121097: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} }
    .  Registered:  device="CPU"

2024-01-24 02:21:31.125859: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} }
    .  Registered:  device="CPU"

2024-01-24 02:21:31.126357: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} }
    .  Registered:  device="CPU"

2024-01-24 02:21:31.137091: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} }
    .  Registered:  device="CPU"

2024-01-24 02:21:31.137578: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} }
    .  Registered:  device="CPU"

2024-01-24 02:21:31.143860: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} }
    .  Registered:  device="CPU"

2024-01-24 02:21:31.144344: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} }
    .  Registered:  device="CPU"
INFO:tensorflow:loss = 4.0, step = 0
INFO:tensorflow:loss = 4.0, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10...
INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpjagdxixr/model.ckpt.
INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpjagdxixr/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10...
INFO:tensorflow:Loss for final step: 1.1510792e-12.
INFO:tensorflow:Loss for final step: 1.1510792e-12.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:35
INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:35
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjagdxixr/model.ckpt-10
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjagdxixr/model.ckpt-10
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.
2024-01-24 02:21:36.170934: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} }
    .  Registered:  device="CPU"

2024-01-24 02:21:36.172154: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} }
    .  Registered:  device="CPU"

2024-01-24 02:21:36.176484: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} }
    .  Registered:  device="CPU"

2024-01-24 02:21:36.176957: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} }
    .  Registered:  device="CPU"

2024-01-24 02:21:36.182511: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} }
    .  Registered:  device="CPU"

2024-01-24 02:21:36.182980: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} }
    .  Registered:  device="CPU"

2024-01-24 02:21:36.196737: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} }
    .  Registered:  device="CPU"

2024-01-24 02:21:36.197227: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} }
    .  Registered:  device="CPU"
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 1.83647s
INFO:tensorflow:Inference Time : 1.83647s
INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:37
INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:37
INFO:tensorflow:Saving dict for global step 10: average_loss = 1.4210855e-14, global_step = 10, label/mean = 1.0, loss = 1.4210855e-14, prediction/mean = 0.99999994
INFO:tensorflow:Saving dict for global step 10: average_loss = 1.4210855e-14, global_step = 10, label/mean = 1.0, loss = 1.4210855e-14, prediction/mean = 0.99999994
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmpjagdxixr/model.ckpt-10
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmpjagdxixr/model.ckpt-10
{'average_loss': 1.4210855e-14,
 'label/mean': 1.0,
 'loss': 1.4210855e-14,
 'prediction/mean': 0.99999994,
 'global_step': 10}

추정기와 케라의 또 다른 차이점은 입력 처리입니다. Keras에서는 데이터 세트의 각 배치가 다중 복제품에 대해 자동으로 분할됩니다. 그러나 추정기에서는 자동 배치 분할을 수행하지 않으며 다른 작업자의 데이터를 자동으로 제공하지 않습니다. 작업자와 장치에 데이터가 배포되기를 원하는 방식을 완전히 제어 할 수 있으며input_fn데이터를 배포하는 방법을 지정합니다.

당신의input_fn근로자 당 한 번 호출되므로 작업자 당 하나의 데이터 세트를 제공합니다. 그런 다음 해당 데이터 세트로부터 하나의 배치가 해당 작업자의 하나의 복제본으로 공급되어 1 명의 작업자에서 N 복제에 대한 N 배치를 소비합니다. 다시 말해, 데이터 세트는input_fn크기의 배치를 제공해야합니다PER_REPLICA_BATCH_SIZE. 그리고 단계의 글로벌 배치 크기는 다음과 같이 얻을 수 있습니다.PER_REPLICA_BATCH_SIZE * strategy.num_replicas_in_sync.

다중 노동자 교육을 수행 할 때는 데이터를 작업자 전체에 분할하거나 각각의 임의의 시드로 셔플해야합니다. 추정 자 튜토리얼을 사용하여 다중 노동자 교육에서이를 수행하는 방법에 대한 예를 확인할 수 있습니다.

마찬가지로 다중 작업자 및 매개 변수 서버 전략도 사용할 수 있습니다. 코드는 동일하게 유지되지만 사용해야합니다.tf.estimator.train_and_evaluate그리고 설정TF_CONFIG클러스터에서 실행되는 각 바이너리에 대한 환경 변수.

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지금 지원되는 것은 무엇입니까?

제외한 모든 전략을 사용하여 추정기를 사용한 훈련에 대한 지원이 제한되어 있습니다.TPUStrategy. 기본 교육 및 평가는 효과가 있지만 다음과 같은 여러 고급 기능v1.train.Scaffold하지 마십시오. 이 통합에는 여러 버그가있을 수 있으며이 지원을 적극적으로 개선 할 계획이 없습니다 (Keras 및 Custom Training Loop 지원에 중점을 둡니다). 가능하다면 사용하는 것을 선호해야합니다tf.distribute대신 그 API와 함께.

| 훈련 API | Mirroredstrategy | tpustrategy | Multiworkermirroredstrategy | CentralStoragestrategy | ParameterserVerstrategy | | —– | —- | —- | —- | —- | —– | | 추정기 API | 제한된 지원 | 지원되지 않음 | 제한된 지원 | 제한된 지원 | 제한된 지원 |

예와 튜토리얼

다음은 추정기와 함께 다양한 전략을 사용하는 방법을 보여주는 몇 가지 엔드 투 엔드 예입니다.

  1. 추정 자 튜토리얼을 사용한 다중 노동자 교육은 여러 근로자와 함께 훈련 할 수있는 방법을 보여줍니다.MultiWorkerMirroredStrategyMNIST 데이터 세트에서.

  2. 유통 전략으로 다중 노동자 교육을 실행하는 엔드 투 엔드 예tensorflow/ecosystemKubernetes 템플릿 사용. 그것은 Keras 모델로 시작하여 그것을 사용하여 추정기로 변환합니다.tf.keras.estimator.model_to_estimatorAPI.

  3. 공식 RESNET50 모델.MirroredStrategy또는MultiWorkerMirroredStrategy.

    \N

::: 정보
원래 게시텐서 플로웹 사이트,이 기사는 새 헤드 라인 아래에 표시되며 CC에 따라 4.0으로 라이센스가 부여됩니다. Apache 2.0 라이센스에 따라 공유 된 코드 샘플.

:::

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