估算器

在 TensorFlow.org 上檢視

在 Google Colab 中執行

在 GitHub 上檢視原始碼

下載筆記本

警告： TensorFlow 2.15 包含 tf-estimator 套件的最終版本。估算器在 TensorFlow 2.16 或更高版本中將無法使用。如需瞭解如何轉換離開估算器的詳細資訊，請參閱遷移指南。

本文件介紹高階 TensorFlow API tf.estimator。估算器封裝下列動作

訓練
評估
預測
匯出以供服務

TensorFlow 實作了數個預先建立的估算器。仍支援自訂估算器，但主要作為回溯相容性措施。新程式碼不應使用自訂估算器。所有估算器 (無論是預先建立或自訂的) 都是以 tf.estimator.Estimator 類別為基礎的類別。

如需快速範例，請試用估算器教學課程。如需 API 設計的概觀，請查看白皮書。

設定

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

優點

類似於 tf.keras.Model，estimator 是模型層級的抽象概念。tf.estimator 提供 tf.keras 目前仍在開發中的部分功能。這些是

以參數伺服器為基礎的訓練
完整 TFX 整合

估算器功能

估算器提供下列優點

您可以在本機主機或分散式多伺服器環境中執行以估算器為基礎的模型，而無需變更模型。此外，您可以在 CPU、GPU 或 TPU 上執行以估算器為基礎的模型，而無需重新編寫模型。
估算器提供安全的分布式訓練迴圈，可控制如何以及何時
- 載入資料
- 處理例外狀況
- 建立檢查點檔案並從失敗中復原
- 儲存 TensorBoard 的摘要

使用估算器編寫應用程式時，您必須將資料輸入管道與模型分開。這種分離簡化了使用不同資料集的實驗。

使用預先建立的估算器

預先建立的估算器讓您能夠在比基本 TensorFlow API 高得多的概念層級上工作。您不再需要擔心建立運算圖或工作階段，因為估算器會為您處理所有「管線」。此外，預先建立的估算器可讓您透過僅進行最少的程式碼變更來實驗不同的模型架構。例如，tf.estimator.DNNClassifier 是一個預先建立的估算器類別，可根據密集的前饋神經網路訓練分類模型。

依賴預先建立的估算器的 TensorFlow 程式通常包含下列四個步驟

1. 撰寫輸入函式

例如，您可以建立一個函式來匯入訓練集，另一個函式來匯入測試集。估算器預期其輸入格式為一對物件

字典，其中鍵是特徵名稱，值是包含對應特徵資料的張量 (或 SparseTensor)
包含一或多個標籤的張量

input_fn 應傳回 tf.data.Dataset，其會產生該格式的配對。

例如，下列程式碼會從鐵達尼號資料集的 train.csv 檔案建立 tf.data.Dataset

def train_input_fn():
  titanic_file = tf.keras.utils.get_file("train.csv", "https://storage.googleapis.com/tf-datasets/titanic/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

input_fn 會在 tf.Graph 中執行，並且也可以直接傳回包含圖張量的 (features_dics, labels) 配對，但在簡單案例 (例如傳回常數) 之外，這很容易出錯。

2. 定義特徵欄。

每個 tf.feature_column 都會識別特徵名稱、其類型和任何輸入預先處理。

例如，下列程式碼片段會建立三個特徵欄。

第一個直接使用 age 特徵作為浮點輸入。
第二個使用 class 特徵作為類別輸入。
第三個使用 embark_town 作為類別輸入，但使用 hashing trick 以避免需要列舉選項，並設定選項數量。

如需更多資訊，請查看特徵欄教學課程。

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. 例項化相關的預先建立估算器。

例如，以下是以 LinearClassifier 命名的預先建立估算器的範例例項化

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}

如需更多資訊，您可以前往線性分類器教學課程。

4. 呼叫訓練、評估或推論方法。

所有估算器都提供 train、evaluate 和 predict 方法。

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 https://storage.googleapis.com/tf-datasets/titanic/train.csv
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.

預先建立估算器的優點

預先建立的估算器編碼最佳實務，提供下列優點

判斷運算圖的不同部分應在何處執行的最佳實務，在單一機器或叢集上實作策略。
事件 (摘要) 寫入和通用實用摘要的最佳實務。

如果您不使用預先建立的估算器，則必須自行實作上述功能。

自訂估算器

每個估算器 (無論是預先建立或自訂的) 的核心都是其模型函式 model_fn，這是一種為訓練、評估和預測建立圖的方法。當您使用預先建立的估算器時，其他人已經實作了模型函式。當您依賴自訂估算器時，您必須自行撰寫模型函式。

注意： 自訂 model_fn 仍會在 1.x 樣式的圖模式中執行。這表示沒有立即執行，也沒有自動控制依附關係。您應計畫從具有自訂 model_fn 的 tf.estimator 遷移。替代 API 是 tf.keras 和 tf.distribute。如果您在訓練的某些部分仍需要 Estimator，則可以使用 tf.keras.estimator.model_to_estimator 轉換器從 keras.Model 建立 Estimator。

從 Keras 模型建立估算器

您可以使用 tf.keras.estimator.model_to_estimator 將現有的 Keras 模型轉換為估算器。如果您想要將模型程式碼現代化，但您的訓練管道仍需要估算器，這會很有幫助。

例項化 Keras MobileNet V2 模型，並使用最佳化工具、損失和指標編譯模型以進行訓練

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 https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5
9406464/9406464 [==============================] - 0s 0us/step

從編譯的 Keras 模型建立 Estimator。Keras 模型的初始模型狀態會保留在建立的 Estimator 中

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.

將衍生的 Estimator 視為與任何其他 Estimator 相同。

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

若要訓練，請呼叫估算器的訓練函式

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>

同樣地，若要評估，請呼叫估算器的評估函式

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}

如需更多詳細資訊，請參閱 tf.keras.estimator.model_to_estimator 的文件。

使用估算器儲存以物件為基礎的檢查點

依預設，估算器會使用變數名稱而非檢查點指南中說明的物件圖來儲存檢查點。tf.train.Checkpoint 將會讀取以名稱為基礎的檢查點，但在將模型的部分移出估算器的 model_fn 之外時，變數名稱可能會變更。為了向前相容性，儲存以物件為基礎的檢查點讓在估算器內訓練模型，然後在估算器外使用模型變得更容易。

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>

然後，tf.train.Checkpoint 可以從其 model_dir 載入估算器的檢查點。

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)

來自估算器的 SavedModel

估算器透過 tf.Estimator.export_saved_model 匯出 SavedModel。

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>

若要儲存 Estimator，您需要建立 serving_input_receiver。此函式會建立 tf.Graph 的一部分，以剖析 SavedModel 收到的原始資料。

tf.estimator.export 模組包含有助於建立這些 receiver 的函式。

下列程式碼會根據 feature_columns 建立接收器，其接受序列化的 tf.Example 通訊協定緩衝區，這些緩衝區通常與 tf-serving 搭配使用。

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 https://tensorflow.dev.org.tw/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py: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 https://tensorflow.dev.org.tw/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://tensorflow.dev.org.tw/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://tensorflow.dev.org.tw/guide/migrate 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

您也可以從 Python 載入並執行該模型

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_fn 可讓您建立輸入函式，其採用原始張量而非 tf.train.Example。

搭配估算器使用 `tf.distribute.Strategy` (有限支援)

tf.estimator 是分散式訓練 TensorFlow API，最初支援非同步參數伺服器方法。tf.estimator 現在支援 tf.distribute.Strategy。如果您使用 tf.estimator，則只需對程式碼進行極少的變更即可變更為分散式訓練。透過此功能，估算器使用者現在可以在多個 GPU 和多個工作站上執行同步分散式訓練，以及使用 TPU。不過，估算器中的此支援有限。請查看下方的「目前支援的功能」章節以取得更多詳細資訊。

搭配估算器使用 tf.distribute.Strategy 與 Keras 案例略有不同。現在，您不再使用 strategy.scope，而是將策略物件傳遞至估算器的 RunConfig。

您可以參閱分散式訓練指南以取得更多資訊。

以下是程式碼片段，其中顯示了搭配預先建立的估算器 LinearRegressor 和 MirroredStrategy 的範例

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}

在這裡，您使用預先建立的估算器，但相同的程式碼也適用於自訂估算器。train_distribute 決定訓練的分散方式，而 eval_distribute 決定評估的分散方式。這與 Keras 的另一個不同之處，在 Keras 中，您對訓練和評估使用相同的策略。

現在您可以使用輸入函式訓練和評估此估算器

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}

估算器和 Keras 之間要強調的另一個差異是輸入處理。在 Keras 中，資料集的每個批次都會自動在多個複本之間分割。不過，在估算器中，您不會執行自動批次分割，也不會自動在不同工作站之間分割資料。您可以完全控制資料在工作站和裝置之間的分散方式，而且您必須提供 input_fn 以指定資料的分散方式。

您的 input_fn 會針對每個工作站呼叫一次，因此每個工作站都會獲得一個資料集。然後，來自該資料集的一個批次會饋送至該工作站上的其中一個複本，因此 1 個工作站上的 N 個複本會耗用 N 個批次。換句話說，input_fn 傳回的資料集應提供大小為 PER_REPLICA_BATCH_SIZE 的批次。而步驟的全域批次大小可以取得為 PER_REPLICA_BATCH_SIZE * strategy.num_replicas_in_sync。

執行多工作站訓練時，您應將資料分散到各個工作站，或在每個工作站上使用隨機種子進行洗牌。您可以在「搭配估算器進行多工作站訓練」教學課程中查看如何執行此操作的範例。

同樣地，您也可以使用多工作站和參數伺服器策略。程式碼保持不變，但您需要使用 tf.estimator.train_and_evaluate，並針對叢集中執行的每個二進位檔設定 TF_CONFIG 環境變數。

目前支援的功能？

除了 TPUStrategy 以外的所有策略，都有限度地支援使用估算器進行訓練。基本訓練和評估應可運作，但許多進階功能 (例如 v1.train.Scaffold) 無法運作。此整合中可能也存在許多錯誤，而且沒有積極改善此支援的計畫 (重點是 Keras 和自訂訓練迴圈支援)。如果可能，您應偏好搭配這些 API 使用 tf.distribute。

訓練 API	MirroredStrategy	TPUStrategy	MultiWorkerMirroredStrategy	CentralStorageStrategy	ParameterServerStrategy
估算器 API	有限支援	不支援	有限支援	有限支援	有限支援

範例和教學課程

以下是一些端對端範例，示範如何搭配估算器使用各種策略

「搭配估算器進行多工作站訓練」教學課程示範如何使用 MNIST 資料集上的 MultiWorkerMirroredStrategy 與多個工作站進行訓練。
使用 Kubernetes 範本在 tensorflow/ecosystem 中執行搭配分散式策略的多工作站訓練的端對端範例。它從 Keras 模型開始，並使用 tf.keras.estimator.model_to_estimator API 將其轉換為估算器。
官方 ResNet50 模型，可以使用 MirroredStrategy 或 MultiWorkerMirroredStrategy 進行訓練。

估算器

設定

優點

估算器功能

使用預先建立的估算器

1. 撰寫輸入函式

2. 定義特徵欄。

3. 例項化相關的預先建立估算器。

4. 呼叫訓練、評估或推論方法。

預先建立估算器的優點

自訂估算器

從 Keras 模型建立估算器

使用估算器儲存以物件為基礎的檢查點

來自估算器的 SavedModel

搭配估算器使用 tf.distribute.Strategy (有限支援)

目前支援的功能？

範例和教學課程

搭配估算器使用 `tf.distribute.Strategy` (有限支援)