TensorFlow估计器被贬值 - 这里是如何(以及为什么)迁移

内容概览 Setup Advantages Estimators Capabilities Using pre-made Estimators Write an input functions Define the feature columns. Instantiate the relevant pre-made Estimator. Call a training, evaluation, or inference method. Benefits of pre-made Estimators Custom Estimators Create an Estimator from a Keras model Saving object-based checkpoints with Estimator SavedModels from Estimators Using tf.distribute.Strategy with Estimator (Limited support) What’s supported now? Examples and tutorial 警告: TensorFlow 2.15 包含 tf 估计器包的最终版本. 估计器不会在 TensorFlow 2.16 或之后提供。 TensorFlow 2.15 包含了最后的版本 估计器在 TensorFlow 2.16 或之后将不可用。 了解有关如何转换估计器的更多信息。 Warning: tf-estimator migration guide 移民指南 本文件引入 一个高级别的 TensorFlow API. Estimators 封装了以下操作: tf.estimator 培训 评价 预测 出口服务 TensorFlow 实现了多种预先制作的估计器,自定义估计器仍然受支持,但主要是作为反向兼容性的衡量。 所有估计器 - 预制或定制 - 都是基于 班级 Custom estimators should not be used for new code tf.estimator.Estimator 一个快速的例子,试试。 对于 API 设计的概述,请检查 . 评估 Tutorials 白皮书 设置 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 优势 类似于A ,一个 这是一个模型级别的抽象。 提供一些目前仍在开发中的能力 这些是: tf.keras.Model estimator tf.estimator tf.keras 基于参数服务器的培训 全面 TFX 集成 评估能力 估计师提供以下好处: 您可以在本地主机或分布式多服务器环境中运行基于 Estimator 的模型,而不改变模型。 Estimators provide a safe distributed training loop that controls how and when to: Load data Handle exceptions Create checkpoint files and recover from failures Save summaries for TensorBoard 使用 Estimators 编写应用时,必须将数据输入管道与模型分开,这种分离简化了使用不同数据集的实验。 使用预制估计器 Pre-made Estimators 允许您在比基本 TensorFlow API 更高的概念层面上工作. 您不再需要担心创建计算图表或会话,因为 Estimators 为您处理所有“导水”。 ,例如,是一种预制的 Estimator 类,该类训练基于密集的、向前传递的神经网络的分类模型。 tf.estimator.DNNClassifier 依靠预制 Estimator 的 TensorFlow 程序通常包括以下四个步骤: 1、写入函数 例如,您可以创建一个函数来导入训练集,另一个函数来导入测试集。 一个词典,其中关键是功能名称,值是 Tensors (或 SparseTensors) 包含相应的功能数据 包含一个或多个标签的 Tensor 该 应该返回A 它在这种格式中产生了对。 input_fn tf.data.Dataset 例如,以下代码构建一个 《泰坦尼克号》数据集 檔案: tf.data.Dataset train.csv 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 该 被执行在A 也可以直接返回 a 包含图形压缩符的对,但这是在简单情况下,如返回常数之外的错误倾向。 input_fn tf.Graph (features_dics, labels) 二、定义特征列。 每 识别功能名称、其类型和任何输入预处理。 tf.feature_column 例如,下面的片段创建了三个功能列。 前者直接使用年龄特征作为浮点输入。 第二个使用类特征作为分类输入。 第三个使用 embark_town 作为分类输入,但使用哈希技巧来避免需要列出选项,并设置选项数量。 有关更多信息,请检查 . 标签: Columns Tutorial 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、即时化相应的预制估计器。 例如,这里是预制 Estimator 的示例实例,名为 : 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} 更多信息,你可以去 . 线性分类器 Tutorial 4、调用训练、评估或推断方法。 所有估计者提供 , ,和 方法。 train evaluate predict 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. 预制估计器的好处 Pre-made Estimators 编码了最佳实践,提供以下好处: 确定计算图的不同部分应该运行的地方的最佳实践,在单个机器或集群上实施策略。 事件(摘要)写作和普遍有用的摘要的最佳实践。 如果您没有使用预编制的 Estimators,则必须自己实现前面的功能。 定制估计 每个 Estimator 的核心 - 无论是预制还是定制 - 都是它的核心。 , ,这是构建图表的方法,用于训练、评估和预测。当您使用预先制作的 Estimator 时,其他人已经实现了模型函数。 功能模型 model_fn 注意:自定义 model_fn 将仍然运行在 1.x 风格的图形模式中,这意味着没有急迫的执行和无自动控制依赖性。您应该计划使用自定义 model_fn 迁移 tf.estimator。 替代的 API 是 tf.keras 和 tf.distribute. 如果您仍然需要一个 Estimator 用于您的培训,您可以使用 tf.keras.estimator.model_to_estimator 转换器从 keras.Model 创建一个 Estimator。 一个习惯 将仍在 1.x 风格图形模式中运行,这意味着没有急迫的执行和无自动控制依赖性。 与习俗 替代的API是 和 如果你仍然需要一个 对于你的训练的一部分,你可以使用 转换 创建一个 从 A . Note: model_fn tf.estimator model_fn tf.keras tf.distribute Estimator tf.keras.estimator.model_to_estimator Estimator keras.Model 从 Keras 模型创建一个 Estimator 您可以将现有的 Keras 模型转换为 Estimators。 这是有用的,如果你想现代化你的模型代码,但你的培训管道仍然需要估计器。 tf.keras.estimator.model_to_estimator 实时化 Keras MobileNet V2 模型,并编译该模型与优化器,损失和指标,以训练: 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 创建一个 从编译的Keras模型。Keras模型的初始模型状态保留在创建的 : Estimator 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. 处理衍生品 就像你在任何其他 . Estimator Estimator 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 要搭乘列车,请拨打 Estimator 的列车函数: 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. 同样,为了评估,请调用 Estimator 的评估函数: 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} 有关更多细节,请参阅文件 . tf.keras.estimator.model_to_estimator 使用 Estimator 保存基于对象的检查点 默认情况下,估计器会保存用变量名称的检查点,而不是描述的对象图表。 是的 会读取基于名称的检查点,但变量名称可能发生变化,当模型的部分移动到 Estimator 的外部时 对于前向兼容性,节省基于对象的检查点可以更容易地在 Estimator 中训练模型,然后在外部使用模型。 检查点指南 tf.train.Checkpoint model_fn 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. 然后可以从它的 Estimator 的检查点加载 . tf.train.Checkpoint model_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 从估计师那里保存模型 估计器通过出口SavedModels . tf.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. 为了拯救一个 你需要创建一个 这个函数构建了一部分 a 这分析了SavedModel收到的原始数据。 Estimator serving_input_receiver tf.Graph 该 模块包含功能来帮助构建这些 . tf.estimator.export receivers 以下代码构建一个接收器,基于 ,它接受了序列化 协议缓冲器,经常使用与 . feature_columns tf.Example TF服务 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://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py: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://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. INFO:tensorflow:Signatures INCLUDED in export for Classify: ['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 您也可以从 python 上加载和运行该模型: 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': , 'probabilities': , 'class_ids': , 'logits': , 'classes': , 'all_class_ids': , 'all_classes': } {'logistic': , 'probabilities': , 'class_ids': , 'logits': , 'classes': , 'all_class_ids': , 'all_classes': } 允许您创建输入函数,而不是使用原始压缩器 斯。 tf.estimator.export.build_raw_serving_input_receiver_fn tf.train.Example 使用 使用 Estimator (有限支持) tf.distribute.战略 tf.distribute.战略 是一个分布式培训 TensorFlow API,最初支持 async 参数服务器方法。 现在支持 如果你正在使用 ,您可以更改为分布式培训,对您的代码进行很少的更改。通过此,Estimator用户现在可以对多个GPU和多个工人进行同步分布式培训,以及使用TPU。 下面的部分为更多细节。 tf.estimator tf.estimator tf.distribute.Strategy tf.estimator 现在支持的是什么 使用 用 Estimator 略有不同于在 Keras 案例中。 ,现在您将策略对象传入 对于估计器来说。 tf.distribute.Strategy strategy.scope RunConfig 你可以指的是 为了更多信息。 分布式培训指南 这里有一个代码片段,用预先估计器来显示这一点 和 : LinearRegressor MirroredStrategy 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': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_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': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_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} 在这里,您使用了预先编制的 Estimator,但同样的代码也与自定义 Estimator 一起工作。 决定如何分配培训,并 它决定了评估将如何分布,这是与Keras的另一个区别,您在培训和评估中使用相同的策略。 train_distribute eval_distribute 现在您可以用输入函数训练和评估这个 Estimator: 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} 在 Estimator 和 Keras 中,另一个值得强调的区别是输入处理。在 Keras 中,数据集的每个批自动分布在多个复制品中。在 Estimator 中,您不会自动分割批量,也不会自动分割数据在不同的工人中。 说明如何分发您的数据。 input_fn 你的 然后,从该数据集的一批被送到该工人的一个复制品中,从而消耗了 N 批对 1 工人的 N 复制品。 应该提供尺寸的包裹 并且,一个步骤的全球批量大小可以获得为 . input_fn input_fn PER_REPLICA_BATCH_SIZE PER_REPLICA_BATCH_SIZE * strategy.num_replicas_in_sync 在进行多人培训时,您应该将数据分割到各个工人中,或者在每个工人中使用随机种子。 教程。 多人培训与 Estimator 同样,您也可以使用多工人和参数服务器策略,代码仍然相同,但您需要使用 ,并设置 在您的集群中运行的每个二进制的环境变量。 tf.estimator.train_and_evaluate TF_CONFIG 现在支持的是什么? 使用 Estimator 训练的支持有限,除了所有策略 基本培训和评估应该起作用,但一些先进的功能如 在此集成中也可能有一些错误,并且没有计划积极改进此支持(重点是Keras和自定义培训循环支持)。 相反,这些APIs。 TPUStrategy v1.train.Scaffold tf.distribute Training API MirroredStrategy TPUStrategy MultiWorkerMirroredStrategy CentralStorageStrategy ParameterServerStrategy Estimator API Limited support Not supported Limited support Limited support Limited support 火花估计 有限支持 未支持 有限支持 有限支持 有限支持 示例和教程 以下是几个端到端的示例,显示如何使用 Estimator 中的各种策略: Multi-worker Training with Estimator 教程展示了如何在 MNIST 数据集上使用 MultiWorkerMirroredStrategy 与多个工人进行培训。 使用 Kubernetes 模板在 tensorflow/生态系统中使用分布策略进行多人培训的端到端示例. 它从 Keras 模型开始,并使用 tf.keras.estimator.model_to_estimator API 将其转换为 Estimator。 官方的ResNet50模型,可以使用MirroredStrategy或MultiWorkerMirroredStrategy进行训练。 本文最初在 TensorFlow 网站上发表,此文在新标题下出现在这里,并在 CC BY 4.0 版下获得许可。 本文最初在 TensorFlow 网站上发表,此文在新标题下出现在这里,并在 CC BY 4.0 版下获得许可。 腾讯 腾讯