# 通过 Keras 模型创建 Estimator > 原文：[https://tensorflow.google.cn/tutorials/estimator/keras_model_to_estimator](https://tensorflow.google.cn/tutorials/estimator/keras_model_to_estimator) ## 概述 TensorFlow 完全支持 TensorFlow Estimator，可以从新的和现有的 [`tf.keras`](https://tensorflow.google.cn/api_docs/python/tf/keras) 模型创建 Estimator。本教程包含了该过程完整且最为简短的示例。 ## 设置 ```py import tensorflow as tf import numpy as np import tensorflow_datasets as tfds ``` ### 创建一个简单的 Keras 模型。 在 Keras 中，需要通过组装*层*来构建*模型*。模型（通常）是由层构成的计算图。最常见的模型类型是一种叠加层：[`tf.keras.Sequential`](https://tensorflow.google.cn/api_docs/python/tf/keras/Sequential) 模型。 构建一个简单的全连接网络（即多层感知器）： ```py model = tf.keras.models.Sequential([ tf.keras.layers.Dense(16, activation='relu', input_shape=(4,)), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(3) ]) ``` 编译模型并获取摘要。 ```py model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer='adam') model.summary() ``` ```py Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 16) 80 _________________________________________________________________ dropout (Dropout) (None, 16) 0 _________________________________________________________________ dense_1 (Dense) (None, 3) 51 ================================================================= Total params: 131 Trainable params: 131 Non-trainable params: 0 _________________________________________________________________ ``` ### 创建输入函数 使用 [Datasets API](https://tensorflow.google.cn/guide/data) 可以扩展到大型数据集或多设备训练。 Estimator 需要控制构建输入流水线的时间和方式。为此，它们需要一个“输入函数”或 `input_fn`。`Estimator` 将不使用任何参数调用此函数。`input_fn` 必须返回 [`tf.data.Dataset`](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset)。 ```py def input_fn(): split = tfds.Split.TRAIN dataset = tfds.load('iris', split=split, as_supervised=True) dataset = dataset.map(lambda features, labels: ({'dense_input':features}, labels)) dataset = dataset.batch(32).repeat() return dataset ``` 测试您的 `input_fn` ```py for features_batch, labels_batch in input_fn().take(1): print(features_batch) print(labels_batch) ``` ```py Downloading and preparing dataset iris/2.0.0 (download: 4.44 KiB, generated: Unknown size, total: 4.44 KiB) to /home/kbuilder/tensorflow_datasets/iris/2.0.0... Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/iris/2.0.0.incompleteQ29ZWS/iris-train.tfrecord Dataset iris downloaded and prepared to /home/kbuilder/tensorflow_datasets/iris/2.0.0\. Subsequent calls will reuse this data. {'dense_input': } tf.Tensor([0 2 1 2 0 1 1 1 0 2 1 0 2 0 0 0 0 0 2 2 2 2 2 0 2 0 2 1 1 1 1 1], shape=(32,), dtype=int64) ``` ### 通过 tf.keras 模型创建 Estimator。 可以使用 [`tf.estimator`](https://tensorflow.google.cn/api_docs/python/tf/estimator) API 来训练 [`tf.keras.Model`](https://tensorflow.google.cn/api_docs/python/tf/keras/Model)，方法是使用 [`tf.keras.estimator.model_to_estimator`](https://tensorflow.google.cn/api_docs/python/tf/keras/estimator/model_to_estimator) 将模型转换为 [`tf.estimator.Estimator`](https://tensorflow.google.cn/api_docs/python/tf/estimator/Estimator) 对象。 ```py import tempfile model_dir = tempfile.mkdtemp() keras_estimator = tf.keras.estimator.model_to_estimator( keras_model=model, model_dir=model_dir) ``` ```py INFO:tensorflow:Using default config. INFO:tensorflow:Using default config. INFO:tensorflow:Using the Keras model provided. INFO:tensorflow:Using the Keras model provided. Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/keras.py:220: set_learning_phase (from tensorflow.python.keras.backend) is deprecated and will be removed after 2020-10-11. Instructions for updating: Simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model. Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/keras.py:220: set_learning_phase (from tensorflow.python.keras.backend) is deprecated and will be removed after 2020-10-11. Instructions for updating: Simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model. INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp13998n2j', '_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, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp13998n2j', '_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, '_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} ``` 训练和评估 Estimator。 ```py keras_estimator.train(input_fn=input_fn, steps=500) eval_result = keras_estimator.evaluate(input_fn=input_fn, steps=10) print('Eval result: {}'.format(eval_result)) ``` ```py WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmp13998n2j/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='/tmp/tmp13998n2j/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={}) INFO:tensorflow:Warm-starting from: /tmp/tmp13998n2j/keras/keras_model.ckpt INFO:tensorflow:Warm-starting from: /tmp/tmp13998n2j/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 4 variables. INFO:tensorflow:Warm-started 4 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 /tmp/tmp13998n2j/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp13998n2j/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 1.5731332, step = 0 INFO:tensorflow:loss = 1.5731332, step = 0 INFO:tensorflow:global_step/sec: 444.326 INFO:tensorflow:global_step/sec: 444.326 INFO:tensorflow:loss = 0.79164267, step = 100 (0.227 sec) INFO:tensorflow:loss = 0.79164267, step = 100 (0.227 sec) INFO:tensorflow:global_step/sec: 515.459 INFO:tensorflow:global_step/sec: 515.459 INFO:tensorflow:loss = 0.5765847, step = 200 (0.193 sec) INFO:tensorflow:loss = 0.5765847, step = 200 (0.193 sec) INFO:tensorflow:global_step/sec: 518.855 INFO:tensorflow:global_step/sec: 518.855 INFO:tensorflow:loss = 0.48571444, step = 300 (0.193 sec) INFO:tensorflow:loss = 0.48571444, step = 300 (0.193 sec) INFO:tensorflow:global_step/sec: 527.318 INFO:tensorflow:global_step/sec: 527.318 INFO:tensorflow:loss = 0.3836534, step = 400 (0.190 sec) INFO:tensorflow:loss = 0.3836534, step = 400 (0.190 sec) INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500... INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp13998n2j/model.ckpt. INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp13998n2j/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500... INFO:tensorflow:Loss for final step: 0.46023262. INFO:tensorflow:Loss for final step: 0.46023262. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically. Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2020-09-22T19:57:20Z INFO:tensorflow:Starting evaluation at 2020-09-22T19:57:20Z INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmp/tmp13998n2j/model.ckpt-500 INFO:tensorflow:Restoring parameters from /tmp/tmp13998n2j/model.ckpt-500 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 : 0.16498s INFO:tensorflow:Inference Time : 0.16498s INFO:tensorflow:Finished evaluation at 2020-09-22-19:57:20 INFO:tensorflow:Finished evaluation at 2020-09-22-19:57:20 INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.33660004 INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.33660004 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp13998n2j/model.ckpt-500 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp13998n2j/model.ckpt-500 Eval result: {'loss': 0.33660004, 'global_step': 500} ```