运行时错误：仅在tf.function或启用急切执行时支持__iter__()。

Question

我是新手，正在尝试学习Tensorflow。我正在尝试在Tensorflow 2.2.0中运行一个估算器LinearClassifier。

1. 导入所有模块并读取tfRecords。

import tensorflow as tf
print(tf.version.VERSION)
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
print (tf.executing_eagerly())
tf.executing_eagerly()
tf.compat.v1.enable_eager_execution()

path = 'train.tfrecord'
filenames = [(path + "/" + name) for name in os.listdir(path) if name.startswith("part")]
print (filenames)

定义解析函数。

def _parse_function(example_proto):
    features = {
        'Age': tf.io.FixedLenFeature([], tf.string),
        'EstimatedSalary': tf.io.FixedLenFeature([], tf.string),
        'Purchased': tf.io.FixedLenFeature([], tf.string)
    }
    tf_records = tf.io.parse_single_example(example_proto, features)
    features_dict = {
        'Age': tf_records['Age'],
        'EstimatedSalary': tf_records['EstimatedSalary']
    }
    return features_dict, tf_records['Purchased']

3. 定义输入函数以传递给估计器。

def input_fn():
    dataset = tf.data.TFRecordDataset(filenames = filenames)
    
    dataset = dataset.map(_parse_function)
    iterator = iter(dataset)
    next_element = iterator.get_next()
    return next_element

4. 初始化估算器

feature_columns = [
    tf.feature_column.numeric_column('Age'),
    tf.feature_column.numeric_column('EstimatedSalary')
]

estimator = tf.estimator.LinearClassifier(feature_columns = feature_columns)
estimator.train(
    input_fn = input_fn
)

运行以下代码会出现错误：

Traceback (most recent call last):
  File "linear_classification.py", line 42, in <module>
    input_fn = input_fn
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 349, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1182, in _train_model
    return self._train_model_default(input_fn, hooks, saving_listeners)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1208, in _train_model_default
    self._get_features_and_labels_from_input_fn(input_fn, ModeKeys.TRAIN))
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1044, in _get_features_and_labels_from_input_fn
    self._call_input_fn(input_fn, mode))
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1137, in _call_input_fn
    return input_fn(**kwargs)
  File "linear_classification.py", line 31, in input_fn
    iterator = iter(dataset)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 406, in __iter__
    raise RuntimeError("__iter__() is only supported inside of tf.function "
RuntimeError: __iter__() is only supported inside of tf.function or when eager execution is enabled.

我尝试过的事情：

1. 强制启用急切执行（即使在tf 2中，默认情况下也已经这样做了）。
2. 尝试搜索现有的StackOverflow：TensorFlow 2.0 dataset.__iter__() 仅在启用急切执行时受支持
3. 在实际的tf源代码中放置打印语句以了解为什么context.executing_eagerly()被设置为False。在context.py中，default_execution_mode由EAGER_MODE初始化，所以我对它为什么变成False感到困惑

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py

这是我的第一篇StackOverflow提问，请见谅如果我没有遵守任何指导方针或规则。非常感谢您的帮助。谢谢。

- Grism Patel

Answer 1

我找到了问题所在。正如错误信息所述RuntimeError: __iter__() is only supported inside of tf.function or when eager execution is enabled。我在input_fn()之前添加了@tf.function。现在我的input_fn()看起来像这样：

@tf.function
def input_fn():
    dataset = tf.data.TFRecordDataset(filenames = filenames)
    
    dataset = dataset.map(_parse_function)
    iterator = iter(dataset)
    next_element = iterator.get_next()
    return next_element

我通过阅读TensorFlow文档得以追踪这个问题：https://www.tensorflow.org/guide/effective_tf2

- Grism Patel