本文整理匯總了Python中tensorflow.python.data.ops.dataset_ops.make_initializable_iterator方法的典型用法代碼示例。如果您正苦於以下問題：Python dataset_ops.make_initializable_iterator方法的具體用法？Python dataset_ops.make_initializable_iterator怎麽用？Python dataset_ops.make_initializable_iterator使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.data.ops.dataset_ops的用法示例。
在下文中一共展示了dataset_ops.make_initializable_iterator方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: dataset_initializer
# 需要導入模塊: from tensorflow.python.data.ops import dataset_ops [as 別名]
# 或者: from tensorflow.python.data.ops.dataset_ops import make_initializable_iterator [as 別名]
def dataset_initializer(self):
"""Returns the dataset's initializer.
The initializer must be run before calling `features_and_labels`.
"""
self._iterator = dataset_ops.make_initializable_iterator(self._dataset)
return self._iterator.initializer 示例2: parse_input_fn_result
# 需要導入模塊: from tensorflow.python.data.ops import dataset_ops [as 別名]
# 或者: from tensorflow.python.data.ops.dataset_ops import make_initializable_iterator [as 別名]
def parse_input_fn_result(result):
"""Gets features, labels, and hooks from the result of an Estimator input_fn.
Args:
result: output of an input_fn to an estimator, which should be one of:
* A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a tuple
(features, labels) with same constraints as below.
* A tuple (features, labels): Where `features` is a `Tensor` or a
dictionary of string feature name to `Tensor` and `labels` is a `Tensor`
or a dictionary of string label name to `Tensor`. Both `features` and
`labels` are consumed by `model_fn`. They should satisfy the expectation
of `model_fn` from inputs.
Returns:
Tuple of features, labels, and input_hooks, where features are as described
above, labels are as described above or None, and input_hooks are a list
of SessionRunHooks to be included when running.
Raises:
ValueError: if the result is a list or tuple of length != 2.
"""
input_hooks = []
if isinstance(result, dataset_ops.DatasetV2):
iterator = dataset_ops.make_initializable_iterator(result)
input_hooks.append(_DatasetInitializerHook(iterator))
result = iterator.get_next()
return parse_iterator_result(result) + (input_hooks,)