tf.estimator.EstimatorSpec讲解

最新推荐文章于 2025-06-05 09:07:35 发布

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最新推荐文章于 2025-06-05 09:07:35 发布

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分类专栏： tensorflow 学习文章标签： tf.estimator.EstimatorSpec

本文链接：https://blog.youkuaiyun.com/qq_32806793/article/details/85010302

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本文深入解析了EstimatorSpec类的作用及其实例化过程，它是TensorFlow中定义在model_fn内的核心组件，用于初始化Estimator实例。文章详细阐述了EstimatorSpec的构造函数参数，包括mode、predictions、loss、train_op等，并解释了这些参数在不同运行模式（训练、评估、预测）下的应用。

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作用

是一个class(类)，是定义在model_fn中的，并且model_fn返回的也是它的一个实例，这个实例是用来初始化Estimator类的
（Ops and objects returned from a model_fn and passed to an Estimator.）

具体细节

Creates a validated EstimatorSpec instance.

@staticmethod
__new__(
    cls,
    mode,
    predictions=None,
    loss=None,
    train_op=None,
    eval_metric_ops=None,
    export_outputs=None,
    training_chief_hooks=None,
    training_hooks=None,
    scaffold=None,
    evaluation_hooks=None,
    prediction_hooks=None
)
'''
Args:
	    mode: A ModeKeys. Specifies if this is training, evaluation or prediction.
		predictions: Predictions Tensor or dict of Tensor.
		loss: Training loss Tensor. Must be either scalar, or with shape [1].
		train_op: Op for the training step.
		eval_metric_ops: Dict of metric results keyed by name. The values of the dict can be one of the following: (1) instance of Metric class. (2) Results of calling a metric function, namely a (metric_tensor, update_op) tuple. metric_tensor should be evaluated without any impact on state (typically is a pure computation results based on variables.). For example, it should not trigger the update_op or requires any input fetching.
		export_outputs: Describes the output signatures to be exported to SavedModel and used during serving. A dict {name: output} where:
		name: An arbitrary name for this output.
		output: an ExportOutput object such as ClassificationOutput, RegressionOutput, or PredictOutput. Single-headed models only need to specify one entry in this dictionary. Multi-headed models should specify one entry for each head, one of which must be named using signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY. If no entry is provided, a default PredictOutput mapping to predictions will be created.
		training_chief_hooks: Iterable of tf.train.SessionRunHook objects to run on the chief worker during training.
		training_hooks: Iterable of tf.train.SessionRunHook objects to run on all workers during training.
		scaffold: A tf.train.Scaffold object that can be used to set initialization, saver, and more to be used in training.
		evaluation_hooks: Iterable of tf.train.SessionRunHook objects to run during evaluation.
		prediction_hooks: Iterable of tf.train.SessionRunHook objects to run during predictions.

Returns:
     A validated EstimatorSpec object.
'''

主要参数说明

predictions: Predictions Tensor or dict of Tensor.(模型的预测输出，主要是在infer阶段，在分类是：预测的类别，在文本生成是：生成的文本)
loss：Training loss Tensor. Must be either scalar, or with shape [1]. 损失。主要用在train 和 dev中
train_op ：Op for the training step.（是一个操作，用来训练）
eval_metric_ops：Dict of metric results keyed by name. The values of the dict can be one of the following: **(1) instance of Metric class **. （2）: Results of calling a metric function, namely a (metric_tensor, update_op) tuple. metric_tensor should be evaluated without any impact on state (typically is a pure computation results based on variables.). For example, it should not trigger the update_op or requires any input fetching.例如可以是 tf.metrics.accuracy(labels,predictions)

说明

创建一个可以利用的EstimatorSpec实例，其根据不同的mode值，需要不同的参数创建不同的实例（主要是训练train，验证dev，测试test）：

For mode==ModeKeys.TRAIN: 需要的参数是 loss and train_op.
For mode==ModeKeys.EVAL:  需要的参数是  loss.
For mode==ModeKeys.PREDICT: 需要的参数是 predictions.

其是定义在一个名为mode_fn的方法中，这个mode_fn可以计算各个mode下的参数需求，定义好的EstimatorSpec 用来初始化一个Estimator实例，同时Estimator实例可以根据mode的不同自动的忽视一些参数（操作），例如：train_op will be ignored in eval and infer modes.

官方给的例子

def my_model_fn(features, labels, mode):
  predictions = ...
  loss = ...
  train_op = ...
  return tf.estimator.EstimatorSpec(
      mode=mode,
      predictions=predictions,
      loss=loss,
      train_op=train_op)