為了實現finetune有如下兩種解決方案：

model_fn裡面定義好模型之後直接賦值

 def model_fn(features,labels,mode,params):
 # .....
 # finetune
 if params.checkpoint_path and (not tf.train.latest_checkpoint(params.model_dir)):
 checkpoint_path = None
 if tf.gfile.IsDirectory(params.checkpoint_path):
  checkpoint_path = tf.train.latest_checkpoint(params.checkpoint_path)
 else:
  checkpoint_path = params.checkpoint_path

 tf.train.init_from_checkpoint(
  ckpt_dir_or_file=checkpoint_path,assignment_map={params.checkpoint_scope: params.checkpoint_scope} # 'OptimizeLoss/':'OptimizeLoss/'
 )

使用鉤子 hooks。

可以在定義tf.contrib.learn.Experiment的時候通過train_monitors引數指定

 # Define the experiment
 experiment = tf.contrib.learn.Experiment(
 estimator=estimator,# Estimator
 train_input_fn=train_input_fn,# First-class function
 eval_input_fn=eval_input_fn,# First-class function
 train_steps=params.train_steps,# Minibatch steps
 min_eval_frequency=params.eval_min_frequency,# Eval frequency
 # train_monitors=[],# Hooks for training
 # eval_hooks=[eval_input_hook],# Hooks for evaluation
 eval_steps=params.eval_steps # Use evaluation feeder until its empty
 )

也可以在定義tf.estimator.EstimatorSpec 的時候通過training_chief_hooks引數指定。

不過個人覺得最好還是在estimator中定義，讓experiment只專注於控制實驗的模式（訓練次數,驗證次數等等）。

def model_fn(features,params):

 # ....

 return tf.estimator.EstimatorSpec(
 mode=mode,predictions=predictions,loss=loss,train_op=train_op,eval_metric_ops=eval_metric_ops,# scaffold=get_scaffold(),# training_chief_hooks=None
 )

這裡順便解釋以下tf.estimator.EstimatorSpec對像的作用。該物件描述來一個模型的方方面面。包括：

當前的模式：

mode: A ModeKeys. Specifies if this is training,evaluation or prediction.

計算圖

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 are the 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.

匯出策略

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.

chief鉤子 訓練時的模型儲存策略鉤子CheckpointSaverHook， 模型恢復等

training_chief_hooks: Iterable of tf.train.SessionRunHook objects to run on the chief worker during training.

worker鉤子 訓練時的監控策略鉤子如： NanTensorHook LoggingTensorHook 等

training_hooks: Iterable of tf.train.SessionRunHook objects to run on all workers during training.

指定初始化和saver

scaffold: A tf.train.Scaffold object that can be used to set initialization,saver,and more to be used in training.

evaluation鉤子

evaluation_hooks: Iterable of tf.train.SessionRunHook objects to run during evaluation.

自定義的鉤子如下：

class RestoreCheckpointHook(tf.train.SessionRunHook):
 def __init__(self,checkpoint_path,exclude_scope_patterns,include_scope_patterns
   ):
 tf.logging.info("Create RestoreCheckpointHook.")
 #super(IteratorInitializerHook,self).__init__()
 self.checkpoint_path = checkpoint_path

 self.exclude_scope_patterns = None if (not exclude_scope_patterns) else exclude_scope_patterns.split(',')
 self.include_scope_patterns = None if (not include_scope_patterns) else include_scope_patterns.split(',')


 def begin(self):
 # You can add ops to the graph here.
 print('Before starting the session.')

 # 1. Create saver

 #exclusions = []
 #if self.checkpoint_exclude_scopes:
 # exclusions = [scope.strip()
 #  for scope in self.checkpoint_exclude_scopes.split(',')]
 #
 #variables_to_restore = []
 #for var in slim.get_model_variables(): #tf.global_variables():
 # excluded = False
 # for exclusion in exclusions:
 # if var.op.name.startswith(exclusion):
 # excluded = True
 # break
 # if not excluded:
 # variables_to_restore.append(var)
 #inclusions
 #[var for var in tf.trainable_variables() if var.op.name.startswith('InceptionResnetV1')]

 variables_to_restore = tf.contrib.framework.filter_variables(
  slim.get_model_variables(),include_patterns=self.include_scope_patterns,# ['Conv'],exclude_patterns=self.exclude_scope_patterns,# ['biases','Logits'],# If True (default),performs re.search to find matches
  # (i.e. pattern can match any substring of the variable name).
  # If False,performs re.match (i.e. regexp should match from the beginning of the variable name).
  reg_search = True
 )
 self.saver = tf.train.Saver(variables_to_restore)


 def after_create_session(self,session,coord):
 # When this is called,the graph is finalized and
 # ops can no longer be added to the graph.

 print('Session created.')

 tf.logging.info('Fine-tuning from %s' % self.checkpoint_path)
 self.saver.restore(session,os.path.expanduser(self.checkpoint_path))
 tf.logging.info('End fineturn from %s' % self.checkpoint_path)

 def before_run(self,run_context):
 #print('Before calling session.run().')
 return None #SessionRunArgs(self.your_tensor)

 def after_run(self,run_context,run_values):
 #print('Done running one step. The value of my tensor: %s',run_values.results)
 #if you-need-to-stop-loop:
 # run_context.request_stop()
 pass


 def end(self,session):
 #print('Done with the session.')
 pass

以上這篇tensorflow estimator 使用hook實現finetune方式就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。