title: How to block estimator from restore weights and global steps for each training
body: <p>I am trying to use tensorflow high-level API to develop a model and I find a issue in training and visualization phase:</p>
  
  <p>For the same model_fn(), every time I try to invoke the estimator.train() method, the global_step will increase from the steps I trained last time. e.g., if run estimator.train() method last time to march to global_step = 2000, then this time when i start training the model, it will be accumulative starting from 2000, 2001, 2002...</p>
  
  <p>My issue is: for some reason, I want to train a model from scratch every single time. that is to say, I don't need estimator to restore the previously-trained weights when i launch a training again. </p>
  
  <p>Currently, I just manually delete the checkpoints and event files before launching a new training every time, and its truly inefficient. However, even so, there still exists residual curves about last time that I can not delete this way.</p>
  
  <p>How can I deal with it?</p>