tensorflow


Tensorflow restore


I read documentation for restoring a session. There is one thing that is not clear to me.
Save/Restore work only for weights and variables defined by tf.variable or it take in account other things like for example the learning rate?
I notice that the learning rate from one session and a following session of training is keep decaying , while my idea was that he restarted. I argued that since I notice very different results from a unique session of, let's say 20000 steps with learning rate that decayed every 2500 steps, and two session with the second that restored the first, using the same network and parameters obiously. Since the 2 session-based is everytime better (in terms of loss/accuracy) I thouth the learning rate was resetted in the second, instead printing it I notice that is decaying from the lasy value of the first session.
Speaking about data, during the first session we largely cover all the data, and we use the same also for the second session.
Are there other parameters that can affect these results?
Thanks

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