What is the difference between saving a summary and saving the model in the logdir?

Question

Using Tensorflow (tf.contrib.slim in particular) we are required to calibrate a few parameters to produce the graphs that we want at tensorboard.

Saving a summary interval is more clear for us what it does. It saves the value (or an average of them?) of a particular point in the graph at the interval provided.

Now checkpoints for saving the model itself why should be required at the training process? Does the model changes?.. Not sure how this works

score 0 · Answer 1 · answered Jan 22 '17 at 13:48

0

You save the model to checkpoints because the Variables in the model, including neural network weights and biases and the global_step counter, keep changing during the training process. The structure of the model doesn't change. The saved checkpoints allow you to load the trained model for serving and to resume training later.

answered Jan 22 '17 at 13:48

Shanqing Cai

3,756
3
23
36

so typically you set these two parameters with the same value? – George Pligoropoulos Jan 22 '17 at 13:57
@GeorgePligor, the summaries and checkpoints can live in the same directory. – Shanqing Cai Jan 22 '17 at 19:39
same value I meant same interval of seconds (I was not referring to the directory). You typically set 5 second both of them or like 5 second the summary interval and 10 seconds for saving the model? – George Pligoropoulos Jan 23 '17 at 10:25

What is the difference between saving a summary and saving the model in the logdir?

1 Answers1