I have been using tf.reset_default_graph() to test out several graphs/models. However, now I would like to train the same model/graph, on different datasets. Therefore, I would like to reset the weights of the created model without having to delete and then create the whole model again. Please note that, I am going to train the model on 20 different datasets. Therefore, maybe resetting the weights only is a simpler operation than deleting the existing model and creating a new one for each dataset. Please correct me if I'm wrong.
How to reset the weights of a graph without having to reset/delete the whole graph in tensorflow?
Viewed 172
1 Answers
Based on this GitHub issue, there is no single function to do it, but the offered workaround is to save your initial weights, then use it to re-initialize them alter (rather than randomly re-initializing them each time).
Initially,
Winit = weights.get_weights() # do this once before training
Later,
weights.set_weights(Winit) # call each time to reset weights
Related