Training ended after 3 epochs even though 5 epochs is given as parameter

[kiran-surya]

[ec2-user@ip-172-31-54-168 deepnl-master]$ time python bin/dl-ner.py ner.dnn -t ~/data/wiki_conll2.iob --vocab ~/data/vocab.txt --vectors ~/data/vectors.txt --caps --suffix --s
uffixes ~/data/suffix.lst --gazetteer ~/data/eng.list -e 5 --variant senna -l 0.0003 -w 5 -n 300 -v
Creating capitalization features...
Generated 5 feature vectors with 5 features each.
Loading suffix list...
Generated 457 feature vectors with 5 features each.
Following is the issue:

Loading gazetteers
Generated 3 feature vectors with 5 features each.
Generated 3 feature vectors with 5 features each.
Generated 3 feature vectors with 5 features each.
Generated 3 feature vectors with 5 features each.
Creating new network...
... with the following parameters:

    Input layer size: 400
    Hidden layer size: 300
    Output size: 17

Starting training with 286490 sentences
Training for up to 5 epochs
.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........
+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+........
.+.........+.........+.........+.........+.........+.........+.........+.........+.........+.......
....+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.........+.......
3 epochs Examples: 20576589 Error: 0.193514 Accuracy: 0.953445 48600 corrections skipped
Saving trained model ...
... to ner.dnn

[kiran-surya]

Hi @attardi

Is this expected behavior ?

[attardi]

It is called early stopping: if there is no progress in an epoch, the train stops.
In your case you had 48600 corrections skipped, i.e. examples that where already correctly classified and were not used.

[kiran-surya]

Thank you. Is there any randomness involved while training. with same training data and same parameters, do we always get same model ? If not, how to get same model with rest remains the same ?

[attardi]

If you use the same initial embeddings and the same training data, you always get the same model, since the network is initialized with random values with a fixed seed.
The seed is set in dl-ner.py. You can comment it out if you want to get different results in different runs.

On 29/ago/2015, at 11:26, kiran-surya notifications@github.com wrote:

Thank you. Is there any randomness involved while training. with same training data and same parameters, do we always get same model ? If not, how to get same model with rest remains the same ?

—
Reply to this email directly or view it on GitHub #10 (comment).

[kiran-surya]

got it.