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How to resume training of a model?
2019 Community Moderator ElectionDeep Learning: Feed Forward for Unbalanced Classes Using Tensor FlowHow to know the model has started overfitting?Neural Network: how to interpret this loss graph?Why is training take so long on my GPU?Problem with keras model loadingTraining Error decreasing with each epochTensorflow CNN sometimes converges, sometimes notWhy does my LSTM perform better when randomizing training subset vs. standard batch training?How to speed up passing of images to a GPUStop CNN model at high accuracy and low loss rate?
$begingroup$
I have not GPU support so it often happens that my model takes hours to train. Can I train my model in batches , for example if I want to have 100 epochs for my model,but due to power cut my training stops(at 50th epoch) but when I retrain my model I want to train it from where it was left (from 50th epoch).
It would be much appreciated if anyone can explain it by some example.
machine-learning python neural-network deep-learning tensorflow
edited Oct 16 '17 at 20:58
ncasas
3,7481131
asked Oct 16 '17 at 17:23
BerryBerry
374
$endgroup$
add a comment |
$begingroup$
I have not GPU support so it often happens that my model takes hours to train. Can I train my model in batches , for example if I want to have 100 epochs for my model,but due to power cut my training stops(at 50th epoch) but when I retrain my model I want to train it from where it was left (from 50th epoch).
It would be much appreciated if anyone can explain it by some example.
machine-learning python neural-network deep-learning tensorflow
edited Oct 16 '17 at 20:58
ncasas
3,7481131
asked Oct 16 '17 at 17:23
BerryBerry
374
$endgroup$
$begingroup$
This is possible with most (all?) mainstream deep learning frameworks by simply storing the model every N training iterations and checking for the last stored model before starting the training. Which framework are you using?
$endgroup$
– ncasas
Oct 16 '17 at 17:56
$begingroup$
I am using tensorflow
$endgroup$
– Berry
Oct 16 '17 at 18:27
add a comment |
$begingroup$
I have not GPU support so it often happens that my model takes hours to train. Can I train my model in batches , for example if I want to have 100 epochs for my model,but due to power cut my training stops(at 50th epoch) but when I retrain my model I want to train it from where it was left (from 50th epoch).
It would be much appreciated if anyone can explain it by some example.
machine-learning python neural-network deep-learning tensorflow
edited Oct 16 '17 at 20:58
ncasas
3,7481131
asked Oct 16 '17 at 17:23
BerryBerry
374
$endgroup$
I have not GPU support so it often happens that my model takes hours to train. Can I train my model in batches , for example if I want to have 100 epochs for my model,but due to power cut my training stops(at 50th epoch) but when I retrain my model I want to train it from where it was left (from 50th epoch).
It would be much appreciated if anyone can explain it by some example.
machine-learning python neural-network deep-learning tensorflow
machine-learning python neural-network deep-learning tensorflow
edited Oct 16 '17 at 20:58
ncasas
3,7481131
asked Oct 16 '17 at 17:23
BerryBerry
374
edited Oct 16 '17 at 20:58
ncasas
3,7481131
asked Oct 16 '17 at 17:23
BerryBerry
374
edited Oct 16 '17 at 20:58
ncasas
3,7481131
edited Oct 16 '17 at 20:58
ncasas
3,7481131
edited Oct 16 '17 at 20:58
ncasas
3,7481131
3,7481131
asked Oct 16 '17 at 17:23
BerryBerry
374
asked Oct 16 '17 at 17:23
BerryBerry
374
asked Oct 16 '17 at 17:23
BerryBerry
374
374
$begingroup$
This is possible with most (all?) mainstream deep learning frameworks by simply storing the model every N training iterations and checking for the last stored model before starting the training. Which framework are you using?
$endgroup$
– ncasas
Oct 16 '17 at 17:56
$begingroup$
I am using tensorflow
$endgroup$
– Berry
Oct 16 '17 at 18:27
add a comment |
$begingroup$
This is possible with most (all?) mainstream deep learning frameworks by simply storing the model every N training iterations and checking for the last stored model before starting the training. Which framework are you using?
$endgroup$
– ncasas
Oct 16 '17 at 17:56
$begingroup$
I am using tensorflow
$endgroup$
– Berry
Oct 16 '17 at 18:27
$begingroup$
This is possible with most (all?) mainstream deep learning frameworks by simply storing the model every N training iterations and checking for the last stored model before starting the training. Which framework are you using?
$endgroup$
– ncasas
Oct 16 '17 at 17:56
$begingroup$
This is possible with most (all?) mainstream deep learning frameworks by simply storing the model every N training iterations and checking for the last stored model before starting the training. Which framework are you using?
$endgroup$
– ncasas
Oct 16 '17 at 17:56
$begingroup$
I am using tensorflow
$endgroup$
– Berry
Oct 16 '17 at 18:27
$begingroup$
I am using tensorflow
$endgroup$
– Berry
Oct 16 '17 at 18:27
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
With tensorflow, currently the most straightforward and easy way to get persistence for your model is to use a tf.train.MonitoredTrainingSession. You just need to use it instead the normal tf.Session() that is frequently used. This an illustrative Python snippet:
with tf.train.MonitoredTrainingSession(checkpoint_dir='/tmp/mymodel',
save_summaries_secs=600) as sess:
_ = sess.run(train_op, feed_dict=...)
With this, your model is automagically saved every 600 secs in /tmp/mymodel and restored the next time you restart the program.
answered Oct 16 '17 at 20:58
ncasasncasas
3,7481131
$endgroup$
add a comment |
$begingroup$
@ncasas could you please give how to do the same using keras ?
answered 7 hours ago
user2351509user2351509
1
New contributor
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
1
$begingroup$
This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From Review
$endgroup$
– oW_
3 hours ago
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
With tensorflow, currently the most straightforward and easy way to get persistence for your model is to use a tf.train.MonitoredTrainingSession. You just need to use it instead the normal tf.Session() that is frequently used. This an illustrative Python snippet:
with tf.train.MonitoredTrainingSession(checkpoint_dir='/tmp/mymodel',
save_summaries_secs=600) as sess:
_ = sess.run(train_op, feed_dict=...)
With this, your model is automagically saved every 600 secs in /tmp/mymodel and restored the next time you restart the program.
answered Oct 16 '17 at 20:58
ncasasncasas
3,7481131
$endgroup$
add a comment |
$begingroup$
With tensorflow, currently the most straightforward and easy way to get persistence for your model is to use a tf.train.MonitoredTrainingSession. You just need to use it instead the normal tf.Session() that is frequently used. This an illustrative Python snippet:
with tf.train.MonitoredTrainingSession(checkpoint_dir='/tmp/mymodel',
save_summaries_secs=600) as sess:
_ = sess.run(train_op, feed_dict=...)
With this, your model is automagically saved every 600 secs in /tmp/mymodel and restored the next time you restart the program.
answered Oct 16 '17 at 20:58
ncasasncasas
3,7481131
$endgroup$
add a comment |
$begingroup$
With tensorflow, currently the most straightforward and easy way to get persistence for your model is to use a tf.train.MonitoredTrainingSession. You just need to use it instead the normal tf.Session() that is frequently used. This an illustrative Python snippet:
with tf.train.MonitoredTrainingSession(checkpoint_dir='/tmp/mymodel',
save_summaries_secs=600) as sess:
_ = sess.run(train_op, feed_dict=...)
With this, your model is automagically saved every 600 secs in /tmp/mymodel and restored the next time you restart the program.
answered Oct 16 '17 at 20:58
ncasasncasas
3,7481131
$endgroup$
With tensorflow, currently the most straightforward and easy way to get persistence for your model is to use a tf.train.MonitoredTrainingSession. You just need to use it instead the normal tf.Session() that is frequently used. This an illustrative Python snippet:
with tf.train.MonitoredTrainingSession(checkpoint_dir='/tmp/mymodel',
save_summaries_secs=600) as sess:
_ = sess.run(train_op, feed_dict=...)
With this, your model is automagically saved every 600 secs in /tmp/mymodel and restored the next time you restart the program.
answered Oct 16 '17 at 20:58
ncasasncasas
3,7481131
edited Oct 16 '17 at 21:04
answered Oct 16 '17 at 20:58
ncasasncasas
3,7481131
answered Oct 16 '17 at 20:58
ncasasncasas
3,7481131
answered Oct 16 '17 at 20:58
ncasasncasas
3,7481131
3,7481131
add a comment |
add a comment |
$begingroup$
@ncasas could you please give how to do the same using keras ?
answered 7 hours ago
user2351509user2351509
1
New contributor
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
1
$begingroup$
This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From Review
$endgroup$
– oW_
3 hours ago
add a comment |
$begingroup$
@ncasas could you please give how to do the same using keras ?
answered 7 hours ago
user2351509user2351509
1
New contributor
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
1
$begingroup$
This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From Review
$endgroup$
– oW_
3 hours ago
add a comment |
$begingroup$
@ncasas could you please give how to do the same using keras ?
answered 7 hours ago
user2351509user2351509
1
New contributor
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
@ncasas could you please give how to do the same using keras ?
answered 7 hours ago
user2351509user2351509
1
New contributor
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 7 hours ago
user2351509user2351509
1
New contributor
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 7 hours ago
user2351509user2351509
1
answered 7 hours ago
user2351509user2351509
1
1
New contributor
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
user2351509 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
1
$begingroup$
This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From Review
$endgroup$
– oW_
3 hours ago
add a comment |
1
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This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From Review
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– oW_
3 hours ago
1
1
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This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From Review
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– oW_
3 hours ago
$begingroup$
This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From Review
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– oW_
3 hours ago
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$begingroup$
This is possible with most (all?) mainstream deep learning frameworks by simply storing the model every N training iterations and checking for the last stored model before starting the training. Which framework are you using?
$endgroup$
– ncasas
Oct 16 '17 at 17:56
$begingroup$
I am using tensorflow
$endgroup$
– Berry
Oct 16 '17 at 18:27