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Can I continue a machine learning process after shutdown?
Can we train a neural network to tell if an object is present or not in an Image?What exactly is a step in Tensorflow prebuilt architectures?Breaking captcha with a neural network - Learning deep learningWhat are differentiable modules used in deep learningHow to extract all information by using idIs there any work done on reconfigurable convolutional neural networks?How can I combine images for Matlab deep learning?Simple Object DetectionHow is Stochastic Gradient Descent done in Faster RCNN?What (and how) should I use for object detection in this case?
$begingroup$
I use Mask RCNN for object detection and instance segmentation.
I am new to neural networks, this is my first project.
I use jupyter notebook. Can I continue learning after shutdown?
I would like start learning from 6 epoch. How can I do it?
One epoch ~ 1h. I don't want to waste time, and continue this process. Is it possible?
deep-learning jupyter
$endgroup$
add a comment |
$begingroup$
I use Mask RCNN for object detection and instance segmentation.
I am new to neural networks, this is my first project.
I use jupyter notebook. Can I continue learning after shutdown?
I would like start learning from 6 epoch. How can I do it?
One epoch ~ 1h. I don't want to waste time, and continue this process. Is it possible?
deep-learning jupyter
$endgroup$
add a comment |
$begingroup$
I use Mask RCNN for object detection and instance segmentation.
I am new to neural networks, this is my first project.
I use jupyter notebook. Can I continue learning after shutdown?
I would like start learning from 6 epoch. How can I do it?
One epoch ~ 1h. I don't want to waste time, and continue this process. Is it possible?
deep-learning jupyter
$endgroup$
I use Mask RCNN for object detection and instance segmentation.
I am new to neural networks, this is my first project.
I use jupyter notebook. Can I continue learning after shutdown?
I would like start learning from 6 epoch. How can I do it?
One epoch ~ 1h. I don't want to waste time, and continue this process. Is it possible?
deep-learning jupyter
deep-learning jupyter
edited Mar 20 at 13:27
Blenzus
446
446
asked Mar 20 at 13:17
BadumBadum
113
113
add a comment |
add a comment |
1 Answer
1
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oldest
votes
$begingroup$
In order to continue the learning process, you will need to use checkpoints.
# source: https://stackoverflow.com/questions/36356004/continue-training-from-a-specific-epoch
weight_save_callback = ModelCheckpoint('/path/to/weights.epoch:02d-val_loss:.2f.hdf5', monitor='val_loss', verbose=0, save_best_only=False, mode='auto')
model.fit(X_train,y_train,batch_size=batch_size,nb_epoch=nb_epoch,callbacks=[weight_save_callback])
# load model
model = Sequential()
model.add(...)
model.load('path/to/weights.hf5')
# use model.fit to continue training.
New contributor
$endgroup$
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
In order to continue the learning process, you will need to use checkpoints.
# source: https://stackoverflow.com/questions/36356004/continue-training-from-a-specific-epoch
weight_save_callback = ModelCheckpoint('/path/to/weights.epoch:02d-val_loss:.2f.hdf5', monitor='val_loss', verbose=0, save_best_only=False, mode='auto')
model.fit(X_train,y_train,batch_size=batch_size,nb_epoch=nb_epoch,callbacks=[weight_save_callback])
# load model
model = Sequential()
model.add(...)
model.load('path/to/weights.hf5')
# use model.fit to continue training.
New contributor
$endgroup$
add a comment |
$begingroup$
In order to continue the learning process, you will need to use checkpoints.
# source: https://stackoverflow.com/questions/36356004/continue-training-from-a-specific-epoch
weight_save_callback = ModelCheckpoint('/path/to/weights.epoch:02d-val_loss:.2f.hdf5', monitor='val_loss', verbose=0, save_best_only=False, mode='auto')
model.fit(X_train,y_train,batch_size=batch_size,nb_epoch=nb_epoch,callbacks=[weight_save_callback])
# load model
model = Sequential()
model.add(...)
model.load('path/to/weights.hf5')
# use model.fit to continue training.
New contributor
$endgroup$
add a comment |
$begingroup$
In order to continue the learning process, you will need to use checkpoints.
# source: https://stackoverflow.com/questions/36356004/continue-training-from-a-specific-epoch
weight_save_callback = ModelCheckpoint('/path/to/weights.epoch:02d-val_loss:.2f.hdf5', monitor='val_loss', verbose=0, save_best_only=False, mode='auto')
model.fit(X_train,y_train,batch_size=batch_size,nb_epoch=nb_epoch,callbacks=[weight_save_callback])
# load model
model = Sequential()
model.add(...)
model.load('path/to/weights.hf5')
# use model.fit to continue training.
New contributor
$endgroup$
In order to continue the learning process, you will need to use checkpoints.
# source: https://stackoverflow.com/questions/36356004/continue-training-from-a-specific-epoch
weight_save_callback = ModelCheckpoint('/path/to/weights.epoch:02d-val_loss:.2f.hdf5', monitor='val_loss', verbose=0, save_best_only=False, mode='auto')
model.fit(X_train,y_train,batch_size=batch_size,nb_epoch=nb_epoch,callbacks=[weight_save_callback])
# load model
model = Sequential()
model.add(...)
model.load('path/to/weights.hf5')
# use model.fit to continue training.
New contributor
New contributor
answered Mar 20 at 13:23
MachineLearnerMachineLearner
32410
32410
New contributor
New contributor
add a comment |
add a comment |
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