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Can I load my own weights?

0

$begingroup$

full code source: https://www.kaggle.com/hmendonca/mask-rcnn-and-coco-transfer-learning-lb-0-155

#Download COCO pre-trained weights
!wget --quiet https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
!ls -lh mask_rcnn_coco.h5

COCO_WEIGHTS_PATH = "mask_rcnn_coco.h5"


model.load_weights(COCO_MODEL_PATH, by_name=True,
 exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
 "mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
 # Load the last model you trained and continue training
 model.load_weights(model.find_last()[1], by_name=True) 

Can I load my own "*.h5" file?
For example: I interrupted my kernel after 5 epochs. Can I load my last epoch?
Can You explain it for me?

It will be continue a process learning?

deep-learning faster-rcnn

share|improve this question

edited Mar 23 at 5:54

Kiritee Gak

1,3591421

asked Mar 23 at 3:04

James_HamesJames_Hames

1

$endgroup$

add a comment | 

0

$begingroup$

full code source: https://www.kaggle.com/hmendonca/mask-rcnn-and-coco-transfer-learning-lb-0-155

#Download COCO pre-trained weights
!wget --quiet https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
!ls -lh mask_rcnn_coco.h5

COCO_WEIGHTS_PATH = "mask_rcnn_coco.h5"


model.load_weights(COCO_MODEL_PATH, by_name=True,
 exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
 "mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
 # Load the last model you trained and continue training
 model.load_weights(model.find_last()[1], by_name=True) 

Can I load my own "*.h5" file?
For example: I interrupted my kernel after 5 epochs. Can I load my last epoch?
Can You explain it for me?

It will be continue a process learning?

deep-learning faster-rcnn

share|improve this question

edited Mar 23 at 5:54

Kiritee Gak

1,3591421

asked Mar 23 at 3:04

James_HamesJames_Hames

1

$endgroup$

add a comment | 

$begingroup$

full code source: https://www.kaggle.com/hmendonca/mask-rcnn-and-coco-transfer-learning-lb-0-155

#Download COCO pre-trained weights
!wget --quiet https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
!ls -lh mask_rcnn_coco.h5

COCO_WEIGHTS_PATH = "mask_rcnn_coco.h5"


model.load_weights(COCO_MODEL_PATH, by_name=True,
 exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
 "mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
 # Load the last model you trained and continue training
 model.load_weights(model.find_last()[1], by_name=True) 

Can I load my own "*.h5" file?
For example: I interrupted my kernel after 5 epochs. Can I load my last epoch?
Can You explain it for me?

It will be continue a process learning?

deep-learning faster-rcnn

share|improve this question

edited Mar 23 at 5:54

Kiritee Gak

1,3591421

asked Mar 23 at 3:04

James_HamesJames_Hames

1

$endgroup$

#Download COCO pre-trained weights
!wget --quiet https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
!ls -lh mask_rcnn_coco.h5

COCO_WEIGHTS_PATH = "mask_rcnn_coco.h5"


model.load_weights(COCO_MODEL_PATH, by_name=True,
 exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
 "mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
 # Load the last model you trained and continue training
 model.load_weights(model.find_last()[1], by_name=True) 

share|improve this question

edited Mar 23 at 5:54

Kiritee Gak

1,3591421

asked Mar 23 at 3:04

James_HamesJames_Hames

1

share|improve this question

edited Mar 23 at 5:54

Kiritee Gak

1,3591421

asked Mar 23 at 3:04

James_HamesJames_Hames

1

edited Mar 23 at 5:54

Kiritee Gak

1,3591421

edited Mar 23 at 5:54

Kiritee Gak

1,3591421

asked Mar 23 at 3:04

James_HamesJames_Hames

1

asked Mar 23 at 3:04

James_HamesJames_Hames

1

1 Answer
1

active

oldest

votes

0

$begingroup$

Can I load my own "*.h5" file?

Yes, you can load your own .h5 file. But to load weights into a model, you need to have a model architecture defined for it. If the weight dimensions or layer sizes does not match, it will throw an error, of course.

But instead of saving weights, if you save the model by using model.save_model, it does have architecture of the model saved allowing you to re-create the model, weights of the model, training configuration (loss, optimizer), state of the optimizer, allowing you to resume training. But this does not load hyperparameters that are present for that epoch like decay in learning rate etc. You have to write a custom callback for this.

Can I load my last epoch?

Yes, if specified. Keras does not automatically save the model for every epoch or anything until you ask it to. So if you had interrupted something, you cant resume it from there. You have to write a custom callback to make it happen. For details on what are callbacks. For details on how to save the model after every epoch, check out this answer.

share|improve this answer

edited Mar 23 at 9:57

answered Mar 23 at 4:35

Kiritee GakKiritee Gak

1,3591421

$endgroup$

add a comment | 

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0

$begingroup$

Can I load my own "*.h5" file?

Yes, you can load your own .h5 file. But to load weights into a model, you need to have a model architecture defined for it. If the weight dimensions or layer sizes does not match, it will throw an error, of course.

But instead of saving weights, if you save the model by using model.save_model, it does have architecture of the model saved allowing you to re-create the model, weights of the model, training configuration (loss, optimizer), state of the optimizer, allowing you to resume training. But this does not load hyperparameters that are present for that epoch like decay in learning rate etc. You have to write a custom callback for this.

Can I load my last epoch?

Yes, if specified. Keras does not automatically save the model for every epoch or anything until you ask it to. So if you had interrupted something, you cant resume it from there. You have to write a custom callback to make it happen. For details on what are callbacks. For details on how to save the model after every epoch, check out this answer.

share|improve this answer

edited Mar 23 at 9:57

answered Mar 23 at 4:35

Kiritee GakKiritee Gak

1,3591421

$endgroup$

add a comment | 

0

$begingroup$

Can I load my own "*.h5" file?

Yes, you can load your own .h5 file. But to load weights into a model, you need to have a model architecture defined for it. If the weight dimensions or layer sizes does not match, it will throw an error, of course.

But instead of saving weights, if you save the model by using model.save_model, it does have architecture of the model saved allowing you to re-create the model, weights of the model, training configuration (loss, optimizer), state of the optimizer, allowing you to resume training. But this does not load hyperparameters that are present for that epoch like decay in learning rate etc. You have to write a custom callback for this.

Can I load my last epoch?

Yes, if specified. Keras does not automatically save the model for every epoch or anything until you ask it to. So if you had interrupted something, you cant resume it from there. You have to write a custom callback to make it happen. For details on what are callbacks. For details on how to save the model after every epoch, check out this answer.

share|improve this answer

edited Mar 23 at 9:57

answered Mar 23 at 4:35

Kiritee GakKiritee Gak

1,3591421

$endgroup$

add a comment | 

$begingroup$

Can I load my own "*.h5" file?

Yes, you can load your own .h5 file. But to load weights into a model, you need to have a model architecture defined for it. If the weight dimensions or layer sizes does not match, it will throw an error, of course.

But instead of saving weights, if you save the model by using model.save_model, it does have architecture of the model saved allowing you to re-create the model, weights of the model, training configuration (loss, optimizer), state of the optimizer, allowing you to resume training. But this does not load hyperparameters that are present for that epoch like decay in learning rate etc. You have to write a custom callback for this.

Can I load my last epoch?

Yes, if specified. Keras does not automatically save the model for every epoch or anything until you ask it to. So if you had interrupted something, you cant resume it from there. You have to write a custom callback to make it happen. For details on what are callbacks. For details on how to save the model after every epoch, check out this answer.

share|improve this answer

edited Mar 23 at 9:57

answered Mar 23 at 4:35

Kiritee GakKiritee Gak

1,3591421

$endgroup$

share|improve this answer

edited Mar 23 at 9:57

answered Mar 23 at 4:35

Kiritee GakKiritee Gak

1,3591421

answered Mar 23 at 4:35

Kiritee GakKiritee Gak

1,3591421

answered Mar 23 at 4:35

Kiritee GakKiritee Gak

1,3591421