Why can't I use data augmentation with a pretrained convnet?
$begingroup$
Reading Deep Learning with Python by François Chollet. In section 5.3.1, we've instantiated a pretrained convnet, VGG16, and are given two options to proceed:
A) Running the convolutional base over your dataset, recording its
output to a Numpy array on disk, and then using this data as input to
a standalone, densely connected classifier similar to those you saw in
part 1 of this book. This solution is fast and cheap to run, because
it only requires running the convolutional base once for every input
image, and the convolutional base is by far the most expensive part of
the pipeline. But for the same reason, this technique won’t allow you
to use data augmentation.
B) Extending the model you have (conv_base) by adding Dense layers on
top, and running the whole thing end to end on the input data. This
will allow you to use data augmentation, because every input image
goes through the convolutional base every time it’s seen by the model.
But for the same reason, this technique is far more expensive than the
first.
Why can't I use data augmentation to generate more training data from existing training samples then go with option A? Seems like I can run the VGG16 base over my augmented dataset and use the output as the input to a standalone densely connected classifier.
convnet beginner data-augmentation
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$begingroup$
Reading Deep Learning with Python by François Chollet. In section 5.3.1, we've instantiated a pretrained convnet, VGG16, and are given two options to proceed:
A) Running the convolutional base over your dataset, recording its
output to a Numpy array on disk, and then using this data as input to
a standalone, densely connected classifier similar to those you saw in
part 1 of this book. This solution is fast and cheap to run, because
it only requires running the convolutional base once for every input
image, and the convolutional base is by far the most expensive part of
the pipeline. But for the same reason, this technique won’t allow you
to use data augmentation.
B) Extending the model you have (conv_base) by adding Dense layers on
top, and running the whole thing end to end on the input data. This
will allow you to use data augmentation, because every input image
goes through the convolutional base every time it’s seen by the model.
But for the same reason, this technique is far more expensive than the
first.
Why can't I use data augmentation to generate more training data from existing training samples then go with option A? Seems like I can run the VGG16 base over my augmented dataset and use the output as the input to a standalone densely connected classifier.
convnet beginner data-augmentation
asked yesterday
Ivan LeeIvan Lee
393
New contributor
Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
Reading Deep Learning with Python by François Chollet. In section 5.3.1, we've instantiated a pretrained convnet, VGG16, and are given two options to proceed:
A) Running the convolutional base over your dataset, recording its
output to a Numpy array on disk, and then using this data as input to
a standalone, densely connected classifier similar to those you saw in
part 1 of this book. This solution is fast and cheap to run, because
it only requires running the convolutional base once for every input
image, and the convolutional base is by far the most expensive part of
the pipeline. But for the same reason, this technique won’t allow you
to use data augmentation.
B) Extending the model you have (conv_base) by adding Dense layers on
top, and running the whole thing end to end on the input data. This
will allow you to use data augmentation, because every input image
goes through the convolutional base every time it’s seen by the model.
But for the same reason, this technique is far more expensive than the
first.
Why can't I use data augmentation to generate more training data from existing training samples then go with option A? Seems like I can run the VGG16 base over my augmented dataset and use the output as the input to a standalone densely connected classifier.
convnet beginner data-augmentation
asked yesterday
Ivan LeeIvan Lee
393
New contributor
Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Reading Deep Learning with Python by François Chollet. In section 5.3.1, we've instantiated a pretrained convnet, VGG16, and are given two options to proceed:
A) Running the convolutional base over your dataset, recording its
output to a Numpy array on disk, and then using this data as input to
a standalone, densely connected classifier similar to those you saw in
part 1 of this book. This solution is fast and cheap to run, because
it only requires running the convolutional base once for every input
image, and the convolutional base is by far the most expensive part of
the pipeline. But for the same reason, this technique won’t allow you
to use data augmentation.
B) Extending the model you have (conv_base) by adding Dense layers on
top, and running the whole thing end to end on the input data. This
will allow you to use data augmentation, because every input image
goes through the convolutional base every time it’s seen by the model.
But for the same reason, this technique is far more expensive than the
first.
Why can't I use data augmentation to generate more training data from existing training samples then go with option A? Seems like I can run the VGG16 base over my augmented dataset and use the output as the input to a standalone densely connected classifier.
convnet beginner data-augmentation
convnet beginner data-augmentation
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asked yesterday
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New contributor
Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
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393
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Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Ivan LeeIvan Lee
393
asked yesterday
Ivan LeeIvan Lee
393
393
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Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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Found the answer in stats.stackexchange.com. Hopefully this helps anyone else with the same question.
feature extraction: freezing convolutional base vs. training on extracted features
edited yesterday
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$begingroup$
Found the answer in stats.stackexchange.com. Hopefully this helps anyone else with the same question.
feature extraction: freezing convolutional base vs. training on extracted features
edited yesterday
Esmailian
3,031320
answered yesterday
Ivan LeeIvan Lee
393
New contributor
Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Found the answer in stats.stackexchange.com. Hopefully this helps anyone else with the same question.
feature extraction: freezing convolutional base vs. training on extracted features
edited yesterday
Esmailian
3,031320
answered yesterday
Ivan LeeIvan Lee
393
New contributor
Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Found the answer in stats.stackexchange.com. Hopefully this helps anyone else with the same question.
feature extraction: freezing convolutional base vs. training on extracted features
edited yesterday
Esmailian
3,031320
answered yesterday
Ivan LeeIvan Lee
393
New contributor
Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Found the answer in stats.stackexchange.com. Hopefully this helps anyone else with the same question.
feature extraction: freezing convolutional base vs. training on extracted features
edited yesterday
Esmailian
3,031320
answered yesterday
Ivan LeeIvan Lee
393
New contributor
Ivan Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
Esmailian
3,031320
edited yesterday
Esmailian
3,031320
edited yesterday
Esmailian
3,031320
3,031320
answered yesterday
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393
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answered yesterday
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answered yesterday
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