tuning a convolution neural net, sample size
$begingroup$
I keep reading that convolution neural net (CNN) performs best with lots and lots (100k+) of data. Is there any rule of thumb, or lower limit for data size during the grid search phase?
For example, if I run a CNN with 100 data points, vary just one parameter (say add an extra layer, or increase a filter size), and get better results, can I reasonably expect better results with those parameters during the actual training phase?
machine-learning cnn convolution hyperparameter-tuning
asked Apr 4 '18 at 15:41
Mohammad AtharMohammad Athar
261111
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bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I keep reading that convolution neural net (CNN) performs best with lots and lots (100k+) of data. Is there any rule of thumb, or lower limit for data size during the grid search phase?
For example, if I run a CNN with 100 data points, vary just one parameter (say add an extra layer, or increase a filter size), and get better results, can I reasonably expect better results with those parameters during the actual training phase?
machine-learning cnn convolution hyperparameter-tuning
asked Apr 4 '18 at 15:41
Mohammad AtharMohammad Athar
261111
$endgroup$
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
It's wrong that you need a lot of images to train a conv-net... I had only trained them with 22 images as trainset and 7 as validation and test.... that also works
$endgroup$
– Aditya
Apr 5 '18 at 2:35
add a comment |
$begingroup$
I keep reading that convolution neural net (CNN) performs best with lots and lots (100k+) of data. Is there any rule of thumb, or lower limit for data size during the grid search phase?
For example, if I run a CNN with 100 data points, vary just one parameter (say add an extra layer, or increase a filter size), and get better results, can I reasonably expect better results with those parameters during the actual training phase?
machine-learning cnn convolution hyperparameter-tuning
asked Apr 4 '18 at 15:41
Mohammad AtharMohammad Athar
261111
$endgroup$
I keep reading that convolution neural net (CNN) performs best with lots and lots (100k+) of data. Is there any rule of thumb, or lower limit for data size during the grid search phase?
For example, if I run a CNN with 100 data points, vary just one parameter (say add an extra layer, or increase a filter size), and get better results, can I reasonably expect better results with those parameters during the actual training phase?
machine-learning cnn convolution hyperparameter-tuning
machine-learning cnn convolution hyperparameter-tuning
asked Apr 4 '18 at 15:41
Mohammad AtharMohammad Athar
261111
asked Apr 4 '18 at 15:41
Mohammad AtharMohammad Athar
261111
asked Apr 4 '18 at 15:41
Mohammad AtharMohammad Athar
261111
asked Apr 4 '18 at 15:41
Mohammad AtharMohammad Athar
261111
asked Apr 4 '18 at 15:41
Mohammad AtharMohammad Athar
261111
261111
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
It's wrong that you need a lot of images to train a conv-net... I had only trained them with 22 images as trainset and 7 as validation and test.... that also works
$endgroup$
– Aditya
Apr 5 '18 at 2:35
add a comment |
$begingroup$
It's wrong that you need a lot of images to train a conv-net... I had only trained them with 22 images as trainset and 7 as validation and test.... that also works
$endgroup$
– Aditya
Apr 5 '18 at 2:35
$begingroup$
It's wrong that you need a lot of images to train a conv-net... I had only trained them with 22 images as trainset and 7 as validation and test.... that also works
$endgroup$
– Aditya
Apr 5 '18 at 2:35
$begingroup$
It's wrong that you need a lot of images to train a conv-net... I had only trained them with 22 images as trainset and 7 as validation and test.... that also works
$endgroup$
– Aditya
Apr 5 '18 at 2:35
add a comment |
1 Answer
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$begingroup$
If you use pre-trained weights, you need significantly lesser data as the initial layers have already learned from a ton of data and you just need to fine tune the later ones.
What you said is not true, you can train on CIFAR10 and get 90%+ and that is not 100k+. It depends on the complexity of the data and how similar the features are. If they are easily Seperable -less data. If the disctintions are harder then the model needs a lot of examples to figure out which of features are seperate.
I would say you could IF you sample was representive of the population.
answered Nov 7 '18 at 17:04
Rahul DeoraRahul Deora
1
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add a comment |
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$begingroup$
If you use pre-trained weights, you need significantly lesser data as the initial layers have already learned from a ton of data and you just need to fine tune the later ones.
What you said is not true, you can train on CIFAR10 and get 90%+ and that is not 100k+. It depends on the complexity of the data and how similar the features are. If they are easily Seperable -less data. If the disctintions are harder then the model needs a lot of examples to figure out which of features are seperate.
I would say you could IF you sample was representive of the population.
answered Nov 7 '18 at 17:04
Rahul DeoraRahul Deora
1
$endgroup$
add a comment |
$begingroup$
If you use pre-trained weights, you need significantly lesser data as the initial layers have already learned from a ton of data and you just need to fine tune the later ones.
What you said is not true, you can train on CIFAR10 and get 90%+ and that is not 100k+. It depends on the complexity of the data and how similar the features are. If they are easily Seperable -less data. If the disctintions are harder then the model needs a lot of examples to figure out which of features are seperate.
I would say you could IF you sample was representive of the population.
answered Nov 7 '18 at 17:04
Rahul DeoraRahul Deora
1
$endgroup$
add a comment |
$begingroup$
If you use pre-trained weights, you need significantly lesser data as the initial layers have already learned from a ton of data and you just need to fine tune the later ones.
What you said is not true, you can train on CIFAR10 and get 90%+ and that is not 100k+. It depends on the complexity of the data and how similar the features are. If they are easily Seperable -less data. If the disctintions are harder then the model needs a lot of examples to figure out which of features are seperate.
I would say you could IF you sample was representive of the population.
answered Nov 7 '18 at 17:04
Rahul DeoraRahul Deora
1
$endgroup$
If you use pre-trained weights, you need significantly lesser data as the initial layers have already learned from a ton of data and you just need to fine tune the later ones.
What you said is not true, you can train on CIFAR10 and get 90%+ and that is not 100k+. It depends on the complexity of the data and how similar the features are. If they are easily Seperable -less data. If the disctintions are harder then the model needs a lot of examples to figure out which of features are seperate.
I would say you could IF you sample was representive of the population.
answered Nov 7 '18 at 17:04
Rahul DeoraRahul Deora
1
answered Nov 7 '18 at 17:04
Rahul DeoraRahul Deora
1
answered Nov 7 '18 at 17:04
Rahul DeoraRahul Deora
1
answered Nov 7 '18 at 17:04
Rahul DeoraRahul Deora
1
1
add a comment |
add a comment |
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$begingroup$
It's wrong that you need a lot of images to train a conv-net... I had only trained them with 22 images as trainset and 7 as validation and test.... that also works
$endgroup$
– Aditya
Apr 5 '18 at 2:35